#unless i need to make corrections or something similar for accuracy/accountability

[sunshinexlollipops]

Just read your post about Kyle. Do people really not see your comparison? You're saying that George Floyd was determined guilty enough to deserve to die over accusations of a counterfeit 20$ bill, and Kyle is found not guilty in spite of traveling to the area, arming himself, and seeking conflict that resulted in him using his weapon that killed 2 people.

yep! precisely anon. (also slight TW for rest of post.)

it's funny how the metric changes based upon who agrees with them and who doesn't.

now, I didn't word it as well as I should've, because I could see how people could think I said George Floyd got no justice.

but the point was that the right says George's death was fine and excusable bc he was a criminal committing a crime -- despite this being an accusation at the time.

(which is so ironic considering their "innocent until proven guilty" chant.)

then, they find "excuses" such as drug use and other things that "rationalize" why police suffocated him for 10min until he passed.

they painted George as a criminal over something as simple as saying he had a fake $20 bill and drugs in his system, and his rightful punishment was death.

but Kyle can go out of his way to insert himself in the situation, aim to cause conflict, arm himself with weapons, and even intended on shooting people before even arriving in the area.

(idk how you can predict you need to shoot someone out of self-defense, bc he's on record for already intending to use his gun to shoot/kill protestors days before interacting w anyone.)

the right doesn't see their actual aggressors as aggressors.

hence "any crime is alright if you're white."

that's why the cops had so much support from the right when the George Floyd murder trial started.

yes, they were held accountable at the end, but it was fought for. justice almost wasn't given.

and the right doesn't agree with the cops being found guilty, as they say they've done nothing wrong, just like Kyle.

the right says Kyle also deserves pity and sparing, and he is just a child.

but then black kids like Treyvon Martin are not spared or given pity. they said this 17yr old child deserved to die bc he was "violent" and a "thug." Treyvon wasn't even armed, and was still shot and killed by a man with the same agenda as Kyle.

I had someone in the notes bring up Breonna Taylor's boyfriend, Kenneth Walker, saying his self-defense claim should be negated since I personally negate Kyle's.

which is a completely incomparable situation, considering Breonna and Kenneth were shot at by police while sleeping and without announcement they were police. additionally, their suspect was already in custody, and they were at the wrong address to boot.

all Kenneth knew is that guns were being fired into his apartment, and his girlfriend was shot and murdered in her sleep.

it's all about painting the VICTIMS as the guilty party, and that's EXACTLY what they did with Kyle's case.

the point is: George Floyd, Joseph Rosenbaum, and Anthony Huber lost their lives unnecessarily, and THEY are painted guilty when they are simply reacting to what people like Kyle do to them.

the right says murders such as these, it's always self-defense.

but really, people like George Floyd die trying to defend themselves from people like Kyle.

the only thing Kyle is defending is the ability to use this system to his advantage, and harm those that he thinks deserve to die, simply bc they don't side with or look like him.

I'm just very disappointed with the world and the way that the justice system is literally Black and White.

[mochacoffee]

I created a 3D model and floor plan of Aziraphale’s bookshop in Good Omens!

I really wanted one for reference and it seemed like many others did too, so I put together my best approximation of where everything is. Beneath the color version, you’ll see I’ve included two simplified, labeled versions of the plan. The verbal labels are so you know what the object is. The numerical labels are there to make it easy to find more information about the object. I’ve put a numbered index below the cut that features the relevant reference images I used for each object and some more information about why I put it where I did/why it’s relevant/etc. I want to be very clear that I did not add anything to this from my own imagination; every single item and feature represents something I actually saw in the shop.

If you have any questions or want more information about this, PLEASE do not hesitate to ask! I put so much time into figuring it out and I would be more than happy to be a resource for anyone who needs it. Also, if you notice any errors, let me know and I’ll update the post. I hope this is helpful!

Update: Here’s a link to an interactive view of the shop! It takes a moment to load. You can click the “3D” tab in the top right to view it in first person and walk around inside. Double click a spot on the floor to move there and pan around by clicking and dragging. The oval symbol next to the person walking gives you a birds-eye view.

Update 2: Here’s a higher quality rendering of the first person perspective! Update 3: I made an alternate first person render here complete with a ceiling, light fixtures, and ambient lighting from outside. This one is optimized for making it seem more like you’re actually there, whereas the previous one is for maximum visibility. This render also has some minor accuracy improvements, which are detailed under the cut in the relevant sections. (The first interactive link with the birds-eye view updates automatically.) Update 4: In case you’re interested in Aziraphale’s books specifically, I’ve made a catalogue of those here.

1. Unknown closet

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There is a door behind Gabriel when he talks to Aziraphale in the backroom. So where does it lead? Well. The wall we can see behind Aziraphale when he encounters Shadwell in the shop (see #17: boxes/storage) doesn’t have a door in it. It’s also facing the wrong direction and it’s in the middle of the southwest wall ⁠— we know this because Aziraphale can see Shadwell in the entrance from there. So the wall behind him at that moment is definitely not the wall of the backroom. We’re left with this door and unaccounted-for corner. The only thing that makes sense to me then is that there’s a closet there between the two spaces. My personal theory is that this closet is “the back” that Aziraphale refers to keeping the Châteauneuf-du-Pape in since I didn’t see any other obvious alcohol storage space in the shop. Update: @n0nb1narydemon has suggested this could be a bathroom for guests or because culturally it’s a room you can use to extricate yourself from situations, which is another possibility! They also asked where I think the doors behind object #20 lead, and I thought it would be good to add here that they might lead to the shop next door or to this unknown room. It’s possible the room actually extends further into the next shop and encompasses the part of the wall where the doors are, but I didn’t have concrete evidence to support that idea so I didn’t include it in the floor plan. Update: I was wrong about the Châteauneuf-du-Pape! In the DVD bookshop tour we learn that the cabinet in the top left corner of the backroom is where Aziraphale keeps his alcohol, including that particular wine. I added a reference photo of Neil pointing it out. Thanks to @fuckyeahgoodomens​ for bringing the existence of this tour to my attention — ya girl got the special edition blu-ray even though I don’t have a blu-ray player yet so I hadn’t actually seen it. Also, there is a chair right next to this cabinet against the wall which I missed in my initial rendering of the shop but have since added.

2. Part of shop next door (top right)

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This was very tricky to figure out because you can see from the exterior of the shop that there is no wall past the back door, but from the interior there is clearly more space there. BUT in a behind the scenes photo of David during the fire scene, you can see on this back wall that there’s actually a nook with two large entryways, similar to the one that makes up the backroom. From the exterior you can see that the area next to the back door is taken up by the window of the next shop, so I concluded that this little square of space was not part of the bookshop’s interior, but the nook did extend further back than where the shop appears to end from the outside. I had to make one bookshelf more nubby than the others to make this work, but after a LOT of trial and error I decided one nubby bookshelf was the only thing that could explain the apparent architecture of the space. Any floor design that accounted for a bookshelf of the same length as the others just did not make sense on a fundamental level.

3. Part of shop next door (bottom left)

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From the exterior of the shop you can see that this window belongs to the adjacent store, as the wall is a different color. Within the bookshop you can also see when Gabriel and Sandalphon enter the backroom, there’s no window behind them; there’s a sink. So it’s definitely not Aziraphale’s window. The wall of the backroom is also further into the shop’s interior than the wall Aziraphale’s desk sits against, so there’s a corner of space inside that’s unaccounted for. At first I assumed it was plumbing from the sink that had been sealed off or something, but when I realized that’s where the window was on the outside, I figured the space is probably part of the next shop over.

4. Aziraphale’s desk

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This is where Aziraphale sits in the shop like 90% of the time. It’s on the Eastern side of the shop because Aziraphale was the guardian of the Eastern gate in Eden and because production designer Michael Ralph is a goddamn genius (source). Shout out to @posted-omens for this fascinating post analyzing the chariot sculpture on his desk. Update: Fun fact, the ladder behind his desk is actually called a library chair, supposedly designed by Benjamin Franklin. It functions as a ladder but you can also fold it into a chair! Neil mentions this in the DVD extra bookshop tour. I added screen caps of it to the reference photos above since I don’t have a specific section for the ladders!

5. Phone Aziraphale calls Crowley from

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I will be honest with you: I think there’s something a little fucked up about this corner. It is my nemesis. I tried so many things to make it work and I just could not get it exactly right, but what you see in the floor plan is my best guess as to what’s going on. The conundrum is that the spot where Aziraphale stands when he’s on the phone with Crowley is definitely closer to the fence around the staircase than it is in my layout. But the table he’s in front of is also clearly against the outside wall of the backroom, and the stairs being where I’ve put them here is the only thing that made sense based on the reference photos. So there’s some weird spacing issue where there’s a little too much room between the fence around the stairs and this phone. If I were to move the walls to close that gap then there would be way too much space in the backroom and way too little space on the southwest side of the shop, so I think the walls are correct as they are. So  ¯\_(ツ)_/¯. What I can say for certain is that the phone is there and it’s on a table next to a lamp, and the table is definitely against the wall of the backroom and behind the staircase. The distance between these things doesn’t hold up perfectly, but their arrangement does. On another note, this is one of two phones in the shop. The other is on the table next to the cash register (see #9) which Aziraphale picks up when Crowley calls to say they need to talk about Armageddon. I believe this is the same one he uses to call Adam’s house in episode two, only he moves it from the table by the register to the top of a pile of books (which I’m pretty sure were stacked on the circular table between his desk and the sofa). Update: OKAY SO it turns out in the behind-the-scenes bookshop tour on the DVD we get two more teeny tiny glimpses of this corner! I added them to the reference photo album above. It appears I was right about the lamp, phone, and bookshelf being where they are, except that the bookshelf and table are touching. There’s also a ladder propped against the shelf. I’d say it’s possible there are actually two bookshelves here; based on the parallax in the DVD tour, the one next to the phone didn’t appear to be against the wall, but we know there is a bookshelf against that wall because we see it in the show. (P.S. There’s also another chair against that wall which I didn’t see because Aziraphale was standing in front of it, so I added that too.) This leads me to believe there’s one against the wall and another one further from it next to the table. But that’s just my speculation, so I won’t change the actual floor plan unless I find more evidence.

6. Where they’re drinking when Crowley realizes Adam has named the hellhound

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When Aziraphale sits down at this table, the background is of the same space he refers to as the “backroom” when Gabriel and Sandalphon show up. He’s across the table from Crowley, behind whom you can see a bookshelf, the staircase, and the coat rack. The table is half in the backroom half out, since the room has two large entryways in its wall. Update: I realized the wall behind this table actually dips back further! It is a weirdly-shaped wall! But in the DVD special tour of the bookshop Neil walks past it and there’s clearly an area that recesses even further, so I’ve modified that in the interactive floor plan :)

7. Bench of books that start the fire

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When Shadwell leaves the book shop and slams the door, one of the candles knocks over and rolls into a pile of books and other papers (including the Sound of Music lmao). You can see it’s the same bench the customer is standing in front of when he gives Gabriel a weird look after he yells about pornography. (I love this customer so much because they gave me a super HD shot of this particular area.) The poles of the fence around the bench, the staircase behind it, and the smaller shelves beside it holding Terry Pratchett’s books make it clear that the bench is in that spot in the shop and that it’s the place the fire starts.

8. Coat rack with Terry’s hat on it

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Aziraphale hangs his coat here right before Crowley calls him to say they need to talk about Armageddon. Out of focus in the frame you can see the lion sculpture that sits on the fence surrounding the stairs (see #11) and a bookshelf. The camera pans past the shelf and we see him walk past his desk to pick up the phone by the cash register, which puts that shelf right next to his sitting area. We can also see the coat rack in the background when Crowley realizes Adam has named the hellhound. The coat rack has Terry Pratchett’s hat and scarf on it in his honor (source).

9. Antique cash register

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You can see this register in the background when Crowley is on the couch and when Aziraphale invites Gabriel and Sandalphon into the backroom. I know it’s an antique cash register because it’s photographed and referenced directly on page 79 of the Good Omens TV Companion. It’s a typewriter in my floor plan because the website I used (floorplanner.com), who knows why, did not have a 3D model of a cash register from the early 1900s.

10. Back door

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Thank you so much to @fuckyeahgoodomens for this post where they figured all this out!! Wonderful work! You can see this door from the exterior of the shop and its existence is referenced in the Good Omens script book on page 94. It’s also in the background of a behind the scenes shot of Aziraphale pulling away the carpet so he can contact heaven. Behind him in that shot you can see the bust (which moves around a lot - see #19) and a grandfather clock, and in the show from one of the aerial shots you can see that the carpet is pulled west, further confirming the door’s location.

11. Fence around the stairs

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I have concluded that this is a fence to keep customers from going up to the second floor. It looks to be made of golden pillars with wooden shelving atop them. The fence crosses beneath the staircase on one side and the other side ends about where the stair’s railing does. You can see this fence behind Crowley when he realizes Adam has named the hellhound, behind Aziraphale when he calls Crowley to tell him he knows where the antichrist is, and next to the customer who gives Gabriel a look after he yells “PORNOGRAPHY!” It’s also visible in one of the aerial shots of the shop. Update: In the DVD extra bookshop tour I noticed the lion sculpture on this fence is not just a lion, but a lion with a woman holding its mane. I think it might also be a lamp? In one of the reference photos, the one that looks down from the second floor, it appears there’s a light in the woman’s other hand. I’d be interested to see if we can track down what this particular sculpture is and what it might mean. Update: @cantdewwrite has suggested here that the light/sculpture could be a replica of one of the bronze statues in the Victoria Memorial, which does look quite similar. I’m fairly certain Aziraphale’s sculpture is of a woman, which would make it the figure in the memorial representing peace.

12. Open book of illustrated story of Adam and Eve

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Shout out to @amuseoffyre for this post where she figured out what this was! Update: I’ve determined that this book is The Gospel in the Old Testament: A series of pictures by Harold Copping. The painting is, naturally, by Harold Copping. It’s called “Adam and Eve after the fall.” Unfortunately this book is out of print and I haven’t been able to track down an ebook or scan of it, so I can’t confirm the text just yet. But based on its premise, I think it’s safe to assume that it’s telling the story of Adam and Eve directly. Aziraphale has a second copy of this book visible on the shelf next to the sofa.

13. Antique computer

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This is the computer Aziraphale does his extremely scrupulous taxes with, as confirmed in this ask that @neil-gaiman answered from @prismatic-bell! It’s an Amstrad, according to the bookshop tour in the DVD extras.

14. Spiral staircase

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These stairs are in many shots of the shop so it was pretty obvious where they were.

15. Sink, teapots, etc.

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You can see this wall right before Gabriel walks into the backroom and behind Aziraphale when he’s drinking with Crowley at the end of episode one. It appears he has two hand towels, a ceramic angel soap dish (aw), some teapots, and a decorated box above it, among other things. On the floor beside the sink is what I believe to be a broom handle, though it could be a mop? Next to that is a bronze statue of an angel atop a small table piled with books. On the other side of the sink is an open book on a stand ⁠— it has a fabric bookmark in it with a crucifix at the end, so I’m assuming it’s a bible. Light reading while you make your tea I guess. Update: Thank you so much to @brightwanderer for pointing out in this post that he has four extra angel wing mugs above the sink as well! I couldn’t figure out what they were! Update: Neil said in this ask that you can see an oven by the sink when Gabriel and Sandalphon walk in. Which you can! It’s real small and there’s a little pot on top of it. I’ve added a screencap of it to the images album for this area. Update: I’m donating my heart and soul to @ack-emma for suggesting in the replies to this ask that the central object above the sink is a samovar!! I had never heard of this so I had absolutely no idea what it was, but I think they hit the nail on the head. Y’all Aziraphale really likes tea.

16. Sculpture

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Thank you @ineffable-endearments, @behold-my-squeees, @srebrnafh, @aethelflaedladyofmercia for contributing to this post about the statue and its potential symbolism! Update: @doctorscienceknowsfandom has added some analysis to the post above suggesting that this is a sculpture of Paris, the figure from Greek mythology. I’m inclined to agree! Update: BINGO! @tifaria​ has found Aziraphale’s exact statue (confirmed Paris!) in this post. Brilliant work!! This community continues to blow me away. Further discussion about the sculpture’s meaning in the context of the show here — be sure to check the notes for further commentary.

17. Boxes/storage

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These boxes and piles of books can be seen behind Aziraphale when he encounters Shadwell in the shop and behind Crowley while he’s rambling drunkenly about why they should stop Armageddon in episode one. They’re in a nook that goes further back than where the shop appears to end from its exterior (see #2 for more info on that!). 

18. Stacks of books

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You can see this stack in one of the aerial shots of Shadwell in the shop. I didn’t include most stacks of books in the floor plan because they’re literally everywhere and I had to manually set how high each book would be from the floor, so putting them in piles got tedious very quickly. But I did include a few notable ones, and this is one of those imo because there’s not much else in that area as far as I can tell.

19. Bust

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This little guy moves around quite a bit, unlike most things in the shop. In some photos/scenes it’s where I put it on the floor plan, but in others it’s closer to the northwestern wall and in this 360 video of the shop it’s right between two of the columns. I chose to put it where I did because it’s there in the scene where Crowley is drunkenly rambling about Armageddon, whereas the other locations I’ve seen it in were from behind the scenes shots and stuff. I’m not sure who the bust is of! It appears to have a little ribbon with a medal around its next though. Update: More speculation about the bust here, courtesy of @aethelflaedladyofmercia! Update: @fuckyeahgoodomens has confirmed in this post that the thing around the bust’s neck is the medal given to Aziraphale by Gabriel in this deleted scene!

20. Divider I think?

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Please for the love of god if you know what this thing is, tell me. My best guess is it’s a room divider because what else looks like that?? But I don’t know why you would put a room divider there. And it still doesn’t look exactly like a divider either. But the decorative element at the top and apparent gap between the metal frame and the red bit leads me to believe it’s not furniture or a box. This mystery object is my second nemesis after the weird corner (#5). Update: @brightwanderer has suggested that it might be an embroidered/tapestry draft screen, which I think makes more sense! Update: In the DVD extra bookshop tour I found a very brief image of this item over Neil’s shoulder which I added to the reference photos above. I think by some miracle I was right and it is a divider. It could be a draft screen but at the very least it is shaped like a divider with at least three sections. Wahoo!

21. Record player

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This is the phonograph that’s playing Franz Schubert’s String Quintet in C major (thank you again to @fuckyeahgoodomens for that info) when we first see Aziraphale in the shop. It also plays Queen’s You’re My Best Friend when Crowley runs into the fire.

22. Terry Pratchett’s books

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Another one of the many little Terry easter eggs in the show is this set of his books! @devoursjohnlock made a post highlighting some other specific books you can find in the shop.

23. Chess set

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I saw a post once pointing out this chess set and the implication that Aziraphale and Crowley must play together sometimes, which I thought was a really nice detail to put into the set. But I can’t find the post to credit it! I will update this with a link if I do. Update: Pretty sure this is the post I saw. Thank you to @losyanya for mentioning it :)

24. Circular entryway

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This is one of many circle motifs that production designer Michael Ralph incorporated into the shop. It’s gorgeous. I think there’s actually more room between the archway and the door than I’ve included in this floor plan; Shadwell takes a few steps through it when he runs out of the shop. But I think the fix is just the door being further out from the entryway rather than the entryway being further in. I didn’t want to fuck with the walls to improve this particular area because when I realized the spacing was wrong, I was almost done and would’ve had to manually move each object in the shop over a few inches over. Made more sense to leave the caveat in a footnote. Update: In the DVD extra bookshop tour you get a brief glimpse of something on the inside wall of the entryway. I think it’s a wall sconce or something along those lines. There’s one on either side. I added them to the reference album above! I also figured out how to extend the walls to accommodate some more space there without having to move everything else, so I did that. Update: Here’s a link to some meta discussion about the cupid sculpture in front of this entryway!

25. Sofa Crowley sits on when he suggests they could be godfathers

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You can see that the sofa is next to Aziraphale’s desk and the cash register, and also that there’s a bookshelf behind it. From the entrance to the shop you can see two bookshelves on either side of the central circle, so it was pretty clear that the couch was on the other side of one of those shelves.

26. Wall crucifix

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I find it very interesting that Aziraphale has this considering Jesus isn’t a big part of angelic lore or heaven’s general priorities in the show. It would make more sense to me that he has it because it’s another memento of his time with Crowley, sort of like the illustrated story of Adam and Eve by his desk (#12). Also, fun fact, the opposite side of this wall segment is where he put up all his maps and notes about the whereabouts of the Antichrist in episode three.

[comedicreliefonlyworkswhenfunny]

I am against the "Americanization" of fandoms.

What this applies to

Holding non American characters (and sometimes even fans) to an American moral standard. This includes

Refusing to take into account that, first things first, America is NOT the target audience, so certain tropes that would or would not pass in the west are different in Japan.

Like seriously, quite a few of the jokes are just not going to pass or hit, because they require background information that is not universal.

Assuming all American experience is standard. (This could mean watering down just how much pressure is placed on Japanese youth irl by saying that sort of thing is universal (while it is, to a degree, Japanese suicide rates are pretty fucking high because of how fast paced and work heavy some of their loads tend to be), and it's really annoying and rude when someone is trying to speak out about how heavy and harsh the standards are placed on them to succeed just for some American whose mom occasionally yells at them to do their homework dropping by to say "it's like that everywhere")

Demonizing (or wubbifying) a character using American morals, including and up to harassing fans over their interpretations or gatekeeping whether or not a character "should" get development (while you shouldn't do that fucking period, it's rude and annoying- this is specifically for the people who use American standards without acknowledging the cultural gap between them and, you know, the fucking target audience) ((Like seriously, saying "It's different in Japan" is not the end all be all excusing someone's actions, but sometimes the author didn't immediately think that maybe (insert vaguely universal thing) was that bad or that heavy of a topic before they put it into their media. If you don't want to see things like that? Pick a different series and stop harassing the fans))

Getting mad at or making fun of Japan's attempts to satirize their own culture. (A good example is Ace Attorney! To most of us, it's just a funny laugh can you imagine if courts were actually like that- guess what? Japan's are! (Not that America's are actually that much better, they just look good on paper))

Making America/American issues the center of your fan spaces

(Usually without sharing or bringing light to the issues that other countries are going through)

Your

Experiences

Are

Not

Univseral!

Seriously, very few things across America, even, are universal. Texas things the hundreds are nothing while Minnesota's like "oh it's only thirty degrees below zero"- so for fucks sake, stop assuming that all other countries work in ways similar to America.

It's good and important to share Ameican issues with your American followers, but guess what? America isn't the only country out there, and it's certainly not the only one going through bullshit. Don't pull shit like "why's no one reblogging this?" or "why should I care about what's happening in (X country)?"

Don't assume everyone lives in America.

Stop assuming everyone lives in America.

America is not and has never been the target audience for anime, and it's certainly not the only country outside of Japan that enjoys it.

Like I said above, sometimes Japan attempts to satirize its own culture. We can't tell what is and isn't meant as satire, because it's not our culture.

Social media activism can be tiring and maybe you don't have the energy to focus on things that are out of your control, but, if someone tells you about the shit they're going through, don't bring American politics up.

For the neurodivergent crowd out there thinking, "But why?" it's because a lot of social media, especially, is very heavily Americanized- sometimes to the point where people assume that everyone is American. Not to mention, it's disheartening. I'm sorry to say, but you're not actually relating to the conversation, you're often diverting the focus away from the topic at hand. Even if you mean well, America is heavily pedestaled and talked about frequently, and people from other countries are tired of America taking precedent over their own issues.

Don't divert non-American issues into American ones. Seriously. It's not your place. Please just support the original issue or move on.

Racist Bullshit

This especially goes for islanders and South Asian characters, as well as poc characters (because, yes, Japan DOES have black people)

Making "funny" racist headcanons. Not fucking cool.

Changing the canon interpretation of an explicit character of color in order to fit racist stereotypes.

Whitewashing or color draining characters. Different artistic skill sets can be hard, yes, but are you seriously going to look at someone and say "I don't feel like accurately portraying you or people that look like you, because it's difficult for me." If someone tries to correct you on your cultural depiction of a character and/or their life style, don't be an ass. (If possible, it would be nice for those that do the corrections to be polite as well, but it does get really frustrating).

Seriously, no offense guys, but, if you want to persue art, you're going to need to learn to depict different body types, skin colors, and/or ethnic features.

On that note, purposefully, willingly, or consistently inaccurately portraying people or characters of color (especially if someone in the fandom has "called you out" or specifically told you that what you're doing comes across as racist and you continue to do it). If you need help or suck at looking things up, there are references for you! Ask your followers if they have tutorials on poc (issue that you're having), whether it be bodily portrayal, facial proportions, or coloring and shading. Art is so much more fun when you can depict a wider variety, and guess what? Before you drew the same skinny, basic, white character over and over, you couldn't even draw that!

Attempting or claiming to DEPECT CULTURAL ACCURACY within a work or meta, while being completely fucking wrong. ESPECIALLY and specifically if someone calls you out, and you refuse to fix, correct, or change anything.

*little side note that the discussion revolving art is a very multilayered conversation, and it has quite a few technical potholes, which I'll bring up again farther into this post.

Fucking history

Stop demonizing or for absolute fucks sake wubbifying Japanese history because UwU Japan ♡0♡ or bringing up shit like "you know they sided with Nazis, right?" It's good to recognize poor past decisions, but literally it's not your country keep your nose out of it. And? A lot of decisions made by countries were not made by their general peoples. Even those that were, often involved heavy propaganda that made them think what they were doing was right.

Seriously, it's not your country, not your history. Unless you have some sort of higher education (but honestly even then a lot of those contain heavy bias), just don't butt in.

^^^ this also goes to all countries that are NOT Japan (specifically when people from non American countries talk about their history while in fandoms and someone wants to Amerisplain to them why "well, actually-"). When we said, "question your sources," we didn't mean "question the people who know better than you, while blindly accepting the (more than likely biased) education you were given in the past."

What this does NOT include:

Fanfiction

FANfiction

FanFICTION

FANFICTION.

Seriously, fanfiction is literally UNPAID WORK from RANDOM FANS- a lot of which who are or have started as kids. ((No, I'm not trying to excuse racist depictions of people just because they're free, please see above where I talk about learning to grow a skill and how it's possible tone bad and get good, on top of the fact that some inaccuracies are not just willful ignorance))

"Looking it up" doesn't work

"Looking it up" almost never works

Please, for fucks sake, you know that most all online search engines are heavily biased, right? Not to mention, not everything is universal across the entirety of Japan. You want to look up how the school system works in Hokkaido? Well it's different from the ones in Osaka!

Most fanfiction is meant to be an idealized version of the world. Homophobia, transphobia, misogyny, ableism, and racism are very prevalent and heavy topics that some fan authors would prefer to avoid. (Keep in mind, this is also used by some people in those minorities often because thinking about how relevant those kinds of things are is to them every day).

A lot of shit that happens in writing is purely because it's an ideal setting. I've seen a few arguments recently about how fan authors portray Japanese schools wrong- listen, I can't tell you how many random school systems I have pulled from my ass purely because (I need them to interact at these points, in these ways). Sometimes the only compliment I can think of is 'I like your shirt' or sometimes I need character A to realize that character B likes the same thing as they do, so I might ignore the fact that most all Japanese schools require uniforms, so that I can put my character in a shirt that will get someone else's attention.

Sometimes it's difficult to find information on different types of systems, and sometimes when you DO know those things, they directly rule out a plot point that needs to happen (like back on the topic of schools (from what I've seen/heard/read- which guess what? Despite being from multiple sources, might still be inaccurate!) Japanese schools don't have mandatory elective classes (outside of like gym and most of them usually learn English or another language- I've seen stuff about art classes? But the information across the board varies.), but, if I need my character to walk in and see someone completely in their element, I'm probably not going to try and gun for accuracy or make up a million and two reasons as to why this (non elective) person would possibly need something from (elective teacher) after school of all things.)

Some experiences ARE universal- or at least overlap American and Japanese norms! Like friends going to fast food places after school doesn't /sound Japanese/ or whatever, but it's not like a horrible inaccuracy to say that your characters ate at McDonald's because they were hungry. Especially when you consider that the Japanese idolization of American "culture" is also a thing.

Also I saw someone complaining about how, in December, a lot of (usually westerners) write Christmas fics! Well, not only are quite a few of those often gift fics, with it being the season if giving and all, but Japanese people do celebrate Christmas! Not as "the birth of Christ," but rather as a popularized holiday about gift giving (also pst: America isn't the only place that celebrates Christmas)

But, on that note, sometimes things like Holidays are "willfully ignorant" of what actually happens (I've made this point several times, but (also this does by no means excuse actual racism)), because, again: plot convenience! Hey what IF they celebrated Halloween by Trick or Treating? What if Easter was a thing and they got to watch their kids or younger siblings crawl around on the ground looking for tiny plastic eggs?

Fanfiction authors can put in hours of work for one or two thousand words- let alone ten thousand words, fifty thousand words, a hundred thousand words. And all of these are free. There is absolutely no (legal) way to make money off of their fanworks, but they spent hours, days, weeks, months- sometimes even years- writing. It is so unnecessary to EXPECT or REQUIRE them to spend even more hours looking up shit that, no offense, almost no one is going to notice. No one is going go care that all of my combini prices are accurate or that I wrote a fic with a Japanese map of a train station that I had to backwards search three times to find an English version that I could read.

Not everyone has the attention span or ability to spend hours of research before writing a single word. Neurodivergent people are literally a thing yall. Instead of producing the perfectly pretty accurate version of Japan that people want to happen, what ACTUALLY happens is that the writer reads and reads and reads and either never finds the information they need or they lose the motivation to write.

^^^ (This does NOT apply to indigenous or native peoples, like Pacific Islanders or tribes that exist in real life. Please make sure that you portray tribal minorities accurately. If you can't find the information you need (assuming that the content of the series is not specifically about a tribe), please just make one up (and for fucks sake, recognize that a lot of what you've been taught about tribal practices, such as shit like human sacrifices or godly worship, is actually just propaganda.)

Not to mention, it often puts a wall in front of readers who would then need to pull up their OWN information (that may or may not be biased) just in order to interact with the fic ((okay, this one has a little bit of arguability when it comes to things like measurements and currency, because Americans don't know what a meter is and no one else knows what a foot is- either way, one of yall is going to have to look up measurements if they want to get a better understanding of the fic)). However, a lot of Americans who do write using 'feet, Fahrenheit, dollars,' also write for their American followers or friends (which really could go both ways).

On a less easily arguable side, most fic readers aren't going to open up a new tab just to search everything that the author has written (re the whole deep topics, not everyone wants to read about those sorts of things, either). Not only are you making it more difficult on the writer, but you're also making it more difficult for the reader who's now wondering why you decided to add in Grandma's Katsudon recipe, and whether or not the details you have added are accurate.

Some series, themselves, ignore Japanese norms! Piercings, hair dye, and incorrectly wearing ones uniform are frowns upon in Japanese schools- sometimes up to inflicting punishment on those students because of it. However, some anime characters still have naturally or dyed blond hair some of them still have piercings or wear their uniforms wrong. Some series aren't set specifically in Japan, but rather in a vague based-off-real-life Japan that's just slightly different (like Haikyuu and all of its different prefectures). Sometimes they're based on real places, but real places that have gone through major changes (like the Hero Academia series with its quirks and shit).

Fandom is not a full time job. Please stop treating it like it is one. Most people in fandoms have to engage in other things like school or work that most definitely take precident over frantically Googling the cultural implications of dying your hair pink in Japan.

Art is also meant to be a creative freedom and is almost always a hobby, so there are a few cracks that tend to spark debate. Like I said, it is still a hobby, something that's meant to be fun (on this note!)

If trying new things and expanding your portfolio is genuinely making you upset, it's okay to take a break from it. You're not going to get it right on the first try and please, please to everyone out there critiquing artists' works, please take this into account before you post things.

I'm sorry to say, but, while it gets frustrating to see the same things done wrong over and over again, some people are genuinely trying. If it matters enough for you to point out, please offer solutions or resources that would possibly help the artist do better (honestly this could be said about a lot of online activism). I get that they should "want" to do better (and maybe they don't and your annoyance towards them is completely justified- again, as I said, if this becomes a repeated offense and they don't listen to or care about the people trying to help them, yeah you can be a bitch if it helps you feel better- just please don't assume that everyone is willfully ignorant of how hurtful/upsetting/annoying a certain way of portraying things is), but also WANTING to do better and ACTUALLY doing better are two different things.

Maybe they didn't realize what they were doing was inaccurate. Maybe they didn't have the right tutorials. Maybe they tried to look it up, but that failed them. Either way, to some- especially neurodivergent artists- just being told that their work is bad or racist or awful isn't going to make them want to search for better resources in order to be more accurate, it's just going to make them give up.

Also! In fic and in writing, no one is going to get it right on the first try. Especially at the stage where we creators ARE merely in fan spaces is a great time to "fuck around and find out", before we bring our willfully or accidentally racist shit into monetized media. Absolutely hold your fan creators to higher standards, but literally fan work has so little actual impact on popular media (and this goes for just about every debate about fan spaces), and constructive criticism as well as routine practice can mean worlds for representation in future media. NOT allowing for mistakes in micro spaces like fandoms is how you get genuinely harmful or just... bad... portrayals of minorities in popularized media that DOES have an impact on the greater public. OR you get a bunch of creators who are too afraid to walk out of their own little bubbles, because what if they get it wrong and everyone turns against them. It's better to just "stick with what they know" (hobbies are something that you are meant to get better at, even if that is a slow road- for all of my writers and artists out there, it does take time, but you will get it. To everyone else, please do speak up about things that are wrong, but don't make it all about what's wrong and please don't be rude. It's frustrating on both ends, so, if you can, please try not to escalate the situation more.)

Anyways, I'm tired of everyone holding fictional characters to American Puritanical standards, but I'm also tired of seeing every "stop Americanizing fandom" somehow loop into fanfiction and how all authors who don't make their fics as accurate as possible are actually just racist and perpetuating or enabling America's take over of the world or some shit.

Fan interpretation of published media is different than fan creation of mon monetized media. Americans dominating or monopolizing spaces meant for all fans (especially in a fandom that was never meant for them to begin with) is annoying and can be harmful sometimes. Americans writing out their own personal experience using random fictional characters (more often than not) isn't.

[wickedbananas]

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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SEO is Not Hard — A step-by-step SEO Tutorial for beginners that will get you ranked every single…

Note: This one of one of the chapters of Secret Sauce: A step-by-step growth hacking guide . Secret Sauce breaks down every channel just like this one, so if you think this is valuable check it out. It’s for sale now.

SEO In One Day

SEO is simply not as hard as people pretend like it is; you can get 95% of the effort with 5% of the work, and you absolutely do not need to hire a professional SEO to do it, nor will it be hard to start ranking for well-picked key terms. Of all the channels we’ll be discussing, SEO is the one that there is the most misinformation about. Some of it is subtle, but some of it is widely spread and believed by so-called SEO consultants who actually don’t know what they’re doing. SEO is very simple, and unless you’re a very large company it’s probably not worth hiring somebody else to do.

How Google Works

In order to understand what we need to do for SEO let’s look back at how Google started, how it’s evolving today, and develop a groundwork from which we can understand how to get ranked on Google.

The Early Days of Google

The idea for PageRank — Google’s early ranking algorithm — stemmed from Einstein. Larry Page and Sergei Brin were students at Stanford, and they noticed how often scientific studies referred to famous papers, such as the theory of relativity. These references acted almost like a vote — the more your work was referenced the more important it must be. If they downloaded every scientific paper and looked at the references, they could theoretically decide which papers were the most important, and rank them. They realized that because of links, the Internet could be analyzed and ranked in a similar way, except instead of using references they could use links. So they set about attempting to “download” (or crawl) the entire Internet, figuring out which sites were linked to the most. The sites with the most links were, theoretically, the best sites. And if you did a search for “university,” they could look at the pages that talked about “university” and rank them.

Google Today

Google works largely the same way today, although with much more sophistication and nuance. For example, not all links carry the same weight. A link from an authoritative site (as seen by how many links a site has pointing at it) is much more valuable than a link from a non-authoritative site. A link from the New York Times is probably worth about 10,000 links from sites that don’t have much authority. At the end of the day the purpose of Google is to find the “best” (or most popular) web page for the words you type into the search bar. All this means is we need to make it clear to google what our page is about, and then make it clear that we’re popular. If we do that we win. In order to do that, we’ll follow a very simple process that works every single time with less effort than you probably think is required.

Gaming the System

Google is a very smart company. The sophistication of the algorithms they write is incredible; bear in mind that there are currently cars driving themselves around Silicon Valley powered by Google’s algorithms. If you get too far into the SEO rabbit hole you’ll start stumbling upon spammy ways to attempt to speed up this process. Automated software like RankerX, GSA SER, and Scrapebox, instructions to create spam or spin content, linkwheels, PBNs, hacking domains, etc. Some of that stuff works very short term, but Google is smart and it is getting smarter. It gets harder to beat Google every day, and Google gets faster at shutting down spammy sites every day. Most don’t even last a week before everything you’ve done disappears and your work evaporates. That’s not the way you should do things. Instead of Internet-based churn and burn we’ll be focusing on building equity in the Internet. So if you see some highly-paid SEO consultant telling you to use software and spun content to generate links, or when you see some blackhatter beating the system, just know that it’s not worth it. We’re going to build authority and get traffic fast, but we’re going to do it in a way that doesn’t disappear or cripple your site in the future.

On-Page SEO

The first step in getting our site ready to rank is making it clear to Google what our site is about. For now we’re going to focus our home page (our landing page) on ranking for one keyword that isn’t our brand or company name. Once we do that and get that ranking we can branch out into other keywords and start to dominate the search landscape, but for now we’ll stay laser focused.

Keyword Research

The first thing we need to do is to figure out what that keyword is. Depending on how popular our site is and how long it’s been around, the level of traffic and difficulty we’ll get from this effort may vary.

The Long Tail

There’s a concept we need to be familiar with known as the “long tail.” If we were to graph “popularity” of most things with “popularity” being the Y axis and the rank order being the X axis, we’d get something like a power law graph: There are some big hits that get the majority of attention, and after a few hits the graph falls sharply. The long-tail theory says that as we become more diverse as a society the yellow end of the above graph will stretch forever and get taller. Think of Amazon. They probably have a few best-selling products, but the majority of their retail revenue comes from a wide variety of things that aren’t bought anywhere nearly as often as their best-selling products. Similarly, if we were to rank the popularity of the songs played in the last 10 years, there would be a few hits that would garner the majority of plays, and an enormous number of songs that have only a few plays. Those less popular products and songs are what we call the long tail . In SEO this matters because, at least in the beginning, we’re going to go after long tail keywords — very exact, intention-driven keywords with lower competition that we know can win, then gradually we’ll work our way to the left. Our site isn’t going to outrank ultra-competitive keywords in the beginning, but by being more specific we can start winning very targeted traffic with much less effort. The keywords we’re looking for we will refer to as “long-tail keywords.”

Finding the Long Tail

In order to find our perfect long-tail keywords, we’re going to use a combination of four tools, all of which are free. The process looks like this:

Use UberSuggest, KeywordShitter and a little bit of brainstorming to come up with some keywords

Export those keywords to the Google Keyword Planner to estimate traffic level

Search for those keywords with the SEOQuake chrome extension installed to analyze the true keyword difficulty

Don’t be intimidated — it’s actually very simple. For this example we’ll pretend like we were finding a keyword for this book (and we’ll probably have to build out a site so you see if we’re ranked there in a few months).

Step 1: Brainstorming and Keyword Generating

In this step we’re simply going to identify a few keywords that seem like they might work. Don’t concentrate too much on culling the list at this point, as most bad keywords will be automatically eliminated as a part of the process. So since this is a book about growth hacking, I’m going to list out a few keywords that would be a good fit: Growth hacking Growth marketing Internet marketing Growth hacking guide Growth hacking book Book about growth hacking What is growth hacking Growth hacking instructions That’s a good enough list to start. If you start running out of ideas go ahead and check out keywordshitter.com . If you plug in one keyword it will start spitting out thousands of variations in just a few minutes. Try to get a solid list of 5–10 to start with. Now we’ll plug each keyword into UberSuggest . When I plug the first one — “growth hacking” — in, I get 246 results. Clicking “view as text” will let us copy and paste all of our keywords into a text editor and create an enormous list. Go through that process with each keyword you came up with. Now we’ll assume you have 500+ keywords. If you don’t, try to start with something more generic and broad as a keyword, and you’ll have that many quickly. Ideally you’ll have over 1500.

Step 2: Traffic Estimating

Now that we have a pretty good list of keywords. Our next step is to figure out if they have enough search volume to be worth our while. You’ll likely notice that some are so far down the long tail they wouldn’t do much for us. For example, my growth hacking list came up with “5 internet marketing techniques.” We probably won’t go after that one, but instead of guessing we can let Google do the work for us. This will be our weeding out step.

Google Keyword Planner

The Google Keyword Planner is a tool meant for advertisers, but it does give us some rough idea of traffic levels. Google doesn’t make any promise of accuracy, so these numbers are likely only directionally correct, but they’re enough to get us on the right track. You’ll have to have an AdWords account to be able to use the tool, but you can create one for free if you haven’t use AdWords in the past. Once you’ve logged in, select “Get search volume data and trends.” Paste in your enormous list of keywords, and click “Get search volume.” Once you’ve done so, you’ll see a lot of graphs and data. Unfortunately the Keyword Planner interface is a little bit of a nightmare to work within, so instead we’re going to export our data to excel with the “download” button and play with it there. Now what we’re going to do is decide what traffic we want to go after. This varies a bit based on how much authority your site has. So let’s try to determine how easy it will be for you to rank. Go to SEMrush.com and enter your URL, looking at the total backlinks in the third column: As a general rule (this may vary based on how old your site is, who the links are from, etc.), based on the number of links you have, this is the maximum level of “difficulty” you should go after. Number of Backlinks Maximum Difficulty < 30:<40 <100:40–50 <1000:50–70 1000+:70+ Go ahead and sort the data by difficulty, and eliminate all of the stuff that is too high for your site (don’t worry, we’ll get those keywords later). For now you can simply delete those rows.

Exact Match

One important thing to note is that Google gives us this volume as “ exact match ” volume. This means that if there is a slight variation of a keyword we will see it if the words are synonyms, but not if they are used in a phrase, so the traffic will be underestimated from what you would expect overall. Now with that disclaimer sort the traffic volume highest to lowest, and from this data pick out five keywords that seem like a good fit. Here are mine: growth hacking strategies growth hacking techniques growth hacking 101 growth hacking instagram growth hacking twitter Mine all look the same, but that may not necessarily be the case.

Keyword Trends

Unfortunately the “keyword difficulty” that Google gives us is based on paid search traffic, not on natural search traffic. First, let’s use Google Trends to view the keyword volume and trajectory simultaneously. You can enter all of the keywords at the same time and see them graphed against each other. For my keywords it looks like this: The ones I’m most excited about are purple and red, which are “Growth hacking techniques” and “Growth hacking Twitter.” Now we’ll take a deeper look at what the competition is like for those two keywords.

Manual Keyword Difficulty Analysis

In order to analyze how difficult it will be to rank for a certain keyword, we’re going to have to look at the keywords manually, one by one. That’s why we started by finding some long-tail keywords and narrowing the list. This process gets a lot easier if you download the SEOQuake Chrome extension . Once you’ve done that, do a Google search and you’ll notice a few changes. With SEOQuake turned on the relevant SEO data of each site is displayed below each search result. We’re going to alter what is displayed, so in the left-hand sidebar click “parameters” and set them to the following: Now when you search, you’ll see something like this SEOQuake adds a ranking number, and the following at the bottom: The Google Index: This is how many pages from this base URL Google has indexed Page Links : The number of pages linking to the exact domain that is ranking according to SEMrush’s index (usually very low compared to reality, but since we’ll be using this number to compare it wil be somewhat apples to apples) URL Links : The number of pages pointing to any page on the base URL Age: The first time the page was indexed by the Internet Archive Traffic : A very rough monthly traffic number for the base URL Looking at these we can try to determine approximately what it would take to overtake the sites in these positions. You’ll notice that the weight of the indicators change. Not all links are from as good of sources, direct page links matter much more than URL links, etc., but if you google around and play with it for a while you’ll get a pretty good idea of what it takes. If you have a brand new site it will take a month or two to start generating the number of links to get to page one. If you have an older site with more links it may just be a matter of getting your on-page SEO in place. Generally it will be a mixture of both. Keep in mind that we’re going to optimize our page for this exact keyword, so we have a bit of an advantage. That said, if you start to see pages from sites like Wikipedia, you will know it’s an uphill battle. Here are a couple of examples so you can see how you should think through these things, starting with “Growth hacking techniques.” Entrepreneur.com is definitely a big name, and “growth hacking techniques” is in the title explicitly. This will be difficult to beat, but there are no links in the SEMRush index that point direct to the page. (By the way, I wonder how hard it would be to write an article for entrepreneur.com — I could probably do that and build a few links to that easily, even linking to my site in the article). Yongfook.com, have never heard of that site. 206 total links, not much traffic, this one I could pass up. It does have quite a bit of age and “Growth hacking tactics” in the title explicitly, so that would make it tough, but this one is doable to pass up after a while. Alright, so quicksprout is relatively popular, a lot of links, good age, lots of traffic, a few links direct to the page but not a ton. But the word “tactics” doesn’t even appear here. This page isn’t optimized for this keyword, so I could probably knock it out by being optimized specifically for “growth hacking tactics.” Let’s jump down a ways to see how hard it would be to get on the front page. 17 total pages indexed? Created in 2014? No links in the index, even to the root URL? This one’s mine. I should be able to front-page easily. So this looks like a good keyword. Now we just have to get the on-page SEO in place and start building a few links.

On-Page SEO

Now that we have our keyword selected, we need to make sure Google knows what our site is about. This is as simple as making sure the right keywords are in the right places. Most of this has to do with html tags, which make up the structure of a webpage. If you don’t know html or understand how it works, just pass this list to a developer and they should be able to help you. Here is a simple checklist you can follow to see if your content is optimized.

On-Page SEO Checklist

☐ Your keyword is in the

tag, ideally at the front (or close to the front) of the tag ☐ Your keyword is close to the beginning of the tag (ideally the first words) ☐ The title tag contains less than the viewable limit of 65 characters (optional but recommended) ☐ Your keyword is in the first

tag (and your page has an

tag) ☐ If your page contains additional header tags (

,

, etc) your keyword or synonyms are in most of them ☐ Any images on the page have an tag that contain your chosen keyword ☐ Your keyword is in the meta description (and there is a meta description) ☐ There is at least 300 words of text on the page ☐ Your keyword appears in the URL (if not the homepage) ☐ Your keyword appears in the first paragraph of the copy ☐ Your keyword (or synonyms — Google recognizes them now) is used other times throughout the page ☐ Your keyword density is between .5% and 2.5% ☐ The page contains dofollow links to other pages (this just means you’re not using nofollow links to every other page) ☐ The page is original content not taken from another page and dissimilar from other pages on your site If you have all of that in place you should be pretty well set from an on-page perspective. You’ll likely be the best-optimized page for your chosen keyword unless you’re in a very competitive space. All we have left now is off-page optimization.

Off-Page SEO

Off-Page SEO is just a fancy way to say links . (Sometimes we call them backlinks , but it’s really the same thing.) Google looks at each link on the web as a weighted vote. If you link to something, in Google’s eyes you’re saying, “This is worth checking out.” The more legit you are the more weight your vote carries.

Link Juice

SEOs have a weird way to describe this voting process; they call it “link juice.” If an authoritative site, we’ll say Wikipedia for example, links to you, they’re passing you “link juice.” But link juice doesn’t only work site to site — if your homepage is very authoritative and it links off to other pages on your site, it passes link juice as well. For this reason our link structure becomes very important.

Checking Link Juice

There are a number of tools that let you check how many links are pointing to a site and what the authority of those pages are. Unfortunately none of them are perfect — the only way to know what links are pointing to your site is to have crawled those pages. Google crawls most popular pages several times per day, but they don’t want you manipulating them, so they update their index pretty slowly. That said, you can check at least a sample of Google’s index in the Google Search Console (formerly known as Webmaster Tools). Once you navigate to your site, In the left-hand side select “Search Traffic” then “Links to your site.” There’s a debate raging over whether or not this actually shows you all of the links Google knows about (I’m 99% convinced it’s only a sample), but it’s at least a representative sample. To see all of your links, click on “More” under “Who links to you the most” then “Download this table.” This, again, seems to only download a sample of what Google knows about. You can also select “Download latest links” which provides more recent links than the other option. Unfortunately this doesn’t let us see much a to the value of the links, nor does it show us links that have dropped or where those links are from. To use those there are a wide variety of tools: If you have a budget I’d go with ahrefs.com as they have the biggest index, followed by Moz’s Open Site Explorer (most of the data you can get with a free account, if not then it’s slightly cheaper than ahrefs), and finally SEMrush, which is free for most purposes we need. MajesticSEO uses a combination of “trust flow” and “citation flow” which also works fairly well to give you an idea as to the overall health and number of links pointing to your site. All of these use different internal metrics to determine the “authority” of a link, but using them to compare apples to apples can be beneficial.

Link Structure

HTML links look something like this: http://www.somesite.com ” title=”keyword”>Anchor text Where http://www.somesite.com is the place the link directs you to, the title is largely a remnant of time gone by, and the linked text — think the words that are blue and you click on — is called the “anchor text.” In addition to the amount of link juice a page has, the relevance of the anchor text matters. Generally speaking you want to use your keyword as the anchor text for your internal linking whenever possible. External linking (from other sites) shouldn’t be very heavily optimized for anchor text. If 90% of your links all have the same anchor text Google can throw a red flag, assuming that you’re doing something fishy. If you’re ever creating links (like we’ll show you in the future) I only ever use something generic like the site name, “here” or the full URL.

Internal Structure

Generally speaking you don’t want orphan pages (those that aren’t linked to by other pages), nor do you want an overly-messy link structure. Some say the ideal link structure for a site is something like this: That’s close, but it gets a couple things wrong. First, you’ll never have a structure that organized, and second, in an ideal world every page would link to every other page on its same level. This can easily be done with a footer that feels like a sitemap or “recommended” pages. That allows you to specify anchor text, and pass link juice freely from page to page. Unfortunately it’s impossible to draw such a web without it becoming a mess, so you’ll just have to imagine what that actually looks like. We have just one more thing to go over before we start getting those first links pointing to our site.

Robots.txt, disavow, nofollow, and other minutia

Most of SEO is managing stuff that can go wrong. There is a lot of that, but we’ll go over what will cover 99% of needs, and you can Google if there’s something really crazy. Robots.txt Almost every site has a page at url.com/robots.txt — even google has one. This is just a plain text file that lets you tell search engine crawlers what to crawl and not to crawl. Most are pretty good about listening, except the Bingbot, which pretty much does whatever it wants no matter what you tell it. (I’m mostly kidding.) If you don’t want Google to crawl a page (maybe it’s a login page you don’t want indexed, a landing page, etc.) you can just “disallow” it in your robots.txt by saying disallow: /somepage. If you add a trailing / to it (e.g. disallow: /somepage/) it will also disallow all child pages. Technically you can specify different rules for different bots (or user agents), but it’s easiest to start your file with “User-agent: *” if you don’t have a need for separate crawling rules. Disavow Google will penalize spammy sites, and unfortunately this causes some bad behavior from bad actors. Say, for example, you wanted to take out a competitor. You could send a bunch of obviously spammy links to their site and get them penalized. This is called “negative SEO,” and is something that happens often in highly contested keywords. Google generally tries to pretend like it doesn’t happen. In the case that this does happen, however, you can “Disavow” links in the Search Console, which is pretty much saying, “Hey Google, don’t count this one.” I hope you’ll never have to use it, but if you hire (or have hired) a bad SEO or are being attacked by a competitor, that is how you combat it. Nofollow A link can have a property called “nofollow” such as this: http://www.somesite.com ” title=”keyword” rel=”nofollow”>Anchor text. If you want to link to somebody but you don’t want it to count as a vote (you don’t want to pass link-juice), or you support user-generated content and want to deter spammers, you can use a nofollow link. Google says it discounts the value of those links. I’m not convinced they discount them heavily, but other SEOs are so they seem to deter spammers if nothing else. Redirects If you’re going to change a URL, but you don’t want its link juice to disappear, you can use a 301 redirect. A 301 will pass a majority of the link juice. Canonical URLs If you have two pages that are virtually the same, you can add something like https://www.someurl.com/somepage ”> to say “hey, treat this page as if it were that page instead, but I don’t want to 301 it.” And with that, we’re ready to build our first links.

Link Building

Link building is where SEO really starts to matter, and where a lot of people end up in a world of hurt. The best way to build links is to not build links. I’ve worked for companies in the past that don’t have to ask for them, they just flow in from press, customer blogs, their awesome blog posts, etc. If this is an option (and we’ll go over a couple of ways to make it more likely) you’re in a great place. If not, at least in the beginning, we’re going to manually create just a few. We’re going to create them in legitimate ways and not hire somebody in India to do so. That is a recipe for disaster, and I can’t even count the number of times I’ve seen that take down a site.

Web 2.0s

The easiest way to build high quality links are what SEOs call “web 2.0s.” That’s just a way to say “social sites” or sites that let you post stuff. Now tweeting a link into the abyss won’t do you anything, but profiles, status pages, etc. do carry some weight. And if they come from a popular domain that counts as a link. Some of the easiest are: Twitter (in your bio) Github (the readme of a repo) YouTube (the description of a video — it has to actually get views) Wordpress (yes, you’ll have to actually create a blog) Blogger (same here) Tumblr Upvote-based sites (HackerNews, GrowthHackers, Inbound.org, etc.) If nothing else you can start there and get a half dozen to a dozen links. There are always big lists of “web 2.0s” you can find online, but keep in mind if you’re going to build something out on a blogging platform you’re going to have to really build something out. That’s a lot of content and time, but you have to do it the right way. We generally keep a bigger list of Web 2.0s here . Some may be out of date, but you should probably only build a half dozen to a dozen Web 2.0s anyway.

Expired Domains

Another way to get link juice is by purchasing an expired domain. This is more difficult to do, but there are a lot of options such as expireddomains.net. (Google “expired domains” and you’ll find dozens of sites monitoring them.) You’ll want to purchase a domain that has expired and restore it as closely as you can to its original form using an archive. These sites likely have some link juice to pass on and you can pass it to yourself.

Link Intersection

Another way to find places you can build links is by using a link intersection tool. These find sites that link to “competitor a” and “competitor b” but not to you. Theoretically, if they link to both of your competitors, they should be willing to link to you. Moz, Ahrefs, LunaMetrics and others have link intersection tools that work quite well. Now that we have a few basic links flowing, we’re going to work on some strategies that will send continual links and press, eventually getting to a point where we don’t have to build any more links.

Your First Drip of Traffic — Becoming an Authority Site

Awesome — you have a site that converts well, your SEO is in place, ready for you to drive traffic. Now what? As you’re probably learned at this point, a site that converts very well but has no traffic flowing to it still converts zero traffic. We’re going to fix that. This section takes a lot of time and effort, and in the beginning you’ll likely wonder if you’re doing anything at all. Remember that class in college that is so difficult it’s the point where most people give up, effectively weeding out the people who aren’t ready to major in a specific subject? Well this is the weeder-out chapter of growth hacking.

Take a Long-Term View

The reason so many people stumble on this step is the same reason people stumble on so many steps that take a little effort under time — losing weight, investing in a 401(k), etc. In the beginning you’re going to have a little seedling of traffic, and you’ll be looking up to those who have giant oak trees, thinking, “I must be doing something wrong.” You’re not doing anything wrong. The traffic starts as a trickle before it becomes a flood. But don’t worry if you’re a startup. Our goal is to get enough traffic that continuing to do this effort will be sustainable (meaning we won’t die before we start to see the rewards), but at the same time we’re building equity in the Internet. The type of traffic we want to build is the type that will compound and will never go away. We want to create traffic today that will still give us a little trickle in five years. Combining hundreds (or thousands) of little trickles, our site that converts, and a great product we will create a giant river. Future chapters will go into depth on the networks we need to drive traffic from, so in this chapter we’re going to focus on traffic that’s network-agnostic. Traffic that we can’t get by tapping any specific network. Just to give you some idea of scale, I’ve seen this process drive over 500,000 visits per day, though the build up to that level took almost a full year. What could you do with 500,000 visits per day?

Monitoring Alerts

To start we’re going to use the keywords we found in the SEO chapter, and inject ourselves (and our company) into the conversation wherever it’s taking place. To do this we’re going to use software called BuzzBundle.

BuzzBundle

This software lets us do a few things:

Constantly monitor all mentions of a specific topic, competitor, or keyword across multiple locations on the Internet (from Facebook groups to Quora questions to blog posts) where comments are available

Allow us to leave a constructive comment that references our product or company

Disclaimer: This is not the SEO comment spam you’ve seen

This step takes thought, effort, and a real human who understands what they’re typing. I don’t often say this, but you cannot effectively automate this step without it becoming spammy. If you’re trying to replicate the automated SEO spam you’ve seen on various blogs and sites this will probably work, but you’ll get banned, your clickthrough will be a fraction of what it could be, and you’ll be banned

Productive Commenting

We’re not going to fire up some awful software to drop spun mentions of garbage onto various comment sections online hoping that brings us SEO traffic. Our comments must do two things:

Be contextual. We are only going to talk about the topic presented in an article or tweet, and only mention our company when it naturally fits in

Contribute to the conversation. I should learn something or have value added to my life by reading your comment

If you do these two things a few changes will take place: First, you’ll notice that people click on your links because you’re a thoughtful person who likes to contribute . Second, people will respect your company because you’re a thoughtful person who likes to contribute. And with that disclaimer, we’ll move on to the nitty gritty of how this is done. Now that all of that is out of the way, let’s fire up BuzzBundle and get to work.

Accounts and Personas

The first thing you’ll want to do in BuzzBundle is go to Accounts -> Add new accounts. This is the starting point for everything we’ll do, as we need accounts to comment. One thing you’ll notice about BuzzBundle is that it lets you use multiple accounts. I find it beneficial to think from multiple perspectives and therefore multiple points of view, but I don’t want to go too far overboard and be spammy. I’d recommend doing something simple — create 2–3 personas, each of whom you identify with (or are you), and enter them into your BuzzBundle accounts. Personally I don’t even change my name, I just use a different one (eg. Austen J. Allred vs. Austen Allred) or use a few photos, just so it isn’t literally the same name and same photo blanketing the Internet.

Disqus

Disqus is a comment system used all over the place, and it carries some caveates. Disqus will ban you if you use the same link in every post, so there are two workarounds:

Use a lot of different accounts, rotating IPs or using a proxy every two days or so

Use your site URL as your “display name”

Both of these work, but the second one is much easier in my view.

UTM Parameters

Using links with our UTM parameters here will be very beneficial. We’ll be able to track traffic back to each individual blog or site, and if necessary double down on the ones that are driving traffic.

Link Shorteners

If you ever start to run into problems with getting your link posted, it may be useful to use a few link shorteners or some 301 redirects. To keep it simple you can use a link shortener that 301s such as bit.ly, or if you want to spend a little more time you can set up your own site and 301 the traffic from a certain page to your money site.

Using BuzzBundle

Let’s get started with the BuzzBundle. First, it’s going to ask you for a keyword. We already have a keyword from the SEO section, but we may want to do something even a bit more generic. For this one I’m going to go with “growth hacking.” Simply hit “go” and let BuzzBundle get started. It will load different content types into different columns, but generally we are going to be scrolling through until we find something that looks compelling and like we can actually contribute to. The first thing I clicked on was this: It’s a review of another book about growth hacking. All I had to do was comment, tag the author, ask him if he were willing to review our book, and offer to send him one for free. (If you’re reading this now it’s going to be pretty awkward). My assumption is this person will find the conversation to be completely authentic, because it is. The fact that there’s now a link on his video that people who are searching for something else will find is just an added bonus. As an aside, I much prefer to hold “shift” and click on a link to open it in my normal browser if I’m just going to be commenting as myself. The next one I found was a roundup of great growth hacking blog posts from the week. I left the following comment: Note how I followed him on Twitter so that it’s obviously personal and not an automated spam comment. I even went a little bit overboard and tweeted at him just for kicks. That is how you get people on your team. As you get further along and have an idea of how to get a good response, I’d recommend starting to sort by reach, ramping up the number of keywords you’re searching for, and possibly even gasp upgrading to the paid version of BuzzBundle.

This content was originally published here.

[roland-terrasold-blog]

Journal 55

After we met with the artist Unae commissioned, we went our separate ways for the day. Unae, Elkin, and Ichibod went to meet with Unae’s father who we’d heard was in town, while Linda went to the market to find a place to sell some of the treasures we’d found in our adventures. As for me, I prepared to meet with Cardinal Zalbrag.

As you can imagine from my previous descriptions of the man, I was not looking forward to meeting with him. If Elkin’s good name wasn’t at stake I likely would have put it off longer than I did. But I needed to get it done, because I was desperately hoping he could be reasoned with and the ‘misunderstanding’ could be put behind us. Sarenrae help me, I can be so naïve sometimes.

Before I went to the church I met with Nel, hoping for some advice. I told him that I wanted to believe that this was something I could sort out by meeting with Zalbrag, and that it was all a misunderstanding, but that I feared that it was likely intentional and that talk would be nothing. Nel essentially told me that I didn’t need the church to follow Sarenrae’s path. He assured me that Zalbrag would try to make it seem like I needed him and his support, but the reality was that I’d done fine so far on my own. I asked Nel if he would come with me to the church, which he agreed to. I’m glad. As much as I put on a strong front to Linda about needing to do this on my own, I was honestly terrified to meet with Zalbrag. While I’d dedicated the last two years of my life to Sarenrae, I’d had almost no contact with the established church. Everything I knew about the Dawnflowers’ teachings I got directly from The Book of Light and Truth. I felt like I was going into this completely blind, and I deeply appreciate that I had Nel for support.

We made our way to the Temple of Desna. The outside of the temple was unchanged, although it was busier than I’d ever seen it. Inside was a different story. Where the shrine to Desna once stood, a number of holy symbols to Sarenrae had mostly dominated the alter. I felt conflicted at the sight. I can hardly complain about shows of worship to the Dawnflower, but this felt disrespectful to Desna and her priests who had allowed the followers of Sarenrae to stay in their temple until they could finish building a temple of their own. Sarenrae and Desna are allied deities, was overpowering Desna’s presence like that really necessary?

Father Zantas met with us not long after we entered. I apologized if my presence had cause him any problems, subtly referring to the alter I’d seen. He assured me that I shouldn’t worry about it. Apparently the church of Sarenrae had made a large donation to the temple to secure a temporary place of worship in Sandpoint. That didn’t really manage to make me feel better about the disrespect being shown in Desna’s temple, but I dropped the subject. I asked Father Zantas if he knew where the Cardinal was, since I was supposed to meet with him, and he pointed me in the direction of the Cardinal’s office.

I was ushered into the office by two clerics who were guarding the door. The Cardinal was filling out paperwork and didn’t so much as look my way when I entered. He was very pointedly taking his time. I’m not a very politically minded person, but this was obviously a power play. He was forcing me to wait to make it clear who held all the authority.

I remained silent and patient. I wasn’t going to give him the satisfaction of giving him a reason to lecture me on the virtues of patience or something if I failed to wait out his little game. Finally Zalbrag set aside the papers he was filling out and pulled out a large stack of papers, which he gave me to sign. He made it clear that this was an “honorary bishop” title, as I had never been a member of the church before and he couldn’t have some upstart having the authority of a true bishop.

Honestly under different circumstances I think I would have been relieved by this. After all, I really don’t know anything about the church or its power structure. All I know are the Dawnflower’s teachings, and from what I’ve seen of Zalbrag that clearly isn’t enough to hold power in the church.

But between the way the town’s been treating Elkin, the way he spoke to me with such disdain, and the little power play he’d pulled, this new slap in the face only managed to make me loathe this man more than I already did.

I held my tongue, however. I needed to be civil if I wanted to reason with him about Elkin, and that was the most vital part of this meeting. Everything else was secondary. So I tried to breach the topic, asking how the stories of my friends came about. The Cardinal said they were compiled from accounts submitted to the church from eyewitnesses. Considering the stories I’ve been hearing I sincerely doubt that, unless they have some really spiteful eyewitnesses. I asked if the church would accept a firsthand account that conflicts with their official story. Zalbrag told me that all stories were checked to see how well they match with other stories for accuracy. This was getting me nowhere, so I just came out and said it: I want the church to make a statement denouncing the inaccurate portrayal of Elkin’s character.

Zalbrag muses on me bringing up Elkin (although he used the less pleasant name for him I mentioned in my previous entry, and bristled when I corrected him). Zalbrag told me that we can’t simply follow the Dawnflower’s teaching of trust and forgiveness blindly, or evil people will take advantage of it. I tried to argue, there’s no point to having a policy of forgiveness and redemption if you refuse to allow people to prove themselves good simply because you believe they can’t be.

Zalbrag revealed to me that he was the sole survivor of the Bearclaw Temple massacre, in which an entire temple of clergy members had all been brutally murdered. The culprit had been Zalbrag’s own brother. A man we’d met once before, under the guise of one Justice Ironbriar. He’d betrayed the church of Sarenrae and murdered everyone he’d been close to, except Zalbrag, all in the name of Norgorber.

I feel conflicted. It’s true, that is a terrible thing to live through. It’s true that a betrayal like that leaves permanent wounds, not to mention to strain of having a huge number of people he knew dead in such a short period of time. It’s true that our group let Ironbriar get away because we were also fooled by him…because I was fooled by him. This is all true. But at the same time, how can someone claim to follow the teachings of Sarenrae and forego one of the basic and most important parts of her teachings? Can someone who doesn’t believe in trust and redemption truly follow the Dawnflower’s path? How could someone with this mindset become a cardinal in the church? He’s been through something terrible. As much as I hate him I do feel bad that he went through something so traumatizing and despicable. But he’s taking his pain and mistrust out on Elkin, and that is unacceptable.

After telling his story, Zalbrag told me to use detect evil on him. He wanted to make a point. When I cast the spell, he appeared totally cloaked in darkness and corruption. For a moment I bristled, fooled by the trick, and admittedly by my own wish that this problem could be solved as easily as dethroning a corrupt and evil cardinal. But then Zalbrag removed a ring he’d been wearing, and the aura vanished. The ring had an enchantment on it, which fooled spells that could view the aura of evil around the most corrupt beings. Zalbrag set out a second ring, and told me that it had a similar enchantment, but one which could fool spells meant to detect good beings instead. Apparently the church sometimes has members go under cover using rings like these, and by the same token evil beings could do the same.

I admitted that this wasn’t unheard of, that evil beings certainly could and do use deception to get close to others before betraying them. But I argued that Elkin showed no signs of this, that Zalbrag had no proof besides Elkin being born a tiefling. Elkin has been nothing but kind and heroic while I’ve known him, when he had plenty of opportunities to stab us in the back if he’d wanted.

In response to this argument, the Cardinal pulled out a large leather bound tome written in what looked like Aklo. I have a passing familiarity of the language’s appearance from living in Westcrown as a child, but I never learned the infernal language myself. Zalbrag was clearly more well-studied than I. He opened the book to a page depicting a demon who I admit looked strikingly similar to Elkin. According to Zalbrag’s translation, the being depicted was a demon lord named Vol’Delkin’Orc, a being of deception.

I argued that Elkin is no demon lord, just a tiefling. At worst maybe he’s descended from the demon lord, although I didn’t risk saying that. I told the Cardinal about the army of beings who all look like Elkin, and argued that any one of them could be the demon lord in question depicted in the book.

Zalbrag said that some beings are simply irredeemably evil, and that demons are one such being. Even if Elkin is only half demon, as he put it “no being can live with only half of what they are”.

Earlier in the conversation Zalbrag had mentioned that good people needed to be tested to prove themselves. Seeing as me vouching for Elkin was getting nowhere, I asked Zalbrag what it would take to prove that Elkin was the good person I know he is.

Death. That’s it. Zalbrag said that the only way Elkin would ever prove himself would be if he died as a martyr on our adventures. And even then, he made it clear that the church would only give a passing word about him, nothing more.

I’d held my tongue for long enough, and this answer crossed the line and then some. I set the damned paperwork aside and told Zalbrag that while I appreciated the offer, I would have to decline even the honorary position. As I walked away the Cardinal quite forcefully told me to sit back down. I didn’t, I only looked back at him and told him exactly what I thought. I won’t choose to be part of a church which slanders a good man because of his heritage, especially when that good man is my dearest friend.

Zalbrag told me that the church couldn’t allow an unregistered rogue cleric spreading his own version of Sarenrae’s words. He told me that if I didn’t do as I was told, he would excommunicate me. Zalbrag had pushed me, so I was feeling gutsy, and I wondered aloud who the people were more likely to side with, Zalbrag or the heroes who saved the town thrice already. The Cardinal none-too-subtly told me that he would slander my name as well, that the only story that people would know of me when this was done would be of a fallen and disgraced cleric.

I was done with this game. I told Zalbrag that if this branch of the church didn’t believe in repentance and acceptance, then it didn’t represent the Dawnflower. And as such I would continue following Sarenrae’s will on my own, as I had been already. I left before Zalbrag could say anything else.

What was it that Elkin said about not rocking the boat? I capsized the boat with that. How much that will come back to bite me later, I guess I’ll see.

Nel was speechless for a while as we left. He’d kept quiet during the talks, but I saw that he was basically gaping at me towards the end. I suppose he didn’t expect me to take his advice quite so far. Honestly, neither had I. Zalbrag had pushed me too far with that comment about Elkin and martyrdom. I feel like I’m going to break my quill just thinking about it. How dare he. How dare he suggest that Elkin only has worth in death? Elkin is a hundred times the man that scummy cardinal is.

I asked Nel afterwards if I’d made the right choice, since I’d likely just brought the wrath of the church down on our group’s heads. Nel told me this: there’s the “right” choice and the “correct” choice. The correct choice would be to bow to Zalbrag to keep from stirring up trouble for their group while they’re already on a dangerous quest. The right choice, however, was to stand up for someone with less agency and to defend the belief that Sarenrae accepts all, consequences be damned.

After this Nel and I talked some more, but I won’t be writing the contents of that particular conversation. Nel told me something private, which I think he’d appreciate I don’t put on paper, especially now that I fear my journal may not be the most private place anymore.

Nel and I returned to the ship. I spent maybe a good hour or two training in the demiplane to get some frustration out before Unae, Elkin, and Ichibod returned. Elkin locked himself in his room, and Unae told me that they’d gone to the temple looking for me, and had the misfortune of running into Zalbrag instead. Because if anything could ruin the progress we’d made cheering Elkin up with the artist this morning, it was meeting Zalbrag. Unae told me about their conversation with him, which went along the same lines as my own experiences. Unae asked me what happened when I met with him, and I gave her a brief summary leading up to being excommunicated. I think my friends are more indignant about it than I am. I feel numb, other than the concern for Elkin which is gnawing at me. I made my decision, I’ll have to face the consequences of that decision. But I stand by Elkin regardless of those consequences.

Linda returned shortly after, and Unae and I had to tell our stories again. Linda tells us that she plans to do some late night shopping. From what I gather, she’s been trying to pawn off a number of enchanted “toys” that she found in the lust wing of Runeforge. To my surprise Ichibod and Unae decided they would go with her. This piqued my curiosity, but when no explanation was offered I more-or-less dropped the subject.

I stayed up late writing the last entry anyways, and I was still awake when the trio returned from their “late night shopping”. I greeted them. Linda told me that they ran into an interesting character while they were shopping. Namely Amara, the woman who taught me magic. Linda told me that Amara had been scrying on us, and in none too many words made it clear that I needed to tell her to never scry on Linda while she was naked again or face the consequences. She also said something about a lich, but she didn’t go into any detail on that subject. Unae was acting dodgy, trying to keep Linda and Ichibod from talking about Amara for some reason. There’s definitely something suspicious about this little trip they took. I intend to ask Amara about it later today.

This is where I left off my previous entry, when I heard footsteps in the night. Unae and I talked about it this morning, because she heard them as well, so it would seem it wasn’t my overactive imagination this time. Unfortunately, Unae panicked and thought that something happened to Elkin because we hadn’t seen him all morning, and I let her panic get the better of me. We went into his room without permission, and he was there and fine, but he was pissed that we invaded his privacy. I feel like an idiot. I try to be the voice of reason in our group, I should have known better. I let myself panic and hurt Elkin in the process. This had not been a good start to the day.

I’m going to head out and find Amara. Maybe her bright and cheery disposition with rub off a little.

[debtlawyer]

What Are My Rights If The Information On My Credit Report is Wrong or Incomplete?

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What Are My Rights If The Information On My Credit Report is Wrong or Incomplete?

The Fair Credit Reporting Act (FCRA) is a federal statute that was passed originally in 1970 and revised in 2003. It was passed with the goal of increasing the accuracy, fairness, and privacy of consumer’s information in the files of credit bureaus and other specialty consumer reporting agencies and in doing so promote efficiency in the banking system. See 15 U.S.C. § 1681. This blog will examine the rights that the FCRA gives to consumers to dispute inaccuracies on their credit reports and in some cases get compensation for a credit bureau’s failure to correct incorrect or inaccurate information.

The FCRA gives you the right to know the information that is contained in your file at a consumer reporting agency. These agencies collect information about you and provide reports to other companies to help them make credit-related decisions. The three most common consumer reporting agencies are the three nationwide credit bureaus: Equifax, TransUnion and Experian.

If your file at a consumer reporting agency contains items that are incorrect, incomplete or inaccurate, you have the right to dispute that information. Some of the most common FCRA violations include the following:

Reports that include inaccurate information

inaccurate balances or payment history;

reporting a debt as charged off when settled or paid in full;

reporting a debt as an open balance when it was discharged in bankruptcy;

reporting a debt that does not belong to you.

Reports that include outdated information

re-aging an account to continue the reporting it;

reporting negative information more than seven years old or bankruptcies more than ten years old.

Reports that include a merging of credit files

duplicating negative credit information with a stranger who shares a similar name, social security number or some other personal identifying information.

Failing to follow proper investigation procedures

failure to conduct a reasonable investigation into your dispute;

failure to inform a creditor of your dispute;

failure to update or delete inaccurate information from your report;

Re-inserting previously deleted information without informing you.

Requesting a credit report for an impermissible purpose

an employer pulling your report without your permission;

a creditor of a paid and closed account pulling your report;

a potential creditor that you have not applied for credit with accessing your report.

Dispute Errors On Your Report

Under the FCRA, both the credit reporting company and the information provider (the person, company, or organization that had provided information about you to the credit reporting company at issue) are responsible for correcting inaccurate or incomplete information in your report. To take advantage of your rights under the FCRA, you must contact both the consumer reporting agency and the information provider. Dispute letters with copies of any supporting documentation should be sent to both entities. The letter should be sent via certified mail with return receipt requested. The credit reporting companies must investigate the items in question unless they consider the dispute to be frivolous. Once disputed, these agencies must correct or remove incorrect, incomplete or inaccurate information in your file within 30 days. This does not mean that they must remove any and all negative information. A consumer reporting agency may continue to report information that it has verified as accurate.

When the investigation is complete, the credit reporting company must give you the results of the investigation. If an investigation doesn’t resolve your dispute with the credit reporting company, you can ask that a statement of the dispute be included in your file and in future reports.

You also can ask the credit reporting company to provide your dispute statement to anyone who received a copy of your report in the recent past. Please note that a consumer reporting agency may charge a fee for this service

File An FCRA Lawsuit

If the dispute process does not correct the problem, the FCRA provides a private right of action against consumer reporting agencies for their willful or negligent failures to comply with the FCRA’s requirements. You may seek compensation for the negative effects of having such inaccurate information on your credit report. For example, have you been unfairly denied credit when applying for a mortgage loan, credit card or auto loan? Or are you paying a high interest rate due to error on your credit report? You may have been damaged by a credit bureau’s inaction in correcting your information. You may be able to sue them in state or federal court. The lawsuit must be filed either two years after the date you discovered the violation, or five years after the date of the violation.

The amount of damages you are entitled to depends on whether the violation is either willful or negligent. If the violation of the FCRA was willful, you may recover either your actual provable damages or statutory damages of $100.00 to $1,000.00 per violation. In order to prove a willful violation of the FCRA, you must either the agency knew about the violation or acted with reckless disregard of their statutory duty. See Safeco Ins. Co. of America v. Burr, 127 S. Ct. 2201 (2007).

If the violation was due to the consumer reposting agency’s negligence, you are only entitled to your actual damages, the economic harm done to you as a result of . In addition, regardless of whether the violation was willful or negligent, the statute provides for compensation with regards to your attorney fees and legal costs. However, the FCRA has a penalty for filing a lawsuit or subsequent court papers that are later determined to have been filed in “bad faith.” If you file papers in bad faith, you may be liable for the other side’s attorney fees

Spokeo, Inc. v. Robins – Injury In Fact Requirement

In Spokeo, Inc. v. Robins, the plaintiff learned that his profile on the Spokeo “people search” website stated he was “an affluent fifty-something, with children, a job, and a graduate degree.” He filed a class action lawsuit because none of those facts were true and having that information on his profile damaged his employment prospects while he was unemployed. He alleged that Spokeo had willfully violated the FCRA by failing to follow reasonable procedures to ensure the accuracy of the information it was disseminating about him. Spokeo challenged Robins’ standing to bring the action, and the case was appealed up to the United States Supreme Court.

Justice Samuel Alito wrote the Court’s 6-2 decision in Spokeo, Inc. v. Robins, 136 S. Ct. 1540 (2016), which narrowly interpreted what constituted an injury under the FCRA. The court found that purely procedural or technical violations of statutory rights are insufficient to establish standing or the ability to bring the lawsuit. Justice Alito wrote that “standing requires a concrete injury even in the context of a statutory violation,” and that Congress has the authority to “elevate to the status of legally cognizable injuries concrete, de facto injuries that were previously inadequate at law.” However, the fact Congress has made a previously intangible injury actionable “does not mean that a plaintiff automatically satisfies the injury-in-fact requirement whenever a statute grants a person a statutory right and purports to authorize that person to sue to vindicate that right.” In other words, you need to show harm or injury that resulted from the inaccurate information in order to bring an FCRA lawsuit. To illustrate this point, Justice Alito point to the example of an incorrect zip code. While it may be technically incorrect, it does not harm or injure an individual’s credit or employment prospects.

The case was sent back to the lower court for further fact finding regarding the injury to Robins caused by the information in the Spokeo profile. In the lower court, Judge O’Scannlain concluded that if one’s age, marital status, educational background, and employment history, were inaccurately listed it was much worse that Justice Alito’s example of the incorrect zip code. He found this fact pattern was likely to harm one’s employment prospects and/or create emotional distress and that “flattering inaccuracies” could hurt one’s employment prospects because they may lead a prospective employer to (1) question the applicant’s veracity or (2) determine that he is overqualified. See Robins v. Spokeo, Inc. 867 F.3d 1108 (9th Cir. 2017).

Despite this Supreme Court’s ruling, FCRA litigation has continued to accelerate. Furthermore, while we have been discussing the individual’s right to bring a lawsuit against consumer reporting agency for inaccurate information on a credit report, some attorneys have been bringing class action lawsuits for violations of the FCRA. In a class action, you sue not just for your violation but for all the similar plaintiffs in your same position.

A recent study released by the Federal Trade Commission found that 23% of consumers identified inaccurate information in their credit reports, which means there are many potential FCRA plaintiffs who have not be compensated for the damage done to their credit because of errors on their credit reports.

We recommend obtaining a free credit report every year from each of the three major credit bureaus from annualcreditreport.com. For consumers who have filed bankruptcy, we recommend checking their credit reports 90 days after discharge to ensure that the bankruptcy is being accurately reflected on their reports.

David I. Pankin is a Brooklyn bankruptcy attorney helping clients with consumer protection matters. If you have any questions about the FCRA or think there may be errors on your credit report, please contact the Law Offices of David I Pankin, PC at 888-529-9600 or by using our easy online contact form.

More information:

Fair Credit Reporting Act 15 USC § 1681

https://www.law.cornell.edu/uscode/text/15/chapter-41/subchapter-III

Request a Free Credit Report

https://www.annualcreditreport.com/

Disputing Errors on Credit Reports

https://www.consumer.ftc.gov/articles/0151-disputing-errors-credit-reports

Spokeo, Inc. v. Robins (Supreme Court Opinion)

https://www.supremecourt.gov/opinions/15pdf/13-1339dif_3m92.pdf

Robins v. Spokeo, Inc. (Later 9th Circuit Opinion)

https://cdn.ca9.uscourts.gov/datastore/opinions/2017/08/15/11-56843.pdf

[tainghekhongdaycomvn]

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

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[lawrenceseitz22]

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

[arthurjeffries]

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics published first on https://steelcitygaragedoors.blogspot.com

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

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Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Trust Your Data: How to Efficiently Filter Spam, Bots, & Other Junk Traffic in Google Analytics

Posted by Carlosesal

There is no doubt that Google Analytics is one of the most important tools you could use to understand your users' behavior and measure the performance of your site. There's a reason it's used by millions across the world.

But despite being such an essential part of the decision-making process for many businesses and blogs, I often find sites (of all sizes) that do little or no data filtering after installing the tracking code, which is a huge mistake.

Think of a Google Analytics property without filtered data as one of those styrofoam cakes with edible parts. It may seem genuine from the top, and it may even feel right when you cut a slice, but as you go deeper and deeper you find that much of it is artificial.

If you're one of those that haven’t properly configured their Google Analytics and you only pay attention to the summary reports, you probably won't notice that there's all sorts of bogus information mixed in with your real user data.

And as a consequence, you won't realize that your efforts are being wasted on analyzing data that doesn't represent the actual performance of your site.

To make sure you're getting only the real ingredients and prevent you from eating that slice of styrofoam, I'll show you how to use the tools that GA provides to eliminate all the artificial excess that inflates your reports and corrupts your data.

Common Google Analytics threats

As most of the people I've worked with know, I’ve always been obsessed with the accuracy of data, mainly because as a marketer/analyst there's nothing worse than realizing that you’ve made a wrong decision because your data wasn’t accurate. That’s why I’m continually exploring new ways of improving it.

As a result of that research, I wrote my first Moz post about the importance of filtering in Analytics, specifically about ghost spam, which was a significant problem at that time and still is (although to a lesser extent).

While the methods described there are still quite useful, I’ve since been researching solutions for other types of Google Analytics spam and a few other threats that might not be as annoying, but that are equally or even more harmful to your Analytics.

Let’s review, one by one.

Ghosts, crawlers, and other types of spam

The GA team has done a pretty good job handling ghost spam. The amount of it has been dramatically reduced over the last year, compared to the outbreak in 2015/2017.

However, the millions of current users and the thousands of new, unaware users that join every day, plus the majority's curiosity to discover why someone is linking to their site, make Google Analytics too attractive a target for the spammers to just leave it alone.

The same logic can be applied to any widely used tool: no matter what security measures it has, there will always be people trying to abuse its reach for their own interest. Thus, it's wise to add an extra security layer.

Take, for example, the most popular CMS: Wordpress. Despite having some built-in security measures, if you don't take additional steps to protect it (like setting a strong username and password or installing a security plugin), you run the risk of being hacked.

The same happens to Google Analytics, but instead of plugins, you use filters to protect it.

In which reports can you look for spam?

Spam traffic will usually show as a Referral, but it can appear in any part of your reports, even in unsuspecting places like a language or page title.

Sometimes spammers will try to fool by using misleading URLs that are very similar to known websites, or they may try to get your attention by using unusual characters and emojis in the source name.

Independently of the type of spam, there are 3 things you always should do when you think you found one in your reports:

Never visit the suspicious URL. Most of the time they'll try to sell you something or promote their service, but some spammers might have some malicious scripts on their site.

This goes without saying, but never install scripts from unknown sites; if for some reason you did, remove it immediately and scan your site for malware.

Filter out the spam in your Google Analytics to keep your data clean (more on that below).

If you're not sure whether an entry on your report is real, try searching for the URL in quotes (“example.com”). Your browser won’t open the site, but instead will show you the search results; if it is spam, you'll usually see posts or forums complaining about it.

If you still can’t find information about that particular entry, give me a shout — I might have some knowledge for you.

Bot traffic

A bot is a piece of software that runs automated scripts over the Internet for different purposes.

There are all kinds of bots. Some have good intentions, like the bots used to check copyrighted content or the ones that index your site for search engines, and others not so much, like the ones scraping your content to clone it.

2016 bot traffic report. Source: Incapsula

In either case, this type of traffic is not useful for your reporting and might be even more damaging than spam both because of the amount and because it's harder to identify (and therefore to filter it out).

It's worth mentioning that bots can be blocked from your server to stop them from accessing your site completely, but this usually involves editing sensible files that require high technical knowledge, and as I said before, there are good bots too.

So, unless you're receiving a direct attack that's skewing your resources, I recommend you just filter them in Google Analytics.

In which reports can you look for bot traffic?

Bots will usually show as Direct traffic in Google Analytics, so you'll need to look for patterns in other dimensions to be able to filter it out. For example, large companies that use bots to navigate the Internet will usually have a unique service provider.

I’ll go into more detail on this below.

Internal traffic

Most users get worried and anxious about spam, which is normal — nobody likes weird URLs showing up in their reports. However, spam isn't the biggest threat to your Google Analytics.

You are!

The traffic generated by people (and bots) working on the site is often overlooked despite the huge negative impact it has. The main reason it's so damaging is that in contrast to spam, internal traffic is difficult to identify once it hits your Analytics, and it can easily get mixed in with your real user data.

There are different types of internal traffic and different ways of dealing with it.

Direct internal traffic

Testers, developers, marketing team, support, outsourcing... the list goes on. Any member of the team that visits the company website or blog for any purpose could be contributing.

In which reports can you look for direct internal traffic?

Unless your company uses a private ISP domain, this traffic is tough to identify once it hits you, and will usually show as Direct in Google Analytics.

Third-party sites/tools

This type of internal traffic includes traffic generated directly by you or your team when using tools to work on the site; for example, management tools like Trello or Asana,

It also considers traffic coming from bots doing automatic work for you; for example, services used to monitor the performance of your site, like Pingdom or GTmetrix.

Some types of tools you should consider:

Project management

Social media management

Performance/uptime monitoring services

SEO tools

In which reports can you look for internal third-party tools traffic?

This traffic will usually show as Referral in Google Analytics.

Development/staging environments

Some websites use a test environment to make changes before applying them to the main site. Normally, these staging environments have the same tracking code as the production site, so if you don’t filter it out, all the testing will be recorded in Google Analytics.

In which reports can you look for development/staging environments?

This traffic will usually show as Direct in Google Analytics, but you can find it under its own hostname (more on this later).

Web archive sites and cache services

Archive sites like the Wayback Machine offer historical views of websites. The reason you can see those visits on your Analytics — even if they are not hosted on your site — is that the tracking code was installed on your site when the Wayback Machine bot copied your content to its archive.

One thing is for certain: when someone goes to check how your site looked in 2015, they don't have any intention of buying anything from your site — they're simply doing it out of curiosity, so this traffic is not useful.

In which reports can you look for traffic from web archive sites and cache services?

You can also identify this traffic on the hostname report.

A basic understanding of filters

The solutions described below use Google Analytics filters, so to avoid problems and confusion, you'll need some basic understanding of how they work and check some prerequisites.

Things to consider before using filters:

1. Create an unfiltered view.

Before you do anything, it's highly recommendable to make an unfiltered view; it will help you track the efficacy of your filters. Plus, it works as a backup in case something goes wrong.

2. Make sure you have the correct permissions.

You will need edit permissions at the account level to create filters; edit permissions at view or property level won’t work.

3. Filters don’t work retroactively.

In GA, aggregated historical data can’t be deleted, at least not permanently. That's why the sooner you apply the filters to your data, the better.

4. The changes made by filters are permanent!

If your filter is not correctly configured because you didn’t enter the correct expression (missing relevant entries, a typo, an extra space, etc.), you run the risk of losing valuable data FOREVER; there is no way of recovering filtered data.

But don’t worry — if you follow the recommendations below, you shouldn’t have a problem.

5. Wait for it.

Most of the time you can see the effect of the filter within minutes or even seconds after applying it; however, officially it can take up to twenty-four hours, so be patient.

Types of filters

There are two main types of filters: predefined and custom.

Predefined filters are very limited, so I rarely use them. I prefer to use the custom ones because they allow regular expressions, which makes them a lot more flexible.

Within the custom filters, there are five types: exclude, include, lowercase/uppercase, search and replace, and advanced.

Here we will use the first two: exclude and include. We'll save the rest for another occasion.

Essentials of regular expressions

If you already know how to work with regular expressions, you can jump to the next section.

REGEX (short for regular expressions) are text strings prepared to match patterns with the use of some special characters. These characters help match multiple entries in a single filter.

Don’t worry if you don’t know anything about them. We will use only the basics, and for some filters, you will just have to COPY-PASTE the expressions I pre-built.

REGEX special characters

There are many special characters in REGEX, but for basic GA expressions we can focus on three:

^ The caret: used to indicate the beginning of a pattern,

$ The dollar sign: used to indicate the end of a pattern,

| The pipe or bar: means "OR," and it is used to indicate that you are starting a new pattern.

When using the pipe character, you should never ever:

Put it at the beginning of the expression,

Put it at the end of the expression,

Put 2 or more together.

Any of those will mess up your filter and probably your Analytics.

A simple example of REGEX usage

Let's say I go to a restaurant that has an automatic machine that makes fruit salad, and to choose the fruit, you should use regular xxpressions.

This super machine has the following fruits to choose from: strawberry, orange, blueberry, apple, pineapple, and watermelon.

To make a salad with my favorite fruits (strawberry, blueberry, apple, and watermelon), I have to create a REGEX that matches all of them. Easy! Since the pipe character “|” means OR I could do this:

REGEX 1: strawberry|blueberry|apple|watermelon

The problem with that expression is that REGEX also considers partial matches, and since pineapple also contains “apple,” it would be selected as well... and I don’t like pineapple!

To avoid that, I can use the other two special characters I mentioned before to make an exact match for apple. The caret “^” (begins here) and the dollar sign “$” (ends here). It will look like this:

REGEX 2: strawberry|blueberry|^apple$|watermelon

The expression will select precisely the fruits I want.

But let’s say for demonstration's sake that the fewer characters you use, the cheaper the salad will be. To optimize the expression, I can use the ability for partial matches in REGEX.

Since strawberry and blueberry both contain "berry," and no other fruit in the list does, I can rewrite my expression like this:

Optimized REGEX: berry|^apple$|watermelon

That’s it — now I can get my fruit salad with the right ingredients, and at a lower price.

3 ways of testing your filter expression

As I mentioned before, filter changes are permanent, so you have to make sure your filters and REGEX are correct. There are 3 ways of testing them:

Right from the filter window; just click on “Verify this filter,” quick and easy. However, it's not the most accurate since it only takes a small sample of data.

Using an online REGEX tester; very accurate and colorful, you can also learn a lot from these, since they show you exactly the matching parts and give you a brief explanation of why.

Using an in-table temporary filter in GA; you can test your filter against all your historical data. This is the most precise way of making sure you don’t miss anything.

If you're doing a simple filter or you have plenty of experience, you can use the built-in filter verification. However, if you want to be 100% sure that your REGEX is ok, I recommend you build the expression on the online tester and then recheck it using an in-table filter.

Quick REGEX challenge

Here's a small exercise to get you started. Go to this premade example with the optimized expression from the fruit salad case and test the first 2 REGEX I made. You'll see live how the expressions impact the list.

Now make your own expression to pay as little as possible for the salad.

Remember:

We only want strawberry, blueberry, apple, and watermelon;

The fewer characters you use, the less you pay;

You can do small partial matches, as long as they don’t include the forbidden fruits.

Tip: You can do it with as few as 6 characters.

Now that you know the basics of REGEX, we can continue with the filters below. But I encourage you to put “learn more about REGEX” on your to-do list — they can be incredibly useful not only for GA, but for many tools that allow them.

How to create filters to stop spam, bots, and internal traffic in Google Analytics

Back to our main event: the filters!

Where to start: To avoid being repetitive when describing the filters below, here are the standard steps you need to follow to create them:

Go to the admin section in your Google Analytics (the gear icon at the bottom left corner),

Under the View column (master view), click the button “Filters” (don’t click on “All filters“ in the Account column):

Click the red button “+Add Filter” (if you don’t see it or you can only apply/remove already created filters, then you don’t have edit permissions at the account level. Ask your admin to create them or give you the permissions.):

Then follow the specific configuration for each of the filters below.

The filter window is your best partner for improving the quality of your Analytics data, so it will be a good idea to get familiar with it.

Valid hostname filter (ghost spam, dev environments)

Prevents traffic from:

Ghost spam

Development hostnames

Scraping sites

Cache and archive sites

This filter may be the single most effective solution against spam. In contrast with other commonly shared solutions, the hostname filter is preventative, and it rarely needs to be updated.

Ghost spam earns its name because it never really visits your site. It’s sent directly to the Google Analytics servers using a feature called Measurement Protocol, a tool that under normal circumstances allows tracking from devices that you wouldn’t imagine that could be traced, like coffee machines or refrigerators.

Real users pass through your server, then the data is sent to GA; hence it leaves valid information. Ghost spam is sent directly to GA servers, without knowing your site URL; therefore all data left is fake. Source: carloseo.com

The spammer abuses this feature to simulate visits to your site, most likely using automated scripts to send traffic to randomly generated tracking codes (UA-0000000-1).

Since these hits are random, the spammers don't know who they're hitting; for that reason ghost spam will always leave a fake or (not set) host. Using that logic, by creating a filter that only includes valid hostnames all ghost spam will be left out.

Where to find your hostnames

Now here comes the “tricky” part. To create this filter, you will need, to make a list of your valid hostnames.

A list of what!?

Essentially, a hostname is any place where your GA tracking code is present. You can get this information from the hostname report:

Go to Audience > Select Network > At the top of the table change the primary dimension to Hostname.

If your Analytics is active, you should see at least one: your domain name. If you see more, scan through them and make a list of all the ones that are valid for you.

Types of hostname you can find

The good ones:

Type

Example

Your domain and subdomains

yourdomain.com

Tools connected to your Analytics

YouTube, MailChimp

Payment gateways

Shopify, booking systems

Translation services

Google Translate

Mobile speed-up services

Google weblight

The bad ones (by bad, I mean not useful for your reports):

Type

Example/Description

Staging/development environments

staging.yourdomain.com

Internet archive sites

web.archive.org

Scraping sites that don’t bother to trim the content

The URL of the scraper

Spam

Most of the time they will show their URL, but sometimes they may use the name of a known website to try to fool you. If you see a URL that you don’t recognize, just think, “do I manage it?” If the answer is no, then it isn't your hostname.

(not set) hostname

It usually comes from spam. On rare occasions it's related to tracking code issues.

Below is an example of my hostname report. From the unfiltered view, of course, the master view is squeaky clean.

Now with the list of your good hostnames, make a regular expression. If you only have your domain, then that is your expression; if you have more, create an expression with all of them as we did in the fruit salad example:

Hostname REGEX (example) yourdomain.com|hostname2|hostname3|hostname4

Important! You cannot create more than one “Include hostname filter”; if you do, you will exclude all data. So try to fit all your hostnames into one expression (you have 255 characters).

The “valid hostname filter” configuration:

Filter Name: Include valid hostnames

Filter Type: Custom > Include

Filter Field: Hostname

Filter Pattern: [hostname REGEX you created]

Campaign source filter (Crawler spam, internal sources)

Prevents traffic from:

Crawler spam

Internal third-party tools (Trello, Asana, Pingdom)

Important note: Even if these hits are shown as a referral, the field you should use in the filter is “Campaign source” — the field “Referral” won’t work.

Filter for crawler spam

The second most common type of spam is crawler. They also pretend to be a valid visit by leaving a fake source URL, but in contrast with ghost spam, these do access your site. Therefore, they leave a correct hostname.

You will need to create an expression the same way as the hostname filter, but this time, you will put together the source/URLs of the spammy traffic. The difference is that you can create multiple exclude filters.

Crawler REGEX (example) spam1|spam2|spam3|spam4

Crawler REGEX (pre-built) As I promised, here are latest pre-built crawler expressions that you just need to copy/paste.

The “crawler spam filter” configuration:

Filter Name: Exclude crawler spam 1

Filter Type: Custom > Exclude

Filter Field: Campaign source

Filter Pattern: [crawler REGEX]

Filter for internal third-party tools

Although you can combine your crawler spam filter with internal third-party tools, I like to have them separated, to keep them organized and more accessible for updates.

The “internal tools filter” configuration:

Filter Name: Exclude internal tool sources

Filter Pattern: [tool source REGEX]

Internal Tools REGEX (example) trello|asana|redmine

In case, that one of the tools that you use internally also sends you traffic from real visitors, don’t filter it. Instead, use the “Exclude Internal URL Query” below.

For example, I use Trello, but since I share analytics guides on my site, some people link them from their Trello accounts.

Filters for language spam and other types of spam

The previous two filters will stop most of the spam; however, some spammers use different methods to bypass the previous solutions.

For example, they try to confuse you by showing one of your valid hostnames combined with a well-known source like Apple, Google, or Moz. Even my site has been a target (not saying that everyone knows my site; it just looks like the spammers don’t agree with my guides).

However, even if the source and host look fine, the spammer injects their message in another part of your reports like the keyword, page title, and even as a language.

In those cases, you will have to take the dimension/report where you find the spam and choose that name in the filter. It's important to consider that the name of the report doesn't always match the name in the filter field:

Report name

Filter field

Language

Language settings

Referral

Campaign source

Organic Keyword

Search term

Service Provider

ISP Organization

Network Domain

ISP Domain

Here are a couple of examples.

The “language spam/bot filter” configuration:

Filter Name: Exclude language spam

Filter Type: Custom > Exclude

Filter Field: Language settings

Filter Pattern: [Language REGEX]

Language Spam REGEX (Prebuilt) \s[^\s]*\s|.{15,}|\.|,|^c$

The expression above excludes fake languages that don't meet the required format. For example, take these weird messages appearing instead of regular languages like en-us or es-es:

Examples of language spam

The organic/keyword spam filter configuration:

Filter Name: Exclude organic spam

Filter Type: Custom > Exclude

Filter Field: Search term

Filter Pattern: [keyword REGEX]

Filters for direct bot traffic

Bot traffic is a little trickier to filter because it doesn't leave a source like spam, but it can still be filtered with a bit of patience.

The first thing you should do is enable bot filtering. In my opinion, it should be enabled by default.

Go to the Admin section of your Analytics and click on View Settings. You will find the option “Exclude all hits from known bots and spiders” below the currency selector:

It would be wonderful if this would take care of every bot — a dream come true. However, there's a catch: the key here is the word “known.” This option only takes care of known bots included in the “IAB known bots and spiders list." That's a good start, but far from enough.

There are a lot of “unknown” bots out there that are not included in that list, so you'll have to play detective and search for patterns of direct bot traffic through different reports until you find something that can be safely filtered without risking your real user data.

To start your bot trail search, click on the Segment box at the top of any report, and select the “Direct traffic” segment.

Then navigate through different reports to see if you find anything suspicious.

Some reports to start with:

Service provider

Browser version

Network domain

Screen resolution

Flash version

Country/City

Signs of bot traffic

Although bots are hard to detect, there are some signals you can follow:

An unnatural increase of direct traffic

Old versions (browsers, OS, Flash)

They visit the home page only (usually represented by a slash “/” in GA)

Extreme metrics:

Bounce rate close to 100%,

Session time close to 0 seconds,

1 page per session,

100% new users.

Important! If you find traffic that checks off many of these signals, it is likely bot traffic. However, not all entries with these characteristics are bots, and not all bots match these patterns, so be cautious.

Perhaps the most useful report that has helped me identify bot traffic is the “Service Provider” report. Large corporations frequently use their own Internet service provider name.

I also have a pre-built expression for ISP bots, similar to the crawler expressions.

The bot ISP filter configuration:

Filter Name: Exclude bots by ISP

Filter Type: Custom > Exclude

Filter Field: ISP organization

Filter Pattern: [ISP provider REGEX]

ISP provider bots REGEX (prebuilt) hubspot|^google\sllc$|^google\sinc\.$|alibaba\.com\sllc|ovh\shosting\sinc\. Latest ISP bot expression

IP filter for internal traffic

We already covered different types of internal traffic, the one from test sites (with the hostname filter), and the one from third-party tools (with the campaign source filter).

Now it's time to look at the most common and damaging of all: the traffic generated directly by you or any member of your team while working on any task for the site.

To deal with this, the standard solution is to create a filter that excludes the public IP (not private) of all locations used to work on the site.

Examples of places/people that should be filtered

Office

Support

Home

Developers

Hotel

Coffee shop

Bar

Mall

Any place that is regularly used to work on your site

To find the public IP of the location you are working at, simply search for "my IP" in Google. You will see one of these versions:

IP version

Example

Short IPv4

1.23.45.678

Long IPv6

2001:0db8:85a3:0000:0000:8a2e:0370:7334

No matter which version you see, make a list with the IP of each place and put them together with a REGEX, the same way we did with other filters.

IP address expression: IP1|IP2|IP3|IP4 and so on.

The static IP filter configuration:

Filter Name: Exclude internal traffic (IP)

Filter Type: Custom > Exclude

Filter Field: IP Address

Filter Pattern: [The IP expression]

Cases when this filter won’t be optimal:

There are some cases in which the IP filter won’t be as efficient as it used to be:

You use IP anonymization (required by the GDPR regulation). When you anonymize the IP in GA, the last part of the IP is changed to 0. This means that if you have 1.23.45.678, GA will pass it as 1.23.45.0, so you need to put it like that in your filter. The problem is that you might be excluding other IPs that are not yours.

Your Internet provider changes your IP frequently (Dynamic IP). This has become a common issue lately, especially if you have the long version (IPv6).

Your team works from multiple locations. The way of working is changing — now, not all companies operate from a central office. It's often the case that some will work from home, others from the train, in a coffee shop, etc. You can still filter those places; however, maintaining the list of IPs to exclude can be a nightmare,

You or your team travel frequently. Similar to the previous scenario, if you or your team travels constantly, there's no way you can keep up with the IP filters.

If you check one or more of these scenarios, then this filter is not optimal for you; I recommend you to try the “Advanced internal URL query filter” below.

URL query filter for internal traffic

If there are dozens or hundreds of employees in the company, it's extremely difficult to exclude them when they're traveling, accessing the site from their personal locations, or mobile networks.

Here’s where the URL query comes to the rescue. To use this filter you just need to add a query parameter. I add “?internal" to any link your team uses to access your site:

Internal newsletters

Management tools (Trello, Redmine)

Emails to colleagues

Also works by directly adding it in the browser address bar

Basic internal URL query filter

The basic version of this solution is to create a filter to exclude any URL that contains the query “?internal”.

Filter Name: Exclude Internal Traffic (URL Query)

Filter Type: Custom > Exclude

Filter Field: Request URI

Filter Pattern: \?internal

This solution is perfect for instances were the user will most likely stay on the landing page, for example, when sending a newsletter to all employees to check a new post.

If the user will likely visit more than the landing page, then the subsequent pages will be recorded.

Advanced internal URL query filter

This solution is the champion of all internal traffic filters!

It’s a more comprehensive version of the previous solution and works by filtering internal traffic dynamically using Google Tag Manager, a GA custom dimension, and cookies.

Although this solution is a bit more complicated to set up, once it's in place:

It doesn’t need maintenance

Any team member can use it, no need to explain techy stuff

Can be used from any location

Can be used from any device, and any browser

To activate the filter, you just have to add the text “?internal” to any URL of the website.

That will insert a small cookie in the browser that will tell GA not to record the visits from that browser.

And the best of it is that the cookie will stay there for a year (unless it is manually removed), so the user doesn’t have to add “?internal” every time.

Bonus filter: Include only internal traffic

In some occasions, it's interesting to know the traffic generated internally by employees — maybe because you want to measure the success of an internal campaign or just because you're a curious person.

In that case, you should create an additional view, call it “Internal Traffic Only,” and use one of the internal filters above. Just one! Because if you have multiple include filters, the hit will need to match all of them to be counted.

If you configured the “Advanced internal URL query” filter, use that one. If not, choose one of the others.

The configuration is exactly the same — you only need to change “Exclude” for “Include.”

Cleaning historical data

The filters will prevent future hits from junk traffic.

But what about past affected data?

I know I told you that deleting aggregated historical data is not possible in GA. However, there's still a way to temporarily clean up at least some of the nasty traffic that has already polluted your reports.

For this, we'll use an advanced segment (a subset of your Analytics data). There are built-in segments like “Organic” or “Mobile,” but you can also build one using your own set of rules.

To clean our historical data, we will build a segment using all the expressions from the filters above as conditions (except the ones from the IP filter, because IPs are not stored in GA; hence, they can’t be segmented).

To help you get started, you can import this segment template.

You just need to follow the instructions on that page and replace the placeholders. Here is how it looks:

In the actual template, all text is black; the colors are just to help you visualize the conditions.

After importing it, to select the segment:

Click on the box that says “All users” at the top of any of your reports

From your list of segments, check the one that says “0. All Users - Clean”

Lastly, uncheck the “All Users”

Now you can navigate through your reaports and all the junk traffic included in the segment will be removed.

A few things to consider when using this segment:

Segments have to be selected each time. A way of having it selected by default is by adding a bookmark when the segment is selected.

You can remove or add conditions if you need to.

You can edit the segment at any time to update it or add conditions (open the list of segments, then click “Actions” then “Edit”).

The hostname expression and third-party tools expression are different for each site.

If your site has a large volume of traffic, segments may sample your data when selected, so if you see the little shield icon at the top of your reports go yellow (normally is green), try choosing a shorter period (i.e. 1 year, 6 months, one month).

Conclusion: Which cake would you eat?

Having real and accurate data is essential for your Google Analytics to report as you would expect.

But if you haven’t filtered it properly, it’s almost certain that it will be filled with all sorts of junk and artificial information.

And the worst part is that if don't realize that your reports contain bogus data, you will likely make wrong or poor decisions when deciding on the next steps for your site or business.

The filters I share above will help you prevent the three most harmful threats that are polluting your Google Analytics and don’t let you get a clear view of the actual performance of your site: spam, bots, and internal traffic.

Once these filters are in place, you can rest assured that your efforts (and money!) won’t be wasted on analyzing deceptive Google Analytics data, and your decisions will be based on solid information.

And the benefits don’t stop there. If you're using other tools that import data from GA, for example, WordPress plugins like GADWP, excel add-ins like AnalyticsEdge, or SEO suites like Moz Pro, the benefits will trickle down to all of them as well.

Besides highlighting the importance of the filters in GA (which I hope I made clear by now), I would also love that for the preparation of these filters to give you the curiosity and basis to create others that will allow you to do all sorts of remarkable things with your data.

Remember, filters not only allow you to keep away junk, you can also use them to rearrange your real user information — but more on that on another occasion.

That’s it! I hope these tips help you make more sense of your data and make accurate decisions.

Have any questions, feedback, experiences? Let me know in the comments, or reach me on Twitter @carlosesal.

Complementary resources:

Google Analytics spam & bots (FAQ): Common data quality questions and concerns answered

Ultimate guide to stopping bots and Google Analytics spam (Always up to date)

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