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SeoTuners offers personalized SEO search engine optimization in Thousand Oaks for businesses aiming for sustainable growth. We craft SEO strategies based on your unique needs, ensuring you get maximum ROI. Our services cover on-page, off-page, and technical SEO to create a holistic strategy that drives lasting results. Let us guide your business to SEO success with customized solutions.
SeoTuners Thousand Oaks 275 E. Hillcrest Dr #160–254, Thousand Oaks, CA 91360 (805) 523–2497
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When NOT to Use SEO
A large part of our business is conducting professional Search Engine Optimization services for small and medium-sized businesses. So you might be surprised to learn that we talk ourselves out of more SEO projects than not. Or, more accurately, we talk our clients out of doing SEO.
Many people see, hear and read about various SEO success stories from friends, colleagues and business associates and think, "Hey, sounds great. How can I use that for MY business?"
The problem with SEO, probably more than any other Internet marketing tactic, is that it's often the wrong tool for the job. No matter how bad you WANT it to be the answer to your problems and help you increase sales it's just not not going to cut it.
sSo to help you evaluate whether SEO can be part of your well-rounded Internet marketing strategy, here's 5 reasons why SEO will NOT help you"
1. You Have a Made Up Product: The whole foundation of SEO is to position your webpages to be the answer to the question regarding relevant searches. If the product or service you offer is something so unique or made up, you can't leverage SEO for the simple reason that nobody knows to search for it. Case in point was a company that came to us to implement an SEO program for "Dog Wine." The problem is two-fold: Wine is actually bad for your dog so nobody would be looking for those keywords together unless maybe it was for "Dog wine poisoning." Secondly, the product wasn't actually wine at all but a type of gravy commonly sold to spruce up kibble. So while the product was real and perfectly OK to feed your dog, the association between the made up product category (dog wine) and the actual product (a kibble additive commonly referred to as "dog gravy") was beyond the scope of SEO. A better marketing solution that we suggested was a trade PR campaign combined with blogger reviews since the product needed explaining in order to "get it."
2. Low Search Volume: The biggest issue we find is that the keyword phrases associated with the company's products and services are in low demand. Nobody wants to hear that what they're selling is not in demand or the greatest thing since sliced bread. SEO exposes weaknesses in branding and product positioning. Sometimes SEO is considered a panacea for a weak product offering or non-existing branding campaign. SEO can't perform magic, and certainly can not create demand where little or none exists. SEO works best when you have a product or service with well-articulated BENEFITS to the person doing the search, since most searches start with "How do I... " or "How to... " Be the answer to that question assuming it's even being asked. Our keyword research will expose those weaknesses right away.
3. No Supporting Efforts: Search Engine Optimization has become increasingly complex in that one-time efforts, while still important, aren't enough to sustain any short term success. And with the rising importance of social media's impact on SEO, businesses will need to invest in ongoing social media efforts to boost their baseline SEO results since all the major search engines have gone on record that social media signals going in and out of a website are a validation of that site's authorityon a particular subject (and set of keywords). So to invest in SEO is really a baseline step that needs to be reinforced with social media marketing and content marketing and blogging on an ongoing basis.
4. Website Design is Not Set Up For SEO: While a website's layout can be changed, this can often lead to additional costs in order to change the layout of the website to make it SEO ready. For instance, the number one problem is when all products and services are listed on a single page. In order to perform proper SEO, each product or service must have it's own page since proper onsite SEO requires unique META tags, Title tags, keywords, etc. Another hurdle is when clients have Flash-heavy websites or resist placing actual text and words on the site preferring mostly pictures. SEO is about content, mainly text.
5. Need Results Immediately: And as you probably suspect, SEO can take time to take effect. In fact, the very nature of GOOD SEO practices is to try and mimic organic linking and social media activity in as natural means as possible. That means slowly, and gradually. And even then, it can take the search engines weeks and even months to index your site and move it up in the rankings - if at all! So if you have a special event or you're planning a product roll out or other timely event, SEO isn't going to be your ticket to the top.
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Thousand Oaks Seo | Elite Internet Technologies
To get quality Thousand Oaks SEO service, contact us Elite Internet Technologies. Our digital marketing team is very talented and has very good experience in this sector. Contact
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How To Pick The Quality Search Engine Optimization Services For Startup
There are a number of digital marketing agencies in India who are providing SEO solutions for startup business to make them profitable around the globe. It is extremely essential for to select the right search engine optimization agency for your company. A wrong choice and you'll end up with downfalls while keeping loss of cash and of course your potential customers too. Here I am writing about SEO just Read on to know about the factors that you need to keep in mind when looking for a professional SEO organization for your needs.
SEO Expertise
Hiring quality SEO solutions is vital for everyone. Ensure that the organization that you seek the assistance of has excellent knowing of various components concerning the market includes every part of digital marketing efforts. They should have comprehensive knowledge of mutual and one-way connecting and other ideas and expertise knowledge of SEO services. They should be prospective enough to offer an innovative research about company data, information of the connecting framework of your site and create prospective techniques in order improve it your overall SEO campaign.
Quality Client service
Unless you don't have professional consultancy for your SEO campaign it is not really possible to reach the preferred factors of achievements you want to get for your website. Of course, you will not want to wait for the digital solutions to your concerns. Examine the digital marketing services organization's customer care guidelines so as to know about their stability of search engine optimization strategies.
Time is money
There is no use of choosing SEO solutions agency if your project is not handling by on time. Ensure that the digital organization you seek the assistance of has the prospective to do the job expertly and implanting all the mandatory SEO solutions for your business. They should not look forward for assistance for small things. How their teams are handling your small code efforts for SEO? It is crucial that the professional digital marketing organization you seek the assistance of has employed extremely certified growth professionals through strategic and possible SEO services.
Campaign Budget
Do not seek the low budget assistance of the very first digital organization that you come across during your searches on internet. Nominee list a few prospective SEO companies who ca n handle your SEO campaign under your budget and evaluate the prices and repair providing of each SEO campaign. Also, evaluate the price with the methods of advertising they are making. Make sure the organization you seek the assistance of offers excellent and fast within best cost of every digital marketing service.
Marketing Efforts report
A serious digital growth solutions (Vargos Technology)organization will provide specific reviews on the site's performance and weekly SEO campaign reporting to give you little insights about how your business is performing. No one prefers anxiety. It’s much better to look into the results in create instead of knowing them vocally. It helps you for future business marketing also. This will further give you the guarantee that the Marketing Industry is working as per your expectations and you are getting best return on investments.
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How To Be on Google's First Page in 2019
Google is the favourite search engine of the people globally on the internet. Imagine 4 Trillion internet users in the world, nearly 2 Trillion websites with almost 8 Million internet traffic in a day, can you tell where you at from this number? There is tremendous competition in the market.
Google uses ranking signals to command organic search page rankings. There are a lot of ranking factors in Google but, you do not need to apply all of them. Focus on the most influential one. Did you know that ranking in Google is all about the relevance of content to search intent and usefulness of content to the user?
Revisit your website and check its content, if you can't find either of the two, then your position from the Trillions of competition, may not even be in the middle yet.
You only need one powerful thing to be on Google's first page in 2019 and beyond - CONTENT!
You need a large volume of related content that matches search and presents the knowledge to the user. Content should be written with the optimization goal in mind.
Q&A formatting
Users enter their search intent with WH questions words like Why, How, What, Who etc. because they're looking for specific answers. You need to know what and how could be a buyer's question and what would be the answer. To meet this need, the question should be present in the content and your answer should give out enough information to the user.
Keywords
Due to the cost of competition in the market, if you are a small business, you can choose keywords that are not very competitive but, these keywords need to be relevant to your business, product and content.
Know your audience
Understanding your audience makes a great impact on your website. If you understand who your target audience is, what they will be searching for, you will be able to create a more effective content that can help you rank in google.
Value your readers
Think about your readers as you write. Is the information you're writing helpful for them? Does it answer their questions? Do you think, they'll ask for more information? Think about sharing a helpful information to a friend who needs to shop, eat, or drink. Do the same with your content. Write and create contents like you are extending help to your friends and family.
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Top 3 SEO Questions Commonly Asked by Beginners
SEO (Search Engine Optimisation) is the process of getting traffic from organic search results on Search Engines. Google, Yahoo and other search engines show a set of primary search results to users and these results are ranked according to their relevancy through Search Engine Optimisation. This guide is to answer the basic questions which pop up in the mind of anyone new to this strategy. From generating traffic for your website, to keywords and the importance of using SEO services, we are going to help you sort out any and all confusion. There are certainly some of the best SEO companies, which are giving their customers the most reliable SEO services with the help of their SEO experts.
Do I need SEO for my website?
Google, Bing, Yahoo are the main source of driving traffic to your website. Although social media also helps you get traffic, but still, its Search Engines that are most commonly used for navigation purposes by online users. Whether your website is related to content generation or your business portfolio, people search for your website on Google before searching on any social media platform.
Why Search Engines are more popular in use than in ever-expanding social media? Well, the indexing takes the targeted audience to your website, looking for the things which you are offering. So, if your website is not SEO friendly, you are surely missing out on some extraordinary opportunities of driving traffic to your website. If your website is optimised, you will benefit in terms of increased revenues and publicity for your product. The idea is that investment in SEO will create more business for you, and in turn create positive ROI (Return on Investment).
Do I need an SEO expert?
Though some people can easily understand the basics, for a layman, SEO is often a bit too technical and complex. If you want to invest your time in understanding SEO then free material is widely available on the internet. If you have time to get decent search engine optimisation education, then combine this knowledge with best practices and you are on your way to become an SEO expert.
However, then your time will become full of the learning of these practices and all the different aspects within SEO. This is when it may make sense to look into an SEO expert to handle this for you. With full command on the subject matter and on-hand experience, an SEO expert can help you develop your website with the maximum optimisation of its content. Some of best SEO companies are giving SEO services with specialised focus on content optimisation.
Will my website be visible on Search Engines without SEO?
With advancement in technology, internet junkies are working to bring improvement so that they can crawl deep in the world of the web to give maximum results to their users. However, marketing work in a specific way; one wrong move can result in your website waning on internet search results due to missteps or violations of policies. With right search engine optimisation and with the help of an SEO expert however, your website can give you an increasing number of visitors and exposure.
As SEO is a marketing tool for increased visibility of your website on the internet, we can't deny the significance of using this tool for revenue generation through our website. A website is your portfolio of your work, so it should be structured in a way that Search Engines can easily connect it in their database.
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Top 3 SEO Questions Commonly Asked by Beginners
SEO (Search Engine Optimisation) is the process of getting traffic from organic search results on Search Engines. Google, Yahoo and other search engines show a set of primary search results to users and these results are ranked according to their relevancy through Search Engine Optimisation. This guide is to answer the basic questions which pop up in the mind of anyone new to this strategy. From generating traffic for your website, to keywords and the importance of using SEO services, we are going to help you sort out any and all confusion. There are certainly some of the best SEO companies, which are giving their customers the most reliable SEO services with the help of their SEO experts.
Do I need SEO for my website?
Google, Bing, Yahoo are the main source of driving traffic to your website. Although social media also helps you get traffic, but still, its Search Engines that are most commonly used for navigation purposes by online users. Whether your website is related to content generation or your business portfolio, people search for your website on Google before searching on any social media platform.
Why Search Engines are more popular in use than in ever-expanding social media? Well, the indexing takes the targeted audience to your website, looking for the things which you are offering. So, if your website is not SEO friendly, you are surely missing out on some extraordinary opportunities of driving traffic to your website. If your website is optimised, you will benefit in terms of increased revenues and publicity for your product. The idea is that investment in SEO will create more business for you, and in turn create positive ROI (Return on Investment).
Do I need an SEO expert?
Though some people can easily understand the basics, for a layman, SEO is often a bit too technical and complex. If you want to invest your time in understanding SEO then free material is widely available on the internet. If you have time to get decent search engine optimisation education, then combine this knowledge with best practices and you are on your way to become an SEO expert.
However, then your time will become full of the learning of these practices and all the different aspects within SEO. This is when it may make sense to look into an SEO expert to handle this for you. With full command on the subject matter and on-hand experience, an SEO expert can help you develop your website with the maximum optimisation of its content. Some of best SEO companies are giving SEO services with specialised focus on content optimisation.
Will my website be visible on Search Engines without SEO?
With advancement in technology, internet junkies are working to bring improvement so that they can crawl deep in the world of the web to give maximum results to their users. However, marketing work in a specific way; one wrong move can result in your website waning on internet search results due to missteps or violations of policies. With right search engine optimisation and with the help of an SEO expert however, your website can give you an increasing number of visitors and exposure.
As SEO is a marketing tool for increased visibility of your website on the internet, we can't deny the significance of using this tool for revenue generation through our website. A website is your portfolio of your work, so it should be structured in a way that Search Engines can easily connect it in their database.
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How to Choose a Good Quality SEO Firm?
You are probably not the only one on the web who is trying to discover how to hire a good SEO company. SEO engineering is a mystery to many. However, I will say that you can not get it for $ 100 per month. That is a lot of hoohah. Here are some good tips. I have seen some SEO companies for years and are partners in a prominent web hosting service. People have asked: "Are they a quality SEO marketing company?" I have wondered if they are of quality. It seems legitimate, as they are on the partner page of a featured hosting site. I would think that many people would scream and shout if their work was really bad and produced bad results.
One of the best ways to measure a company's quality is to verify the results of its previous clients. That's what you pay at the end ... strong search results and high rank in Google, Yahoo and Bing (msn) for your keywords. I'll say you should look around some places for reviews. Of course, we are an SEO company that offers SEO services. I'll stop there so I do not push too hard and be accused of marketing. This is for your education on how to choose a good company and what to look for in the SEO services offered by reputable Internet companies.
Here are some of the basic elements of search engine ranking that you should look for in a quality search engine optimization company or in an internet marketing company proposal: You should use SEO optimization tools and techniques on page and out of page. Many Internet marketers could benefit from basic "white hat" SEO information.
What does 'white hat' mean? Are they jeans with clean clothes? No, this means that you are actively using "best practice" techniques that are compatible with SEO for Google algorithms. By the way, he usually does not say white hat openly.
What is a search engine algorithm? It is the computer code in web spiders that crawls all over the Internet and decides which 10 websites are most relevant to the specific search of users of the millions of websites on the Internet.
I will not reveal everything as some techniques take years to learn and are owned by our company. However, these basic white hat SEO techniques should be in the Internet marketing services that offer you:
The SEO techniques on the page that cause good "organic" SEO ratings are:
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o Google xml sitemap
o No Flash or sloppy Javascript code
Off-page SEO techniques for your website that cause higher ranking in the search engines:
o Increase in relevant inbound links
o Reciprocal: relevant exchange links
o One Way: inbound links to the home page
o Blog / Article posts: relevant keyword content
o High ranking directory presentations
o Social bookmarks: Twitter, Facebook. LinkedIn
o An SEO analysis report that compares your site with 1-2 competitors of your choice. Monthly reports:
o Track web traffic source
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California SEO Service Providers Make Popular Your Brand Everywhere
Today’s world is the world of technology where everything is based on the technology. Here, many difficult tasks accomplished simply with the help of highly advanced tools and gadgets. The popularity of SEO And Web Development Company is increasing day by day, which makes various software and applications and promote it to bring world around you.
Presently, many people enter into the digital marketing industry and try to set their business by serving best SEO services to their clients. The SEO Service Provider works dedicatedly to give a new identity to your business and make famous your brand in all over the world. In this service, the SEO experts make promotion of the client’s website, raise your site above your competitors and also increase the traffic of your site.
In the Digital Marketing Company, professionals contribute great efforts to make high rank of client’s website. Generally, the SEO experts know that how to increase ranking of a website, how to promote any brand or company on the web. The SEO companies have set a fixed strategy for online marketing. At the initial stage, the professionals observe the client’s website minutely and find out some wrong things of website which makes their site dull and uninteresting. And then experts modify the site with some changes like they upload some meaningful and interested content to the website, update meta tag and description of the website.
SEO service providers provide two types of services like on page and off page. In the on page service, experts update and set all the on page data of the website. In the off page service, professionals use some online marketing techniques to increase the popularity of any brand or company. Generally, promotion is the best way to increase your recognition and sales too. But now the way of promotion is totally changed. Now the online marketing is on the peak and everyone chooses this way to get high rank on search engine.
There are many companies in the market which brings SEO services, but choosing the perfect one is quite different for every business owner. We can get the help of the internet where we can find many reliable and reputed companies which provide needful services at very reasonable rate. We should compare all the companies with their services and rate and then choose the best one. It is essential to know each and everything about the company which we are going to hire for our brand promotion. We should also get the help of some online reviews where many people share their good and bad experience with all. We should read the reviews and make aware with the company’s policy, working culture and services. Choosing the best SEO Company can help us to make best in our industry.
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At SeoTuners Thousand Oaks, we provide affordable website SEO packages in Ventura that are designed to boost your online presence and drive traffic to your site. Our team of SEO experts uses the latest techniques and tools to ensure your website ranks higher on search engines. Our affordable website SEO packages include on-page and off-page optimization, keyword analysis, content creation, and more.
SeoTuners Thousand Oaks 275 E. Hillcrest Dr #160–254, Thousand Oaks, CA 91360 (805) 523–2497
My Official Website: https://www.seotuners.com/ Google Plus Listing: https://www.google.com/maps?cid=13188952195076580524
Service We Offer:
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SEO SERVICES IN CALIFORNIA AND TEXAS
Nowadays, many so-called SEO services are being offered on the Internet. Unfortunately, most of these "companies" deliver dubious results. Excellent SEO services are rare today since everybody seems to be to getting into online business marketing. More and more websites are clamoring to get into USA's top 10 in terms of search engine ranking. With this scenario in mind, companies and small businesses should be careful in their search for SEO companies with competent SEO services.
Before you dole out your hard-earned dollars to get social media marketing packages, read below for some essential tips when scouting around for an SEO company:
1) Always ask for a case study. An SEO expert should definitely present you with a case study containing a thorough evaluation of your business' strengths and weaknesses, as well as an overview of your competitors' marketing strategies. This gives you a jump start on what your company lacks in comparison to others. This could be very helpful before you hire SEO services.
2) Review an SEO company's web design and development and content management system. Ask for copies of their client's web pages or check out how their client's websites look like in the Internet. This will give you an idea of the kind of output that you are most likely to be getting from a prospective SEO firm.
3) Collect and collect, then select. It may be a bit time-consuming, but any wise spender knows that canvassing and comparing notes rather than hiring the SEO services of the first SEO company that comes your way eventually leads to money well spent.
4) Don't forget to inquire about SEO strategy and planning services. Find out as much as you can how an SEO consultant creates a marketing plan, more importantly for businesses that are similar to yours. Some SEO services firms are adept at formulating SEO keywords while others are more inclined to churning out award-winning web designs. Others have a knack for link-building strategies and there are SEO companies that offer social media marketing packages.
5) Ask several companies that offer SEO services to present you with a variety of search engine optimization services. Most SEO firms can offer different rates and SEO strategies can work depending on the client's budget and preferences. Also, ask for a detailed rate card that includes separate pricing for say,SEO audit, web design, web copywriting services, content management system, and web design and development. This way, you will get an idea of how much you should be paying for each kind of SEO services you are getting.
6) Ask for referrals. With a bit of effort and time, you can check out USA's top 10 SEO companies and see if you can afford them. If not, the next best thing to do is ask SEO firms to provide you with a list of their existing clients so you can inquire about the quality of work from the people that have enlisted their SEO services.
After all is said and done and you have made a good with an SEO firm, don't forget to read the fine print well before signing the contract for the SEO services. This is just plain and simple good business practice.
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5 Reasons Why SMB's Should Use SEO
As the owner of a small or medium-sized business, you may be under the impression that "SEO" (Search Engine Optimisation) is just a fancy marketing term for big businesses and corporations. And you're not alone - in a recent survey, it was found that while 56% of small businesses have their own websites, only 28% think about SEO.
The importance of SEO in this day and age could not be more prevalent. Neglecting to apply SEO strategies to your website may be as crippling for your business as locking your front doors. So what does SEO do? In a world where most consumers use search engines to find nearby companies, SEO is the difference between being visible or invisible. Here are 5 reasons why SMB's should use SEO today.
1) Traffic Boost
The traffic going to your website is driven mainly by search engines. Simply putting up a website is not enough to get seen; people have to know you exist in the first place. Every time they use Google or another search engine to find their new job, designer handbag, car repair (or anything else), Google uses special algorithms to determine which websites will show up on the front page.
By using certain SEO strategies, you can be one of the first sites to pop up every time someone needs something related to your business. Without SEO, you will be buried beneath a mountain of competitors.
2) It's Cost Effective
Most marketing and advertising costs big money, but the only cost of SEO is how much time you spend learning SEO strategies and writing content, or paying an SEO specialist to improve your Google rankings. With a proper SEO campaign maintained over several months, you can expect an ROI several times greater than any Facebook or AdWords advertising, up to 800% or more.
3) Small Businesses Can Beat Big Businesses with Specific Keywords and Other Localized Advantages
Once you get into SEO, you will start to learn that it's all about the keywords, which are simply the words and phrases that people enter into search engines. The overall goal of SEO is to write content in which these keywords are used and focused on, making your site stronger and higher ranked when people Google that certain keyword.
So you might think that your chances at success are hopeless; after all, how are you supposed to out-SEO corporations and large businesses that have been doing this for years? Well, small businesses actually have a greater advantage due to long tail keywords. Long tail keywords are long and specific keywords or phrases. As a small business, you also have a smaller and more specific target audience (for example, everyone looking for your product or service in your town or city, as opposed to a global audience). You can hone in on long tail keywords that include your specific location or audience, that big businesses won't bother tackling. Sure, Toys R Us may get all the "new toys" searches, but your small toy store can capture all the "new toys for young boys in London" searches.
And this isn't the only thing that helps out small businesses, search engines generally take into account the user's location with Local Search, meaning you have a better chance to be seen by people in your area. This will only improve with recent updates in which business owners can further personalize their listings.
In addition to this there is also what's known as "off-page SEO": this is less about the SEO work you do on your website, and more about improving your reputation in the real world and through social media. It turns out that people are more than willing to talk up a small local business that provide a good service, and the more people are talking about you on the web, the more likely your rankings will rise.
4) Competitors Will Always Have an Advantage If You Don't Jump Onboard
The internet boom has come and gone, and ignoring what it can offer is just irresponsible for your business. SEO is fact of life these days and most, if not all, of your competitors are certain to be doing this, so you need to get onboard.
You may argue that you already have a stable group of loyal customers who will keep your business afloat, customers with whom you have built up trust before the SEO and social media years. Sure, that may be the case now, but how long until those customers stop needing or wanting the service or product you provide? New customers will not flock to your store like the old ones. Why? Because new customers will be swept away by your competition long before they even hear your name.
5) SEO Will Always Be Around
SEO isn't something that we'll ride out. The way search engines work, and the fact Google has an increasingly monopolized hold over search engines and various social media platforms, SEO will always be relevant. The best option is to start working on your SEO as soon as possible if you don't want your business to drown under the current.
So what are you waiting for? Don't let the marketing acronyms and jargon scare you away; at the end of the day, SEO is as simple as every other part of running a business. The longer you wait, the further you fall behind. And if you still don't feel comfortable handling all the technical aspects of SEO, that's fine; that's what SEO specialists are for.
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Tackling Tag Sprawl: Crawl Budget, Duplicate Content, and User-Generated Content
Tackling Tag Sprawl: Crawl Budget, Duplicate Content, and User-Generated Content
Posted by rjonesx.
Alright, so here's the situation. You have a million-product website. Your competitors have a lot of the same products. You need unique content. What do you do? The same thing everyone does — you turn to user-generated content. Problem solved, right?
User-generated content (UGC) can be an incredibly valuable source of content and organization, helping you build natural language descriptions and human-driven organization of site content. One common feature used by sites to take advantage of user-created content are tags, found everywhere from e-commerce sites to blogs. Webmasters can leverage tags to power site search, create taxonomies and categories of products for browsing, and to provide rich descriptions of site content.
This is a logical and practical approach, but can cause intractable SEO problems if left unchecked. For mega-sites, manually moderating millions of user-submitted tags can be cumbersome (if not wholly impossible). Leaving tags unchecked, though, can create massive problems with thin content, duplicate content, and general content sprawl. In our case study below, three technical SEOs from different companies joined forces to solve a massive tag sprawl problem. The project was led by Jacob Bohall, VP of Marketing at Hive Digital, while computational statistics services were provided by J.R. Oakes of Adapt Partners and Russ Jones of Moz. Let's dive in.
What is tag sprawl?
We define tag sprawl as the unchecked growth of unique, user-contributed tags resulting in a large amount of near-duplicate pages and unnecessary crawl space. Tag sprawl generates URLs likely to be classified as doorway pages, pages appearing to exist only for the purpose of building an index across an exhaustive array of keywords. You’ve probably seen this in its most basic form in the tagging of posts across blogs, which is why most SEOs recommend a blanket “noindex, follow” across tag pages in Wordpress sites. This simple approach can be an effective solution for small blog sites, but is not often the solution for major e-commerce sites that rely more heavily on tags for categorizing products.
The three following tag clouds represent a list of user-generated terms associated with different stock photos. Note: User behavior is generally to place as many tags as possible in an attempt to ensure maximum exposure for their products.
USS Yorktown, Yorktown, cv, cvs-10, bonhomme richard, revolutionary war-ships, war-ships, naval ship, military ship, attack carriers, patriots point, landmarks, historic boats, essex class aircraft carrier, water, ocean
ship, ships, Yorktown, war boats, Patriot pointe, old war ship, historic landmarks, aircraft carrier, war ship, naval ship, navy ship, see, ocean
Yorktown ship, Warships and aircraft carriers, historic military vessels, the USS Yorktown aircraft carrier
As you can see, each user has generated valuable information for the photos, which we would want to use as a basis for creating indexable taxonomies for related stock images. However, at any type of scale, we have immediate threats of:
Thin content: Only a handful of products share the user-generated tag when a user creates a more specific/defining tag, e.g. "cvs-10"
Duplicate and similar content: Many of these tags will overlap, e.g. "USS Yorktown" vs. "Yorktown," "ship" vs. "ships," "cv" vs. "cvs-10," etc.
Bad content: Created by improper formatting, misspellings, verbose tags, hyphenation, and similar mistakes made by users.
Now that you understand what tag sprawl is and how it negatively effects your site, how can we address this issue at scale?
The proposed solution
In correcting tag sprawl, we have some basic (at the surface) problems to solve. We need to effectively review each tag in our database and place them in groups so further action can be taken. First, we determine the quality of a tag (how likely is someone to search for this tag, is it spelled correctly, is it commercial, is it used for many products) and second, we determine if there is another tag very similar to it that has a higher quality.
Identify good tags: We defined a good tag as term capable of contributing meaning, and easily justifiable as an indexed page in search results. This also entailed identifying a "master" tag to represent groups of similar terms.
Identify bad tags: We wanted to isolate tags that should not appear in our database due to misspellings, duplicates, poor format, high ambiguity, or likely to cause a low-quality page.
Relate bad tags to good tags: We assumed many of our initial "bad tags" could be a range of duplicates, i.e. plural/singular, technical/slang, hyphenated/non-hyphenated, conjugations, and other stems. There could also be two phrases which refer to the same thing, like "Yorktown ship" vs. "USS Yorktown." We need to identify these relationships for every "bad" tag.
For the project inspiring this post, our sample tag database comprised over 2,000,000 "unique" tags, making this a nearly impossible feat to accomplish manually. While theoretically we could have leveraged Mechanical Turk or similar platform to get "manual" review, early tests of this method proved to be unsuccessful. We would need a programmatic method (several methods, in fact) that we could later reproduce when adding new tags.
The methods
Keeping the goal in mind of identifying good tags, labeling bad tags, and relating bad tags to good tags, we employed more than a dozen methods, including: spell correction, bid value, tag search volume, unique visitors, tag count, Porter stemming, lemmatization, Jaccard index, Jaro-Winkler distance, Keyword Planner grouping, Wikipedia disambiguation, and K-Means clustering with word vectors. Each method either helped us determine whether the tag was valuable and, if not, helped us identify an alternate tag that was valuable.
Spell correction
Method: One of the obvious issues with user-generated content is the occurrence of misspellings. We would regularly find misspellings where semicolons are transposed for the letter “L” or words have unintended characters at the beginning or end. Luckily, Linux has an excellent built-in spell checker called Aspell which we were able to use to fix a large volume of issues.
Benefits: This offered a quick, early win in that it was fairly easy to identify bad tags when they were composed of words that weren’t included in the dictionary or included characters that were simply inexplicable (like a semicolon in the middle of a word). Moreover, if the corrected word or phrase occurred in the tag list, we could trust the corrected phrase as a potentially good tag, and relate the misspelled term to the good tag. Thus, this method help us both filter bad tags (misspelled terms) and find good tags (the spell-corrected term)
Limitations: The biggest limitation with this methodology was that combinations of correctly spelled words or phrases aren’t necessarily useful for users or the search engine. For example, many of the tags in the database were concatenations of multiple tags where the user space-delimited rather than comma-delimited their submitted tags. Thus, a tag might consist of correctly spelled terms but still be useless in terms of search value. Moreover, there were substantial dictionary limitations, especially with domain names, brand names, and Internet slang. In order to accommodate this, we added a personal dictionary that included a list of the top 10,000 domains according to Quantcast, several thousand brands, and a slang dictionary. While this was helpful, there were still several false recommendations that needed to be handled. For example, we saw "purfect" correct to "perfect," despite being a pop-culture reference for cat images. We also noticed some users reference this saying as "purrfect," "purrrfect," "purrrrfect," "purrfeck," etc. Ultimately, we had to rely on other metrics to determine whether we trusted the misspelling recommendations.
Bid value
Method: While a tag might be good in the sense that it is descriptive, we wanted tags that were commercially relevant. Using the estimated cost-per-click of the tag or tag phrase proved useful in making sure that the term could attract buyers, not just visitors.
Benefits: One of the great features of this methodology is that it tends to have a high signal-to-noise ratio. Most tags that have high CPCs tend to be commercially relevant and searched frequently enough to warrant inclusion as "good tags." In many cases we could feel confident that a tag was good just on this metric alone.
Limitations: However, the bid value metric comes with some pretty big limitations, too. For starters, Google Keyword Planner’s disambiguation problem is readily apparent. Google combines related keywords together when reporting search volume and CPC data, which means a tag like “facbook” would return the same data as “facebook.” Obviously, we would prefer to map “facbook” to “facebook” rather than keep both tags, so in some cases the CPC metric wasn’t sufficient to identify good tags. A further limitation of the bid value was the difficulty of acquiring CPC data. Google now requires running active Adwords campaigns to get access to CPC value. It is no simple feat to look up 5,000,000 keywords in Google Keyword Planner, even if you have a sufficient account. Luckily, we felt comfortable that historical data would be trustworthy enough, so we didn't need to acquire fresh data.
Tag search volume
Method: Similar to CPC, we could use search volume to determine the potential value of a tag. We had to be careful not to rely on the tag itself, though, since the tag could be so generic that it earns traffic unrelated to the product itself. For example, the tag “USS Yorktown” might get a few hundred searches a month, but “USS Yorktown T-shirt” gets 0. For all of the tags in our index, we tracked down the search volume for the tag plus the product name, in order to make sure we had good estimates of potential product traffic.
Benefits: Like CPC, this metric did a very good job of consolidating our tag data set to just keywords that were likely to deliver traffic. In the vast majority of cases, if “tag + product” had search volume, we could feel confident that it is a good term.
Limitations: Unfortunately, this method fell victim to the same disambiguation problem that CPC presents. Because Google groups terms together, it is possible that on some occasions two tags will be given the same metrics. For example: “pontoons boat,” “pontoonboat,” “pontoon boats,” “pontoon boat,” “pontoon boating,” and “pontoons boats” were in the same traffic volume group which also included tags like “yacht” and “yachts.” Moreover, there is no accounting for keyword difficulty in this metric. Some tags, when combined with product types, produce keywords that receive substantial traffic but will always be out of reach for a templated tag page.
Unique visitors
Method: This method was a no-brainer: protect the tags that already receive traffic from Google. We exported all of the tags from Google Analytics that had received search traffic from Google in the last 12 months. Generally speaking, this should be a fairly safe list of terms.
Benefits: When doing experimental work with a client, it is always nice to be able to give them a scenario that almost guarantees improvement. Because we were able to protect tags that already receive traffic by labeling them as good (in the vast majority of cases), we could ensure that the client had a high probability of profiting from the changes we made and minimal risk of any traffic loss.
Limitations: Unfortunately, even this method wasn’t perfect. If a product (or set of products) with high enough authority included a poor variation of a tag, then the bad variant would rank and receive traffic. We had to use other strategies to verify our selections from this method and devise a method to encourage a tag swap in the index for the correct version of a term.
Tag count
Description: The frequency with which a tag was used on the site was often a strong signal that we could trust the tag, especially when compared with other similar tags. By counting the number of times each tag was used on the site, we could bias our final set of trusted tags in favor of these more popular terms.
Benefits: This was a great tie-breaker metric when we had two tags that were very similar but needed to choose just one. For example, sometimes two variants of a phrase were completely acceptable (such as a version with and without a hyphen). We could simply defer to the one with a higher tag count.
Limitations: The clear limitation of tag frequency is that many of the most frequent tags were too generic to be useful. The tag “blue” isn’t particularly useful when it just helps people find “blue t-shirts.” The term is too generic and too competitive to warrant inclusion. Additionally, the inclusion of too broad of a tag would simply create a very large crawl vs. traffic-potential ratio. A common tag will have hundreds if not thousands of matching products, creating many pages of products for the single tag. If a tag produces 50 paginated product listings, but only has the potential to drive 10 visitors a year, it might not be worth it.
Porter stemming
Method: Stemming is a method used to identify the root word from a tag by scanning the word right to left and using various pattern matching rules to remove characters (suffixes) until you arrive at the word’s stem. There are a couple of popular stemmers available, but we found Porter stemming to be more accurate as a tool for seeing alternative word forms. You can geek out by looking at the Porter stemming algorithm in Snowball here, or you can play with a JS version here.
Benefits: Plural and possessive terms can be grouped by their stem for further analysis. Running Porter stemming on the terms “pony” and “ponies” will return “poni” as the stem, which can then be used to group terms for further analysis. You can also run Porter stemming on phrases. For example, “boating accident,” “boat accidents,” “boating accidents,” etc. share the stem “boat accid.” This can be a crude and quick method for grouping variations. Porter stemming also is able to clean text more kindly, where others stemmers can be too aggressive for our efforts; e.g., Lancaster stemmer reduces "woman" to "wom," while Porter stemmer leaves it as "woman."
Limitations: Stemming is intended for finding a common root for terms and phrases, and does not create any type of indication as to the proper form of a term. The Porter stemming method applies a fixed set of rules to the English language by blanket removing trailing “s,” “e,” “ance,” “ing,” and similar word endings to try and find the stem. For this to work well, you have to have all of the correct rules (and exceptions) in place to get the correct stems in all cases. This can be particularly problematic with words that end in S but are not plural, like "billiards" or "Brussels." Additionally, this method does not help with mapping related terms such as “boat crash,” “crashed boat,” “boat accident,” etc. which would stem to “boat crash,” “crash boat,” and “boat acci."
Lemmatization
Method: Lemmatization works similarly to stemming. However, instead of using a rule set for editing words by removing letters to arrive at a stem, lemmatization attempts to map the term to its most simple dictionary form, such as WordNet, and return a canonical “lemma” of the word. A crude way to think about lemmatization is just simplifying a word. Here’s an API to check out.
Benefits: This method often works better than stemming. Terms like “ship,” “shipped,” and “ships” are all mapped to “ship” by this method, while “shipping” or “shipper,” which are terms that have distinct meaning despite the same stem, are retained. You can create an array of “lemma” from phrases which can be compared to other phrases resolving word order issues. This proved to be a more reliable method for grouping variations than stemming.
Limitations: As with many of the methods, context for mapping related terms can be difficult. Lemmatization can provide better filters for context, but to do so generally relies on identifying the word form (noun, adjective, etc) to appropriately map to a root term. Given the inconsistency of the user-generated content, it is inaccurate to assume all words are in adjective form (describing a product), or noun form (the product itself). This inconsistency can present wild results. For example, “strip socks” could be intended as as a tag for socks with a strip of color on them, such as as “striped socks,” or it could be “stripper socks” or some other leggings that would be a match only found if there other products and tags to compare for context. Additionally, it doesn’t create associations between all related words, just textual derivatives, so you are still seeking out a canonical between mailman, courier, shipper, etc.
Jaccard index
Method: The Jaccard index is a similarity coefficient measured by Intersection over Union. Now, don’t run off just yet, it is actually quite straightforward. Imagine you had two piles of with 3 marbles in each: Red, Green, and Blue in the first, Red, Green and Yellow in the second. The “Intersection” of these two piles would be Red and Green, since both piles have those two colors. The “Union” would be Red, Green, Blue and Yellow, since that is the complete list of all the colors. The Jaccard index would be 2 (Red and Green) divided by 4 (Red, Green, Blue, and Yellow). Thus, the Jaccard index of these two piles would be .5. The higher the Jaccard index, the more similar the two sets. So what does this have to do with tags? Well, imagine we have two tags: “ocean” and “sea.” We can get a list of all of the products that have the tag “ocean” and “sea.” Finally, we get the Jaccard index of those two sets. The higher the score, the more related they are. Perhaps we find that 70% of the products with the tag “ocean” also have the tag “sea”; we now know that the two are fairly well-related. However, when we run the same measurement to compare “basement” or “casement,” we find that they only have a Jaccard index of .02. Even though they are very similar in terms of characters, they mean quite different things. We can rule out mapping the two terms together.
Benefits: The greatest benefit of using the Jaccard index is that it allows us to find highly related tags which may have absolutely no textual characteristics in common, and are more likely to have an overly similar or duplicate results set. While most of the the metrics we have considered so far help us find “good” or “bad” tags, the Jaccard index helps us find “related” tags without having to do any complex machine learning.
Limitations: While certainly useful, the Jaccard index methodology has its own problems. The biggest issue we ran into had to do with tags that were used together nearly all the time but weren’t substitutes of one another. For example, consider the tags “babe ruth” and his nickname, “sultan of swat.” The latter tag only occurred on products which also had the “babe ruth” tag (since this was one of his nicknames), so they had quite a high Jaccard index. However, Google doesn't map these two terms together in search, so we would prefer to keep the nickname and not simply redirect it to "babe ruth." We needed to dig deeper if we were to determine when we should keep both tags or when we should redirect one to another. As a standalone, this method also was not sufficient at identifying cases where a user consistently misspelled tags or used incorrect syntax, as their products would essentially be orphans without “union.”
Jaro-Winkler distance
Method: There are several edit distance and string similarity metrics that we used throughout this process. Edit Distance is simply some measurement of how difficult it is to change one word to another. For example, the most basic edit distance metric, Levenshtein distance, between "Russ Jones" and "Russell Jones" is 3 (you have to add "E","L", and "L" to transform Russ to Russell). This can be used to help us find similar words and phrases. In our case, we used a particular edit distance measure called "Jaro-Winkler distance" which gives higher precedence to words and phrases that are similar at the beginning. For example, "Baseball" would be closer to "Baseballer" than to “Basketball” because the differences are at the very end of the term.
Benefits: Edit distance metrics helped us find many very similar variants of tags, especially when the variants were not necessarily misspellings. This was particularly valuable when used in conjunction with the Jaccard index metrics, because we could apply a character-level metric on top of a character-agnostic metric (i.e. one that cares about the letters in the tag and one that doesn’t).
Limitations: Edit distance metrics can be kind of stupid. According to Jaro-Winkler distance, "Baseball” and "Basketball" are far more related to one another than "Baseball" and "Pitcher" or "Catcher." "Round" and "Circle" have a horrible edit distance metric, while "Round" and "Pound" look very similar. Edit distance simply cannot be used in isolation to find similar tags.
Keyword Planner grouping
Method: While Google's choice to combine similar keywords in Keyword Planner has been problematic for predicting traffic, it has actually offered us a new method to identify highly related terms. Whenever two tags share identical metrics from Google Keyword Planner (average monthly traffic, historical traffic, CPC, and competition), we can conclude that there is an increased chance the two are related to one another.
Benefits: This method is extremely useful for acronyms (which are particularly difficult to detect). While Google groups together COO and Chief Operating Officer, you can imagine that standard methods like those outlined above might have problems detecting the relationship.
Limitations: The greatest drawback for this methodology was that it created numerous false positives among less popular terms. There are just too many keywords which have an annual search volume average of 10, are searched 10 times monthly, and have a CPC and competition of 0. Thus, we had to limit the use of this methodology to more popular terms where there were only a handful of matches.
Wikipedia disambiguation
Method: Many of the methods above are great for grouping similar/related terms, but do not provide a high-confidence method for determining the "master" term or phrase to represent a grouping of related/duplicate terms. While considerations can be made for testing all tags against an English language model, the lack of pop culture references and phrases makes it unreliable. To do this effectively, we found Wikipedia to be a trusted source for identifying the proper spelling, tense, formatting, and word order for any given tag. For example, if users tagged a product as "Lord of the Rings," "LOTR," and "The Lord of the Rings," it can be difficult to determine which tag should be preferred (certainly we don't need all 3). If you search Wikipedia for these terms, you will see that they redirect you to the page titled "The Lord of the Rings." In many cases, we can trust their canonical variant as the "good tag." Please note that we don’t encourage scraping any website or violating their terms of use. Wikipedia does offer an export of their entire database that can be used for research purposes.
Benefits: When a tag could be mapped to a Wikipedia entry, this method proved to be a highly effective at providing validation that a tag had potential value, or creating a point of reference for related tags. If the Wikipedia community felt a tag or tag phrase was important enough to have an article dedicated to it, then the tag was more likely to be a valuable term vs. random entry or keyword stuffing by the user. Further, the methodology allows for grouping related terms without any bias on word order. Doing a search on Wikipedia creates a search results page (“pontoon boats”), or redirects you to a correction of the article (“disneyworld” becomes “Walt Disney World”). Wikipedia also tends to have entries for some pop culture references, so things that would get flagged as a misspelling, such as "lolcats," can be vindicated by the existence of a matching Wikipedia article.
Limitations: While Wikipedia is effective at delivering a consistent formal tag for disambiguation, it can at times be more sterile than user-friendly. This can run counter to other signals such as CPC or traffic volume methods. For example, "pontoon boats" becomes "Pontoon (Boat)", or “Lily” becomes "lilium." All signals indicate the former case as the most popular, but Wikipedia disambiguation suggests the latter to be the correct usage. Wikipedia also contains entries for very broad terms, like each number, year, letter, etc. so simply applying a rule that any Wikipedia article is an allowed tag would continue to contribute to tag sprawl problems.
K-means clustering with word vectors
Method: Finally, we attempted to transform the tags into a subset of more meaningful tags using word embeddings and k-means clustering. Generally, the process involved transforming the tags into tokens (individual words), then refining by part-of-speech (noun, verb, adjective), and finally lemmatizing the tokens ("blue shirts" becomes "blue shirt"). From there, we transformed all the tokens into a custom Word2Vec embedding model based on adding the vectors of each resulting token array. We created a label array and a vector array of each tag in the dataset, then ran k-means with 10 percent of the total count of the tags as the value for number of centroids. At first we tested on 30,000 tags and obtained reasonable results. Once k-means had completed, we pulled all of the centroids and obtained their nearest relative from the custom Word2Vec model, then we assigned the tags to their centroid category in the main dataset.
Tag TokensTag PosTag Lemm.Categorization['beach', 'photographs'][('beach', 'NN'), ('photographs', 'NN')]['beach', 'photograph']beach photo['seaside', 'photographs'][('seaside', 'NN'), ('photographs', 'NN')]['seaside', 'photograph']beach photo['coastal', 'photographs'][('coastal', 'JJ'), ('photographs', 'NN')]['coastal', 'photograph']beach photo['seaside', 'photographs'][('seaside', 'NN'), ('photographs', 'NN')]['seaside', 'photograph']beach photo['seaside', 'posters'][('seaside', 'NN'), ('posters', 'NNS')]['seaside', 'poster']beach photo['coast', 'photographs'][('coast', 'NN'), ('photographs', 'NN')]['coast', 'photograph']beach photo['beach', 'photos'][('beach', 'NN'), ('photos', 'NNS')]['beach', 'photo']beach photo
The Categorization column above was the centroid selected by Kmeans. Notice how it handled the matching of "seaside" to "beach" and "coastal" to "beach."
Benefits: This method seemed to do a good job of finding associations between the tags and their categories that were more semantic than character-driven. "Blue shirt" might be matched to "clothing." This was obviously not possible without the semantic relationships found within the vector space.
Limitations: Ultimately, the chief limitation that we encountered was trying to run k-means on the full two million tags while ending up with 200,000 categories (centroids). Sklearn for Python allows for multiple concurrent jobs, but only across the initialization of the centroids, which in this case was 11 — meaning that even if you ran on a 60-core processor, the number of concurrent jobs was limited by the number of initialization, which in this case, was again 11. We tried PCA (principal component analysis) to reduce the vector sizes (300 to 10) but the results were overall poor. Finally, because embeddings are generally built based on probabilistic closeness of terms in the corpus on which they were trained, there were matches that you could understand why they matched, but would obviously not have been the correct category (eg "19th century art" was picked as a category for "18th century art"). Finally, context matters and the word embeddings obviously suffer from understanding the difference between "duck" (the animal) and "duck" (the action).
Bringing it all together
Using a combination of the methods above, we were able to develop a series of methodology confidence scores that could be applied to any tag in our dataset, generating a heuristic for how to consider each tag going forward. These were case-level strategies to determine the appropriate methodology. We denoted these as follows:
Good Tags: This mostly started as our “do not touch” list of terms which already received traffic from Google. After some confirmation exercises, the list was expanded to include unique terms with rankings potential, commercial appeal, and unique product sets to deliver to customers. For example, a heuristic for this category might look like this:
If tag is identical to Wikipedia entry and
Tag + product has estimated search traffic and
Tag has CPC value then
Mark as "Good Tag"
Okay Tags: This represents terms that we would like to retain associated with products and their descriptions, as they could be used within the site to add context to a page, but do not warrant their own indexable space. These tags were mapped to be redirected or canonicaled to a “master,” but still included on a page for topical relevancy, natural language queries, long-tail searches, etc. For example, a heuristic for this category might look like this:
If tag is identical to Wikipedia entry but
Tag + product has no search volume
Vector tag matches a "Good Tag"
Mark as "Okay Tag" and redirect to "Good Tag"
Bad Tags to Remap: This grouping represents bad tags that were mapped to a replacement. These tags would literally be deleted and replaced with a corrected version. These were most often misspellings or terms discovered through stemming/lemmatization/etc. where a dominant replacement was identified. For example, a heuristic for this category might look like this:
If tag is not identical to either Wikipedia or vector space and
Tag + product has no search volume
Tag has no volume
Tag Wikipedia entry matches a "Good Tag"
Mark as "Bad Tag to Remap"
Bad Tags to Remove: These are tags that were flagged as bad tags that could not be related to a good tag. Essentially, these needed to be removed from our database completely. This final group represented the worst of the worst in the sense that the existence of the tag would likely be considered a negative indicator of site quality. Considerations were made for character length of tags, lack of Wikipedia entries, inability to map to word vectors, no previous traffic, no predicted traffic or CPC value, etc. In many cases, these were nonsense phrases.
All together, we were able to reduce the number of tags by 87.5%, consolidating the site down to a reasonable, targeted, and useful set of tags which properly organized the corpus without wasting either crawl budget or limiting user engagement.
Conclusions: Advanced white hat SEO
It was nearly nine years ago that a well-known black hat SEO called out white hat SEO as being simple, stale, and bereft of innovation. He claimed that "advanced white hat SEO" was an oxymoron — it simply did not exist. I was proud at the time to respond to his claims with a technique Hive Digital was using which I called "Second Page Poaching." It was a great technique, but it paled in comparison to the sophistication of methods we now see today. I never envisioned either the depth or breadth of technical proficiency which would develop within the white hat SEO community for dealing with unique but persistent problems facing webmasters. I sincerely doubt most of the readers here will have the specific tag sprawl problem described above. I'd be lucky if even a few of you have run into it. What I hope is that this post might disabuse us of any caricatures of white hat SEO as facile or stagnant and inspire those in our space to their best work.
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Tackling Tag Sprawl: Crawl Budget, Duplicate Content, and User-Generated Content
Posted by rjonesx.
Alright, so here's the situation. You have a million-product website. Your competitors have a lot of the same products. You need unique content. What do you do? The same thing everyone does — you turn to user-generated content. Problem solved, right?
User-generated content (UGC) can be an incredibly valuable source of content and organization, helping you build natural language descriptions and human-driven organization of site content. One common feature used by sites to take advantage of user-created content are tags, found everywhere from e-commerce sites to blogs. Webmasters can leverage tags to power site search, create taxonomies and categories of products for browsing, and to provide rich descriptions of site content.
This is a logical and practical approach, but can cause intractable SEO problems if left unchecked. For mega-sites, manually moderating millions of user-submitted tags can be cumbersome (if not wholly impossible). Leaving tags unchecked, though, can create massive problems with thin content, duplicate content, and general content sprawl. In our case study below, three technical SEOs from different companies joined forces to solve a massive tag sprawl problem. The project was led by Jacob Bohall, VP of Marketing at Hive Digital, while computational statistics services were provided by J.R. Oakes of Adapt Partners and Russ Jones of Moz. Let's dive in.
What is tag sprawl?
We define tag sprawl as the unchecked growth of unique, user-contributed tags resulting in a large amount of near-duplicate pages and unnecessary crawl space. Tag sprawl generates URLs likely to be classified as doorway pages, pages appearing to exist only for the purpose of building an index across an exhaustive array of keywords. You’ve probably seen this in its most basic form in the tagging of posts across blogs, which is why most SEOs recommend a blanket “noindex, follow” across tag pages in Wordpress sites. This simple approach can be an effective solution for small blog sites, but is not often the solution for major e-commerce sites that rely more heavily on tags for categorizing products.
The three following tag clouds represent a list of user-generated terms associated with different stock photos. Note: User behavior is generally to place as many tags as possible in an attempt to ensure maximum exposure for their products.
USS Yorktown, Yorktown, cv, cvs-10, bonhomme richard, revolutionary war-ships, war-ships, naval ship, military ship, attack carriers, patriots point, landmarks, historic boats, essex class aircraft carrier, water, ocean
ship, ships, Yorktown, war boats, Patriot pointe, old war ship, historic landmarks, aircraft carrier, war ship, naval ship, navy ship, see, ocean
Yorktown ship, Warships and aircraft carriers, historic military vessels, the USS Yorktown aircraft carrier
As you can see, each user has generated valuable information for the photos, which we would want to use as a basis for creating indexable taxonomies for related stock images. However, at any type of scale, we have immediate threats of:
Thin content: Only a handful of products share the user-generated tag when a user creates a more specific/defining tag, e.g. "cvs-10"
Duplicate and similar content: Many of these tags will overlap, e.g. "USS Yorktown" vs. "Yorktown," "ship" vs. "ships," "cv" vs. "cvs-10," etc.
Bad content: Created by improper formatting, misspellings, verbose tags, hyphenation, and similar mistakes made by users.
Now that you understand what tag sprawl is and how it negatively effects your site, how can we address this issue at scale?
The proposed solution
In correcting tag sprawl, we have some basic (at the surface) problems to solve. We need to effectively review each tag in our database and place them in groups so further action can be taken. First, we determine the quality of a tag (how likely is someone to search for this tag, is it spelled correctly, is it commercial, is it used for many products) and second, we determine if there is another tag very similar to it that has a higher quality.
Identify good tags: We defined a good tag as term capable of contributing meaning, and easily justifiable as an indexed page in search results. This also entailed identifying a "master" tag to represent groups of similar terms.
Identify bad tags: We wanted to isolate tags that should not appear in our database due to misspellings, duplicates, poor format, high ambiguity, or likely to cause a low-quality page.
Relate bad tags to good tags: We assumed many of our initial "bad tags" could be a range of duplicates, i.e. plural/singular, technical/slang, hyphenated/non-hyphenated, conjugations, and other stems. There could also be two phrases which refer to the same thing, like "Yorktown ship" vs. "USS Yorktown." We need to identify these relationships for every "bad" tag.
For the project inspiring this post, our sample tag database comprised over 2,000,000 "unique" tags, making this a nearly impossible feat to accomplish manually. While theoretically we could have leveraged Mechanical Turk or similar platform to get "manual" review, early tests of this method proved to be unsuccessful. We would need a programmatic method (several methods, in fact) that we could later reproduce when adding new tags.
The methods
Keeping the goal in mind of identifying good tags, labeling bad tags, and relating bad tags to good tags, we employed more than a dozen methods, including: spell correction, bid value, tag search volume, unique visitors, tag count, Porter stemming, lemmatization, Jaccard index, Jaro-Winkler distance, Keyword Planner grouping, Wikipedia disambiguation, and K-Means clustering with word vectors. Each method either helped us determine whether the tag was valuable and, if not, helped us identify an alternate tag that was valuable.
Spell correction
Method: One of the obvious issues with user-generated content is the occurrence of misspellings. We would regularly find misspellings where semicolons are transposed for the letter “L” or words have unintended characters at the beginning or end. Luckily, Linux has an excellent built-in spell checker called Aspell which we were able to use to fix a large volume of issues.
Benefits: This offered a quick, early win in that it was fairly easy to identify bad tags when they were composed of words that weren’t included in the dictionary or included characters that were simply inexplicable (like a semicolon in the middle of a word). Moreover, if the corrected word or phrase occurred in the tag list, we could trust the corrected phrase as a potentially good tag, and relate the misspelled term to the good tag. Thus, this method help us both filter bad tags (misspelled terms) and find good tags (the spell-corrected term)
Limitations: The biggest limitation with this methodology was that combinations of correctly spelled words or phrases aren’t necessarily useful for users or the search engine. For example, many of the tags in the database were concatenations of multiple tags where the user space-delimited rather than comma-delimited their submitted tags. Thus, a tag might consist of correctly spelled terms but still be useless in terms of search value. Moreover, there were substantial dictionary limitations, especially with domain names, brand names, and Internet slang. In order to accommodate this, we added a personal dictionary that included a list of the top 10,000 domains according to Quantcast, several thousand brands, and a slang dictionary. While this was helpful, there were still several false recommendations that needed to be handled. For example, we saw "purfect" correct to "perfect," despite being a pop-culture reference for cat images. We also noticed some users reference this saying as "purrfect," "purrrfect," "purrrrfect," "purrfeck," etc. Ultimately, we had to rely on other metrics to determine whether we trusted the misspelling recommendations.
Bid value
Method: While a tag might be good in the sense that it is descriptive, we wanted tags that were commercially relevant. Using the estimated cost-per-click of the tag or tag phrase proved useful in making sure that the term could attract buyers, not just visitors.
Benefits: One of the great features of this methodology is that it tends to have a high signal-to-noise ratio. Most tags that have high CPCs tend to be commercially relevant and searched frequently enough to warrant inclusion as "good tags." In many cases we could feel confident that a tag was good just on this metric alone.
Limitations: However, the bid value metric comes with some pretty big limitations, too. For starters, Google Keyword Planner’s disambiguation problem is readily apparent. Google combines related keywords together when reporting search volume and CPC data, which means a tag like “facbook” would return the same data as “facebook.” Obviously, we would prefer to map “facbook” to “facebook” rather than keep both tags, so in some cases the CPC metric wasn’t sufficient to identify good tags. A further limitation of the bid value was the difficulty of acquiring CPC data. Google now requires running active Adwords campaigns to get access to CPC value. It is no simple feat to look up 5,000,000 keywords in Google Keyword Planner, even if you have a sufficient account. Luckily, we felt comfortable that historical data would be trustworthy enough, so we didn't need to acquire fresh data.
Tag search volume
Method: Similar to CPC, we could use search volume to determine the potential value of a tag. We had to be careful not to rely on the tag itself, though, since the tag could be so generic that it earns traffic unrelated to the product itself. For example, the tag “USS Yorktown” might get a few hundred searches a month, but “USS Yorktown T-shirt” gets 0. For all of the tags in our index, we tracked down the search volume for the tag plus the product name, in order to make sure we had good estimates of potential product traffic.
Benefits: Like CPC, this metric did a very good job of consolidating our tag data set to just keywords that were likely to deliver traffic. In the vast majority of cases, if “tag + product” had search volume, we could feel confident that it is a good term.
Limitations: Unfortunately, this method fell victim to the same disambiguation problem that CPC presents. Because Google groups terms together, it is possible that on some occasions two tags will be given the same metrics. For example: “pontoons boat,” “pontoonboat,” “pontoon boats,” “pontoon boat,” “pontoon boating,” and “pontoons boats” were in the same traffic volume group which also included tags like “yacht” and “yachts.” Moreover, there is no accounting for keyword difficulty in this metric. Some tags, when combined with product types, produce keywords that receive substantial traffic but will always be out of reach for a templated tag page.
Unique visitors
Method: This method was a no-brainer: protect the tags that already receive traffic from Google. We exported all of the tags from Google Analytics that had received search traffic from Google in the last 12 months. Generally speaking, this should be a fairly safe list of terms.
Benefits: When doing experimental work with a client, it is always nice to be able to give them a scenario that almost guarantees improvement. Because we were able to protect tags that already receive traffic by labeling them as good (in the vast majority of cases), we could ensure that the client had a high probability of profiting from the changes we made and minimal risk of any traffic loss.
Limitations: Unfortunately, even this method wasn’t perfect. If a product (or set of products) with high enough authority included a poor variation of a tag, then the bad variant would rank and receive traffic. We had to use other strategies to verify our selections from this method and devise a method to encourage a tag swap in the index for the correct version of a term.
Tag count
Description: The frequency with which a tag was used on the site was often a strong signal that we could trust the tag, especially when compared with other similar tags. By counting the number of times each tag was used on the site, we could bias our final set of trusted tags in favor of these more popular terms.
Benefits: This was a great tie-breaker metric when we had two tags that were very similar but needed to choose just one. For example, sometimes two variants of a phrase were completely acceptable (such as a version with and without a hyphen). We could simply defer to the one with a higher tag count.
Limitations: The clear limitation of tag frequency is that many of the most frequent tags were too generic to be useful. The tag “blue” isn’t particularly useful when it just helps people find “blue t-shirts.” The term is too generic and too competitive to warrant inclusion. Additionally, the inclusion of too broad of a tag would simply create a very large crawl vs. traffic-potential ratio. A common tag will have hundreds if not thousands of matching products, creating many pages of products for the single tag. If a tag produces 50 paginated product listings, but only has the potential to drive 10 visitors a year, it might not be worth it.
Porter stemming
Method: Stemming is a method used to identify the root word from a tag by scanning the word right to left and using various pattern matching rules to remove characters (suffixes) until you arrive at the word’s stem. There are a couple of popular stemmers available, but we found Porter stemming to be more accurate as a tool for seeing alternative word forms. You can geek out by looking at the Porter stemming algorithm in Snowball here, or you can play with a JS version here.
Benefits: Plural and possessive terms can be grouped by their stem for further analysis. Running Porter stemming on the terms “pony” and “ponies” will return “poni” as the stem, which can then be used to group terms for further analysis. You can also run Porter stemming on phrases. For example, “boating accident,” “boat accidents,” “boating accidents,” etc. share the stem “boat accid.” This can be a crude and quick method for grouping variations. Porter stemming also is able to clean text more kindly, where others stemmers can be too aggressive for our efforts; e.g., Lancaster stemmer reduces "woman" to "wom," while Porter stemmer leaves it as "woman."
Limitations: Stemming is intended for finding a common root for terms and phrases, and does not create any type of indication as to the proper form of a term. The Porter stemming method applies a fixed set of rules to the English language by blanket removing trailing “s,” “e,” “ance,” “ing,” and similar word endings to try and find the stem. For this to work well, you have to have all of the correct rules (and exceptions) in place to get the correct stems in all cases. This can be particularly problematic with words that end in S but are not plural, like "billiards" or "Brussels." Additionally, this method does not help with mapping related terms such as “boat crash,” “crashed boat,” “boat accident,” etc. which would stem to “boat crash,” “crash boat,” and “boat acci."
Lemmatization
Method: Lemmatization works similarly to stemming. However, instead of using a rule set for editing words by removing letters to arrive at a stem, lemmatization attempts to map the term to its most simple dictionary form, such as WordNet, and return a canonical “lemma” of the word. A crude way to think about lemmatization is just simplifying a word. Here’s an API to check out.
Benefits: This method often works better than stemming. Terms like “ship,” “shipped,” and “ships” are all mapped to “ship” by this method, while “shipping” or “shipper,” which are terms that have distinct meaning despite the same stem, are retained. You can create an array of “lemma” from phrases which can be compared to other phrases resolving word order issues. This proved to be a more reliable method for grouping variations than stemming.
Limitations: As with many of the methods, context for mapping related terms can be difficult. Lemmatization can provide better filters for context, but to do so generally relies on identifying the word form (noun, adjective, etc) to appropriately map to a root term. Given the inconsistency of the user-generated content, it is inaccurate to assume all words are in adjective form (describing a product), or noun form (the product itself). This inconsistency can present wild results. For example, “strip socks” could be intended as as a tag for socks with a strip of color on them, such as as “striped socks,” or it could be “stripper socks” or some other leggings that would be a match only found if there other products and tags to compare for context. Additionally, it doesn’t create associations between all related words, just textual derivatives, so you are still seeking out a canonical between mailman, courier, shipper, etc.
Jaccard index
Method: The Jaccard index is a similarity coefficient measured by Intersection over Union. Now, don’t run off just yet, it is actually quite straightforward. Imagine you had two piles of with 3 marbles in each: Red, Green, and Blue in the first, Red, Green and Yellow in the second. The “Intersection” of these two piles would be Red and Green, since both piles have those two colors. The “Union” would be Red, Green, Blue and Yellow, since that is the complete list of all the colors. The Jaccard index would be 2 (Red and Green) divided by 4 (Red, Green, Blue, and Yellow). Thus, the Jaccard index of these two piles would be .5. The higher the Jaccard index, the more similar the two sets. So what does this have to do with tags? Well, imagine we have two tags: “ocean” and “sea.” We can get a list of all of the products that have the tag “ocean” and “sea.” Finally, we get the Jaccard index of those two sets. The higher the score, the more related they are. Perhaps we find that 70% of the products with the tag “ocean” also have the tag “sea”; we now know that the two are fairly well-related. However, when we run the same measurement to compare “basement” or “casement,” we find that they only have a Jaccard index of .02. Even though they are very similar in terms of characters, they mean quite different things. We can rule out mapping the two terms together.
Benefits: The greatest benefit of using the Jaccard index is that it allows us to find highly related tags which may have absolutely no textual characteristics in common, and are more likely to have an overly similar or duplicate results set. While most of the the metrics we have considered so far help us find “good” or “bad” tags, the Jaccard index helps us find “related” tags without having to do any complex machine learning.
Limitations: While certainly useful, the Jaccard index methodology has its own problems. The biggest issue we ran into had to do with tags that were used together nearly all the time but weren’t substitutes of one another. For example, consider the tags “babe ruth” and his nickname, “sultan of swat.” The latter tag only occurred on products which also had the “babe ruth” tag (since this was one of his nicknames), so they had quite a high Jaccard index. However, Google doesn't map these two terms together in search, so we would prefer to keep the nickname and not simply redirect it to "babe ruth." We needed to dig deeper if we were to determine when we should keep both tags or when we should redirect one to another. As a standalone, this method also was not sufficient at identifying cases where a user consistently misspelled tags or used incorrect syntax, as their products would essentially be orphans without “union.”
Jaro-Winkler distance
Method: There are several edit distance and string similarity metrics that we used throughout this process. Edit Distance is simply some measurement of how difficult it is to change one word to another. For example, the most basic edit distance metric, Levenshtein distance, between "Russ Jones" and "Russell Jones" is 3 (you have to add "E","L", and "L" to transform Russ to Russell). This can be used to help us find similar words and phrases. In our case, we used a particular edit distance measure called "Jaro-Winkler distance" which gives higher precedence to words and phrases that are similar at the beginning. For example, "Baseball" would be closer to "Baseballer" than to “Basketball” because the differences are at the very end of the term.
Benefits: Edit distance metrics helped us find many very similar variants of tags, especially when the variants were not necessarily misspellings. This was particularly valuable when used in conjunction with the Jaccard index metrics, because we could apply a character-level metric on top of a character-agnostic metric (i.e. one that cares about the letters in the tag and one that doesn’t).
Limitations: Edit distance metrics can be kind of stupid. According to Jaro-Winkler distance, "Baseball” and "Basketball" are far more related to one another than "Baseball" and "Pitcher" or "Catcher." "Round" and "Circle" have a horrible edit distance metric, while "Round" and "Pound" look very similar. Edit distance simply cannot be used in isolation to find similar tags.
Keyword Planner grouping
Method: While Google's choice to combine similar keywords in..
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Tackling Tag Sprawl: Crawl Budget, Duplicate Content, and User-Generated Content
Posted by rjonesx.
Alright, so here's the situation. You have a million-product website. Your competitors have a lot of the same products. You need unique content. What do you do? The same thing everyone does — you turn to user-generated content. Problem solved, right?
User-generated content (UGC) can be an incredibly valuable source of content and organization, helping you build natural language descriptions and human-driven organization of site content. One common feature used by sites to take advantage of user-created content are tags, found everywhere from e-commerce sites to blogs. Webmasters can leverage tags to power site search, create taxonomies and categories of products for browsing, and to provide rich descriptions of site content.
This is a logical and practical approach, but can cause intractable SEO problems if left unchecked. For mega-sites, manually moderating millions of user-submitted tags can be cumbersome (if not wholly impossible). Leaving tags unchecked, though, can create massive problems with thin content, duplicate content, and general content sprawl. In our case study below, three technical SEOs from different companies joined forces to solve a massive tag sprawl problem. The project was led by Jacob Bohall, VP of Marketing at Hive Digital, while computational statistics services were provided by J.R. Oakes of Adapt Partners and Russ Jones of Moz. Let's dive in.
What is tag sprawl?
We define tag sprawl as the unchecked growth of unique, user-contributed tags resulting in a large amount of near-duplicate pages and unnecessary crawl space. Tag sprawl generates URLs likely to be classified as doorway pages, pages appearing to exist only for the purpose of building an index across an exhaustive array of keywords. You’ve probably seen this in its most basic form in the tagging of posts across blogs, which is why most SEOs recommend a blanket “noindex, follow” across tag pages in Wordpress sites. This simple approach can be an effective solution for small blog sites, but is not often the solution for major e-commerce sites that rely more heavily on tags for categorizing products.
The three following tag clouds represent a list of user-generated terms associated with different stock photos. Note: User behavior is generally to place as many tags as possible in an attempt to ensure maximum exposure for their products.
USS Yorktown, Yorktown, cv, cvs-10, bonhomme richard, revolutionary war-ships, war-ships, naval ship, military ship, attack carriers, patriots point, landmarks, historic boats, essex class aircraft carrier, water, ocean
ship, ships, Yorktown, war boats, Patriot pointe, old war ship, historic landmarks, aircraft carrier, war ship, naval ship, navy ship, see, ocean
Yorktown ship, Warships and aircraft carriers, historic military vessels, the USS Yorktown aircraft carrier
As you can see, each user has generated valuable information for the photos, which we would want to use as a basis for creating indexable taxonomies for related stock images. However, at any type of scale, we have immediate threats of:
Thin content: Only a handful of products share the user-generated tag when a user creates a more specific/defining tag, e.g. "cvs-10"
Duplicate and similar content: Many of these tags will overlap, e.g. "USS Yorktown" vs. "Yorktown," "ship" vs. "ships," "cv" vs. "cvs-10," etc.
Bad content: Created by improper formatting, misspellings, verbose tags, hyphenation, and similar mistakes made by users.
Now that you understand what tag sprawl is and how it negatively effects your site, how can we address this issue at scale?
The proposed solution
In correcting tag sprawl, we have some basic (at the surface) problems to solve. We need to effectively review each tag in our database and place them in groups so further action can be taken. First, we determine the quality of a tag (how likely is someone to search for this tag, is it spelled correctly, is it commercial, is it used for many products) and second, we determine if there is another tag very similar to it that has a higher quality.
Identify good tags: We defined a good tag as term capable of contributing meaning, and easily justifiable as an indexed page in search results. This also entailed identifying a "master" tag to represent groups of similar terms.
Identify bad tags: We wanted to isolate tags that should not appear in our database due to misspellings, duplicates, poor format, high ambiguity, or likely to cause a low-quality page.
Relate bad tags to good tags: We assumed many of our initial "bad tags" could be a range of duplicates, i.e. plural/singular, technical/slang, hyphenated/non-hyphenated, conjugations, and other stems. There could also be two phrases which refer to the same thing, like "Yorktown ship" vs. "USS Yorktown." We need to identify these relationships for every "bad" tag.
For the project inspiring this post, our sample tag database comprised over 2,000,000 "unique" tags, making this a nearly impossible feat to accomplish manually. While theoretically we could have leveraged Mechanical Turk or similar platform to get "manual" review, early tests of this method proved to be unsuccessful. We would need a programmatic method (several methods, in fact) that we could later reproduce when adding new tags.
The methods
Keeping the goal in mind of identifying good tags, labeling bad tags, and relating bad tags to good tags, we employed more than a dozen methods, including: spell correction, bid value, tag search volume, unique visitors, tag count, Porter stemming, lemmatization, Jaccard index, Jaro-Winkler distance, Keyword Planner grouping, Wikipedia disambiguation, and K-Means clustering with word vectors. Each method either helped us determine whether the tag was valuable and, if not, helped us identify an alternate tag that was valuable.
Spell correction
Method: One of the obvious issues with user-generated content is the occurrence of misspellings. We would regularly find misspellings where semicolons are transposed for the letter “L” or words have unintended characters at the beginning or end. Luckily, Linux has an excellent built-in spell checker called Aspell which we were able to use to fix a large volume of issues.
Benefits: This offered a quick, early win in that it was fairly easy to identify bad tags when they were composed of words that weren’t included in the dictionary or included characters that were simply inexplicable (like a semicolon in the middle of a word). Moreover, if the corrected word or phrase occurred in the tag list, we could trust the corrected phrase as a potentially good tag, and relate the misspelled term to the good tag. Thus, this method help us both filter bad tags (misspelled terms) and find good tags (the spell-corrected term)
Limitations: The biggest limitation with this methodology was that combinations of correctly spelled words or phrases aren’t necessarily useful for users or the search engine. For example, many of the tags in the database were concatenations of multiple tags where the user space-delimited rather than comma-delimited their submitted tags. Thus, a tag might consist of correctly spelled terms but still be useless in terms of search value. Moreover, there were substantial dictionary limitations, especially with domain names, brand names, and Internet slang. In order to accommodate this, we added a personal dictionary that included a list of the top 10,000 domains according to Quantcast, several thousand brands, and a slang dictionary. While this was helpful, there were still several false recommendations that needed to be handled. For example, we saw "purfect" correct to "perfect," despite being a pop-culture reference for cat images. We also noticed some users reference this saying as "purrfect," "purrrfect," "purrrrfect," "purrfeck," etc. Ultimately, we had to rely on other metrics to determine whether we trusted the misspelling recommendations.
Bid value
Method: While a tag might be good in the sense that it is descriptive, we wanted tags that were commercially relevant. Using the estimated cost-per-click of the tag or tag phrase proved useful in making sure that the term could attract buyers, not just visitors.
Benefits: One of the great features of this methodology is that it tends to have a high signal-to-noise ratio. Most tags that have high CPCs tend to be commercially relevant and searched frequently enough to warrant inclusion as "good tags." In many cases we could feel confident that a tag was good just on this metric alone.
Limitations: However, the bid value metric comes with some pretty big limitations, too. For starters, Google Keyword Planner’s disambiguation problem is readily apparent. Google combines related keywords together when reporting search volume and CPC data, which means a tag like “facbook” would return the same data as “facebook.” Obviously, we would prefer to map “facbook” to “facebook” rather than keep both tags, so in some cases the CPC metric wasn’t sufficient to identify good tags. A further limitation of the bid value was the difficulty of acquiring CPC data. Google now requires running active Adwords campaigns to get access to CPC value. It is no simple feat to look up 5,000,000 keywords in Google Keyword Planner, even if you have a sufficient account. Luckily, we felt comfortable that historical data would be trustworthy enough, so we didn't need to acquire fresh data.
Tag search volume
Method: Similar to CPC, we could use search volume to determine the potential value of a tag. We had to be careful not to rely on the tag itself, though, since the tag could be so generic that it earns traffic unrelated to the product itself. For example, the tag “USS Yorktown” might get a few hundred searches a month, but “USS Yorktown T-shirt” gets 0. For all of the tags in our index, we tracked down the search volume for the tag plus the product name, in order to make sure we had good estimates of potential product traffic.
Benefits: Like CPC, this metric did a very good job of consolidating our tag data set to just keywords that were likely to deliver traffic. In the vast majority of cases, if “tag + product” had search volume, we could feel confident that it is a good term.
Limitations: Unfortunately, this method fell victim to the same disambiguation problem that CPC presents. Because Google groups terms together, it is possible that on some occasions two tags will be given the same metrics. For example: “pontoons boat,” “pontoonboat,” “pontoon boats,” “pontoon boat,” “pontoon boating,” and “pontoons boats” were in the same traffic volume group which also included tags like “yacht” and “yachts.” Moreover, there is no accounting for keyword difficulty in this metric. Some tags, when combined with product types, produce keywords that receive substantial traffic but will always be out of reach for a templated tag page.
Unique visitors
Method: This method was a no-brainer: protect the tags that already receive traffic from Google. We exported all of the tags from Google Analytics that had received search traffic from Google in the last 12 months. Generally speaking, this should be a fairly safe list of terms.
Benefits: When doing experimental work with a client, it is always nice to be able to give them a scenario that almost guarantees improvement. Because we were able to protect tags that already receive traffic by labeling them as good (in the vast majority of cases), we could ensure that the client had a high probability of profiting from the changes we made and minimal risk of any traffic loss.
Limitations: Unfortunately, even this method wasn’t perfect. If a product (or set of products) with high enough authority included a poor variation of a tag, then the bad variant would rank and receive traffic. We had to use other strategies to verify our selections from this method and devise a method to encourage a tag swap in the index for the correct version of a term.
Tag count
Description: The frequency with which a tag was used on the site was often a strong signal that we could trust the tag, especially when compared with other similar tags. By counting the number of times each tag was used on the site, we could bias our final set of trusted tags in favor of these more popular terms.
Benefits: This was a great tie-breaker metric when we had two tags that were very similar but needed to choose just one. For example, sometimes two variants of a phrase were completely acceptable (such as a version with and without a hyphen). We could simply defer to the one with a higher tag count.
Limitations: The clear limitation of tag frequency is that many of the most frequent tags were too generic to be useful. The tag “blue” isn’t particularly useful when it just helps people find “blue t-shirts.” The term is too generic and too competitive to warrant inclusion. Additionally, the inclusion of too broad of a tag would simply create a very large crawl vs. traffic-potential ratio. A common tag will have hundreds if not thousands of matching products, creating many pages of products for the single tag. If a tag produces 50 paginated product listings, but only has the potential to drive 10 visitors a year, it might not be worth it.
Porter stemming
Method: Stemming is a method used to identify the root word from a tag by scanning the word right to left and using various pattern matching rules to remove characters (suffixes) until you arrive at the word’s stem. There are a couple of popular stemmers available, but we found Porter stemming to be more accurate as a tool for seeing alternative word forms. You can geek out by looking at the Porter stemming algorithm in Snowball here, or you can play with a JS version here.
Benefits: Plural and possessive terms can be grouped by their stem for further analysis. Running Porter stemming on the terms “pony” and “ponies” will return “poni” as the stem, which can then be used to group terms for further analysis. You can also run Porter stemming on phrases. For example, “boating accident,” “boat accidents,” “boating accidents,” etc. share the stem “boat accid.” This can be a crude and quick method for grouping variations. Porter stemming also is able to clean text more kindly, where others stemmers can be too aggressive for our efforts; e.g., Lancaster stemmer reduces "woman" to "wom," while Porter stemmer leaves it as "woman."
Limitations: Stemming is intended for finding a common root for terms and phrases, and does not create any type of indication as to the proper form of a term. The Porter stemming method applies a fixed set of rules to the English language by blanket removing trailing “s,” “e,” “ance,” “ing,” and similar word endings to try and find the stem. For this to work well, you have to have all of the correct rules (and exceptions) in place to get the correct stems in all cases. This can be particularly problematic with words that end in S but are not plural, like "billiards" or "Brussels." Additionally, this method does not help with mapping related terms such as “boat crash,” “crashed boat,” “boat accident,” etc. which would stem to “boat crash,” “crash boat,” and “boat acci."
Lemmatization
Method: Lemmatization works similarly to stemming. However, instead of using a rule set for editing words by removing letters to arrive at a stem, lemmatization attempts to map the term to its most simple dictionary form, such as WordNet, and return a canonical “lemma” of the word. A crude way to think about lemmatization is just simplifying a word. Here’s an API to check out.
Benefits: This method often works better than stemming. Terms like “ship,” “shipped,” and “ships” are all mapped to “ship” by this method, while “shipping” or “shipper,” which are terms that have distinct meaning despite the same stem, are retained. You can create an array of “lemma” from phrases which can be compared to other phrases resolving word order issues. This proved to be a more reliable method for grouping variations than stemming.
Limitations: As with many of the methods, context for mapping related terms can be difficult. Lemmatization can provide better filters for context, but to do so generally relies on identifying the word form (noun, adjective, etc) to appropriately map to a root term. Given the inconsistency of the user-generated content, it is inaccurate to assume all words are in adjective form (describing a product), or noun form (the product itself). This inconsistency can present wild results. For example, “strip socks” could be intended as as a tag for socks with a strip of color on them, such as as “striped socks,” or it could be “stripper socks” or some other leggings that would be a match only found if there other products and tags to compare for context. Additionally, it doesn’t create associations between all related words, just textual derivatives, so you are still seeking out a canonical between mailman, courier, shipper, etc.
Jaccard index
Method: The Jaccard index is a similarity coefficient measured by Intersection over Union. Now, don’t run off just yet, it is actually quite straightforward. Imagine you had two piles of with 3 marbles in each: Red, Green, and Blue in the first, Red, Green and Yellow in the second. The “Intersection” of these two piles would be Red and Green, since both piles have those two colors. The “Union” would be Red, Green, Blue and Yellow, since that is the complete list of all the colors. The Jaccard index would be 2 (Red and Green) divided by 4 (Red, Green, Blue, and Yellow). Thus, the Jaccard index of these two piles would be .5. The higher the Jaccard index, the more similar the two sets. So what does this have to do with tags? Well, imagine we have two tags: “ocean” and “sea.” We can get a list of all of the products that have the tag “ocean” and “sea.” Finally, we get the Jaccard index of those two sets. The higher the score, the more related they are. Perhaps we find that 70% of the products with the tag “ocean” also have the tag “sea”; we now know that the two are fairly well-related. However, when we run the same measurement to compare “basement” or “casement,” we find that they only have a Jaccard index of .02. Even though they are very similar in terms of characters, they mean quite different things. We can rule out mapping the two terms together.
Benefits: The greatest benefit of using the Jaccard index is that it allows us to find highly related tags which may have absolutely no textual characteristics in common, and are more likely to have an overly similar or duplicate results set. While most of the the metrics we have considered so far help us find “good” or “bad” tags, the Jaccard index helps us find “related” tags without having to do any complex machine learning.
Limitations: While certainly useful, the Jaccard index methodology has its own problems. The biggest issue we ran into had to do with tags that were used together nearly all the time but weren’t substitutes of one another. For example, consider the tags “babe ruth” and his nickname, “sultan of swat.” The latter tag only occurred on products which also had the “babe ruth” tag (since this was one of his nicknames), so they had quite a high Jaccard index. However, Google doesn't map these two terms together in search, so we would prefer to keep the nickname and not simply redirect it to "babe ruth." We needed to dig deeper if we were to determine when we should keep both tags or when we should redirect one to another. As a standalone, this method also was not sufficient at identifying cases where a user consistently misspelled tags or used incorrect syntax, as their products would essentially be orphans without “union.”
Jaro-Winkler distance
Method: There are several edit distance and string similarity metrics that we used throughout this process. Edit Distance is simply some measurement of how difficult it is to change one word to another. For example, the most basic edit distance metric, Levenshtein distance, between "Russ Jones" and "Russell Jones" is 3 (you have to add "E","L", and "L" to transform Russ to Russell). This can be used to help us find similar words and phrases. In our case, we used a particular edit distance measure called "Jaro-Winkler distance" which gives higher precedence to words and phrases that are similar at the beginning. For example, "Baseball" would be closer to "Baseballer" than to “Basketball” because the differences are at the very end of the term.
Benefits: Edit distance metrics helped us find many very similar variants of tags, especially when the variants were not necessarily misspellings. This was particularly valuable when used in conjunction with the Jaccard index metrics, because we could apply a character-level metric on top of a character-agnostic metric (i.e. one that cares about the letters in the tag and one that doesn’t).
Limitations: Edit distance metrics can be kind of stupid. According to Jaro-Winkler distance, "Baseball” and "Basketball" are far more related to one another than "Baseball" and "Pitcher" or "Catcher." "Round" and "Circle" have a horrible edit distance metric, while "Round" and "Pound" look very similar. Edit distance simply cannot be used in isolation to find similar tags.
Keyword Planner grouping
Method: While Google's choice to combine similar keywords in..
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