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a-alex-hammer · 6 years ago

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Getting started in Messenger Marketing – Chatbots Life

I’m regularly asked what Messenger marketing is, and exactly how it can benefit a business, so I’ve decided to write an article covering some of the main points, and showing you how you can get started today.

First of all, I want to point out that there are 1.3 billion FaceBook Messenger users, and not to mention millions more on FaceBook’s other messaging platforms.

And what’s most interesting to me is that those 1.3 billion users are having 7 billion conversations each day! That means there’s a hell of a lot of activity across FaceBook Messenger each day.

Platforms like FaceBook Messenger are the new trend for Social Media marketing, since they allow businesses to reach out to consumers on a large scale through advertisements, and can then interact with them on a more individual level. I believe that a well-designed Messenger chatbot can effectively do the work of an entire marketing department. Chatbot’s will allow you to provide instant responses to your customers questions, and alert real people when there’s a question that needs to be answered by a human.

Trending Articles on Chatbot Marketing:

1. How to Get to 1 Million Users for your Chatbot

2. How to Get Users for Free using a Viral Loop

3. How I grew JokeBot from 26k subscribers to 117k subscribers

4.Chatbot Conference in San Francisco

Why choose Messenger marketing?

When marketing a business or product, your aim should be to target your customers across the platforms that they are most active with. Messenger marketing will allow businesses to engage with customers and leads in a more conversational way than traditional email marketing. This is giving businesses an opportunity to develop personalised relationships with their customers.

Messenger Is Quickly Becoming A Mainstream Channel For Businesses!

When email first came out, email marketing was born, everyone was sending emails and this was a perfect opportunity for businesses to communicate with their customers. The same happened when phones became common, telemarketing came out. Today, people communicate across Messenger apps and the popularity with this channel keeps on growing, year by year!

How to use Messenger marketing

There are a number of ways in which you can use Messenger marketing, but I tend to break the different methods into the following:

Lead generation: Lead generation is one of the biggest reasons why many businesses are adopting Messenger marketing. Build out a marketing funnel which uses Messenger as a warm way to introduce your business to your target audience. For example, let’s say you’re a restaurant that wants to drive new people down to your business…

You could boost a FaceBook post promoting a special offer to new customers. You can then setup a Chatbot to automatically respond to people who leave a comment on the post with a specific keyword. You then run both Messenger ads and post engagement ads driving people to leave comments, or click into your Messenger to claim that offer. Each time this happens, the page can automatically respond with the offer.

You then have this user subscribed to your Messenger marketing list. This means you can keep running promotional messages to this user from your chosen bot platform, without having to pay for ads. It’s the same process as running an email campaign to your email subscribers.

Content delivery:

The majority of sites send new content to users through people who have opted in through email, but this is now something that you can do through Messenger. You can ask your users to optin through Messenger to be delivered new content through their inboxes.

Most bot platforms allow you to connect an RSS feed with your site, so for each time you post, it’s delivered into their inbox automatically.

You can also offer your users to search for specific content, by adding in some categories to your Chatbot. Tech Crunch has a great example for doing this.

One of the best features with content delivery is that you can personalise the content that you deliver to your users. For example, adding tags to your Messenger subscribers will allow you to fire more personalised/desired content.

Customer support:

As well as being able to provide your subscribers with instant customer support, there are many other ways of being able to support your customers with Messenger marketing.

One of my favourite ways is to achieve valuable feedback from your customers. If you’re running an event, broadcast messages to your attenders asking for feedback on how well the event went. This method is often used in eCommerce, as you can ask your customers to leave reviews on the products that they’ve purchased.

You’ll also find a much higher feedback response through Messenger rather than email!

Some interesting facts about Messenger marketing:

Messenger has a 4x higher open rate.

Messenger has an 8x-12.5x higher click-through rate.

Messenger Generates 1.6x more revenue.

Our latest client achieved 16K in table bookings

Our average Messenger campaign gets an open rate of 96%

We’ve helped a startup achieve 11,500 Messenger subscribers in a short period of time!

Start your first campaign today!

If you’ve got this far, then you may be considering a Messenger campaign. If that’s the case then I encourage you to continue with those thoughts! If you’re a little stuck, then here are some steps for launching your first Messenger marketing campaign:

Find a platform to build your Chatbot with. I’d recommend either ManyChat or ChatFuel. In these instructions I’ll talk about ManyChat. Create an account with them, and connect the bot to your page.

2. Create a post on FaceBook with a great offer. Think of something catchy that will instantly capture the attention of your audience. Tell them to comment a keyword which will enable them to claim their offer. The page will message those who comment with the relevant keyword. In this case I’ll use the keyword “interested”.

3. Go back to ManyChat, head over to the growth tools, click new growth tool and select the FaceBook comment tool.

4. Here’s where it may get tricky, I’ll write an in depth article about how to correctly set this up in a few days but, you basically want to select the post you have created with the offer in, and connect the comment tool. You want to setup the tool to only reply to users who have left a specific keyword in their comment. In this case, it’s for anyone who commented “interested”

You should see something that looks like this:

Make sure you’ve selected that checkbox below the “change post” button. Then click next.

5. Now create an opt in message. As your page is sending a message to someone who left a comment on your post, I always recommend putting something like:

“Hey Full Name! We’re messaging you because you left a comment on our recent post. Reply with “yes” to be sent the offer.”

When someone leaves a comment saying “yes” they become subscribed to your Messenger marketing list, because they’ve technically opted into your bot by leaving a reply.

6. When you reach the optin message section, you want to select “send only to users who reply with a specific keyword”. This is where you’ll enter the keyword “yes” in our case. You’ll then see a field which is your optin message.

Think of this as the response to the user, after they have replied with “yes”. For now, keep the default message in there, but remember what it’s called. You can then press save and activate.

Now whenever someone leaves a comment with “interested” and responds with “yes” the page will send across the offer, and also add the user to the pages Messenger subscriber list. Also, make sure that it’s set to active in the top left and not draft.

7. Now head to the flows tab. You should be able to see “optin messages”, click on this and select that optin message that you took note of before, when we setup the comment response. You need to click on this, and edit the flow.

Whatever text is inside of the flow will be what is sent to users who respond with “yes”. Once you’re finished with your message, click publish and now that response will be fired to the user once they have replied.

8. The very final step is to now boost that post you created earlier on. I’d recommend boosting it with the objective of post engagements, to drive the most comments for the budget that you have. Make sure you’re targeting the right people, and you’ll soon start seeing comments.

You’ve now created your first Messenger ad!

This can go really advanced, such as subscribing these users to a specific sequence, or adding tags to them so you know where the leads have come from. If you’re a restaurant then you could even fire up a second message asking when they’d like to book a table.. the possibilities are endless!

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Source/Repost=> http://technewsdestination.com/getting-started-in-messenger-marketing-chatbots-life/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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a-alex-hammer · 6 years ago

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YouTube’s Upcoming AR Ads Let You Try On Makeup During Beauty Vlogs

Try on makeup directly from the YouTube app with AR Beauty Try-On.

Hot on the heels of their recent announcement regarding the introduction of 3D AR models to Google search results, the company revealed this week the arrival of AR Beauty Try-On, a new augmented reality-powered feature for the official YouTube app that allows users to try-on makeup as they watch their favorite beauty vloggers.

Those who have spent at least a couple hours on YouTube can tell you that beauty channels run by vloggers who provide reviews, tips, and inspirations for hair, fashion, and cosmetic, account for a wide portion of YouTube’s 2 billion monthly active users. In an effort to provide a more personalized and effective experience for viewers as well as brands looking to advertise over specific content, AR Beauty Try-On enables viewers to try-on real cosmetic products in real-time without even having to pause the video.

Image Credit: Google / YouTube

So say you’re watching a popular beauty channel famous for its in-depth reviews of the hottest lipstick brands. If the channel has AR Beauty Try-On activated, instead of seeing a standard banner ad as you would normally, you’d instead be offered a second window in which to try-on a realistic AR rendition of the product being reviewed in the video above.

In a quiet test run conducted earlier this year alongside several prominent Beauty brands, Google claims 30% of viewers activated the augmented advertisements, with a majority spending around 80 seconds trying on AR lipstick. The first company taking part in the campaign will be M·A·C Cosmetics, which will offer a series of AR options when AR Beauty Try-On launches later this summer.

Creators interested in participating can join up using Famebit, YouTube’s branded content platform.

Image Credit: Google / YouTube

Along with the announcement of AR Beauty Try-On, Google is also introducing Swirl, their first immersive display format designed specifically for mobile web via Display & Video 360 which allows users to interact with advertisements in a variety of new ways. To assist brands with creating Swirl-based interactive advertisements, Google is also introducing a new editor to Google’s 3D platform, Poly. This new addition offers developers more creative control over 3D objects, allowing them to change animation settings, customize backgrounds, and add realistic reflections.

You can head over to Google’s official blog to learn more about these new features.

Source/Repost=> http://technewsdestination.com/youtubes-upcoming-ar-ads-let-you-try-on-makeup-during-beauty-vlogs/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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a-alex-hammer · 6 years ago

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One-Shot Learning with Siamese Networks, Contrastive Loss, and Triplet Loss for Face Recognition

One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future.

This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given one or a few template photos.

Modern face recognition systems approach the problem of one-shot learning via face recognition by learning a rich low-dimensional feature representation, called a face embedding, that can be calculated for faces easily and compared for verification and identification tasks.

Historically, embeddings were learned for one-shot learning problems using a Siamese network. The training of Siamese networks with comparative loss functions resulted in better performance, later leading to the triplet loss function used in the FaceNet system by Google that achieved then state-of-the-art results on benchmark face recognition tasks.

In this post, you will discover the challenge of one-shot learning in face recognition and how comparative and triplet loss functions can be used to learn high-quality face embeddings.

After reading this post, you will know:

One-shot learning are classification tasks where many predictions are required given one (or a few) examples of each class, and face recognition is an example of one-shot learning.

Siamese networks are an approach to addressing one-shot learning in which a learned feature vector for the known and candidate example are compared.

Contrastive loss and later triplet loss functions can be used to learn high-quality face embedding vectors that provide the basis for modern face recognition systems.

Let’s get started.

One-Shot Learning with Siamese Networks, Contrastive, and Triplet Loss for Face Recognition Photo by Heath Cajandig, some rights reserved.

Overview

This tutorial is divided into four parts; they are:

One-Shot Learning and Face Recognition

Siamese Network for One-Shot Learning

Contrastive Loss for Dimensionality Reduction

Triplet Loss for Learning Face Embeddings

One-Shot Learning and Face Recognition

Typically, classification involves fitting a model given many examples of each class, then using the fit model to make predictions on many examples of each class.

For example, we may have thousands of measurements of plants from three different species. A model can be fit on these examples, generalizing from the commonalities among the measurements for a given species and contrasting differences in the measurements across species. The result, hopefully, is a robust model that, given a new set of measurements in the future, can accurately predict the plant species.

One-shot learning is a classification task where one example (or a very small number of examples) is given for each class, that is used to prepare a model, that in turn must make predictions about many unknown examples in the future.

In the case of one-shot learning, a single exemplar of an object class is presented to the algorithm.

— Knowledge transfer in learning to recognize visual objects classes, 2006.

This is a relatively easy problem for humans. For example, a person may see a Ferrari sports car one time, and in the future, be able to recognize Ferraris in new situations, on the road, in movies, in books, and with different lighting and colors.

Humans learn new concepts with very little supervision – e.g. a child can generalize the concept of “giraffe” from a single picture in a book – yet our best deep learning systems need hundreds or thousands of examples.

— Matching Networks for One Shot Learning, 2017.

One-shot learning is related to but different from zero-shot learning.

This should be distinguished from zero-shot learning, in which the model cannot look at any examples from the target classes.

— Siamese Neural Networks for One-shot Image Recognition, 2015.

Face recognition tasks provide examples of one-shot learning.

Specifically, in the case of face identification, a model or system may only have one or a few examples of a given person’s face and must correctly identify the person from new photographs with changes to expression, hairstyle, lighting, accessories, and more.

In the case of face verification, a model or system may only have one example of a persons face on record and must correctly verify new photos of that person, perhaps each day.

As such, face recognition is a common example of one-shot learning.

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Siamese Network for One-Shot Learning

A network that has been popularized given its use for one-shot learning is the Siamese network.

A Siamese network is an architecture with two parallel neural networks, each taking a different input, and whose outputs are combined to provide some prediction.

It is a network designed for verification tasks, first proposed for signature verification by Jane Bromley et al. in the 2005 paper titled “Signature Verification using a Siamese Time Delay Neural Network.”

The algorithm is based on a novel, artificial neural network, called a “Siamese” neural network. This network consists of two identical sub-networks joined at their outputs.

— Signature Verification using a “Siamese” Time Delay Neural Network, 2005.

Two identical networks are used, one taking the known signature for the person, and another taking a candidate signature. The outputs of both networks are combined and scored to indicate whether the candidate signature is real or a forgery.

Verification consists of comparing an extracted feature vector with a stored feature vector for the signer. Signatures closer to this stored representation than a chosen threshold are accepted, all other signatures are rejected as forgeries.

— Signature Verification using a “Siamese” Time Delay Neural Network, 2005.

Example of a Siamese Network for Signature Verification. Taken from: Signature Verification using a “Siamese” Time Delay Neural Network.

Siamese networks were used more recently, where deep convolutional neural networks were used in parallel image inputs in a 2015 paper by Gregory Koch, et al. titled “Siamese Neural Networks for One-Shot Image Recognition.”

The deep CNNs are first trained to discriminate between examples of each class. The idea is to have the models learn feature vectors that are effective at extracting abstract features from the input images.

Example of Image Verification Used to Train a Siamese Network. Taken from: Siamese Neural Networks for One-Shot Image Recognition.

The models are then re-purposed for verification to predict whether new examples match a template for each class.

Specifically, each network produces a feature vector for an input image, which are then compared using the L1 distance and a sigmoid activation. The model was applied to benchmark handwritten character datasets used in computer vision.

Example of One-Shot Image Classification Used to Test a Siamese Network. Taken from: Siamese Neural Networks for One-Shot Image Recognition.

The Siamese Network is interesting for its approach to solving one-shot learning by learning feature representations (feature vectors) that are then compared for verification tasks.

An example of a face recognition system that was developed using a Siamese Network is DeepFace, described by Yaniv Taigman, et al. in the 2014 paper titled “DeepFace: Closing the Gap to Human-Level Performance in Face Verification.”

Their approach involved first training the model for face identification, then removing the classifier layer of the model and using the activations as a feature vector that were then calculated and compared for two different faces for face verification.

We have also tested an end-to-end metric learning approach, known as Siamese network: once learned, the face recognition network (without the top layer) is replicated twice (one for each input image) and the features are used to directly predict whether the two input images belong to the same person.

— DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014.

Contrastive Loss for Dimensionality Reduction

Learning a vector representation of a complex input, like an image, is an example of dimensionality reduction.

Dimensionality reduction aims to translate high dimensional data to a low dimensional representation such that similar input objects are mapped to nearby points on a manifold.

— Dimensionality Reduction by Learning an Invariant Mapping, 2006.

The goal of effective dimensionality reduction is to learn a new lower dimensional representation that preserves the structure of the input such that distances between output vectors meaningfully capture the differences in the input. Yet, the vectors must capture the invariant features in the input.

The problem is to find a function that maps high dimensional input patterns to lower dimensional outputs, given neighborhood relationships between samples in input space.

— Dimensionality Reduction by Learning an Invariant Mapping, 2006.

Dimensionality reduction is the approach that Siamese networks use to address one-shot learning.

In their 2006 paper titled “Dimensionality Reduction by Learning an Invariant Mapping,” Raia Hadsell, et al. explore using a Siamese network for dimensionality reduction with convolutional neural networks with image data and propose training the models via contrastive loss.

Unlike other loss functions that may evaluate the performance of a model across all input examples in the training dataset, contrastive loss is calculated between pairs of inputs, such as between the two inputs provided to a Siamese network.

Pairs of examples are provided to the network, and the loss function penalizes the model differently based on whether the classes of the samples are the same or different. Specifically, if the classes are the same, the loss function encourages the models to output feature vectors that are more similar, whereas if the classes differ, the loss function encourages the models to output feature vectors that are less similar.

The contrastive loss requires face image pairs and then pulls together positive pairs and pushes apart negative pairs. […] However, the main problem with the contrastive loss is that the margin parameters are often difficult to choose.

— Deep Face Recognition: A Survey, 2018.

The loss function requires that a margin is selected that is used to determine the limit to which examples from different pairs are penalized. Choosing this margin requires careful consideration and is one downside of using the loss function.

Plot of Contrastive Loss Calculation for Similar (red) and Dissimilar (blue) Pairs. Taken From: Dimensionality reduction by learning an invariant mapping

Contrastive loss can be used to train a face recognition system, specifically for the task of face verification. Further, this can be achieved without the need for parallel models used in the Siamese network architecture by providing pairs of examples sequentially and saving the predicted feature vectors before calculating the loss and updating the model.

An example is the DeepID2 and subsequent systems (DeepID2+ and DeepID3) that used deep convolutional neural networks, but not a Siamese network architecture, and achieved then state-of-the-art results on benchmark face recognition datasets.

The verification signal directly regularize DeepID2 and can effectively reduce the intra-personal variations. Commonly used constraints include the L1/L2 norm and cosine similarity. We adopt the following loss function based on the L2 norm, which was originally proposed by Hadsell et al. for dimensionality reduction.

— Deep Learning Face Representation by Joint Identification-Verification, 2014.

Triplet Loss for Learning Face Embeddings

The idea of comparative loss can be further extended from two examples to three, called triplet loss.

Triplet loss was introduced by Florian Schroff, et al. from Google in their 2015 paper titled “FaceNet: A Unified Embedding for Face Recognition and Clustering.”

Rather than calculating loss based on two examples, triplet loss involves an anchor example and one positive or matching example (same class) and one negative or non-matching example (differing class).

The loss function penalizes the model such that the distance between the matching examples is reduced and the distance between the non-matching examples is increased.

It requires the face triplets, and then it minimizes the distance between an anchor and a positive sample of the same identity and maximizes the distance between the anchor and a negative sample of a different identity.

— Deep Face Recognition: A Survey, 2018.

Example of The Effect on Anchor, Positive, and Negative Both Before and After Applying Triplet Loss. Taken from: Facenet: A unified embedding for face recognition and clustering.

The result is a feature vector, referred to as a ‘face embedding,’ that has a meaningful Euclidean relationship, such that similar faces produce embeddings that have small distances (e.g. can be clustered) and different examples of the same face produce embeddings that are very small and allow verification and discrimination from other identities.

This approach is used as the basis behind the FaceNet system that achieved then state-of-the-art results on benchmark face recognition datasets.

In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity.

— Facenet: A unified embedding for face recognition and clustering, 2015.

The triplets that are used to train the model are carefully chosen.

Triplets that are easy, result in a small loss, and are not effective at updating the model. Instead, hard triplets are sought that encourage changes to the model and the predicted face embeddings.

Choosing which triplets to use turns out to be very important for achieving good performance and, inspired by curriculum learning, we present a novel online negative exemplar mining strategy which ensures consistently increasing difficulty of triplets as the network trains.

— Facenet: A unified embedding for face recognition and clustering, 2015.

Triplets are generated in an online manner, and so-called hard positive (matching) and hard negative (non-matching) cases are found and used in the estimate of the loss for the batch.

It is crucial to select hard triplets, that are active and can therefore contribute to improving the model.

— Facenet: A unified embedding for face recognition and clustering, 2015.

The approach of directly training face embeddings, such as via triplet loss, and using the embeddings as the basis for face identification and face verification models, such as FaceNet, is the basis for modern and state-of-the-art methods for face recognition.

… for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.

— In Defense of the Triplet Loss for Person Re-Identification, 2017.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Papers

Knowledge transfer in learning to recognize visual objects classes, 2006.

Matching Networks for One Shot Learning, 2017.

Siamese Neural Networks for One-shot Image Recognition, 2015.

Signature Verification using a “Siamese” Time Delay Neural Network, 2005.

DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014.

Dimensionality Reduction by Learning an Invariant Mapping, 2006.

Deep Face Recognition: A Survey, 2018.

Deep Learning Face Representation by Joint Identification-Verification, 2014.

Facenet: A unified embedding for face recognition and clustering, 2015.

In Defense of the Triplet Loss for Person Re-Identification, 2017.

Articles

Summary

In this post, you discovered the challenge of one-shot learning in face recognition and how comparative and triplet loss functions can be used to learn high-quality face embeddings.

Specifically, you learned:

One-shot learning are classification tasks where many predictions are required given one (or a few) examples of each class, and face recognition is an example of one-shot learning.

Siamese networks are an approach to addressing one-shot learning in which a learned feature vector for the known and candidate example are compared.

Contrastive loss and later triplet loss functions can be used to learn high-quality face embedding vectors that provide the basis for modern face recognition systems.

Do you have any questions? Ask your questions in the comments below and I will do my best to answer.

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Source/Repost=> http://technewsdestination.com/one-shot-learning-with-siamese-networks-contrastive-loss-and-triplet-loss-for-face-recognition/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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a-alex-hammer · 6 years ago

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Watch 20 Minutes of Asgard’s Wrath Gameplay – Road to VR

Sanzaru Games and Oculus Studios showed off a new demo for their upcoming melee adventure Asgard’s Wrath at this year’s E3. We got a chance to not only jump in to see more of the game’s questing, but also get a feel for the scope of the world ahead.

I had around 20 minutes in the new demo—only just enough to sample other important components of the game besides rote combat, which I saw a fair bit of in my first hands-on at GDC in March.

This time around I got to create chimeric buddies from the world’s beasts, solve light puzzles while dungeoning, and accumulate loot for crafting along the way. What most impressed me about the game though was its visual fidelity and rich environments, something I hope to explore more during the game’s purported 30+ hour duration.

If you’re short on viewing time, check out my article ‘Asgard’s Wrath Shows Titanic Ambition & Visual Finesse’ for the play-by-play and my impressions of the E3 demo.

Asgard’s Wrath is slated to arrive exclusively on Rift sometime in 2019.

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Source/Repost=> http://technewsdestination.com/watch-20-minutes-of-asgards-wrath-gameplay-road-to-vr/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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a-alex-hammer · 6 years ago

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Snapchat announces new augmented reality platform – Haptical

NEW EXPERIENCES

Garuda launches inflight virtual reality entertainment

Garuda Indonesia’s latest inflight entertainment option is the first VR system in Asia Pacific to have been certified with an international flight safety approval, according to a statement from the airline… (Jakarta Post)

Full-body virtual reality lets you feel the Amazon rainforest

In recent years, an increasing number of pop-ups and other location-based virtual reality arcades have allowed people to go beyond simply strapping on a headset by incorporating multi-sensory interactions like motion and scent… (ABC News)

Inside Muse and Microsoft’s virtual reality tour experience

Muse has partnered with Microsoft to offer fans a one-of-a-kind virtual reality experience during their Simulation Theory world tour, which kicked off earlier this year… (Billboard)

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Source/Repost=> http://technewsdestination.com/snapchat-announces-new-augmented-reality-platform-haptical/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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a-alex-hammer · 6 years ago

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How to Check Your Ads With a Text Overlay Tool

The best billboards demand your attention with bold fonts, in-your-face messages, and bright, eye-catching graphics. The best Facebook ads take the exact opposite approach.

If you want to reach and engage with potential customers on Facebook, you need to create ads that blend as seamlessly as possible into the rest of the content on their newsfeeds. This means focusing on simple, high-quality images, straightforward messages, and most importantly: minimal text.

Facebook knows that the best performing ads include images with little to no text, which is why they created the 20% rule. This rule states that in order to run an image-based ad on Facebook, your image(s) must contain less than 20% text.

For a complete guide to creating Facebook ads, check out our article here.

Facebook 20% Rule

Facebook advertisers are not allowed to cover their ads’ images with more than 20% text. This rule applies to both single image and carousel ads run on Facebook and Instagram. Ads with more than 20% text covering any images might be rejected by Facebook’s review team or might be shown less frequently. There are a few key exceptions — discussed here.

It’s important to note that the 20% rule only applies to text that covers images attached to your ad. It does not include text on your ad outside of images, like the description copy or call-to-action button.

There are a few exceptions to the 20% rule, including images of book covers, album covers, event posters, video games, and some product images that contain text (e.g., a cereal box). Text-based logos are not an exception to the 20% rule, and will be counted as text when Facebook reviews your images.

So, why exactly does the Facebook 20% rule exist? It all comes down to what users want to see and engage with in their newsfeeds. Ads with less overlay text actually perform significantly better than images crowded with text, so the rule actually creates a better experience for both users and advertisers.

Facebook Text Overlay Tool

When Facebook reviews your ad images, they examine how much of your images are covered by text. While you’re creating an ad, it can be tricky to evaluate the exact percentage of text covering your image — fortunately, Facebook provides a tool you can use to check before you even submit your ad for review. You can access that tool right here.

Here’s an example of an image with an ideal amount of text:

Your best approach when creating a Facebook ad is to use little to no text. In this example of an ideal ad image, there’s only a small text-based logo and no other copy. An ad with a simple image like this will blend more easily into users’ newsfeeds and is much more likely to gain exposure and engagement among your target audience.

In the next example, there’s an extra line of text:

This image technically passes the 20% rule, but the extra line of text means you risk your ad being seen by fewer people. Instead of adding copy to your image, try adding it directly into the body copy of your ad.

This final example is exactly what Facebook does not want to see:

This ad contains too much text over the image. The information displayed here could easily be incorporated into the body copy of your ad, creating a much cleaner look in users’ newsfeeds. While it’s tempting to throw important information onto your images like this, you risk having your ad rejected by Facebook or alienating users who are turned off by the busy copy.

Here’s a simple rule to remember: the best way to capture users’ attention on Facebook is to use an eye-catching image with no text. The 20% rule isn’t just an arbitrary standard — it helps advertisers reach their target audiences more effectively, and prevents users’ newsfeeds from becoming overwhelmed with disruptive advertisements.

Originally published Jun 20, 2019 7:00:00 AM, updated June 20 2019

Topics:

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Source/Repost=> http://technewsdestination.com/how-to-check-your-ads-with-a-text-overlay-tool/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

#success #motivation #AlexHammer #Founder #CEO #Ecommerce #ROI #Charlotte #Secrets #win

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