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Targeted Mobile A/B Testing Distribution It feels good to be able to announce new features. It's a combination of getting to share exciting new things with people and the anticipation of seeing how people will use what we've been working on. Today, I get to announce a new feature that I'm really excited about, targeted distribution. With targeted distribution we give our users the ability to deliver experiments and hotfixes to the users they want, based off of certain qualities those users possess.

First, we had the segmentation of results, and that was great. With segmenting and refining results you got to see how different cohorts of your users reacted to your experiment without having to think about logical cohorts ahead of time. All you have to do is run your experiment, head to the experiment summary page and set the properties that you want to filter your results by. Amazing, right? Right! This has been extremely useful for understanding how to tweak your hypotheses as you create follow-up experiments.

Now, we have taken that great feature and brought it forward in the process, all the way to when you're setting the distribution. We've done this because you may not want all users to be a part of an experiment or a hotfix. As a specific example, let’s say you have an experiment or hotfix that really only makes sense to one cohort of users, people with a certain screen size, this could be all users with an iPhone 5 or newer.

Mobile A/B Testing Targeted Distribution Filters

Well now all you have to do when you set your distribution is create a filter (like the one in the image above) that distributes only to the iPhone 5, 5S and 5C devices. You can then set your distribution percentages for the variations just as before and you can be sure that the experiment or hotfix that you are pushing live will only be delivered to those 3 types of phones.

This feature is extremely powerful and opens up a wide range of opportunities for our customers. When it comes to hotfixes we all know that bugs are elusive because they tend to show up on that one device type or OS version you didn't test. Now you can push a fix instantly just for that specific device or version without needing an update or making a change to all of those versions that are functioning perfectly. And when it comes to experiments, this allows you to deliver more tailored experiments to your users. Clearly, mobile app users are distinct and require unique experiences. Targeted distribution and segmentation of your experiments allows you to deliver a more unique experience to different segments of your userbase. The result being, great retention, engagement and revenue!

As always if you're interested in learning more about how to target users effectively or get the most out of mobile A/B testing feel free to shoot me an email or throw a question, in the comments section below.

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Cobi Druxerman

Co-Founder and CMO of Taplytics


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