Mobile

Why cohort analysis is a game-changer for mobile app businesses

This sponsored post is produced by Simon Kendall, the product manager and head of analytics operations at Adeven GmbH, a mobile app tracking and analytics firm.

There’s an arms race in mobile ad tech — and the name of the game is cohort analysis. Even though most providers now offer a solution of some sort, most are still left with the burning question: How can my app business benefit from this? Cohort analysis has been around for a while in medicine, academic research, business, and other fields where there is a need to compare apples with apples. Its potential applications in the mobile marketing world are as obvious as they are thrilling.

All mobile users have a lifespan, and these lifespans express a trend that you can manipulate. When you try to identify changes in user behaviour from tweaking or optimizing your various channels and features, you’ll often find that the trends or changes are too weak to draw any conclusions. This usually occurs when your user base is varied, with different levels of engagement and at different stages in a user lifespan.

Cohort analysis is designed to solve this problem. A cohort, in its strictest definition, is a group of users who share some common criteria. In a cohort analysis, you then compare these groups, observing their performance over days, weeks, or months, enabling you to observe the trends and movements that are otherwise hidden.

Let’s say you’re a winemaker and you’ve just made some modification to your, er, grape-squeezing, and you want to know if that was an improvement. So you pour a glass from your most recent bottle and a glass from an older vintage, and you then ask your sommelier to do a blind test. This probably would not be the most astute comparison. It’s easy to see the flaw here — your vintage bottle has aged and matured, changing its flavor, and as a winemaker, you should know that a wine’s taste changes over time. In this moment, the bottles are simply not comparable — and with this method, they probably never will be. There is a difference between the glasses, or what we like to call “interference.”

User lifespans are a rainbow

Your mobile users change over their lifespan in the same way. They will stay for a certain period of time, and they generate different activity when they first start using the app than after a few weeks or months. Cohort analysis is a way of removing the interference — it’s as if you taste, monitor, and record the flavor of your wine on each day of the week during a specific period of maturation.

Let’s start simple — we segment the users in your mobile app by the date on which they downloaded the app. Remaining performance data is then aggregated by the install-week segments. This already provides for a whole new definition — pick out the revenue figures from a single install week, on a given number of weeks after they installed.

Once you have these segments, you’ll want to compare their lifespans. Simply line two cohorts up so that you’re comparing metrics for the first week after install, second week after install, and so on.

Since you’re directly comparing users at equivalent times in your app, you’ve removed the interference that comes out of their morphing lifespans. There are plenty of questions you can answer here – and plenty of new questions you previously couldn’t ask.

Cohorts give you the capability to track user segments from a specific time period. Engagement rates vary not just between different users but also between different stages of those users’ lifespans. Efforts at optimization — be it marketing, reengagement, or product updates — will often move the needle for some groups and not for others.

For example, you might want to segment users based on which week they installed your app postlaunch and track their behavior as a cohort over the user life cycle. You can also test the effectiveness of different marketing campaigns to see which channels give you loyal, profitable users in the long run. You may have been able to improve the app for your longest-lasting users, perhaps those who use it most intensely, but is your focus to monetize those users or are you more concerned about growth?

Applications?

Cohort analysis removes the confusion that can arise from running simultaneous improvements and campaigns, so you can get a better sense of what is yielding the best results and is therefore worth investing in. It is a tool that enables you to look at lifespans in isolation — aggregating users at similar points in their lifespans. From there it enables you to form ambitions about how they should play out and ideas about how to accomplish that.

Let’s say you update your checkout process, and you’d like to see if your revenues improve. As the change affects all users, we’ll look at revenues across the board — with no other segmentation.

Can you simply look at your total revenues and see if there was an upward trend? No, because new users coming in would warp this. Could you calculate average revenue per user? Not really, because a large amount of the users that make up this average are at different stages of use within the app. Only by employing cohort analysis can you isolate what you’re looking for: Whether your update improved the way your users go through with purchases, not just if those users would have gone through with it anyway. This is a critical difference.

Knowing your users helps serve your users

By using a fully fledged cohort analysis, you can look at how these metrics change and move over time, in a higher definition — meaning you can pull up data on your retention rate from a given day in a given period. Looking at how many users you retain from week to week in this manner, you can see at what point people lose interest in your app. This enables you to point out exactly where and when you may want to push higher retention. It’s more than a lot of nifty charts — cohort analysis enables you to dive deeper and create much better segments out of your users. The only way of spotting trends in heaps of usage data is by respecting the lifetimes of those users. Only by doing that can you have a truly comparable base to optimize for growth.


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