predictive analytics can help you retain app users
Editorial

Retain Your App Users With Predictive Analytics

4 minute read
Aditya Vempaty avatar

Mobile apps are everywhere and people can’t stop using them.

Recent data shows that 90 percent of a consumer’s time spent on mobile is spent in apps. So, even if you haven’t been involved in marketing mobile apps yet, you probably will soon.

To take advantage of this move to mobile, marketers need to synergize with app developers to come up with the best strategies to acquire and retain users. Predictive analytics can inform a solid mobile marketing or product development strategy.

The Importance of User Retention

Mobile app developers spend thousands of dollars on paid user acquisition. If you don’t have users, you can’t monetize them, and if you can’t monetize, you’ll quickly cease to be a profitable business.

But getting users is only half the story. Businesses need to retain users and maximize the cash value they bring to stay competitive. If the cost of acquiring a user is higher than their lifetime value, you’ll lose money fast.

Every iteration of your app should be focused around getting your users to stick around longer. The higher your retention rate, the higher the value of each user. The question then becomes: how do you increase user retention?

User Behavior Analytics Increases Retention

User behavior analytics can give you deep insight into what your users are doing within your app, why they’re doing it, as well as insight into what core experience your product delivers.

To understand why some users stay while others churn, data analysts can compare the retention of cohorts of users who perform different actions within the app. Your high-value “power users,” for example, may be using a certain feature or performing specific actions that could be causing them to be better retained than the rest.

Understanding how the group of users who are retained behave in the long-term can inform the right product changes and marketing campaigns that will increase retention overall.

The challenge, however, is that it’s often difficult to form hypotheses around which users are likely to be retained and which are likely to churn. It can be difficult to know what behaviors to start investigating, especially if you’re running several experiments in parallel.

Learning Opportunities

That’s where predictive analytics comes into play.

Predicting User Behavior

More and more self-service analytics tools are including “predictive” features that will tell you which users are likely to convert, which users are likely to churn, which users are “high-value,” which users will retain. Some vendors integrate these features with push notifications and email campaigns to re-engage users who are at high risk for churn.

On a more sophisticated level, some predictive analytics features can tell you both: 1) how correlated a specific user behavior is with retention and 2) the user behaviors that are most highly correlated with retention. Predictive tools like these give you a starting place to formulate hypotheses about the way users engage with and use your product.

Mobile trivia and social gaming app QuizUp, for example, discovered one such finding using a predictive analytics tool. It found that the use of social features within their app was well correlated with retention.

In fact, users who used social features within the first week of downloading the app have 60 percent higher retention than other users. With the next iteration of their app, QuizUp could leverage this finding to make social sharing a key step in their new user onboarding process.

Correlation, Not Causation

Predictive analytics uses machine learning and statistical modeling to look at the correlation of how users behave with their likelihood to (in this case) stay retained. QuizUp, for example, can’t claim that using social features caused increased retention.

Businesses should be wary about attributing causation to what predictive analytics features show them. But these predictions open your eyes to data insights that you may otherwise be blind to. They provide a foundation upon which you can formulate hypotheses about how users actually use your mobile app.

About the author

Aditya Vempaty

Aditya Vempaty, head of marketing at Amplitude, has more than 10 years experience building, selling and marketing innovative high tech companies that range from storage infrastructure to analytics. He helped launch Nutanix's converged storage platform, Coho Data's Datastream storage platform and now Amplitude's analytics platform.

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