Fifty-eight percent of users churn within the first 30 days.

Seventy-one percent of users within the first 60 days.

Seventy-five percent of users within the first 90 days.

The risk of app churn is real, and historically, marketers haven’t had the tools or capacity to identify churn risks within data, make the correct optimizations or enable automated marketing that aims to curb churn. In 2016, however, this is all possible. 

The first step is to uncover and define what factors are indicators of churn risk in your app. Without creating a definition of churn as it relates to your app, you cannot start to measure or model it for predicting which users are at risk. With the right analytics tracking software in place, you should have access to the insights needed to create this definition.

How users behave in your app and the various attributes they bring to the table (such as location, language, purchase history, etc.) both play a role in defining and uncovering reasons for churn. Most likely, you will reach a point in this process in which you have multiple definitions of churn based on these different factors in order to get a broad sense of the many possible risks (Tip: you can use this blog post to identify baseline churn definitions and identify the corresponding factors in your app).

Once you’ve uncovered this, you can optimize your roadmap for better features, functionality, and to eliminate the experiential elements that lead to churn, and you can run targeted campaigns, aimed at high-risk users, to re-engage them with your platform and prevent churn.