The ultimate guide to user churn prediction
If you’ve read this far, app user churn is a familiar topic for you. But just as a refresher: it’s the loss of app users over time. It’s a critical measure of how successful an app is or is not. The average churn rate without any intervention can be as high as 70% depending on the app.
As we said at the outset, some user churn is inevitable. However poor user retention and high user churn rates can lead to:
- Slow app growth as fewer users stay in the app
- Decreased revenue and profitability as high value users who would otherwise make conversions churn
- Increased costs as more efforts and resources are needed to bring in new users to replace those who have churned
It’s clear that most of the pain points on this list are related: investment in UA leads to a big group of users joining the app. Without any significant retention efforts, an average of 70% of those app users churn by day 7. More budget and resources must be spent to acquire a new batch of users. Those users then follow the same cycle, and the majority have churned by day 7 after install, leading to all of the above points all over again. For some apps, the boom and bust of acquiring new users and losing them is fine, but for the vast majority it’s a problem that requires constant attention and resources.
The test-and-learn method for figuring out the best strategy to retain your app users is a good one. It requires testing out multiple strategies and audience segments, from push notifications to retargeting lapsed payers, and figuring out which combination of methods offers the best ROI.
With user churn prediction algorithms, app marketers can cut down on some of this test-and-learn work. Using machine learning, it’s possible to predict which users are likely to churn and give them a score to assess the probability of them leaving your app. Armed with this knowledge, app marketers can make more informed decisions about where to target their resources.
Predicting churn allows marketers to deploy targeted strategies to increase user retention. While encouraging lapsed users to return to the app through retargeting can be effective if they’ve previously shown an interest in the app (purchase, download, sign-up, etc), preventing them from leaving altogether is even better. Combined with retention strategies such as retargeting, cross-promotion, push notifications, and others, user churn prediction allows for data-driven decision making about which users are important to the app’s growth.
Leveraging user churn prediction scores can:
- Improve targeted retention strategies to reduce churn
- Help boost user LTV by identifying and targeting users at risk of leaving
- Lead to lower UA costs as more and more users are remaining in the app
- Increase app marketers’ ability to foresee and respond to changes in user behavior or market trends
- Allow for more confident decision making thanks to data-driven insights
Building a user churn prediction model is an article in and of itself. The essential idea is that machine learning models analyze an app audience, and score users for churn prediction probability. Users with a high probability of churn are put into an audience segment and can be targeted for remarketing activities.
Data gained from these prediction algorithms can be applied to a host of different user retention strategies
Churn prediction models can identify an app’s weak points. For example, if the overall user churn score is lower after a specific point in the user journey, the product team can look into what’s going on there to cause users to leave. It can also help in developing a pricing strategy that is both lucrative for the developer and acceptable for the user: if the developer raises the price of certain items and sees the user churn score drop significantly, it’s clear that this pricing strategy isn’t working out and should be adjusted.
For paid media campaigns such as retargeting, marketers can use the data from churn prediction models to segment users and target them with personalized messaging. Retargeting can increase in-app actions, drive incremental revenue from existing users, and prevent users from leaving the app.
As the adtech industry changes, it’s essential for app marketers to continue innovating and exploring all avenues to improve their product, and make their messaging as relevant as possible for their users. User churn prediction is just one such tool. Instead of relying on broad-based marketing strategies, user churn prediction has the ability to touch almost every part of the app marketing journey, whether it be targeted advertising through retargeting or push notifications to signal that it’s time for a user to play again.
SOLVE THE USER CHURN PUZZLE
Predict which users are likely to leave your app for good and keep them