How to spot churn before it happens
Churn-proof your app with early detection.

Churn is a barometer for the health of your subscription business. Whether it’s voluntary churn, where users actively decide to leave, or involuntary churn caused by payment failures, the implications are the same. Churn can eat into your profits, reduce long-term customer value, and hinder growth.
This blog will guide you through the many early signals that indicate that churn is on the way.
Early warning signs of churn (and how you can respond)
Reduced app engagement
There is no substitute for keeping a close eye on app usage metrics to monitor engagement. Here’s what to look out for:
- Frequency of usage: Monitor how often users open your app. A decline suggests decreasing interest. Keep an eye on both short-term and long-term trends.
- Session length: Pay attention to how long users engage. Shorter sessions may indicate waning interest.
- Feature engagement: Keep track of which app features are popular. A drop in use can be a warning. Consider offering guides or tutorials for underutilized features.
Remember that reduced engagement with a feature might indicate changing needs, not dissatisfaction. For instance, a user might stop using a period-tracking app during pregnancy or a fitness app after New Year’s resolutions fade. The response and messaging to prevent churn vary for each situation. While preventing churn isn’t always possible, like with the period-tracking app, you can aim to re-engage users at the appropriate time.
Learn more about app engagement metrics.
Later, I’ll discuss categorizing users based on predicted outcomes, like subscribing or churning. For now, let’s touch on the “habit path” from Nir Eyal’s “Hooked”. Identify and map out the journey of your habitual users. For example, early on, Twitter found users who followed a certain number of accounts became regulars. Examine your app’s feature engagement and identify its habit path. Preventing churn often means converting users into habitual ones.
What do you do once you’ve noticed a decline in engagement?
Once you notice these reduced engagement, it’s time to take action. Personalized in-app messages or push notifications can remind users about the value your app provides. Special offers or loyalty rewards can also incentivize users to re-engage with your app.
In this blog, I’ll discuss how customer relationship management (CRM) can engage users to prevent churn. For more, see our guide on CRM optimization for subscription apps.
Negative user feedback
Direct complaints: Monitor customer support tickets for any increase in complaints or issues.
Poor app store reviews: Users often express dissatisfaction through app store reviews before churning, so watch out for patterns in low ratings and critical comments. Monitor these yourself or use one of a variety of tools to automate tracking such as Appfigures or App Radar.
Many app businesses don’t take customer support seriously enough. For Talking Parents CEO Vince Mayfield, charging for a premium subscription means that you should be offering premium support.
“Every experience that somebody has with your brand sets a mental image of who you are. And so, if I’m gonna charge a premium price, then they need to have premium support… We go in and reply to every app review with a human response… It’s about radical focus on the customer. If they feel like they’re heard and that we’re listening to them, they’re gonna continue to stay on a subscription with us, and we’re gonna have great retention rates.”
Vince Mayfield, Talking Parents
There’s an interesting story on the Sub Club community in which a developer turned a 1-star review “Worst App Ever!” into a 5-star review. The first step was taking on board the feedback. The second step was responding in a thoughtful way.
Sentiment tracking: Use online, in-app or email surveys, to gauge the satisfaction of your users. While you can look to gain detailed feedback, for sentiment tracking over time, you might prefer to ask a question like “How likely would you be to recommend [your app] to a friend or colleague?”, with an accompanying score of 1-10, to allow you to record a Net Promoter Score (NPS).
Tracking NPS scores is an important tool for your app growth in general (this Phiture article by Alice Muir is a great resource). But from the context of churn detection, monitor your NPS over time. A decline can indicate a problem. And with CRM, you can trigger campaigns targeting detractors (i.e. those users that would rate your app from 0-6) to attempt to turn their negative experience around.
Billing and payment issues
Failed payments and involuntary churn: Involuntary churn, often caused by failed payments due to expired or changed credit card details, or new devices, poses a significant problem that frequently goes unchallenged by many apps.
While both Apple and Google have implemented measures to address this, it is vital to actively manage this issue rather than solely relying on the app stores’ mechanisms. You should complement them with proactive measures such as real-time notifications for payment failures. Use the information provided in real time by Apple and Google to trigger automated emails or push notifications to your customers, encouraging them to update their payment details. And remember that grace periods on iOS and Android need enabling — they will not be turned on by default.
For web payments, you have a greater challenge. Not only will you need to handle most of the communications yourself, you rely on the user coming back to update their details for your app, specifically. Generally speaking, users are more likely to keep their details up-to-date on the app stores, given that so many other services and apps depend on them.
Downgrades: Keep an eye out for users who downgrade their subscriptions or remove add-ons. These actions can be a sign of customers contemplating cancellation. Proactively reaching out to these users with tailored communications and offers may help sway them back or at least provide insight into their concerns.
Using predictive modeling to anticipate churn
Predictive analytics, or modeling, harnesses data to forecast user actions like subscribing, renewing, or churning. Such correlation analysis — which pinpoints behaviors linked to specific outcomes — is common among data-rich app companies. These insights are either extracted via analytics tools, like Amplitude or Mixpanel, or through data analysts utilizing data warehouses. Note: Amplitude suggests a base of “over 100,000 monthly average users” for accurate predictions.