Churn prediction

Churn prediction helps identify users likely to stop using your product. Most product analytics tools include built-in features that can detect early warning signs of user dropout. These tools monitor basic engagement patterns and alert teams when users show signs of decreasing activity.

The prediction process relies on common usage metrics that most analytics platforms track automatically. This includes how often users log in, which features they use, and how much time they spend in the product. When these numbers start dropping below normal patterns, analytics tools can flag these users as potential churners.

Teams can typically set up custom alert thresholds in their analytics platform to catch concerning patterns early. This automated monitoring helps teams take action before users actually leave, rather than analyzing churn after it happens. This might include sending helpful tips, offering support, or asking for feedback to understand what's wrong.^[1]