Every click, every session, every feature invocation in your product is a data point that tells you something about how customers are experiencing value. The teams that are winning on retention are reading this data differently than their competitors.
The usage signals that consistently predict churn 60-90 days out:
Depth regression. A customer who used to use advanced features and now only uses basic features is regressing in their relationship with the product. This isn't the same as using fewer features — it's a specific movement from high-value features toward low-value features. Depth regression is more predictive of churn than overall usage decline.
Session duration compression. When average session duration drops by more than 30% over a 60-day period, customers are spending less time getting value. They're logging in to check a specific thing and leaving. They're not exploring. This is an engagement quality signal, not just a quantity signal.
Export frequency increase. Customers who are preparing to leave often start exporting more data. They're building the migration package. Export events without corresponding import or workflow events are a specific combination worth monitoring.
Collaboration metric decline. In products with collaboration features, the decline of multi-user engagement (comments, shares, assignments) is more predictive than individual usage decline. When a team stops using your product collaboratively, the product has become a personal tool for one person — and that person's departure equals full churn.
API access with declining UI engagement. When API usage holds steady but UI logins decline, someone is maintaining an integration while the team has stopped using the product. The integration is being maintained for data portability, not for active use.
Build dashboards that surface these patterns by account. Prioritize intervention based on signal strength and account ACV. Act on the data before the renewal conversation forces you to.