Churn prediction models analyze behavioral signals such as login frequency, feature usage, and support activity to flag accounts at risk of canceling. Early identification allows customer success teams to intervene before the customer leaves.

Example: A SaaS platform detects that customers who have not used a key feature within 30 days of onboarding are 3x more likely to churn. The CS team is automatically alerted to reach out to those accounts proactively.