Analytics
Churn Prediction
Churn prediction uses machine learning models to identify customers who are likely to cancel or stop purchasing before they actually do. Early identification enables proactive retention interventions that can save at-risk accounts.
Examples
A telecom company's churn prediction model identifies subscribers whose usage has declined 40% over three months and triggers a personalized retention offer, saving 25% of at-risk accounts.
Best Practices
Include both product usage and engagement signals in your model, set actionable prediction timeframes that allow for intervention, and continuously validate model accuracy against actual churn.