Predictive Analytics for Churn
Use predictive analytics to identify customers at risk of churning.
What is Churn Prediction?
Churn prediction uses behavioral, support, and billing data to score each customer or account by likelihood to churn in a given window (e.g. next 30 or 90 days). That lets you focus retention efforts on the right accounts before they cancel.
Signals That Predict Churn
Common leading indicators: declining usage (DAU/MAU, sessions, feature adoption), support strain (rising tickets, unresolved issues), billing issues (failed payments, downgrades), lack of expansion (no upgrades or add-ons), and lifecycle (e.g. post-trial, near renewal). Combining several signals usually beats any single metric.
Building a Simple Model
Start with rules: e.g. “risk = high if (usage down 30%+ and support tickets > 2) or payment failed.” Then move to a scored model (logistic regression or ML) using historical churners vs survivors. Validate on a holdout period and refresh regularly.
Acting on Predictions
Route high-risk, high-value accounts to CS for outreach; use automated emails or in-app flows for lower-touch segments. Track which interventions actually reduce churn and double down on those.