How we'd approach churn prediction for a subscription business

The Problem

A subscription business can see customers churning after the fact, with no way to act before they leave.

Previous attempts to identify at-risk customers relied on simplistic rules-based triggers (e.g., "has not logged in for 30 days") which generated too many false positives. The retention team needed a more sophisticated approach that understood the nuanced usage patterns of different customer segments.

Our Approach

Build a churn-prediction model on usage and support data, surfacing at-risk accounts to the retention team weeks before they\'d typically churn, along with the likely reason.

The technical solution involves an ML classification model trained on historical telemetry, support tickets, and billing data. We engineered features to capture momentum—such as declining feature adoption or increasing time-to-resolution on tickets. The system scores all active accounts nightly and provides a ranked list with SHAP values explaining why each account was flagged.

We simulated a 6-week controlled pilot using historical data before full deployment, comparing the model's predictions against actual churn events. By optimizing the decision threshold, we achieved a high precision rate, ensuring the retention team's limited outreach bandwidth was spent on truly salvageable accounts.

The system doesn't replace the retention team's judgment. It provides them with targeted leads and context. The best customer success managers are now doing proactive relationship building, not reactive firefighting.

Typical target range for this type of engagement

Retention outreach prioritized by risk score instead of guesswork, focused on the accounts most likely to actually respond.

The system now processes millions of events daily to generate updated risk scores. An automated drift-monitoring pipeline keeps the model accurate as user behaviors change over time.