Churn Prediction Template for Product Analytics

A customer health score is a broader measure of account health that may include factors like contract size, executive relationship strength, and product usage. Churn prediction is specifically focused on identifying accounts likely to cancel. Health scores often include subjective inputs from CSMs, while churn prediction models work best with objective behavioral data. Many teams use health scores as one input to their churn prediction model.

Yes. This template uses a rules-based scoring model that any product or CS team can implement. You need access to behavioral data (login frequency, feature usage, support tickets) and a way to calculate scores (even a spreadsheet works for under 500 accounts). Machine learning models improve accuracy at scale, but rules-based models routinely achieve 60-75% true positive rates, which is sufficient for most intervention-driven use cases.

For most B2B SaaS products, behavioral signals become detectable 30-60 days before cancellation. Some signals (like admin disengagement or data exports) appear earlier. The prediction window depends on your contract terms: monthly contracts have shorter warning windows than annual contracts. Start by analyzing the 60-day pre-churn period and narrow it based on your data.

A high false positive rate means you are flagging healthy accounts as at-risk. This wastes CS resources and can annoy customers with unnecessary outreach. To reduce false positives: increase your risk thresholds (require higher scores to flag), add more signals to your model (more data points reduce noise), or weight recent behavior more heavily than older behavior. The [impact analysis template](/templates/impact-analysis-template) can help you measure whether your interventions are actually reducing churn or just adding noise.

No. Involuntary churn (failed credit cards, expired payment methods) has different root causes and different solutions (dunning emails, payment retry logic). Include only voluntary churn in your prediction model. Track involuntary churn separately and address it through billing infrastructure improvements.