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Run 6–10 weeks
Build Predictive Churn Models
Move from reactive risk identification to predictive churn modeling that surfaces at-risk accounts before traditional signals appear.
Why This Matters
At the run stage, your health score catches known risk patterns. But predictive modeling catches the unknown — the accounts that look healthy on the surface but match historical churn profiles. This is the shift from 'we react to red accounts' to 'we prevent accounts from turning red.' It requires data maturity, which run-stage teams finally have.
Action Plan
- 01 Aggregate your historical churn data: which accounts churned, when, and what their engagement patterns looked like in the 90 days before churn
- 02 Identify predictive features: usage decline velocity, support ticket sentiment, stakeholder departure, login frequency drops, feature abandonment
- 03 Build a baseline model — even a logistic regression on 5–7 features outperforms gut feel
- 04 Validate the model against historical data: what's the false positive rate? False negative rate?
- 05 Integrate predictions into CSM workflows: surface predicted-risk accounts in daily/weekly reports
- 06 Create intervention playbooks specifically for predicted-risk accounts (distinct from confirmed-risk playbooks)
- 07 Track model accuracy quarterly and retrain as your customer base and product evolve
Metrics to Watch
Common Pitfalls
- Over-engineering the first model — a simple model you act on beats a sophisticated model that sits in a notebook
- Not validating against holdout data — in-sample accuracy is meaningless if the model doesn't generalize
- Alerting CSMs on every prediction without confidence thresholds — too many false positives cause alert fatigue