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Run 10–16 weeks
Build a Predictive Customer Intelligence Model
Build an intelligence layer that moves from predicting churn to anticipating the full range of customer signals: churn risk, expansion readiness, advocacy likelihood, and stakeholder changes.
Why This Matters
Run-stage teams have the data volume and operational maturity to move beyond reactive health scoring into genuine predictive intelligence. This means not just predicting churn, but predicting expansion readiness, identifying advocacy candidates, and surfacing stakeholder risk, all automatically. The first step is building the churn model; the destination is a model-ranked work queue that tells every CSM exactly where to focus.
Action Plan
- 01 Consolidate your customer data sources into a unified view: product usage, support, billing, CRM, engagement, and sentiment. This is the foundation every model needs.
- 02 Aggregate your historical churn data: which accounts churned, when, and what their engagement patterns looked like in the 90 days before churn
- 03 Identify predictive features: usage decline velocity, support ticket sentiment, stakeholder departure, login frequency drops, feature abandonment
- 04 Build a baseline churn model. Even a logistic regression on 5–7 features outperforms gut feel
- 05 Validate the model against historical data: what is the false positive rate? False negative rate?
- 06 Extend to multi-type prediction once your churn model is validated: expansion propensity, advocacy likelihood, and stakeholder departure risk add compounding value with the same data infrastructure
- 07 Build an automated insight delivery layer: CSMs receive a prioritized, model-ranked intervention list each day, not raw dashboards or data exports
- 08 Create intervention playbooks for each prediction type: predicted-churn accounts get different plays than predicted-expansion accounts
- 09 Track model accuracy quarterly and retrain as your customer base and product evolve
Metrics to Watch
Related Principles
Common Pitfalls
- Building models before consolidating data. Model quality is bounded by data quality: incomplete signals produce unreliable predictions.
- Over-automating without human oversight. Models make mistakes on edge cases. Keep CSMs in the loop for high-stakes decisions.
- Treating the churn model as the finish line. Churn prediction is the entry point. The goal is a full intelligence layer across all customer signals.