Pilot Predictive Health Scoring

Why This Matters

Rule-based health scores plateau. They find patterns you already know to look for. An ML model trained on historical churn and expansion data discovers non-obvious signal combinations (product usage sequences, support ticket patterns, stakeholder engagement gaps) that static thresholds miss. Walk-stage teams have enough historical data (12–18 months of customer outcomes) to run a meaningful pilot without a data science team.

Action Plan

1. 01 Export 12–18 months of customer outcome data: which accounts churned, expanded, or stayed flat, with their health signals at the time
2. 02 Identify 8–12 input features available in your CS platform or data warehouse: login frequency, feature breadth, support volume, stakeholder coverage, NPS, days since last CSM touchpoint
3. 03 Use your CS platform's built-in AI scoring (Gainsight, ChurnZero, Vitally all have this) or a simple logistic regression in a spreadsheet/Python notebook
4. 04 Run the predictive score in parallel with your existing rule-based score for 6–8 weeks without changing CSM workflows
5. 05 Compare: which score flagged at-risk accounts earlier? Which produced more false positives?
6. 06 If predictive outperforms rule-based, plan a migration. Keep the rule-based score as a fallback for new accounts with insufficient history
7. 07 Document which features had the highest predictive weight. This tells you what signals matter most for your customer base

Metrics to Watch

Related Principles

Common Pitfalls

• Replacing the rule-based score too quickly before validating accuracy. Run both in parallel for at least two monthly cohorts
• Using too few historical outcomes. Fewer than 50 churned accounts makes the model unreliable
• Ignoring new customers. Predictive models need tenure to work, so new accounts should still use rule-based scoring as a baseline