Customer Health Scoring Model
The score that predicts which accounts stay.
Workflow
Step 1Health Metrics Selection
ObjectiveIdentify metrics that predict customer health and renewal
InputHistorical data, customer feedback, usage patterns
OutputSelected health metrics with definitions
Key activities
Analyze historical churn patterns, identify leading indicators, define health dimensions (adoption, engagement, support, sentiment), establish metric definitions
Decision points
Metrics predictive? Data available? Actionable?
Tools: Data analysis, historical CRM dataRoles: Analytics (lead), CSM (validation)
Success: 5-10 health metrics selected and defined
Quality gate: Metrics approved, data sources identified
Why it matters
- A calibrated health score turns scattered signals into one number teams can act on. It's the backbone of proactive CS.
Best practices
- Pick metrics that correlate with retention.
- Build the model on real data, then validate.
- Deploy with thresholds that trigger action.
- Improve the model continuously.
Common mistakes
- A score that's just usage, ignoring sentiment.
- Thresholds nobody owns or acts on.
- A model that never gets recalibrated.
Key questions
- What is the leading indicator of churn in our data?
- Who is responsible when a score drops?
Agents that drive this
Health MonitorChurn Predictor
Tools we use · alternates in [ ]
Metabase[ Looker Studio, Power BI, Grafana ]Supabase[ Postgres, Firebase ]