How to Build Customer Health Scores That Predict Churn

Customer health scoring takes multiple signals - product usage, support interactions, billing status, engagement metrics - and combines them into a composite score that predicts how likely a customer is to churn or expand. A good health score enables proactive intervention before customers decide to leave.

Building effective health scores requires understanding which signals actually predict churn in your business, weighting them appropriately, and creating workflows that act on the scores. This guide walks through the process.

Choosing Health Score Signals

The signals you include should actually predict churn, not just feel important. Start with these categories and test which signals correlate with actual outcomes:

Product Usage Signals

• Login frequency and recency
• Feature adoption breadth and depth
• Time spent in product
• Key action completion
• Usage trend (increasing, stable, declining)

Engagement Signals

• Email open and click rates
• Response to outreach
• Webinar or event attendance
• Documentation or resource usage
• Community participation

Support Signals

• Ticket volume and trend
• Ticket sentiment and severity
• Time to resolution
• Escalation frequency
• CSAT scores on tickets

Relationship Signals

• NPS or satisfaction scores
• Champion engagement level
• Stakeholder changes
• Expansion conversations
• Referral activity

Billing Signals

• Payment history (failures, recoveries)
• Contract value trend
• Time to renewal
• Discount or concession history
• Plan changes (upgrades, downgrades)

Weighting and Scoring

Not all signals matter equally. A support escalation might predict churn 3x better than a missed login. Weight signals based on their predictive power, which you discover through analysis of historical churn data.

Simple Approach

Start with a simple weighted average. Assign each signal a score (1-10) and a weight based on perceived importance. Calculate: (Signal1 x Weight1 + Signal2 x Weight2 + ...) / Sum of Weights.

Validated Approach

As you gather data, validate which signals actually predict churn. Run correlation analysis between each signal and churn outcomes. Adjust weights based on actual predictive power. Remove signals that don't add value.

Health Tiers

Translate numeric scores into actionable tiers:

• Healthy (80-100): Low intervention needed, focus on expansion
• Stable (60-79): Monitor closely, proactive engagement
• At Risk (40-59): Active intervention required
• Critical (0-39): Urgent save efforts needed

Acting on Health Scores

Health scores are useless without action. Define interventions for each health tier:

Automated Actions

• Trigger email sequences when health drops
• Assign tasks to CSMs for at-risk accounts
• Send alerts when critical accounts emerge
• Update CRM records with health data

Human Actions

• CSM outreach for at-risk accounts
• Executive involvement for critical accounts
• QBR scheduling based on health trends
• Escalation procedures for rapidly declining accounts

Validating Your Health Score

Regularly check whether your health score actually predicts churn:

• Do low-health accounts actually churn more than high-health accounts?
• What percentage of churned customers were flagged as at-risk?
• Are there false positives (flagged but didn't churn)?
• Are there false negatives (churned without being flagged)?

If your health score isn't predictive, adjust signals and weights until it correlates with actual outcomes.

Tools for Health Scoring

Customer success platforms like Gainsight, ChurnZero, Vitally, and Custify provide health scoring capabilities. For email-focused retention, tools like Sequenzy include health signals integrated with retention email automation.

Start simple and iterate. A basic health score that triggers action beats a sophisticated score that sits in a dashboard unused.