Definition
A customer health score is a composite metric that estimates churn risk using product usage, support signals, and account context.
How to use it
- Build health scores from behaviors that predict churn in your cohorts (not opinions).
- Use scores to trigger interventions, then validate impact on retention.
Common mistakes
- Using a health score that is not validated against churn outcomes.
- Changing score definitions without versioning, which breaks comparisons.
Why this matters
This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.
Practical checklist
- Write a 1-line definition for "Customer Health Score" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Sanity-check with a related calculator from the same category on MetricKit.
- Read the related guide (e.g., Retention & churn hub: cohorts, GRR/NRR, and retention curves) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- LTV:CAC Calculator: Compute LTV:CAC ratio and CAC payback using ARPA, gross margin, churn, and CAC.
- CAC Payback Period Calculator: Estimate how many months it takes to recover CAC (months to recover CAC) using gross profit.
- CAC Payback Sensitivity Calculator: See how CAC payback months change as ARPA and gross margin vary (simple 3x3 sensitivity).
- Churn Rate Calculator: Calculate customer churn rate for a period and compare retention across segments or cohorts.
- Retention Rate Calculator: Calculate retention rate for a period accounting for new customers.
Guides
- Retention & churn hub: cohorts, GRR/NRR, and retention curves: A practical hub for retention measurement: churn rate, GRR/NRR, cohort retention curves, and how to set retention targets without getting misled by noise.
- Cohort analysis playbook: retention curves, LTV forecasting, and payback: A practical cohort analysis workflow: build retention curves, forecast LTV, and translate retention quality into payback and growth decisions.