customer-health-score

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: customer-health-score
description: Design, validate, and govern a composite customer health score - product usage and engagement signals combined into one weighted, normalized, decayed, banded account score that flags both churn risk and expansion readiness, backtested against real churn outcomes and recalibrated. Use whenever the user mentions a customer health score, health scoring model, account health, signal weighting, score bands, at-risk account flagging, churn risk score, expansion risk, or says "green accounts keep churning" - even if they never say "health score". Covers B2B account-level rollup and B2C/PLG continuous scoring. Do NOT use for discovering which indicators actually predict churn - use mbfinotti/revops-skills@customer-churn-signals instead.
license: MIT
metadata:
  author: Maya-Beth Finotti
  version: "1.0.1"
---

# Customer Health Score

Design, validate, and maintain one composite score that tells a post-sale team which accounts are drifting toward churn and which are ready for an expansion conversation. In ChurnZero's 2025 Customer Revenue Leadership Study (~800 CS and post-sales leaders surveyed), 73% said their current health score does not reliably predict churn.

- **Performative score.** Someone picks ten metrics, weights them by intuition; CS ignores the result because it predicts nothing.
- **Predictive score.** Derives its weights from what churned accounts actually did before churning, then proves itself in backtest.

This skill owns the composition and validation that turns one into the other:

- Normalization
- Weighting
- Decay
- Banding
- Segment calibration
- Override governance
- Wiring every band to a play

Which indicators to feed it, and how predictive each one is, is discovery work owned by `mbfinotti/revops-skills@customer-churn-signals`. Treat signals here at the category level (product usage, relationship/engagement, support, commercial, sentiment) and take the discovered indicator list as input.

Draw on the field's named canon where it genuinely helps:

- **Gainsight's DEAR framework** (Deployment, Engagement, Adoption, ROI): a balanced-scorecard model of 4-6 weighted metric groups summed into bands. Gainsight cautions that weighting "is highly unique to each company".
- **Lincoln Murphy's Success Potential**: ties expansion to achieved health ("No Customer Success... No Expansion Revenue").
- **RFM-style reads**: recency, frequency, and depth of usage.
- **Leading-vs-lagging split**: adoption trend, sponsor engagement, and response-time trend lead; churn, NRR, and renewal rate only confirm afterward. Score the leading ones.

One composite score, or a separate expansion model:

- **One composite, top band gates expansion (default).** Already the score being built and validated; a second model costs a quarter to build and two validation loops to keep. Gating expansion plays on the top band is the dominant practitioner pattern: low bands trigger retention, the top band triggers expansion/advocacy motions.
- **A separate expansion model.** Justified only when expansion-specific inputs are systematically tracked; otherwise it is a second unvalidated score.

Only vendor case studies support the claim that high health causally predicts expansion; no rigorous published study does. Treat health as necessary but not sufficient for expansion: the top band earns an account the conversation, and fit, whitespace, and budget decide it.

B2B and B2C/PLG share most of the mechanics: per-seat normalization, trend-over-level, an action wired to every band, and the backtest loop are identical in both. Say so when asked. What genuinely differs:

- **B2B.** Rolls user-level signals up to account level with extra weight on champion/sponsor continuity: renewals hinge on the buying organization, not one user, and flat aggregate usage can hide a disengaged champion (the "silent churn" pattern). Risk concentrates around a renewal date, so weight renewal proximity inside the score.
- **B2C/PLG.