Customer Health Score

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. No CSM and no renewal cliff: scoring is continuous and system-driven, ownership sits with growth/product rather than CS ops, and an involuntary-churn layer (failed payments, dunning) belongs in the score because payment failures correlate with engagement decline.

Interview

Ask before designing. One question per message; offer multiple-choice answers when possible. Skip anything already answered.

• Which motion? (a) CSM-covered B2B with contract renewal dates, (b) PLG/self-serve continuous subscription, (c) B2C consumer subscription.
• How many accounts, and roughly how many churn events in the last 12-24 months? (Decides statistical weighting vs the rules-based cold-start path.)
• Which data sources are actually connected today - product usage data, engagement/meeting records, support system, billing? Rough coverage of each?
• Has signal discovery been done - is there an evidenced list of indicators that preceded past churn (see mbfinotti/revops-skills@customer-churn-signals), or only a gut list?
• Does a health score already exist? If yes: diagnose first or rebuild? (Diagnosis path: see Diagnosing a Broken Score.)
• What segments exist - enterprise vs SMB, onboarding vs mature? (A 70 that is healthy for enterprise is a red flag for an onboarding SMB; segment calibration is mandatory, not optional.)
• How many at-risk accounts can the team actually work per month? (The red band is sized to this, not to statistics.)
• Who owns the scoring logic, who acts on the score day-to-day, and who may override it?
• By what date must the score be live and acted on? (A hard date - a renewal cycle, a board review - promotes the rules-based proxy and the top two signal rungs; a distant date makes statistically fitted weights and a survey category affordable.)
• One-off win or compounding asset - this quarter's at-risk list, or a model that keeps getting better? (A one-off list drops decay, segment calibration, and the standing recalibration cadence; a compounding mandate promotes all three plus fitted weights.)
• What is the effort ceiling - analyst hours, whose data-engineering time, and how much manual logging CS will genuinely do? (Manual logging is what makes stakeholder-continuity signals real; if nobody will log, that category drops to whatever the CRM captures on its own.)

Build order

Build the score rung by rung, ranked by churn actually prevented per hour of analyst time - not by what is cheapest to wire, and not by what scores best in backtest. Effort here is analyst hours, data the product must already emit, ongoing re-fitting, and how legible the score stays to the people who must act on it: a score CSMs cannot reconstruct is expensive no matter how accurate it is.

• efficiency: usage/adoption > commercial/billing > support > stakeholder continuity > sentiment survey
• value (churn caught): stakeholder continuity > usage/adoption > support == commercial/billing > sentiment survey
• effort:
• usage/adoption and commercial/billing: near-zero - the product and billing system already emit the events.
• support: an hour to map tickets to rates and trends.
• stakeholder continuity: a standing job - humans must log meetings and sponsor changes for the input to stay true.
• sentiment survey: a week to stand up, plus a standing job to run - low response rates keep it unstable.

Support and commercial tie on value because each catches a blind spot the other cannot - escalation clusters, invoice disputes - and neither substitutes for the other.

Start from the top two rungs plus whatever discovery already evidenced, validate that, then add rungs.

What this order starves:

• Stakeholder continuity. The highest-value input, the one that catches silent churn, and the worst ratio because it depends on humans logging contact. Promote it to rung one in any B2B book with renewal dates, or the moment one green account churns after a champion left - the continuity rule in step 5 makes it mandatory regardless of its ratio.
• Statistically fitted weights and a second, dedicated expansion model. Both lose every round on ratio and are still right eventually. Both need an account base large enough to fit and validate on its own outcomes (tens of churn events per segment), and both buy better signal once it exists.

This ordering is a default, not a law. It shifts with context and with who executes it - re-rank it against everything already known about the user before proposing it:

• A product already emitting rich usage telemetry locks usage at rung one for near-zero effort.
• A customer-success team of one demotes everything that needs manual logging and promotes automated usage and billing signals.