AI导哈

设计与多媒体

customer-health-score

mbfinotti/revops-skills

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.

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安装命令

npx skills add https://github.com/mbfinotti/revops-skills --skill customer-health-score

SKILL.md

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.

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