This page is part of the ForgeSDLC knowledge base — an AI-assisted, human-directed methodology for taking product work from concept to production. For the core operating model and vocabulary, see Forge SDLC overview and What is ForgeSDLC?.
Customer health scoring and risk detection
Overview
Customer health scoring turns scattered signals — usage, support, sentiment, billing, and outcomes — into a prioritized view of risk and opportunity. It supports proactive customer management: who needs attention now, with what playbook, and whether interventions work. This guide covers components, methods, tiers, and implementation — without prescribing a single vendor or model.
Health score components
| Component | Typical signals |
|---|---|
| Product engagement | DAU / WAU / MAU, depth (breadth of features, key events), recency and frequency, seat utilization |
| Support health | Ticket volume and trend, severity / priority mix, reopen rate, CSAT / CES after resolution |
| Relationship health | NPS or pulse surveys, executive sponsor engagement, QBR attendance, stakeholder coverage |
| Financial health | Payment status, expansion vs. contraction signals, contract timeline and renewal window |
| Outcome achievement | Success plan milestones, implementation checklist completion, stated goal progress |
Weight components by what predicts your outcomes (retention, expansion, advocacy) — not by what is easiest to measure.
Health score architecture
Scoring methodology comparison
| Method | Pros | Cons | Data requirements |
|---|---|---|---|
| Weighted average | Transparent; easy to explain to CSMs and leadership | May miss nonlinear interactions | Clean metrics per component; calibration against outcomes |
| Machine learning | Can capture complex patterns | “Black box” risk; governance harder | Historical churn/expand labels; feature store discipline |
| Rule-based | Fast to ship; aligns to known failure modes | Brittle if product/market shifts | Documented business rules; regular review |
| Hybrid | Rules for known risks + model for edge cases | More operational complexity | Both rule definitions and labeled outcomes |
Start simple where culture and data maturity are low; add sophistication when actions and measurement keep pace.
Weight calibration
- Hypothesize drivers from churn interviews, support themes, and product analytics.
- Correlate component metrics with churn, downgrade, or negative expansion (and with positive outcomes you want to reinforce).
- Iterate weights quarterly or when major product changes alter behavior.
- Validate with CSMs: false positives erode trust; false negatives miss saves.
Document lineage: which fields feed which sub-score, refresh cadence, and known gaps.
Risk tier framework
| Tier | Score range (example) | Characteristics | Automated actions | CSM actions | Escalation |
|---|---|---|---|---|---|
| Healthy (green) | Upper band | Strong usage, low support distress, positive or neutral sentiment | Positive triggers (expansion cues, advocacy asks) | Proactive QBRs; growth plays | As needed for strategic accounts |
| At-risk (yellow) | Middle band | Mixed signals; usage dip or support spike | Alerts to owner; suggested playbooks | Outreach, success plan refresh, training | Manager visibility on aged yellow |
| Critical (red) | Lower band | Severe usage collapse, exec churn, payment risk, or explicit risk statements | High-priority routing; exec notification rules | Rescue plan, exec sponsor, commercial levers per policy | VP / cross-functional war room where warranted |
Thresholds should be calibrated to your base rates — a “red” that fires on 40% of accounts is not operational.
Risk detection through intervention and learning
Close the loop: track whether tier changes and plays correlate with improved usage, renewal, or save rate.
Leading vs. lagging indicators
| Type | Examples | Use |
|---|---|---|
| Leading | Login frequency drop, key feature abandonment, support escalation pattern, champion departure, stalled onboarding milestones | Early warning; trigger playbooks before renewal crisis |
| Lagging | Cancellation request, payment failure, signed non-renewal, contract expiry without engagement | Confirms outcome; feeds model training and post-mortems |
Health scores should emphasize leading signals for actionability; lagging signals validate and tune the model.
Health score by business model
| Model | Emphasis |
|---|---|
| SaaS (seat / usage) | Product depth, seat activation, admin health |
| Marketplace | Supply and demand balance, liquidity, quality / dispute signals |
| API platform | Call volume, error rates, latency SLO adherence, key integration health |
| Enterprise | Stakeholder map coverage, security / procurement milestones, executive engagement |
One global score rarely fits; consider sub-scores by motion (e.g. product vs. relationship) with a composite for prioritization.
Implementation roadmap
| Phase | Focus |
|---|---|
| Phase 1 — Manual scoring | Spreadsheet or CRM fields; CSM judgment + weekly review; document definitions |
| Phase 2 — Automated data collection | Pipeline from product, support, billing; consistent account IDs; data quality checks |
| Phase 3 — Predictive model | Labeled outcomes; validate lift over rules; governance and explainability policy |
| Phase 4 — Prescriptive actions | Recommended next best action; experiment framework for plays |
Skipping Phase 2 quality usually wastes model effort.
Technology stack
| Layer | Examples |
|---|---|
| CS platforms | Gainsight, ChurnZero, Totango, Vitally — health scores, plays, dashboards |
| Custom | Warehouse + dbt + reverse ETL or in-app flags; full control, higher build cost |
| Integration | Event pipelines, CRM as system of record for account, ticketing sync |
| Dashboards | Executive rollup, CSM workbench, manager queue — different detail density |
Design dashboards for decisions, not vanity: “what do I do Monday morning?”
Anti-patterns
| Anti-pattern | Effect |
|---|---|
| Too many components | Noise; unstable scores; CSM distrust |
| Equal weighting | Ignores true drivers of churn |
| No action triggers | Score is analytics theater |
| No benchmarks | Cannot tell normal seasonality from crisis |
| Gaming the score | Incentives drive theater metrics instead of customer outcomes |
External references
- Gainsight — Customer Success resources and health score playbooks.
- Customer Success (Mehta et al.) — Foundational framing for CS operations and metrics.
- ChurnZero — Blog and materials on engagement scoring and plays.
- TSIA — Frameworks for service and success economics and maturity.
Keep project-specific customer success documentation in docs/product/customer-success/ and support playbooks in docs/operations/, not in this file.