Customer Health Analyst
Expert guidance for customer health scoring, predictive analytics, and data-driven customer success strategies. Transform raw customer data into actionable insights that prevent churn and drive expansion.
Philosophy
Customer health is not a single metric — it's a predictive system:
- Measure what matters — Health scores should predict outcomes, not just track activity
- Lead, don't lag — Focus on indicators that predict churn before it's too late
- Segment for action — Different customers need different interventions
- Automate detection — Scale health monitoring across your entire customer base
- Close the loop — Analytics without action is just expensive data collection
How This Skill Works
When invoked, apply the guidelines in rules/ organized by:
health-* — Health score design, weighting, and calibrationindicators-* — Leading vs lagging indicator analysischurn-* — Prediction modeling and early warning systemsusage-* — Analytics and adoption metricsrisk-* — Identification, escalation, and interventiondata-* — Enrichment and customer 360 developmentcohort-* — Analysis and benchmarkingexecutive-* — Reporting and dashboardssegmentation-* — Customer tiers and scoring models
Core Frameworks
The Health Score Hierarchy
┌─────────────────────────────────────────────────────────────────┐
│ COMPOSITE HEALTH SCORE │
│ (0-100) │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ PRODUCT │ │ENGAGEMENT│ │ GROWTH │ │ SUPPORT │ │
│ │ USAGE │ │ │ │ SIGNALS │ │ HEALTH │ │
│ │ (35%) │ │ (25%) │ │ (20%) │ │ (20%) │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
│ │
├─────────────────────────────────────────────────────────────────┤
│ COMPONENT METRICS │
│ │
│ Usage: Engagement: Growth: Support: │
│ - DAU/MAU - NPS score - Seat trend - Ticket volume │
│ - Features - CSM meetings - Usage trend - Resolution time │
│ - Depth - Email opens - Expansion - Sentiment │
│ - Breadth - Logins - Contract - Escalations │
│ │
└─────────────────────────────────────────────────────────────────┘
Leading vs Lagging Indicators
| Type | Definition | Examples | Action Window |
|---|
| Leading | Predict future outcomes | Usage decline, engagement drop | 60-90 days |
| Coincident | Move with outcomes | Support sentiment, NPS | 30-60 days |
| Lagging | Confirm after the fact | Churn, revenue loss | Too late |
Customer Health States
┌─────────────────────────────────────────────────────────────────┐
│ │
│ THRIVING ──→ HEALTHY ──→ NEUTRAL ──→ AT-RISK ──→ CRITICAL │
│ (85+) (70-84) (50-69) (30-49) (<30) │
│ │
│ Expand Monitor Engage Intervene Escalate │
│ │
└─────────────────────────────────────────────────────────────────┘
Health Score Components
| Component | Weight | Key Metrics | Why It Matters |
|---|
| Product Usage | 30-40% | DAU/MAU, feature adoption, depth | Usage predicts value realization |
| Engagement | 20-25% | NPS, CSM contact, responsiveness | Relationship strength indicator |
| Growth Signals | 15-20% | Seat expansion, usage trend | Investment signals commitment |
| Support Health | 15-20% | Ticket volume, sentiment, resolution | Frustration predicts churn |
| Financial | 5-10% | Payment history, contract length | Financial commitment level |
Churn Risk Factors
| Factor | Risk Weight | Detection Method |
|---|
| Champion departure | Critical | Contact tracking, LinkedIn |
| Usage decline >30% | High | Product analytics |
| Negative NPS (0-6) | High | Survey responses |
| Support escalations | High | Ticket analysis |
| Missed renewal meeting | High | CSM activity tracking |
| Contract downgrade | Very High | Billing data |
| Competitor mentions | High | Call transcripts, tickets |
| Budget review mentions | Medium | CSM notes |
The Analytics Stack
| Layer | Purpose | Tools/Methods |
|---|
| Collection | Gather raw data | Product events, CRM, support |
| Processing | Clean and transform | ETL, data pipelines |
| Calculation | Compute scores | Scoring algorithms |
| Storage | Historical tracking | Data warehouse |
| Visualization | Present insights | Dashboards, reports |
| Action | Trigger interventions | Alerting, automation |
Key Metrics
| Metric | Formula | Target |
|---|
| Health Score Accuracy | Churn predicted / Actual churn | >70% |
| Leading Indicator Correlation | Correlation to outcomes | >0.6 |
| Score Distribution | % in each health tier | Bell curve |
| Intervention Success Rate | Saved / Intervened | >40% |
| Time to Detection | Days before risk → action | <14 days |
| False Positive Rate | False alerts / Total alerts | <20% |
Executive Dashboard KPIs
| KPI | Definition | Benchmark |
|---|
| Gross Revenue Retention | Retained ARR / Starting ARR | 85-95% |
| Net Revenue Retention | (Retained + Expansion) / Starting | 100-130% |
| Logo Retention | Retained customers / Starting | 90-95% |
| Health Score Average | Mean across customer base | 65-75 |
| At-Risk Revenue | ARR with health <50 | <15% |
| Expansion Rate | Customers expanded / Total | 15-30% |
Cohort Analysis Framework
| Cohort Type | Segments By | Use Case |
|---|
| Time-based | Sign-up month/quarter | Retention trends |
| Behavioral | Feature usage patterns | Activation success |
| Value-based | ARR tier | Segment economics |
| Industry | Vertical | Product-market fit |
| Acquisition | Channel/source | Marketing efficiency |
Anti-Patterns
- Vanity health scores — Scores that look good but don't predict outcomes
- Over-weighted product usage — Ignoring relationship and sentiment signals
- Lagging indicator focus — Measuring what already happened
- One-size-fits-all thresholds — Same scores mean different things for different segments
- Manual-only health tracking — Can't scale without automation
- Score without action — Calculating risk without intervention playbooks
- Annual calibration only — Health models need continuous refinement
- Ignoring data quality — Garbage in, garbage out