customer-health-analyst
Expert customer health scoring and analytics guidance. Use when designing health scores, building churn prediction models, analyzing usage metrics, identifying at-risk accounts, creating executive dashboards, or performing cohort analysis. Use for leading indicator development, customer data enrichment, risk escalation frameworks, and retention analytics.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ncklrs/startup-os-skills --skill customer-health-analyst- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads customer-health-analyst automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
FAQ
What does the customer-health-analyst skill do?
Expert customer health scoring and analytics guidance. Use when designing health scores, building churn prediction models, analyzing usage metrics, identifying at-risk accounts, creating executive dashboards, or performing cohort analysis. Use for leading indicator development, customer data enrichment, risk escalation frameworks, and retention analytics.
How do I install customer-health-analyst?
Run npx skills add https://github.com/ncklrs/startup-os-skills --skill customer-health-analyst in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/customer-health-analyst/ (personal) or .claude/skills/customer-health-analyst/ (project).
Which AI agents can use customer-health-analyst?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is customer-health-analyst safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains customer-health-analyst?
It is published by ncklrs in ncklrs/startup-os-skills. It has 152 recorded installs.
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