Agent-Skills.md

Agent Skills: 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.

UncategorizedID: ncklrs/startup-os-skills/customer-health-analyst

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ncklrs/startup-os-skills
ncklrs
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pnpm dlx add-skill https://github.com/ncklrs/startup-os-skills/tree/HEAD/skills/customer-health-analyst

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skills/customer-health-analyst/SKILL.md

Skill Metadata

Name
customer-health-analyst
Description
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.

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:

  1. Measure what matters — Health scores should predict outcomes, not just track activity
  2. Lead, don't lag — Focus on indicators that predict churn before it's too late
  3. Segment for action — Different customers need different interventions
  4. Automate detection — Scale health monitoring across your entire customer base
  5. 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 calibration
  • indicators-* — Leading vs lagging indicator analysis
  • churn-* — Prediction modeling and early warning systems
  • usage-* — Analytics and adoption metrics
  • risk-* — Identification, escalation, and intervention
  • data-* — Enrichment and customer 360 development
  • cohort-* — Analysis and benchmarking
  • executive-* — Reporting and dashboards
  • segmentation-* — 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
Customer Health Analyst Skill | Agent Skills