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

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

Churn Risk Factors

The Analytics Stack

Key Metrics

Executive Dashboard KPIs

Cohort Analysis Framework

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