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