customer-health-analyst

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