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