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: Measure what matters — Health scores should predict outcomes, not just track activity Lead, don't lag — Focus on indicators that predict churn before it's too late Segment for action — Different customers need different interventions Automate detection — Scale health monitoring across your entire customer base 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