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 calibrationindicators-* — Leading vs lagging indicator analysischurn-* — Prediction modeling and early warning systemsusage-* — Analytics and adoption metricsrisk-* — Identification, escalation, and interventiondata-* — Enrichment and customer 360 developmentcohort-* — Analysis and benchmarkingexecutive-* — Reporting and dashboardssegmentation-* — 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
1---2name: customer-health-analyst3description: 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.4---5
6# Customer Health Analyst
7
8Expert 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.
9
10## Philosophy
11
12Customer health is not a single metric — it's a predictive system:
13
141. **Measure what matters** — Health scores should predict outcomes, not just track activity
152. **Lead, don't lag** — Focus on indicators that predict churn before it's too late
163. **Segment for action** — Different customers need different interventions
174. **Automate detection** — Scale health monitoring across your entire customer base
185. **Close the loop** — Analytics without action is just expensive data collection
19
20## How This Skill Works
21
22When invoked, apply the guidelines in `rules/` organized by:
23
24- `health-*` — Health score design, weighting, and calibration
25- `indicators-*` — Leading vs lagging indicator analysis
26- `churn-*` — Prediction modeling and early warning systems
27- `usage-*` — Analytics and adoption metrics
28- `risk-*` — Identification, escalation, and intervention
29- `data-*` — Enrichment and customer 360 development
30- `cohort-*` — Analysis and benchmarking
31- `executive-*` — Reporting and dashboards
32- `segmentation-*` — Customer tiers and scoring models
33
34## Core Frameworks
35
36### The Health Score Hierarchy
37
38```
39┌─────────────────────────────────────────────────────────────────┐
40│ COMPOSITE HEALTH SCORE │
41│ (0-100) │
42├─────────────────────────────────────────────────────────────────┤
43│ │
44│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
45│ │ PRODUCT │ │ENGAGEMENT│ │ GROWTH │ │ SUPPORT │ │
46│ │ USAGE │ │ │ │ SIGNALS │ │ HEALTH │ │
47│ │ (35%) │ │ (25%) │ │ (20%) │ │ (20%) │ │
48│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
49│ │
50├─────────────────────────────────────────────────────────────────┤
51│ COMPONENT METRICS │
52│ │
53│ Usage: Engagement: Growth: Support: │
54│ - DAU/MAU - NPS score - Seat trend - Ticket volume │
55│ - Features - CSM meetings - Usage trend - Resolution time │
56│ - Depth - Email opens - Expansion - Sentiment │
57│ - Breadth - Logins - Contract - Escalations │
58│ │
59└─────────────────────────────────────────────────────────────────┘
60```
61
62### Leading vs Lagging Indicators
63
64| Type | Definition | Examples | Action Window |
65|------|------------|----------|---------------|
66| **Leading** | Predict future outcomes | Usage decline, engagement drop | 60-90 days |
67| **Coincident** | Move with outcomes | Support sentiment, NPS | 30-60 days |
68| **Lagging** | Confirm after the fact | Churn, revenue loss | Too late |
69
70### Customer Health States
71
72```
73┌─────────────────────────────────────────────────────────────────┐
74│ │
75│ THRIVING ──→ HEALTHY ──→ NEUTRAL ──→ AT-RISK ──→ CRITICAL │
76│ (85+) (70-84) (50-69) (30-49) (<30) │
77│ │
78│ Expand Monitor Engage Intervene Escalate │
79│ │
80└─────────────────────────────────────────────────────────────────┘
81```
82
83### Health Score Components
84
85| Component | Weight | Key Metrics | Why It Matters |
86|-----------|--------|-------------|----------------|
87| **Product Usage** | 30-40% | DAU/MAU, feature adoption, depth | Usage predicts value realization |
88| **Engagement** | 20-25% | NPS, CSM contact, responsiveness | Relationship strength indicator |
89| **Growth Signals** | 15-20% | Seat expansion, usage trend | Investment signals commitment |
90| **Support Health** | 15-20% | Ticket volume, sentiment, resolution | Frustration predicts churn |
91| **Financial** | 5-10% | Payment history, contract length | Financial commitment level |
92
93### Churn Risk Factors
94
95| Factor | Risk Weight | Detection Method |
96|--------|-------------|------------------|
97| Champion departure | Critical | Contact tracking, LinkedIn |
98| Usage decline >30% | High | Product analytics |
99| Negative NPS (0-6) | High | Survey responses |
100| Support escalations | High | Ticket analysis |
101| Missed renewal meeting | High | CSM activity tracking |
102| Contract downgrade | Very High | Billing data |
103| Competitor mentions | High | Call transcripts, tickets |
104| Budget review mentions | Medium | CSM notes |
105
106### The Analytics Stack
107
108| Layer | Purpose | Tools/Methods |
109|-------|---------|---------------|
110| **Collection** | Gather raw data | Product events, CRM, support |
111| **Processing** | Clean and transform | ETL, data pipelines |
112| **Calculation** | Compute scores | Scoring algorithms |
113| **Storage** | Historical tracking | Data warehouse |
114| **Visualization** | Present insights | Dashboards, reports |
115| **Action** | Trigger interventions | Alerting, automation |
116
117### Key Metrics
118
119| Metric | Formula | Target |
120|--------|---------|--------|
121| **Health Score Accuracy** | Churn predicted / Actual churn | >70% |
122| **Leading Indicator Correlation** | Correlation to outcomes | >0.6 |
123| **Score Distribution** | % in each health tier | Bell curve |
124| **Intervention Success Rate** | Saved / Intervened | >40% |
125| **Time to Detection** | Days before risk → action | <14 days |
126| **False Positive Rate** | False alerts / Total alerts | <20% |
127
128### Executive Dashboard KPIs
129
130| KPI | Definition | Benchmark |
131|-----|------------|-----------|
132| **Gross Revenue Retention** | Retained ARR / Starting ARR | 85-95% |
133| **Net Revenue Retention** | (Retained + Expansion) / Starting | 100-130% |
134| **Logo Retention** | Retained customers / Starting | 90-95% |
135| **Health Score Average** | Mean across customer base | 65-75 |
136| **At-Risk Revenue** | ARR with health <50 | <15% |
137| **Expansion Rate** | Customers expanded / Total | 15-30% |
138
139### Cohort Analysis Framework
140
141| Cohort Type | Segments By | Use Case |
142|-------------|-------------|----------|
143| **Time-based** | Sign-up month/quarter | Retention trends |
144| **Behavioral** | Feature usage patterns | Activation success |
145| **Value-based** | ARR tier | Segment economics |
146| **Industry** | Vertical | Product-market fit |
147| **Acquisition** | Channel/source | Marketing efficiency |
148
149## Anti-Patterns
150
151- **Vanity health scores** — Scores that look good but don't predict outcomes
152- **Over-weighted product usage** — Ignoring relationship and sentiment signals
153- **Lagging indicator focus** — Measuring what already happened
154- **One-size-fits-all thresholds** — Same scores mean different things for different segments
155- **Manual-only health tracking** — Can't scale without automation
156- **Score without action** — Calculating risk without intervention playbooks
157- **Annual calibration only** — Health models need continuous refinement
158- **Ignoring data quality** — Garbage in, garbage out
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