customer-health-analyst

Name: customer-health-analyst
Author: ncklrs

129installs3forks

How to install customer-health-analyst?

ncklrs/startup-os-skills/customer-health-analyst

$npx -y skills add ncklrs/startup-os-skills --skill customer-health-analyst

Installs into the current project.

Summary

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, c

Files · 1

View on GitHub

SKILL.md

1	# Customer Health Analyst
2
3	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.
4
5	## Philosophy
6
7	Customer health is not a single metric — it's a predictive system:
8
9	1. Measure what matters — Health scores should predict outcomes, not just track activity
10	2. Lead, don't lag — Focus on indicators that predict churn before it's too late
11	3. Segment for action — Different customers need different interventions
12	4. Automate detection — Scale health monitoring across your entire customer base
13	5. Close the loop — Analytics without action is just expensive data collection
14
15	## How This Skill Works
16
17	When invoked, apply the guidelines in `rules/` organized by:
18
19	- `health-*` — Health score design, weighting, and calibration
20	- `indicators-*` — Leading vs lagging indicator analysis
21	- `churn-*` — Prediction modeling and early warning systems
22	- `usage-*` — Analytics and adoption metrics
23	- `risk-*` — Identification, escalation, and intervention
24	- `data-*` — Enrichment and customer 360 development
25	- `cohort-*` — Analysis and benchmarking
26	- `executive-*` — Reporting and dashboards
27	- `segmentation-*` — Customer tiers and scoring models
28
29	## Core Frameworks
30
31	### The Health Score Hierarchy
32
33	```
34	┌─────────────────────────────────────────────────────────────────┐
35	│ COMPOSITE HEALTH SCORE │
36	│ (0-100) │
37	├─────────────────────────────────────────────────────────────────┤
38	│ │
39	│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
40	│ │ PRODUCT │ │ENGAGEMENT│ │ GROWTH │ │ SUPPORT │ │
41	│ │ USAGE │ │ │ │ SIGNALS │ │ HEALTH │ │
42	│ │ (35%) │ │ (25%) │ │ (20%) │ │ (20%) │ │
43	│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
44	│ │
45	├─────────────────────────────────────────────────────────────────┤
46	│ COMPONENT METRICS │
47	│ │
48	│ Usage: Engagement: Growth: Support: │
49	│ - DAU/MAU - NPS score - Seat trend - Ticket volume │
50	│ - Features - CSM meetings - Usage trend - Resolution time │
51	│ - Depth - Email opens - Expansion - Sentiment │
52	│ - Breadth - Logins - Contract - Escalations │
53	│ │
54	└─────────────────────────────────────────────────────────────────┘
55	```
56
57	### Leading vs Lagging Indicators
58
59	\| Type \| Definition \| Examples \| Action Window \|
60	\|------\|------------\|----------\|---------------\|
61	\| Leading \| Predict future outcomes \| Usage decline, engagement drop \| 60-90 days \|
62	\| Coincident \| Move with outcomes \| Support sentiment, NPS \| 30-60 days \|
63	\| Lagging \| Confirm after the fact \| Churn, revenue loss \| Too late \|
64
65	### Customer Health States
66
67	```
68	┌─────────────────────────────────────────────────────────────────┐
69	│ │
70	│ THRIVING ──→ HEALTHY ──→ NEUTRAL ──→ AT-RISK ──→ CRITICAL │
71	│ (85+) (70-84) (50-69) (30-49) (<30) │
72	│ │
73	│ Expand Monitor Engage Intervene Escalate │
74	│ │
75	└─────────────────────────────────────────────────────────────────┘
76	```
77
78	### Health Score Components
79
80	\| Component \| Weight \| Key Metrics \| Why It Matters \|
81	\|-----------\|--------\|-------------\|----------------\|
82	\| Product Usage \| 30-40% \| DAU/MAU, feature adoption, depth \| Usage predicts value realization \|
83	\| Engagement \| 20-25% \| NPS, CSM contact, responsiveness \| Relationship strength indicator \|
84	\| Growth Signals \| 15-20% \| Seat expansion, usage trend \| Investment signals commitment \|
85	\| Support Health \| 15-20% \| Ticket volume, sentiment, resolution \| Frustration predicts churn \|
86	\| Financial \| 5-10% \| Payment history, contract length \| Financial commitment level \|
87
88	### Churn Risk Factors
89
90	\| Factor \| Risk Weight \| Detection Method \|
91	\|--------\|-------------\|------------------\|
92	\| Champion departure \| Critical \| Contact tracking, LinkedIn \|
93	\| Usage decline >30% \| High \| Product analytics \|
94	\| Negative NPS (0

People who liked this also viewed

6 picks

Type

customer-health-analyst

129installs3forks

View on GitHub

How to install customer-health-analyst?

ncklrs/startup-os-skills/customer-health-analyst

$npx -y skills add ncklrs/startup-os-skills --skill customer-health-analyst

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Customer Health Analyst
2
3	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.
4
5	## Philosophy
6
7	Customer health is not a single metric — it's a predictive system:
8
9	1. Measure what matters — Health scores should predict outcomes, not just track activity
10	2. Lead, don't lag — Focus on indicators that predict churn before it's too late
11	3. Segment for action — Different customers need different interventions
12	4. Automate detection — Scale health monitoring across your entire customer base
13	5. Close the loop — Analytics without action is just expensive data collection
14
15	## How This Skill Works
16
17	When invoked, apply the guidelines in `rules/` organized by:
18
19	- `health-*` — Health score design, weighting, and calibration
20	- `indicators-*` — Leading vs lagging indicator analysis
21	- `churn-*` — Prediction modeling and early warning systems
22	- `usage-*` — Analytics and adoption metrics
23	- `risk-*` — Identification, escalation, and intervention
24	- `data-*` — Enrichment and customer 360 development
25	- `cohort-*` — Analysis and benchmarking
26	- `executive-*` — Reporting and dashboards
27	- `segmentation-*` — Customer tiers and scoring models
28
29	## Core Frameworks
30
31	### The Health Score Hierarchy
32
33	```
34	┌─────────────────────────────────────────────────────────────────┐
35	│ COMPOSITE HEALTH SCORE │
36	│ (0-100) │
37	├─────────────────────────────────────────────────────────────────┤
38	│ │
39	│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
40	│ │ PRODUCT │ │ENGAGEMENT│ │ GROWTH │ │ SUPPORT │ │
41	│ │ USAGE │ │ │ │ SIGNALS │ │ HEALTH │ │
42	│ │ (35%) │ │ (25%) │ │ (20%) │ │ (20%) │ │
43	│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
44	│ │
45	├─────────────────────────────────────────────────────────────────┤
46	│ COMPONENT METRICS │
47	│ │
48	│ Usage: Engagement: Growth: Support: │
49	│ - DAU/MAU - NPS score - Seat trend - Ticket volume │
50	│ - Features - CSM meetings - Usage trend - Resolution time │
51	│ - Depth - Email opens - Expansion - Sentiment │
52	│ - Breadth - Logins - Contract - Escalations │
53	│ │
54	└─────────────────────────────────────────────────────────────────┘
55	```
56
57	### Leading vs Lagging Indicators
58
59	\| Type \| Definition \| Examples \| Action Window \|
60	\|------\|------------\|----------\|---------------\|
61	\| Leading \| Predict future outcomes \| Usage decline, engagement drop \| 60-90 days \|
62	\| Coincident \| Move with outcomes \| Support sentiment, NPS \| 30-60 days \|
63	\| Lagging \| Confirm after the fact \| Churn, revenue loss \| Too late \|
64
65	### Customer Health States
66
67	```
68	┌─────────────────────────────────────────────────────────────────┐
69	│ │
70	│ THRIVING ──→ HEALTHY ──→ NEUTRAL ──→ AT-RISK ──→ CRITICAL │
71	│ (85+) (70-84) (50-69) (30-49) (<30) │
72	│ │
73	│ Expand Monitor Engage Intervene Escalate │
74	│ │
75	└─────────────────────────────────────────────────────────────────┘
76	```
77
78	### Health Score Components
79
80	\| Component \| Weight \| Key Metrics \| Why It Matters \|
81	\|-----------\|--------\|-------------\|----------------\|
82	\| Product Usage \| 30-40% \| DAU/MAU, feature adoption, depth \| Usage predicts value realization \|
83	\| Engagement \| 20-25% \| NPS, CSM contact, responsiveness \| Relationship strength indicator \|
84	\| Growth Signals \| 15-20% \| Seat expansion, usage trend \| Investment signals commitment \|
85	\| Support Health \| 15-20% \| Ticket volume, sentiment, resolution \| Frustration predicts churn \|
86	\| Financial \| 5-10% \| Payment history, contract length \| Financial commitment level \|
87
88	### Churn Risk Factors
89
90	\| Factor \| Risk Weight \| Detection Method \|
91	\|--------\|-------------\|------------------\|
92	\| Champion departure \| Critical \| Contact tracking, LinkedIn \|
93	\| Usage decline >30% \| High \| Product analytics \|
94	\| Negative NPS (0

People who liked this also viewed

6 picks

Type