Customer Health Analyst
Design health scores, churn signals, and executive CS dashboards when you need to retain and expand B2B customers without a full data team.
Overview
Customer Health Analyst is an agent skill most often used in Grow (also Operate and Validate) that structures health scoring, churn prediction, and CS reporting for solo builders retaining B2B customers.
Install
npx skills add https://github.com/ncklrs/startup-os-skills --skill customer-health-analystWhat is this skill?
- 8 capability areas from health-score architecture through executive reporting
- Churn prediction, leading vs lagging indicators, and intervention timing
- Usage analytics, cohort retention curves, and adoption benchmarking
- Risk identification with escalation frameworks and save strategies
- Data enrichment and 360-degree customer view governance
- 8 section areas covering health design through executive reporting
- Multiple areas marked CRITICAL impact (health, indicators, churn, risk)
Adoption & trust: 1 installs on skills.sh; 27 GitHub stars; 2/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You have usage and billing data but no shared health model, so churn surprises you and saves happen too late.
Who is it for?
Indie SaaS founders with early paid accounts who need churn early-warning and lifecycle playbooks without hiring customer success first.
Skip if: Pre-revenue ideas with no customers to score, or teams that only need one-off SQL charts without a retention framework.
When should I use this skill?
You have paying customers and need structured health scoring, churn prediction, cohort views, or CS executive reporting—not ad-hoc spreadsheet patches.
What do I get? / Deliverables
You leave with component-weighted health scores, indicator priorities, risk playbooks, and executive-ready KPI narratives you can wire to your analytics stack.
- Health score architecture with weights and thresholds
- Churn risk signals and intervention playbook outline
- Executive KPI and dashboard narrative
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Customer health and churn prevention sit in Grow because solo founders compound revenue through retention, expansion, and proactive saves—not only acquisition. Lifecycle is the canonical shelf: health scoring, intervention playbooks, and risk escalation map directly to account lifecycle management.
Where it fits
Define health-score weights and save playbooks before renewal season for your first 50 accounts.
Benchmark feature adoption and usage patterns to prioritize onboarding fixes that lift retention.
Turn weekly risk escalations into product fixes when leading indicators show declining engagement.
Use cohort curves from pilot customers to sanity-check packaging before scaling sales motion.
How it compares
Use for customer-success methodology and score design, not a plug-in monitoring agent or generic SQL assistant.
Common Questions / FAQ
Who is customer-health-analyst for?
Solo and indie builders on subscription or usage-based SaaS who own retention, expansion, and save motions while still shipping product.
When should I use customer-health-analyst?
In Grow when you define lifecycle dashboards and save triggers; in Operate when production usage patterns feed risk signals; in Validate when pricing and scope assumptions need cohort proof from pilot customers.
Is customer-health-analyst safe to install?
Treat it as advisory playbooks—review the Security Audits panel on this page before pointing an agent at production CRM or billing credentials.
SKILL.md
READMESKILL.md - Customer Health Analyst
## 1. Health Score Design (health) **Impact:** CRITICAL **Description:** Health score architecture, component selection, weight assignment, scoring algorithms, threshold calibration, and model validation. ## 2. Leading vs Lagging Indicators (indicators) **Impact:** CRITICAL **Description:** Indicator identification, predictive signal development, correlation analysis, signal prioritization, and action trigger design. ## 3. Churn Prediction (churn) **Impact:** CRITICAL **Description:** Prediction model development, feature engineering, risk scoring, early warning systems, and intervention timing optimization. ## 4. Usage Analytics (usage) **Impact:** HIGH **Description:** Engagement measurement, feature adoption tracking, usage patterns, behavioral analysis, and adoption benchmarking. ## 5. Risk Identification (risk) **Impact:** CRITICAL **Description:** Risk signal detection, escalation frameworks, intervention playbooks, stakeholder communication, and save strategies. ## 6. Data Enrichment (data) **Impact:** HIGH **Description:** Data source integration, enrichment strategies, data quality management, 360-degree customer view, and data governance. ## 7. Cohort Analysis (cohort) **Impact:** HIGH **Description:** Cohort definition, retention curve analysis, comparative benchmarking, segment performance, and trend identification. ## 8. Executive Reporting (executive) **Impact:** HIGH **Description:** KPI selection, dashboard design, data storytelling, executive presentations, and board reporting. ## 9. Segmentation & Scoring (segmentation) **Impact:** MEDIUM-HIGH **Description:** Customer tier definition, behavioral clustering, value-based segmentation, scoring model design, and segment-specific strategies. --- title: Churn Prediction Modeling impact: CRITICAL tags: churn-prediction, machine-learning, risk-scoring, early-warning --- ## Churn Prediction Modeling **Impact: CRITICAL** Effective churn prediction gives you 60-90 days of lead time to intervene. A well-calibrated model can reduce churn by 15-30% by enabling proactive outreach to at-risk accounts before they decide to leave. ### The Churn Prediction Pipeline ``` ┌──────────────────────────────────────────────────────────────────┐ │ CHURN PREDICTION PIPELINE │ ├──────────────────────────────────────────────────────────────────┤ │ │ │ DATA FEATURES MODEL SCORING │ │ COLLECTION → ENGINEERING → TRAINING → & ALERTS │ │ │ │ • Product • Usage decay • Logistic • Daily risk │ │ • CRM • Engagement • Random • Threshold │ │ • Support • Sentiment • XGBoost • Routing │ │ • Financial • Growth • Neural • Actions │ │ │ ├──────────────────────────────────────────────────────────────────┤ │ FEEDBACK LOOP │ │ │ │ Actual Outcomes → Model Refinement → Improved Accuracy │ │ │ └──────────────────────────────────────────────────────────────────┘ ``` ### Feature Categories for Churn Models | Category | Features | Predictive Value | |----------|----------|------------------| | **Usage Metrics** | DAU/MAU, feature adoption, session depth | High | | **Usage Trends** | 30/60/90-day slopes, velocity changes | Very High | | **Engagement** | NPS, CSM touchpoints, email responsiveness | High | | **Support** | Ticket volume, sentiment, escalations | High | | **Financial** | Payment issues, contract length, pricing tier | Medium | | **Organizational** | Champion status, stakeholder changes | High | |