When churn is creeping up and renewal season is here, /customer-success-manager scores your portfolio and surfaces at-risk accounts before they leave. — Claude Skill
A Claude Skill for Claude Code by Alireza Rezvani — run /customer-success-manager in Claude·Updated ·v1.0.0
Score customer health, predict churn, find expansion plays
- Health scoring across 4 dimensions: usage (30%), engagement (25%), support (20%), relationship (25%)
- Risk tiers: Critical (executive escalation), High (CSM intervention), Medium (proactive outreach), Low (monitor)
- Expansion scoring: upsell, cross-sell, seat expansion with revenue estimates
- Segment-aware benchmarks for Enterprise, Mid-Market, and SMB — calibrated thresholds per segment
- QBR-ready output with prioritized accounts, intervention playbooks, and revenue impact
Who this is for
Score 50+ customer accounts before QBR and surface at-risk accounts before they churn
See skills for this roleSee expansion pipeline by account with revenue estimates for the board deck
See skills for this roleStandardize churn risk scoring across the portfolio with weighted dimensions
See skills for this roleWhat it does
Your VP wants account scores for the QBR tomorrow morning. /customer-success-manager runs the health scorer on your portfolio JSON, classifies every account Green/Yellow/Red, and gives you prioritized intervention recommendations — in 10 minutes.
Q4 renewals start in 90 days and you need to know which accounts are at risk. /customer-success-manager runs churn analyzer with 5 signal categories (usage decline, engagement drop, support issues, relationship signals, commercial factors) and surfaces the Critical and High tier.
VP Sales asks for expansion revenue forecast for the board. /customer-success-manager scores upsell, cross-sell, and seat expansion across the portfolio — with revenue estimates per account based on contract gaps and product adoption.
New CSM, 80 accounts, no health scores anywhere. /customer-success-manager builds your baseline in one pass — segment-aware benchmarks for Enterprise/Mid-Market/SMB so you know which accounts deserve weekly attention vs quarterly check-ins.
How it works
Export customer data: usage, engagement, support, and relationship signals as JSON
Run health_score_calculator on portfolio — get Green / Yellow / Red classification per account
Run churn_risk_analyzer — get Critical / High / Medium / Low risk tiers with intervention playbooks
Run expansion_opportunity_scorer — get upsell, cross-sell, and seat expansion ranked by revenue
Get a QBR-ready report: prioritized accounts, recommended actions, and pipeline by tier
Example
50 accounts, $4.2M ARR Segments: Enterprise (12), Mid-Market (22), SMB (16) Q2 renewals: 18 accounts, $1.6M ARR Last quarter NRR: 108%
Green: 28 accounts (56%) — $2.4M ARR Yellow: 19 accounts (38%) — $1.5M ARR Red: 3 accounts (6%) — $300K ARR
Acme Corp $180K Usage -40%, no exec sponsor, NPS dropped 8→4 Beta Inc $80K Support tickets +200% Q3, escalations to engineering weekly Charlie LLC $40K Champion left August, no replacement engaged
8 accounts ready for upgrade conversation: NorthStar +$60K seat expansion (utilizing 95%+ seats) DataFlow +$45K cross-sell to Analytics module SummitTech +$80K plan tier upgrade (using power features) + 5 more
→ Critical: exec escalation this week (Acme, Beta, Charlie) — VP-level outreach required → Renewal risk: 4 Yellow accounts in Q2 renewals — schedule QBRs by EOM → Expansion: NorthStar ready for upgrade conversation NOW — strike while usage is hot
Metrics this improves
Works with
Alternative support data source for the support dimension
Export customer health, usage, and engagement data as JSON input for the scoring scripts
Pull customer account, contract, and renewal data for the churn risk analyzer
Source customer engagement and ticket data for health scoring
Pull support ticket volume, escalations, and CSAT for the support dimension
Want to use Customer Success Manager?
Choose how to get started.
Install and run this skill locally on your computer.
Open a terminal on your computer and paste this command:
This downloads the skill with all its files to your computer:
Add -g at the end to make it available in all your projects.
Start Claude Code, then type the command:
Customer Success Manager
Production-grade customer success analytics with multi-dimensional health scoring, churn risk prediction, and expansion opportunity identification. Three Python CLI tools provide deterministic, repeatable analysis using standard library only -- no external dependencies, no API calls, no ML models.
Table of Contents
- Input Requirements
- Output Formats
- How to Use
- Scripts
- Reference Guides
- Templates
- Best Practices
- Limitations
Input Requirements
All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples and sample data.
Health Score Calculator
Required fields per customer object: customer_id, name, segment, arr, and nested objects usage (login_frequency, feature_adoption, dau_mau_ratio), engagement (support_ticket_volume, meeting_attendance, nps_score, csat_score), support (open_tickets, escalation_rate, avg_resolution_hours), relationship (executive_sponsor_engagement, multi_threading_depth, renewal_sentiment), and previous_period scores for trend analysis.
Churn Risk Analyzer
Required fields per customer object: customer_id, name, segment, arr, contract_end_date, and nested objects usage_decline, engagement_drop, support_issues, relationship_signals, and commercial_factors.
Expansion Opportunity Scorer
Required fields per customer object: customer_id, name, segment, arr, and nested objects contract (licensed_seats, active_seats, plan_tier, available_tiers), product_usage (per-module adoption flags and usage percentages), and departments (current and potential).
Output Formats
All scripts support two output formats via the --format flag:
text(default): Human-readable formatted output for terminal viewingjson: Machine-readable JSON output for integrations and pipelines
How to Use
Quick Start
# Health scoring
python scripts/health_score_calculator.py assets/sample_customer_data.json
python scripts/health_score_calculator.py assets/sample_customer_data.json --format json
# Churn risk analysis
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json
# Expansion opportunity scoring
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json --format json
Workflow Integration
# 1. Score customer health across portfolio
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json
# Verify: confirm health_results.json contains the expected number of customer records before continuing
# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json
# Verify: confirm risk_results.json is non-empty and risk tiers are present for each customer
# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json
# Verify: confirm expansion_results.json lists opportunities ranked by priority
# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md
Error handling: If a script exits with an error, check that:
- The input JSON matches the required schema for that script (see Input Requirements above)
- All required fields are present and correctly typed
- Python 3.7+ is being used (
python --version) - Output files from prior steps are non-empty before piping into subsequent steps
Scripts
1. health_score_calculator.py
Purpose: Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.
Dimensions and Weights:
| Dimension | Weight | Metrics |
|---|---|---|
| Usage | 30% | Login frequency, feature adoption, DAU/MAU ratio |
| Engagement | 25% | Support ticket volume, meeting attendance, NPS/CSAT |
| Support | 20% | Open tickets, escalation rate, avg resolution time |
| Relationship | 25% | Executive sponsor engagement, multi-threading depth, renewal sentiment |
Classification:
- Green (75-100): Healthy -- customer achieving value
- Yellow (50-74): Needs attention -- monitor closely
- Red (0-49): At risk -- immediate intervention required
Usage:
python scripts/health_score_calculator.py customer_data.json
python scripts/health_score_calculator.py customer_data.json --format json
2. churn_risk_analyzer.py
Purpose: Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.
Risk Signal Weights:
| Signal Category | Weight | Indicators |
|---|---|---|
| Usage Decline | 30% | Login trend, feature adoption change, DAU/MAU change |
| Engagement Drop | 25% | Meeting cancellations, response time, NPS change |
| Support Issues | 20% | Open escalations, unresolved critical, satisfaction trend |
| Relationship Signals | 15% | Champion left, sponsor change, competitor mentions |
| Commercial Factors | 10% | Contract type, pricing complaints, budget cuts |
Risk Tiers:
- Critical (80-100): Immediate executive escalation
- High (60-79): Urgent CSM intervention
- Medium (40-59): Proactive outreach
- Low (0-39): Standard monitoring
Usage:
python scripts/churn_risk_analyzer.py customer_data.json
python scripts/churn_risk_analyzer.py customer_data.json --format json
3. expansion_opportunity_scorer.py
Purpose: Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.
Expansion Types:
- Upsell: Upgrade to higher tier or more of existing product
- Cross-sell: Add new product modules
- Expansion: Additional seats or departments
Usage:
python scripts/expansion_opportunity_scorer.py customer_data.json
python scripts/expansion_opportunity_scorer.py customer_data.json --format json
Reference Guides
| Reference | Description |
|---|---|
references/health-scoring-framework.md | Complete health scoring methodology, dimension definitions, weighting rationale, threshold calibration |
references/cs-playbooks.md | Intervention playbooks for each risk tier, onboarding, renewal, expansion, and escalation procedures |
references/cs-metrics-benchmarks.md | Industry benchmarks for NRR, GRR, churn rates, health scores, expansion rates by segment and industry |
Templates
| Template | Purpose |
|---|---|
assets/qbr_template.md | Quarterly Business Review presentation structure |
assets/success_plan_template.md | Customer success plan with goals, milestones, and metrics |
assets/onboarding_checklist_template.md | 90-day onboarding checklist with phase gates |
assets/executive_business_review_template.md | Executive stakeholder review for strategic accounts |
Best Practices
- Combine signals: Use all three scripts together for a complete customer picture
- Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
- Calibrate thresholds: Adjust segment benchmarks based on your product and industry per
references/health-scoring-framework.md - Prepare with data: Run scripts before every QBR and executive meeting; reference
references/cs-playbooks.mdfor intervention guidance
Limitations
- No real-time data: Scripts analyze point-in-time snapshots from JSON input files
- No CRM integration: Data must be exported manually from your CRM/CS platform
- Deterministic only: No predictive ML -- scoring is algorithmic based on weighted signals
- Threshold tuning: Default thresholds are industry-standard but may need calibration for your business
- Revenue estimates: Expansion revenue estimates are approximations based on usage patterns
Last Updated: February 2026 Tools: 3 Python CLI tools Dependencies: Python 3.7+ standard library only