| name | customer-success-manager |
| description | Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments. |
| compatibility | opencode |
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
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
python scripts/health_score_calculator.py assets/sample_customer_data.json
python scripts/health_score_calculator.py assets/sample_customer_data.json --format json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json
python scripts/churn_risk_analyzer.py assets/sample_customer_data.json --format json
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
python scripts/health_score_calculator.py customer_portfolio.json --format json > health_results.json
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json
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.md for 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
