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.

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

# Expansion opportunity scoring
python scripts/expansion_opportunity_scorer.py assets/sample_customer_data.json

All scripts support --format text (default) and --format json for integrations.

Scripts

1. health_score_calculator.py

Multi-dimensional customer health scoring with trend analysis and segment-aware benchmarking.

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), Yellow (50-74), Red (0-49)

2. churn_risk_analyzer.py

Identify at-risk accounts with behavioral signal detection and tier-based intervention recommendations.

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), High (60-79), Medium (40-59), Low (0-39)

3. expansion_opportunity_scorer.py

Identify upsell, cross-sell, and expansion opportunities with revenue estimation and priority ranking.

Expansion Types: Upsell (higher tier), Cross-sell (new modules), Expansion (additional seats/departments)

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete schema examples. Required fields vary per script -- see the script --help output for details.

Best Practices

1. Combine signals: Use all three scripts together for a complete customer picture
2. Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
3. Calibrate thresholds: Adjust segment benchmarks per references/health-scoring-framework.md
4. Prepare with data: Run scripts before every QBR and executive meeting

Reference Guides

Reference	Description
references/health-scoring-framework.md	Complete health scoring methodology, dimension definitions, threshold calibration
references/cs-playbooks.md	Intervention playbooks for each risk tier, onboarding, renewal, expansion procedures
references/cs-metrics-benchmarks.md	Industry benchmarks for NRR, GRR, churn rates, health scores by segment

Templates

Template	Purpose
assets/qbr_template.md	Quarterly Business Review presentation structure
assets/success_plan_template.md	Customer success plan with goals and milestones
assets/onboarding_checklist_template.md	90-day onboarding checklist with phase gates

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
• Revenue estimates: Expansion revenue estimates are approximations based on usage patterns