customer-success-manager

name	customer-success-manager
description	Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success
license	MIT
metadata	{"version":"1.0.0","author":"Alireza Rezvani","category":"business-growth","domain":"customer-success","updated":"2026-02-06T00:00:00.000Z","python-tools":"health_score_calculator.py, churn_risk_analyzer.py, expansion_opportunity_scorer.py","tech-stack":"customer-success, saas-metrics, health-scoring"}

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

• Capabilities
• Input Requirements
• Output Formats
• How to Use
• Scripts
• Reference Guides
• Templates
• Best Practices
• Limitations

---

Capabilities

• Customer Health Scoring: Multi-dimensional weighted scoring across usage, engagement, support, and relationship dimensions with Red/Yellow/Green classification
• Churn Risk Analysis: Behavioral signal detection with tier-based intervention playbooks and time-to-renewal urgency multipliers
• Expansion Opportunity Scoring: Adoption depth analysis, whitespace mapping, and revenue opportunity estimation with effort-vs-impact prioritization
• Segment-Aware Benchmarking: Configurable thresholds for Enterprise, Mid-Market, and SMB customer segments
• Trend Analysis: Period-over-period comparison to detect improving or declining trajectories
• Executive Reporting: QBR templates, success plans, and executive business review templates

---

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_customer_data.json for complete examples.

Health Score Calculator

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "usage": {
        "login_frequency": 85,
        "feature_adoption": 72,
        "dau_mau_ratio": 0.45
      },
      "engagement": {
        "support_ticket_volume": 3,
        "meeting_attendance": 90,
        "nps_score": 8,
        "csat_score": 4.2
      },
       
         
         
         
      
       
         
         
         
      
       
         
         
         
         
      
    
  

Churn Risk Analyzer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract_end_date": "2026-06-30",
      "usage_decline": {
        "login_trend": -15,
        "feature_adoption_change": -10,
        "dau_mau_change": -0.08
      },
      "engagement_drop": {
        "meeting_cancellations": 2,
        "response_time_days": 5,
        "nps_change": -3
      },
       
         
         
         
      
       
         
         
         
      
       
         
         
         
      
    
  

Expansion Opportunity Scorer

{
  "customers": [
    {
      "customer_id": "CUST-001",
      "name": "Acme Corp",
      "segment": "enterprise",
      "arr": 120000,
      "contract": {
        "licensed_seats": 100,
        "active_seats": 95,
        "plan_tier": "professional",
        "available_tiers": ["professional", "enterprise", "enterprise_plus"]
      },
      "product_usage": {
        "core_platform": {"adopted": true  
            
            
            
            
      
       
          
           
      
    
  

---

Output Formats

All scripts support two output formats via the --format flag:

• text (default): Human-readable formatted output for terminal viewing
• json: 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

# 2. Identify at-risk accounts
python scripts/churn_risk_analyzer.py customer_portfolio.json --format json > risk_results.json

# 3. Find expansion opportunities in healthy accounts
python scripts/expansion_opportunity_scorer.py customer_portfolio.json --format json > expansion_results.json

# 4. Prepare QBR using templates
# Reference: assets/qbr_template.md

---

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

1. Score regularly: Run health scoring weekly for Enterprise, bi-weekly for Mid-Market, monthly for SMB
2. Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow
3. Combine signals: Use all three scripts together for a complete customer picture
4. Calibrate thresholds: Adjust segment benchmarks based on your product and industry
5. Document interventions: Track what actions you took and outcomes for playbook refinement
6. Prepare with data: Run scripts before every QBR and executive meeting

---

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