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.

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

json

{
  "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
      },
      "support": {
        "open_tickets": 2,
        "escalation_rate": 0.05,
        "avg_resolution_hours": 18
      },
      "relationship": {
        "executive_sponsor_engagement": 80,
        "multi_threading_depth": 4,
        "renewal_sentiment": "positive"
      },
      "previous_period": {
        "usage_score": 70,
        "engagement_score": 65,
        "support_score": 75,
        "relationship_score": 60
      }
    }
  ]
}

Churn Risk Analyzer

json

{
  "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
      },
      "support_issues": {
        "open_escalations": 1,
        "unresolved_critical": 0,
        "satisfaction_trend": "declining"
      },
      "relationship_signals": {
        "champion_left": false,
        "sponsor_change": false,
        "competitor_mentions": 1
      },
      "commercial_factors": {
        "contract_type": "annual",
        "pricing_complaints": false,
        "budget_cuts_mentioned": false
      }
    }
  ]
}

Expansion Opportunity Scorer

json

{
  "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, "usage_pct": 85},
        "analytics_module": {"adopted": true, "usage_pct": 60},
        "integrations_module": {"adopted": false, "usage_pct": 0},
        "api_access": {"adopted": true, "usage_pct": 40},
        "advanced_reporting": {"adopted": false, "usage_pct": 0}
      },
      "departments": {
        "current": ["engineering", "product"],
        "potential": ["marketing", "sales", "support"]
      }
    }
  ]
}

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

bash

# 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

bash

# 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:

bash

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:

bash

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:

bash

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

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

customer-success-manager

NPX Install

Tags

SKILL.md Content

Customer Success Manager

Table of Contents

Capabilities

Input Requirements

Health Score Calculator

Churn Risk Analyzer

Expansion Opportunity Scorer

Output Formats

How to Use

Quick Start

Workflow Integration

Scripts

1. health_score_calculator.py

2. churn_risk_analyzer.py

3. expansion_opportunity_scorer.py

Reference Guides

Templates

Best Practices

Limitations