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Customer Success Manager

Domain: Business & Growth | Skill: customer-success-manager | Source: business-growth/customer-success-manager/SKILL.md

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 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
# 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

  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 based on your product and industry per references/health-scoring-framework.md
  4. 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