customer-success-manager

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.

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Table of Contents

• [Input Requirements](#input-requirements)
• [Output Formats](#output-formats)
• [How to Use](#how-to-use)
• [Scripts](#scripts)
• [Reference Guides](#reference-guides)
• [Templates](#templates)
• [Best Practices](#best-practices)
• [Limitations](#limitations)

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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).

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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

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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

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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

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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 |

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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 |

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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

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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

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Last Updated: February 2026

Tools: 3 Python CLI tools

Dependencies: Python 3.7+ standard library only