customer-success-manager

$28

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

• Combine signals: Use all three scripts together for a complete customer picture

• Act on trends, not snapshots: A declining Green is more urgent than a stable Yellow

• Calibrate thresholds: Adjust segment benchmarks based on your product and industry per references/health-scoring-framework.md

• 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