customer-success-manager

Calculate a multi-dimensional customer health score by processing JSON signals across usage, engagement, support, and relationship dimensions. The framework applies trend analysis and renewal urgency to produce actionable recommendations for SaaS portfolios.

Predict SaaS churn risk by analyzing customer signals with churn_risk_analyzer.py, which evaluates renewal urgency and tiers. It generates prioritized intervention playbooks to help customer success teams plan renewals and prevent revenue loss.

Use expansion_opportunity_scorer.py to score expansion opportunities and estimate revenue across Enterprise, Mid-Market, and SMB segments. The Python CLI tool processes JSON data to output prioritized expansion bets for SaaS portfolios.

Churn risk analysis handles renewal urgency and support trends by evaluating JSON signals across four dimensions. It applies trend analysis to support and engagement metrics, producing tiered playbooks that prioritize at-risk customers.

Format SaaS analytics data as JSON before running health_score_calculator.py. The deterministic CLI tools process JSON signal files, like the included sample_customer_data.json, to generate human-readable text and machine-readable JSON outputs.