customer-success-manager

SaaS customer health scores predict churn risk by applying deterministic, weights-based scoring models across usage, engagement, support, relationship, and commercial signals. This skill processes JSON inputs to generate per-customer health scores and Green/Yellow/Red tier classifications.

Expansion opportunity scoring analyzes adoption depth, whitespace, and revenue potential to prioritize growth across enterprise, mid-market, and SMB segments. It evaluates multi-dimensional signals to quantify revenue potential and surface actionable account expansion targets.

Churn risk analysis runs via a production-grade Python CLI requiring only the standard library with no external dependencies. The tool accepts JSON inputs, detects risk signals, and outputs both human-readable and JSON formats with structured metadata.

Intervention playbooks are generated alongside churn risk analysis by detecting specific risk signals within customer data. The CLI tool evaluates support and engagement metrics to output targeted intervention recommendations for at-risk accounts.

Customer health scoring applies across enterprise, mid-market, and SMB segments by evaluating usage, engagement, support, relationship, and commercial signals. The deterministic weights-based model adjusts scoring dimensions to fit each segment's specific characteristics.

SaaS customer churn and expansion analysis requires JSON inputs containing usage, engagement, support, relationship, and commercial signals. The Python CLI tools process this JSON data to output structured metadata in both human-readable and JSON formats.