customer-success-manager

SaaS customer health scores are calculated by applying fixed dimension weights to usage, engagement, support, and relationship signals. This Skill processes a JSON file containing a customers array and outputs deterministic risk scores using Python standard library only.

Churn risk and expansion opportunities are identified by applying renewal-urgency multipliers and weighted scoring models to customer data. The analysis covers enterprise, mid-market, and SMB portfolios, producing tiered intervention playbooks and upsell recommendations.

Yes, expansion opportunity scoring is handled by a Python script that evaluates account data to identify upsell, cross-sell, and seat or department expansions. It requires only the Python standard library and reads input from a JSON file with a customers array.

No external dependencies are required. The Skill relies entirely on the Python standard library to analyze customer data, generate health scores, and produce both JSON and text reports containing churn risk analysis and expansion recommendations.

Churn risk analyzers generate intervention playbooks by applying renewal-urgency multipliers to weighted customer health dimensions. The resulting playbooks are tiered based on risk severity and output alongside deterministic health scores in JSON and text report formats.

Customer data must be structured as a JSON file containing a customers array. The Skill reads this input to evaluate usage, engagement, support, and relationship signals, then outputs health scores, churn risk assessments, and expansion recommendations in JSON and text formats.