customer-success-managerlisted

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.
Elfredaaroused655/claude-skills · ★ 2 · AI & Automation · score 78

Install: claude install-skill Elfredaaroused655/claude-skills

# 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. --- ## 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) --- ## 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