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customer-success-manager

Score customer health, churn risk, and expansion potential from JSON portfolio data.

6|1|Updated Apr 7, 2026
Search Tags:#customer-success#health-score#portfolio-analysis#deterministic-scripts#saas-metrics#churn-risk#expansion-opportunities
One-click install
npx skills add https://github.com/kmshihab7878/claude-code-setup --skill customer-success-manager-kmshihab7878
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-success-manager
Source: https://github.com/kmshihab7878/claude-code-setup/tree/main/skills/customer-success-manager
Command: npx skills add https://github.com/kmshihab7878/claude-code-setup --skill customer-success-manager-kmshihab7878

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

  • Multi-dimensional health scoring to quantify customer health across segments and time.
  • Churn risk tiers with actionable intervention guidance for different risk levels.
  • Expansion opportunity scoring to prioritize upsell, cross-sell, and expansion initiatives across Enterprise, Mid-Market, and SMB.

Quick Start

Run the three Python CLI tools on a JSON dataset to generate health scores, churn risk tiers, and expansion recommendations.

Frequently Asked Questions about customer-success-manager

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate customer health scores and predict SaaS churn risk?▼

Predicting SaaS churn risk requires analyzing customer portfolio data with deterministic Python scripts. These tools evaluate usage, engagement, support, and relationship metrics from a JSON dataset to output structured churn risk tiers and intervention guidance.

How do I identify upsell and expansion opportunities across Enterprise and Mid-Market segments?▼

Identify expansion opportunities across Enterprise, Mid-Market, and SMB segments by applying weighted scoring models to customer portfolio data. This generates structured scores to prioritize upsell, cross-sell, and expansion revenue initiatives.

What data format is needed to analyze customer retention and churn likelihood?▼

Analyzing customer retention and churn likelihood requires a JSON input with defined fields for usage, engagement, support, and relationship metrics. This structured portfolio data allows the Python CLI scripts to evaluate customer health and output risk tiers.

Can I automate customer portfolio analysis for SaaS metrics using Python?▼

Yes, you can automate SaaS customer portfolio analysis using three deterministic Python CLI scripts. These tools process JSON data to automatically output multi-dimensional health scores, churn risk tiers, and expansion recommendations.

What is the best way to score at-risk customers for retention and renewal strategies?▼

The best way to score at-risk customers is by applying multi-dimensional health scoring to SaaS portfolio data. This method quantifies customer health across segments to generate churn risk tiers with actionable intervention guidance for retention and renewal strategies.

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