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customer-success-manager

Analyze customer health, predict churn risk, and score expansion opportunities with Python CLI tools.

Updated Mar 12, 2026
Search Tags:#account management#SaaS metrics#customer success#health scoring#churn prediction#revenue expansion
One-click install
npx skills add https://github.com/Fantasia1999/claude-skills-zh --skill customer-success-manager-fantasia1999
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-success-manager
Source: https://github.com/Fantasia1999/claude-skills-zh/tree/main/translations/business-growth/customer-success-manager
Command: npx skills add https://github.com/Fantasia1999/claude-skills-zh --skill customer-success-manager-fantasia1999

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive suite of tools to proactively manage customer health, predict churn, and identify expansion opportunities, ensuring customer satisfaction and driving revenue growth.

Core Features & Use Cases

  • Customer Health Scoring: Quantifies customer health across multiple dimensions (Usage, Engagement, Support, Relationship).
  • Churn Risk Prediction: Identifies at-risk accounts based on behavioral and commercial signals.
  • Expansion Opportunity Identification: Pinpoints upsell, cross-sell, and expansion potential within the customer base.
  • Use Case: A Customer Success Manager can use these tools to analyze their entire portfolio, flag accounts needing immediate attention, and prioritize outreach for potential revenue expansion.

Quick Start

Run the health score calculator for customer data in sample_customer_data.json.

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 using SaaS metrics?▼

The Skill identifies churn risk by processing customer account data and reviewing behavioral and commercial retention signals. It flags at-risk accounts needing immediate attention across enterprise, mid-market, and SMB segments.

How do I identify revenue expansion opportunities in my customer base?▼

Predicting churn risk involves analyzing customer account data for behavioral and commercial signals that indicate at-risk accounts. The Python tools review these retention metrics to flag accounts needing immediate attention.

Can I analyze customer health scores for enterprise, mid-market, and SMB segments?▼

Yes, you can analyze customer health scores across enterprise, mid-market, and SMB segments. The tools process segment-specific account data to score at-risk accounts and identify expansion opportunities for each tier.

Do I need external API calls to run churn prediction on customer data?▼

No, you do not need external API calls to run churn prediction. The tools operate using standard Python libraries without external dependencies, processing your local customer data deterministically.

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