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customer-success-manager

Analyze customer success metrics to provide health scores and churn risk assessments.

Updated Mar 4, 2026
Search Tags:#account management#SaaS metrics#customer success#customer health#churn prediction#expansion revenue
One-click install
npx skills add https://github.com/Tonybleything76/more-claude-skills --skill customer-success-manager-tonybleything76
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-success-manager
Source: https://github.com/Tonybleything76/more-claude-skills/tree/main/business-growth/customer-success-manager
Command: npx skills add https://github.com/Tonybleything76/more-claude-skills --skill customer-success-manager-tonybleything76

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps customer success teams proactively manage their portfolio by identifying at-risk accounts, predicting churn, and uncovering expansion opportunities.

Core Features & Use Cases

  • Customer Health Scoring: Continuously monitors customer health across usage, engagement, support, and relationship dimensions.
  • Churn Risk Analysis: Predicts the likelihood of churn based on behavioral signals and commercial factors.
  • Expansion Opportunity Scoring: Identifies upsell, cross-sell, and expansion potential within the existing customer base.
  • Use Case: A CSM can use this Skill to get a daily health score for all their accounts, flag those trending towards 'Red', and then drill down into the specific reasons and recommended actions to prevent churn or identify upsell opportunities.

Quick Start

Analyze the customer health for the provided data.

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 to predict SaaS churn risk?▼

Customer health scores are calculated by analyzing SaaS usage patterns, engagement levels, support interactions, and commercial factors to predict churn risk and identify at-risk accounts for proactive retention strategies.

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

Expansion revenue opportunities are identified by scoring upsell and cross-sell potential using deterministic Python analysis of customer data, evaluating behavioral signals and commercial factors to drive growth.

What data sources are needed to perform churn prediction analysis?▼

Churn prediction analysis requires customer data covering usage patterns, engagement levels, support interactions, and commercial factors to accurately assess the likelihood of customer churn.

Can I use Python scripts for deterministic account management analysis?▼

Yes, this approach utilizes Python scripts for deterministic analysis of customer data, enabling automated customer health scoring and churn risk assessments across your account portfolio.

What is the best way to monitor SaaS metrics for customer retention?▼

The best way to monitor SaaS metrics for retention is continuously tracking customer health across usage, engagement, support, and relationship dimensions to flag accounts trending towards risk.

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