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tomtoto757
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customer-analytics

Analyze customer order histories to detect churn risk and segment customers by RFM scoring.

14|3|Updated Mar 22, 2026
Search Tags:#churn-prediction#cohort-analysis#rfm#customer-segmentation#clv#customer-retention#purchase-frequency
One-click install
npx skills add https://github.com/tomtoto757/ecomm-ai-skills-hub --skill customer-analytics-tomtoto757
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-analytics
Source: https://github.com/tomtoto757/ecomm-ai-skills-hub/tree/main/skills/analytics-reporting/finsilabs/data-analytics/customer-analytics
Command: npx skills add https://github.com/tomtoto757/ecomm-ai-skills-hub --skill customer-analytics-tomtoto757

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Convert raw order and customer records into actionable retention intelligence so marketing and customer success teams can identify loyal customers, flag at-risk users, and prioritize interventions that improve lifetime value.

Core Features & Use Cases

  • RFM Scoring & Segmentation: Compute recency, frequency, and monetary scores to classify customers (champions, at_risk, lost, etc.) for targeted campaigns.
  • Cohort & Retention Analysis: Produce monthly cohort matrices and second-purchase rate metrics to track retention over time and by acquisition channel.
  • Churn Prediction & Actions: Estimate per-customer churn risk using differentiated logic for single-purchase vs repeat customers and recommend actions (win-back email, personal outreach).
  • Integrations & Exports: Supports platform-specific workflows (Shopify/Klaviyo, WooCommerce/Metorik, BigCommerce/Glew) and SQL/CSV exports for data warehouses and marketing tools.

Quick Start

Analyze the last 12 months of order data to compute RFM scores, identify at-risk customers, generate a cohort retention matrix, and export a prioritized list for win-back campaigns.

Frequently Asked Questions about customer-analytics

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

FAQPage Schema
How do I predict customer churn using e-commerce order history data?▼

To predict customer churn, this Skill analyzes cleaned order histories to estimate per-customer churn risk using differentiated logic for single-purchase versus repeat buyers, outputting prioritized risk scores for win-back campaigns.

What is RFM scoring and how does it segment customers for retention campaigns?▼

RFM scoring computes recency, frequency, and monetary metrics from order data to classify customers into segments like champions, at_risk, and lost, enabling targeted marketing and retention interventions.

Can I use this for cohort retention analysis on Shopify or WooCommerce?▼

Yes, this Skill supports Shopify, WooCommerce, and BigCommerce platforms to generate monthly cohort matrices and track retention metrics like second-purchase rates over time.

Do I need to clean my order data before calculating customer lifetime value and churn?▼

Yes, you must provide cleaned order and customer data that explicitly excludes cancelled and refunded orders to ensure accurate CLV calculations and churn risk scoring.

How do I export at-risk customer lists for Klaviyo or Metorik integrations?▼

You can export per-customer risk scores and segment lists via SQL or CSV formats, supporting platform-specific workflows for Klaviyo, Metorik, and data warehouses to execute win-back emails.

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