customer-analytics

Name: customer-analytics
Availability: InStock
Author: finsilabs

Analyzes customer behavior with RFM scoring, churn prediction, and cohort analysis.

44|7|Updated Mar 12, 2026

Search Tags:#retention #segmentation #cohort analysis #rfm #customer analytics #churn prediction

Ready to run

npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill customer-analytics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: customer-analytics
Source: https://github.com/finsilabs/awesome-ecommerce-skills/tree/main/skills/data-analytics/customer-analytics
Command: npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill customer-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill transforms raw customer order data into actionable insights, helping you understand customer behavior, predict churn, and improve retention strategies.

Core Features & Use Cases

RFM Scoring: Segment customers based on Recency, Frequency, and Monetary value.
Churn Prediction: Identify customers at risk of leaving.
Purchase Frequency Analysis: Understand how often customers buy.
Cohort Analysis: Track customer retention over time.
Use Case: Identify your "Champions" (recent, frequent, high-spending customers) and create a VIP program for them, while simultaneously targeting "At Risk" customers with win-back campaigns.

Quick Start

Use the customer-analytics skill to analyze customer purchase frequency and identify at-risk customers.

Frequently Asked Questions about customer-analytics

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

FAQPage Schema

How do I use RFM scoring to segment customers for targeted marketing?▼

RFM scoring segments customers by evaluating their Recency, Frequency, and Monetary value. This process identifies actionable groups like Champions or At Risk customers, enabling targeted marketing campaigns and customer success interventions.

Can I analyze customer churn prediction data from Shopify and WooCommerce?▼

Yes, customer churn prediction works with platforms like Shopify, WooCommerce, and BigCommerce. It analyzes purchase behavior to identify at-risk customers, and also supports data-first approaches via data warehouses.

What is the best way to track customer retention over time using cohort analysis?▼

Cohort analysis tracks customer retention by grouping buyers based on their initial purchase date. This reveals behavioral patterns over time, helping optimize retention strategies by comparing how different cohorts maintain purchase frequency.

How do I predict which customers are at risk of churning from purchase history?▼

Churn prediction analyzes purchase frequency tracking and historical order data to identify customers likely to leave. It flags at-risk segments so you can deploy targeted win-back campaigns before they stop buying.

Do I need a data warehouse to analyze customer behavior and purchase frequency?▼

No, a data warehouse is optional. You can analyze customer behavior and purchase frequency directly from e-commerce platforms like Shopify or BigCommerce, but the skill also supports data-first approaches via data warehouses.