Customer-Segmentation-with-RFM-Analysis

🥈Silver

Context A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', i

1830Updated 2mo ago

Intermediate30min to implementmarketing

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/mathchi/Customer-Segmentation-with-RFM-Analysis.git

Works with:

Claude

Overview

About This Skill

This skill implements RFM (Recency, Frequency, Monetary) analysis on a real-world UK online retail dataset spanning 2009–2011. It uses transaction data including invoice numbers, product codes, quantities, prices, and customer IDs to segment customers into meaningful groups. The dataset contains over two years of transactions from a non-store online retailer selling all-occasion gift-ware, with many wholesale customers. RFM analysis helps retailers identify high-value customers, at-risk segments, and optimize marketing spend. The skill is grounded in academic research on customer profitability prediction and data mining for retail.

Use Cases

Identify high-value customer segments for targeted retention campaigns

Detect at-risk customers with declining purchase recency or frequency

Allocate marketing budgets to the most profitable customer groups

Analyze wholesale vs. retail customer behavior patterns

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/mathchi/Customer-Segmentation-with-RFM-Analysis

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the Online Retail II dataset to perform customer segmentation using RFM (Recency, Frequency, Monetary) analysis. The dataset contains transactions from [COMPANY], a UK-based online retailer, between 2009 and 2011. The company sells unique gift-ware, with many wholesalers as customers. Focus on identifying high-value customer segments for targeted marketing campaigns. Provide actionable insights based on the RFM scores.

Example Output

# Customer Segmentation with RFM Analysis

## Key Insights

- **High-Value Customers**: 15% of customers contribute to 60% of total revenue.
- **Churn Risk**: 20% of customers have not made a purchase in the last 6 months.
- **Potential Loyalists**: 10% of customers show increasing purchase frequency and monetary value.

## Customer Segments

### Champions (High Recency, High Frequency, High Monetary)
- **Count**: 1,200 customers
- **Revenue Contribution**: 35%
- **Action**: Reward with exclusive offers and loyalty programs.

### At Risk (Low Recency, High Frequency, High Monetary)
- **Count**: 800 customers
- **Revenue Contribution**: 20%
- **Action**: Win-back campaigns with personalized discounts.

### Hibernating (Low Recency, Low Frequency, High Monetary)
- **Count**: 500 customers
- **Revenue Contribution**: 10%
- **Action**: Reactivation campaigns with special promotions.

## Recommendations

- Focus on retaining and reactivating high-value segments.
- Implement targeted marketing campaigns for each segment.
- Monitor RFM scores quarterly to track changes in customer behavior.