Does this skill support Chinese language data?

Yes, it is specifically optimized for Chinese e-commerce data formats, including field names and visualization labels.

How are customer segments determined?

It uses the K-means clustering algorithm on RFM dimensions and automatically applies the elbow method to find the optimal number of segments.

What types of files does the analysis output?

It generates segmented CSV files, a ranked VIP list, summary statistics, and a PNG visualization dashboard.

What data is required for the RFM analysis?

The skill requires e-commerce transaction data containing user IDs, purchase dates, item quantities, and unit prices.

Can I use this for non-retail data?

While optimized for e-commerce, it can be used for any transactional data that involves identifiable users, transaction dates, and monetary values.

RFM Customer Segmentation Analyst

Name: RFM Customer Segmentation Analyst
Author: liangdabiao

byliangdabiao

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Data Science & ML

Performs automated RFM analysis and K-means clustering on e-commerce transaction data to identify and rank high-value customer segments.

This skill streamlines the process of customer value analysis by processing raw e-commerce transaction data into actionable marketing insights. It automatically handles data cleaning, calculates Recency, Frequency, and Monetary metrics, and applies intelligent K-means clustering to segment customers into tiers like VIP, high-value, and at-risk. With built-in support for Chinese language fields and automated dashboard generation, it's designed for marketers and data analysts who need to quickly identify top customers and develop targeted activation strategies without manual data wrangling or complex statistical programming.

Key Features

01Native support for Chinese e-commerce data formats and field names

02Automated RFM metric calculation (Recency, Frequency, Monetary)

0378 GitHub stars

04Comprehensive visual dashboards and scatter plot generation

05Intelligent K-means clustering with elbow method optimization

06Exportable VIP customer lists and statistical summary reports

Use Cases

01Generating revenue contribution reports by customer segment to optimize marketing spend

02Identifying the top 5% most valuable customers for exclusive loyalty programs

03Detecting at-risk customer segments to initiate targeted re-activation campaigns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add liangdabiao/claude-data-analysis-ultra-main rfm-customer-segmentation

For use in Claude.ai and ChatGPT

Download Skill