MCPInstall

rfm-customer-segmentation

by liangdabiaoMarketing & SEO

Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.

Installs
62
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill rfm-customer-segmentation
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads rfm-customer-segmentation automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

RFM Customer Segmentation Analysis

A comprehensive customer segmentation skill that automatically analyzes e-commerce transaction data to identify customer value segments using RFM (Recency, Frequency, Monetary) analysis with K-means clustering.

Instructions

1. Data Analysis

When users provide e-commerce data or ask about customer segmentation:

  • Load and validate the transaction data
  • Clean data by removing invalid orders (negative quantities, zero prices)
  • Calculate RFM metrics for each customer:
    • Recency: Days since last purchase
    • Frequency: Number of purchases
    • Monetary: Total purchase amount
  • Use K-means clustering on RFM dimensions
  • Automatically determine optimal number of clusters using elbow method

2. Customer Segmentation

  • Create customer value segments: High, Medium, Low value customers
  • Score each customer on RFM dimensions (1-3 scale)
  • Calculate overall customer value scores
  • Identify and rank VIP customers for marketing campaigns

3. Visualization and Reporting

  • Generate comprehensive customer segmentation dashboard
  • Create pie charts for segment distribution and revenue share
  • Build RFM scatter plots to visualize customer patterns
  • Generate box plots showing value distribution by segment
  • Export detailed CSV reports with VIP customer lists

4. Marketing Insights

  • Provide actionable marketing recommendations for each segment
  • Generate executive summary with key findings
  • Create customer activation strategies for different value tiers
  • Export VIP customer lists for targeted marketing campaigns

Usage Examples

Basic Customer Segmentation

Analyze these e-commerce orders and segment customers by value:
[CSV data with order_id, user_id, purchase_date, quantity, unit_price]

VIP Customer Identification

Find the top 100 most valuable customers from our sales data for marketing campaign

Customer Value Analysis

Create a customer segmentation report showing revenue contribution by customer segment

Key Features

  • Automatic Data Cleaning: Handles Chinese e-commerce data formats, removes invalid orders
  • Intelligent Clustering: Uses elbow method to determine optimal cluster count
  • Chinese Language Support: Full support for Chinese field names and visualizations
  • Comprehensive Reports: Generates HTML reports, PNG dashboards, and CSV exports
  • Marketing Ready: Provides VIP customer lists and actionable insights

File Requirements

The skill works with e-commerce transaction data containing:

  • user_id: Customer identification code (用户码)
  • order_date: Purchase date (消费日期)
  • quantity: Order quantity (数量)
  • unit_price: Item unit price (单价)
  • product_info: Product details (optional)

Output Files Generated

  • customer_segments.csv: Complete customer segmentation data
  • vip_customers_list.csv: Ranked VIP customer list for marketing
  • segment_summary_statistics.csv: Detailed statistics by segment
  • customer_segmentation_dashboard.png: Visual analytics dashboard
  • data_validation_report.txt: Data quality and analysis validation

Dependencies

  • pandas, numpy for data processing
  • scikit-learn for K-means clustering
  • matplotlib, seaborn for visualization (with Chinese font support)
  • Standard Python libraries for file operations

Best Practices

  • Ensure date fields are in consistent format (YYYY-MM-DD recommended)
  • Remove or handle missing values before analysis
  • Use sufficient data volume (1000+ orders recommended for reliable clustering)
  • Consider business context when interpreting segment results
  • Validate results with domain knowledge when possible

FAQ

What does the rfm-customer-segmentation skill do?

Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.

How do I install rfm-customer-segmentation?

Run npx skills add https://github.com/liangdabiao/claude-data-analysis-ultra-main --skill rfm-customer-segmentation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rfm-customer-segmentation/ (personal) or .claude/skills/rfm-customer-segmentation/ (project).

Which AI agents can use rfm-customer-segmentation?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is rfm-customer-segmentation safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains rfm-customer-segmentation?

It is published by liangdabiao in liangdabiao/claude-data-analysis-ultra-main. It has 62 recorded installs.

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