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
• vipcustomerslist.csv: Ranked VIP customer list for marketing
• segmentsummarystatistics.csv: Detailed statistics by segment
• customersegmentationdashboard.png: Visual analytics dashboard
• datavalidationreport.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