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