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rfm-customer-segmentation

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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.

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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