AgentSkillsCN

rfm-customer-segmentation

对电商数据进行 RFM（最近性、频率、货币价值）客户细分分析。在需要分析客户价值、识别 VIP 客户或创建营销细分时使用。自动清理数据，计算 RFM 指标，应用 K-means 聚类，并生成支持中文语言的可视化报告。

SKILL.md
--- frontmatter
name: rfm-customer-segmentation
description: 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.
allowed-tools: Read, Write, Bash, Glob

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

code
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

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

Customer Value Analysis

code
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