Customer Segmentation Using RFM Analysis

This project demonstrates how to apply RFM (Recency, Frequency, Monetary) analysis for customer segmentation using order transaction data. The objective is to understand customer purchasing behaviors and group them into segments that can be targeted with personalized marketing strategies.

📊 Project Objectives

• Calculate Recency, Frequency, and Monetary metrics for each customer.
• Assign RFM scores based on business rules.
• Segment customers based on RFM scores.
• Provide actionable insights to improve customer engagement and retention.

📁 Dataset

The dataset contains customer order information including:

• Customer ID
• Frequency of purchases
• Recency (days since last purchase)
• Total monetary value of purchases

⚙️ Tools Used

• Python (pandas, numpy)
• Microsoft Excel
• RFM scoring logic
• Documentation in Word format

🔍 Methodology

1. Preprocessed the transaction data to extract R, F, and M metrics.
2. Applied percentile-based segmentation to assign scores (1-3 scale).
3. Calculated the RFM score by combining individual scores.
4. Segmented customers into groups (e.g., high value, loyal, at-risk).
5. Derived key business insights.

🧠 Key Insights

• 33% of customers purchased within the last 49 days.
• Customers with RFM scores like 333, 331, etc. are highly engaged and valuable.
• Customers with scores like 111, 112 may need win-back strategies.

📌 Project Files

• Orders.xlsx – Source dataset with RFM calculation.
• RFM_Analysis_Project_Documentation.docx – Full project report.
• README.md – Project overview and instructions.

📈 Potential Extensions

• Add customer segmentation visualizations (bar charts, heatmaps).
• Build dashboards using Tableau or Power BI.
• Deploy customer segmentation model using Streamlit or Flask.

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Author: Ayan Jawaid