RFM (Recency, Frequency, Monetary) Analysis - Segment 8000+ customer purchases to identify your best customers and drive targeted business strategies.
This project implements RFM segmentation logic to analyze customer behavior from 8,000+ purchase records. It automatically scores and categorizes customers based on:
- Recency: How recently they made a purchase
- Frequency: How often they purchase
- Monetary: How much they spend
Use these insights to optimize marketing campaigns, customer retention, and revenue optimization.
| Metric | Definition |
|---|---|
| Recency (R) | Days since last purchase (lower is better) |
| Frequency (F) | Number of purchases in period (higher is better) |
| Monetary (M) | Total amount spent (higher is better) |
Scoring: Each metric is divided into 5 quintiles (scores 1-5), then combined into an RFM score.
✅ Automated RFM Scoring - Calculate R, F, M scores from raw transaction data
✅ Customer Segmentation - Group customers into actionable segments
✅ Data Visualization - Charts and insights in Excel outputs
✅ Scalable Pipeline - Processes 8,000+ transactions efficiently
✅ Export Ready - Results in structured Excel formats
Python 3.7+
pandas
openpyxl
numpy