🚀 Customer Segementation Using RFM(Recency, Frequency, Monetary)

A complete end-to-end customer segmentation case study using RFM analysis, from raw data cleaning and star-schema design to Python automation and interactive Tableau dashboards.

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🔍 Overview

• Business Goal: Identify high-value and at-risk customers to boost retention and revenue.
• Data: 38 k customers, €43 M sales, multiple European markets.
• Approach:
  1. EDA & Cleaning – handle missing CustomerIDs, shipping costs, warehouse locations
  2. Star Schema – build fact & dimension tables for fast analytics
  3. RFM Scoring – recency, frequency, monetary quantiles → composite score
  4. Segmentation – Champions, Loyal, Regular, At-Risk, Lost
  5. Visualization – dynamic Tableau dashboard + key screenshots
  6. Business Impact – +20 % Q4 performance, targeted retention campaigns

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📁 Repo Structure

├── notebooks/ # 01_EDA_and_Cleaning.ipynb │ # 02_RFM_Scoring_and_Segmentation.ipynb ├── data/ # raw → processed → final CSVs ├── tableau/ # Retail_RFM_Dashboard.twbx ├── screenshots/ # annotated dashboard PNGs ├── presentation/ # Case_Study.pptx / .pdf └── README.md # This file

🎯 Results & Next Steps

• Key Finding: 0.6 % of customers (“Champions”) drive 9 % of revenue; 44 % are “Lost” yet contribute only 14 %.
• Action: Tailored campaigns for “Regular” → “Loyal” uplift; churn-risk mitigation for “At-Risk.”
• Future: Predictive churn modeling, real-time segmentation updates, automated reporting.