Segment customers based on purchasing behavior using RFM (Recency, Frequency, Monetary) analysis to identify high-value users, detect churn risk, and improve retention strategies.
- 📊 Dataset: Olist E-commerce (Brazil)
- 🛠 Tools: SQL, Python, Tableau
- 🎯 Technique: RFM Segmentation
- 📈 Goal: Customer segmentation & revenue optimization
| Folder / File | Content |
|---|---|
| 📁 Pandas | RFM.py - Python EDA> |
| 📁 dashboard | RFM.png & rfm_python.png — Tableau visual assets. |
| 📁 data | RFM.csv & RFM Segmentation.csv — Insights generated from SQL queries. |
| 📁 insights | insights.md — Detailed business analysis summary. |
| 📁 sql | RFM.sql & RFM Segmentation Analysis.sql — Technical documentation and script logic. |
| 📄 README.md | Project overview and navigation. |
| ⚖️ LICENSE | MIT License information. |
| Segment | Customers | Revenue | Revenue % |
|---|---|---|---|
| 🟢 Loyal | 41,740 | 5.05M | 32.12% |
| 🔵 Others | 25,402 | 4.09M | 26.00% |
| 🟡 Champions | 13,354 | 4.09M | 25.98% |
| 🔴 Lost | 12,593 | 2.07M | 13.18% |
| 🟠 At Risk | 1,900 | 0.43M | 2.72% |
- Loyal customers form the largest segment and contribute the highest revenue share
- Champions generate high revenue despite a smaller customer base
- Revenue is heavily concentrated among repeat customers
- Lost customers contribute a significant portion of revenue, indicating churn impact
- At-Risk customers represent early churn signals and require timely intervention
- Retain high-value customers (Champions) through personalized engagement
- Increase revenue from Loyal customers via upselling and cross-selling
- Re-engage At-Risk users before churn
- Run win-back campaigns for Lost customers
Dataset not included due to size limitations.
Source: https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce
Mohammad Ammar
Aspiring Data Analyst | SQL | Tableau
- E-commerce Sales Analysis : https://github.com/theammarngp-makes/olist-sales-analysis
- Cohort Retention Analysis : https://github.com/theammarngp-makes/E-commerce-cohort-retention-analysis
This project demonstrates the ability to apply customer segmentation techniques to derive actionable insights for improving retention, revenue, and marketing strategies.