This project performs RFM segmentation and churn analysis on e-commerce customer data using Python and Power BI, helping derive actionable insights for marketing strategies.

• Customer Segments by RFM scores
• Churn Insights (retained vs lost customers)
• Top Value Customers filter
• Heatmap of R-F segments with average spending

Built in: Power BI
Data processed with: Python (pandas, sklearn)

• RFM Segmentation (Recency, Frequency, Monetary)
• Churn Labeling (Churned column from Recency)
• Grouping + scoring using quantiles
• Visualization in Power BI

📦 
├── README.md
├── data
  ├── Online Retail.xlsx
  ├── data.csv
├── images
  ├── dashboard_preview.png
├── notebook
  ├── rfm_churn.csv
  ├── rfm_churn_analysis.ipynb
├── powerbi
  ├── RFM_Churn_Report.pbix.pbix

©generated by GitTree

1. Clone the repo
2. Open notebooks/rfm_churn_analysis.ipynb to see Python part
3. Open powerbi/RFM_Churn_Report.pbix in Power BI Desktop
4. Use filters to explore insights