Customer Segmentation Using RFM Analysis in E-Commerce

📌 Project Overview

This project applies RFM (Recency, Frequency, Monetary) analysis to an e-commerce dataset to segment customers based on their purchasing behavior. The goal is to identify high-value customers, at-risk segments, and optimize marketing strategies using data-driven insights.

🛠️ Tools & Technologies Used

• Python 🐍 (Data analysis, RFM calculations)
• Jupyter Notebook 📓 (Exploratory analysis, data processing)
• Pandas, Matplotlib, Seaborn 📊 (Data manipulation & visualization)
• Power BI 📈 (Dashboard creation & insights visualization)
• Git & GitHub 🗂️ (Version control, repository management)

📂 Dataset

Source

• The dataset is sourced from UC Irvine ML Repository's Online Retail Dataset.
• It is licensed under CC BY 4.0, allowing modifications and redistribution with attribution.
• Citation: Chen, D. (2015). Online Retail Dataset. UCI Machine Learning Repository. https://doi.org/10.24432/C5BW33..

Details

• This is a transactional dataset which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
• Key columns include Invoice Number, Stock Code, Description, Quantity, Invoice Date, Unit Price, Customer ID, and Country.
• The data is cleaned and preprocessed before performing RFM analysis.

🛠 Methodology

1. Data Preprocessing:
   • Handling missing values, duplicates, and formatting date fields.
   • Removing non-product stockcodes and cancelled orders.
2. RFM Score Calculation:
   • Recency (R): Days since last purchase.
   • Frequency (F): Number of purchases made.
   • Monetary (M): Total amount spent.
3. Customer Segmentation: Assigning RFM scores and categorizing customers into segments.
4. Pareto Principle Application:
   • Identifying that 27% of customers contribute to 80% of sales.
   • Identifying that 21% of products contribute to 80% of sales.
5. Dashboard Creation: Visualizing insights with plots and charts.

📊 Visualizations & Dashboard

• Analyzing Customer Trends: Patterns and Insights Over Time.
• Heatmap: Correlation between R, F, and M scores.
• Customer Segmentation: Distribution of customer groups.
• Dashboard: Interactive representation of key insights.

📡 Presentation

You can view the project presentation here.

📌 Key Insights

• A small percentage of customers contribute to the majority of sales (Pareto 80/20 Rule).
• High-value customers can be targeted with personalized offers to increase retention.
• At-risk customers can be re-engaged with special incentives.

💡 Future Improvements

• Implement machine learning for customer segmentation to enhance the accuracy of grouping customers.
• Analyze cancelled orders to gain deeper insights into customer dissatisfaction and potential improvements.
• Expand segmentation using demographic and behavioral data for more personalized marketing strategies.

📝 Author

👤 Reet Chandra
📧 reetphy@gmail.com
🔗 LinkedIn

⭐ Contributing

Feel free to open issues or submit pull requests to improve this project!

📜 License

This project is licensed under the Creative Commons (CC BY 4.0).