📊 E-commerce Customer Behavior Analysis & Segmentation

🎯 Project Overview

This project explores how customers interact with an online store by analyzing their purchase behavior and transaction patterns over time. It transforms raw e-commerce data into actionable business insights through data cleaning, exploratory analysis, RFM (Recency, Frequency, Monetary) segmentation, and machine learning-based clustering.

The goal is to help e-commerce businesses understand their customers better, identify valuable segments, and make data-driven decisions to improve marketing strategies, boost retention, and maximize revenue.

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🚀 Key Objectives

• 📈 Understand Customer Patterns: Analyze buying behavior, order frequency, and spending habits
• 🎯 Segment Customers: Group customers into meaningful segments using RFM analysis and clustering
• 💡 Generate Insights: Identify high-value customers, at-risk customers, and new customer opportunities
• 📊 Actionable Recommendations: Provide marketing and retention strategies for each customer segment
• 🔮 Predictive Analytics: Build models to predict customer lifetime value and churn risk

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🛠️ Tech Stack

Language & Environment:

• Python 3.8+
• Jupyter Notebook / JupyterLab

Core Libraries:

• Data Manipulation: pandas, numpy
• Visualization: matplotlib, seaborn, plotly
• Machine Learning: scikit-learn
• Statistical Analysis: scipy

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📁 Project Structure

E-commerce-Customer-Behavior-Analysis-Segmentation/
│
├── data/
│   ├── raw/                    # Original dataset files
│   └── processed/              # Cleaned and feature-engineered data
│
├── notebooks/
│   ├── 01_data_loading_cleaning.ipynb
│   ├── 02_eda_customer_behavior.ipynb
│   ├── 03_rfm_feature_engineering.ipynb
│   ├── 04_customer_segmentation.ipynb
│   └── 05_insights_recommendations.ipynb
│
├── src/
│   ├── data_processing.py      # Data cleaning utilities
│   ├── rfm_calculator.py       # RFM metrics calculation
│   └── visualization.py        # Custom plotting functions
│
├── reports/
│   ├── figures/                # Generated plots and charts
│   └── summary.md              # Final analysis summary
│
├── requirements.txt            # Python dependencies
├── LICENSE                     # MIT License
└── README.md                   # This file

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📊 Workflow & Methodology

1️⃣ Data Collection & Cleaning

• Load raw e-commerce transaction data
• Handle missing values, duplicates, and data type conversions
• Validate data integrity and consistency

2️⃣ Exploratory Data Analysis (EDA)

• Analyze customer demographics and purchase patterns
• Visualize distributions of orders, revenue, and products
• Identify trends and anomalies in the data

3️⃣ Feature Engineering

• Create customer-level aggregated metrics
• Calculate RFM scores:
  • Recency: Days since last purchase
  • Frequency: Total number of orders
  • Monetary: Total amount spent

4️⃣ Customer Segmentation

• Apply K-Means clustering on RFM features
• Identify optimal number of clusters using Elbow Method and Silhouette Score
• Label segments with business-friendly names

5️⃣ Insights & Recommendations

• Analyze characteristics of each segment
• Provide targeted marketing strategies
• Calculate segment revenue contribution

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🎯 Expected Customer Segments

Segment	Description	Marketing Strategy
🏆 Champions	High RFM scores - best customers	VIP rewards, early access, personalized offers
💎 Loyal Customers	High frequency, regular purchases	Loyalty programs, referral incentives
🌱 Potential Loyalists	Recent buyers with potential	Engagement campaigns, product recommendations
⚠️ At Risk	Previously active, now declining	Win-back campaigns, special discounts
🆕 New Customers	Recent first-time buyers	Onboarding emails, welcome offers
💤 Hibernating	Low recency and frequency	Re-engagement campaigns, surveys

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📈 Key Results

🔄 In Progress - Results will be updated as the project progresses through each phase.

Preliminary Findings:

• Total customers analyzed: TBD
• Number of segments identified: TBD
• Revenue contribution by segment: TBD
• Retention rate insights: TBD

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🚀 How to Run This Project

Prerequisites

# Python 3.8 or higher
python --version

# Install pip if not already installed
pip --version

Installation

1. Clone the repository

git clone https://github.com/13-Prabhat/E-commerce-Customer-Behavior-Analysis-Segmentation.git
cd E-commerce-Customer-Behavior-Analysis-Segmentation

2. Create a virtual environment (optional but recommended)

python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Launch Jupyter Notebook

jupyter notebook

5. Run notebooks in sequence

• Start with 01_data_loading_cleaning.ipynb
• Follow the numbered order for best results

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📊 Dataset

This project uses the Online Retail Dataset containing transaction data from a UK-based online retailer.

Source: UCI Machine Learning Repository - Online Retail Dataset

Features:

• InvoiceNo: Transaction ID
• StockCode: Product code
• Description: Product description
• Quantity: Number of items
• InvoiceDate: Transaction date and time
• UnitPrice: Price per unit
• CustomerID: Unique customer identifier
• Country: Customer location

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🤝 Contributing

Contributions are welcome! If you'd like to improve this project:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Prabhat

• GitHub: @13-Prabhat
• Project Link: E-commerce Customer Behavior Analysis

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🎓 Learning Outcomes

This project demonstrates:

• ✅ Data cleaning and preprocessing techniques
• ✅ Exploratory data analysis and visualization
• ✅ Feature engineering for customer analytics
• ✅ Unsupervised machine learning (clustering)
• ✅ Business intelligence and actionable insights
• ✅ Professional documentation and version control

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⭐ If you find this project helpful, please give it a star!

Made with ❤️ by Prabhat