🛒 Customer Behavior Analysis Project

This project aims to explore customer behavior patterns and purchasing trends using a structured e-commerce dataset. Through data cleaning, visualization, and statistical analysis, the goal is to extract actionable insights that can guide business decisions around customer satisfaction, sales strategy, and operational improvements.

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📊 Dataset Overview

The dataset contains customer-level transaction and behavior data with the following key features:

• Demographics: Age, Gender, Location
• Purchase Behavior: Product Category, Purchase Amount, Number of Items, Discount Usage
• Website Interaction: Time Spent on Website, Device Type
• Order Fulfillment: Delivery Time, Payment Method
• Customer Attributes: Return Customer Status, Subscription Status
• Feedback Metrics: Review Score (1–5), Customer Satisfaction (Target Variable)

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🧠 Objectives

• Identify key drivers of customer satisfaction
• Analyze the effect of delivery time, payment methods, and product categories
• Segment customers by location, return status, and subscription
• Recommend actionable strategies to boost retention, loyalty, and revenue

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📌 Major Insights

1. Fast delivery improves customer satisfaction significantly.
2. Returning customers are more loyal and spend more.
3. Toys, Books, and Home Essentials generate the highest revenue.
4. Cash on Delivery users report higher satisfaction despite lower usage.
5. Purchase behavior varies greatly across locations, especially in urban areas.
6. Subscribed customers show higher spending and satisfaction.
7. Device type and gender influence browsing and purchase behavior.
8. Discount usage increases purchase volume and satisfaction.

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📁 Project Structure

📦 Customer-Behavior-Analysis

│

├── 📊 data/

│ └── customer_data.csv

│

├── 📓 notebooks/

│ └── EDA_and_Insights.ipynb

│

├── 📈 visuals/

│ └── plots, bar charts, pie charts, etc.

│

├── 📄 README.md

└── requirements.txt

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⚙️ Technologies Used

• Python (Pandas, NumPy)
• Matplotlib, Seaborn
• Jupyter Notebook
• Scikit-learn (for further modeling, if needed)

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/Customer-Behavior-Analysis.git
   cd Customer-Behavior-Analysis

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # For Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Launch the notebook:

   jupyter notebook notebooks/EDA_and_Insights.ipynb
   

📌 Future Work

• Predict customer satisfaction using machine learning.

• Add clustering for customer segmentation.

• Deploy dashboards using Streamlit or Dash.