🛍️ Customer Shopping Behavior Analysis

An end-to-end Data Analytics project analyzing customer purchasing patterns using Python, SQL, and Power BI.

The project transforms raw customer transaction data into business insights through data preparation, SQL-based analysis, and interactive dashboard visualization.

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🎯 Project Objective

The objective of this project is to understand customer purchasing behavior, revenue patterns, product performance, and customer segments and translate the findings into actionable business insights.

Key objectives

• Analyze customer purchasing behavior
• Identify revenue patterns across customer segments
• Analyze product and category performance
• Evaluate subscription and shipping behavior
• Identify repeat and loyal customers
• Analyze discount usage and its relationship with purchasing
• Understand customer demographics
• Present findings through an interactive Power BI dashboard

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🛠️ Tech Stack

Tool	Purpose
Python	Data preparation and preprocessing
Pandas	Data manipulation and analysis
SQL	Business analysis and querying
SQLite / Database	Data storage and querying
Power BI	Interactive dashboard and visualization
Jupyter Notebook	Development and analysis

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📂 Repository Structure

Customer-Shopping-Behavior-Analysis/
│
├── DATASET/
│   └── customer_shopping_behavior.csv
│
├── SQL/
│   └── customer_behavior.sql
│
├── PYTHON/
│   └── Customer_Shopping_Behaviour_Analysis.ipynb
│
├── POWER BI/
│   └── customer_behavior_dashboard.pbix
│
├── IMAGES/
│   └── customer_behavior_dashboard.png
│
├── README.md
└── LICENSE

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🔄 Project Workflow

Raw Customer Data
        │
        ▼
Python + Pandas
(Data Preparation)
        │
        ▼
Database
        │
        ▼
SQL Analysis
(Business Questions)
        │
        ▼
Power BI
(Dashboard & Visualization)
        │
        ▼
Business Insights

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🐍 Python Analysis

Python and Pandas are used for:

• Loading the customer shopping dataset
• Inspecting the dataset
• Cleaning and preparing the data
• Transforming data for analysis
• Preparing the dataset for database and visualization workflows

The complete analysis notebook is available in the PYTHON directory.

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🗄️ SQL Analysis

The SQL analysis answers several business questions related to customer and product performance.

Key analyses include:

• Revenue by gender
• Customers spending above average after receiving discounts
• Top-rated products
• Average purchase amount by shipping type
• Subscription status analysis
• Products with the highest discount usage
• Customer segmentation
• Top products within each category
• Repeat buyer analysis
• Revenue contribution by age group

SQL concepts demonstrated

• Aggregate Functions
• CASE Statements
• Common Table Expressions (CTEs)
• Window Functions
• Subqueries
• GROUP BY
• ORDER BY
• Ranking Functions

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📊 Power BI Dashboard

The Power BI dashboard provides an interactive view of customer shopping behavior.

The dashboard helps explore:

• Customer demographics
• Revenue performance
• Product category performance
• Purchasing behavior
• Subscription analysis
• Shipping preferences
• Customer segmentation

Dashboard Preview

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💡 Business Questions

This project helps answer questions such as:

1. Which customer segments contribute the most revenue?
2. Which products and categories perform best?
3. How does subscription status relate to purchasing behavior?
4. Which shipping methods are most commonly used?
5. Which customers are repeat or loyal buyers?
6. Which products receive the highest discounts?
7. How do purchasing patterns differ across age groups?
8. How do discounts relate to customer spending?

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📌 Skills Demonstrated

• Data Cleaning
• Data Preparation
• Python
• Pandas
• SQL
• Exploratory Data Analysis
• Business Analysis
• Data Visualization
• Power BI Dashboard Development
• Customer Segmentation
• Analytical Thinking

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🚀 How to Run the Project

1. Clone the repository

git clone https://github.com/Shrey2503/Customer-Shopping-Behavior-Analysis.git
cd Customer-Shopping-Behavior-Analysis

2. Install dependencies

pip install pandas sqlalchemy

Install any additional database driver required by your specific database setup.

3. Run the analysis

1. Open the Jupyter Notebook from the PYTHON directory.
2. Load the customer shopping dataset.
3. Perform the required data preparation.
4. Execute the SQL queries from the SQL directory.
5. Open the Power BI file to explore the dashboard.

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🔮 Future Improvements

• Customer segmentation using Machine Learning
• Sales forecasting
• Predictive customer analysis
• Product recommendation system
• Interactive web-based analytics dashboard

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👨‍💻 Author

Shreyash Vats

Aspiring Data Analyst SQL | Python | Power BI | Excel

GitHub · LinkedIn

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📄 License

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