Customer Shopping Data Analysis

Project Overview

This project analyzes customer shopping behavior using PostgreSQL. The objective is to identify revenue drivers, customer preferences, payment trends, demographic patterns, and shopping mall performance through SQL-based analysis.

Dataset

The dataset contains transaction-level shopping data, including:

• Customer ID
• Gender
• Age
• Product Category
• Quantity Purchased
• Price
• Payment Method
• Invoice Date
• Shopping Mall

SQL Concepts Used

• SELECT
• COUNT
• DISTINCT
• SUM
• AVG
• GROUP BY
• ORDER BY
• LIMIT
• EXTRACT
• TO_CHAR
• Aggregate Functions

Business Questions Answered

1. How many unique customers made purchases?
2. Which category generated the highest revenue?
3. Which payment method was used most frequently?
4. What is the average spending amount by gender?
5. Which shopping mall generated the highest revenue?
6. How did sales revenue vary across different months?
7. Who are the top 5 customers by spending?
8. How does revenue contribution differ between genders?
9. What is the average customer age for each product category?
10. Which payment methods generated the highest revenue?

Key Findings

• The dataset contains 99,457 unique customers.
• Clothing generated the highest revenue, contributing 11,399,679.04 in sales.
• Cash was the most frequently used payment method with 44,447 transactions.
• Cash also generated the highest revenue contribution of 112,832,243.02.
• January recorded the highest monthly sales revenue of 28,891,525.59.
• Female customers generated 150,207,136.02 in revenue, outperforming male customers.
• Mall of Istanbul generated the highest total sales revenue of 50,872,481.68.
• Average customer age remained consistent across product categories at approximately 43 years.

Tools Used

• PostgreSQL
• SQL
• GitHub

Project Structure

customer-shopping-analysis/
│
├── customer_shopping_data.csv
├── shopping_analysis.sql
├── insights.md
├── README.md
└── screenshots/

Author

Sak_