This project explores an E-commerce Sales Dataset using SQL to uncover insights on:
- Product Performance 📊
- Customer Behaviour 👥
- Regional Sales Trends 🌍
It demonstrates data exploration, cleaning, and analysis through optimized SQL queries.
- Identify top-performing products and categories.
- Analyze customer purchase behaviour and demographics.
- Track regional sales and revenue trends.
- Generate insights to help make data-driven business decisions.
| Column | Description |
|---|---|
| Order_ID | Unique order identifier |
| Order_Date | Date of the order |
| Customer_ID | Unique customer identifier |
| Customer_Name | Name of the customer |
| Gender | Gender of the customer |
| Age | Age of the customer |
| City | City of purchase |
| Region | Sales region |
| Product_ID | Product identifier |
| Product_Name | Name of the product |
| Category | Product category |
| Quantity | Number of units sold |
| Unit_Price | Price per unit |
| Total_Sales | Total revenue for the order |
| Payment_Method | Payment type used |
See the sales_table.sql file for table creation script.
The file ecommerce_analysis_queries.sql includes all key analytical queries such as:
- Product Performance Analysis
- Customer Behaviour Analysis
- Regional Sales Trends
- Additional Exploration Queries
- 🔹 Which products generate the most revenue?
- 🔹 Which city or region contributes most to total sales?
- 🔹 What is the average spending by gender?
- 🔹 How does age affect buying behaviour?
- SQL
- Excel (for dataset review)
- GitHub (for documentation)
👤 Anmol Raj
📧 Data Analyst | SQL | Python | EXCEL | Tableau
🔗 LinkedIn | GitHub