📅 Date: [August, 2025]

This project analyzes sales data from a small e-commerce platform using SQL.
The goal is to demonstrate advanced SQL skills such as joins, aggregations, CTEs, and window functions to extract meaningful business insights.

• Customers_data.csv → Customer details (ID, name, city, signup date)
• Products_data.csv → Product details (ID, name, category, price)
• Orders_data.csv → Orders placed by customers (ID, product, quantity, date)

• SQL Joins (INNER JOIN, aggregations)
• Common Table Expressions (CTEs)
• Window Functions (RANK, Running Totals)
• Business-Oriented Query Writing
• Data Analysis & Reporting

1. Who are the top spending customers across all cities?
2. Which cities contribute the most to total revenue
3. What are the daily sales trends and cumulative growth over time?
4. Which customers shop across multiple product categories?
5. What are the most popular products in each city?
6. Which product categories drive the most revenue? (extra since you have more categories now)

• 🏆 Top Customers: Ava Sharma, Ananya Iyer, and Rahul Mehta are the highest spenders.
• 🌆 Top Cities: Mumbai and Delhi generate the most revenue overall.
• 📈 Sales Trends: Daily sales are steady with cumulative revenue showing consistent growth.
• 🛍️ Multi-Category Buyers: 8 customers shop across multiple categories, led by Ava Sharma.
• 🏙️ Popular Products by City: Laptops dominate Mumbai, Smartphones lead Delhi, T-Shirts top Bangalore.
• 📦 Category Revenue: Electronics contribute ~55% of revenue, making them the main driver.

• Customers_data.csv
• Products_data.csv
• Orders_data.csv
• SQL_queries.sql
• README.md (this file)

1. Import the CSVs into your SQL database (PostgreSQL, MySQL, or SQLite).
2. Run queries from SQL_queries.sql.
3. Explore insights or modify queries to extend the analysis.

Siva Satya Varaprasad Vasamsetti

• 📧 sivavasamsetty001@gmail.com
• 🔗 LinkedIn