Ecommerce Sales Analysis using SQL

Project Overview

This project analyzes e-commerce sales data using SQL to uncover valuable business insights related to product performance, revenue generation, customer reviews, sales trends, and category-wise analysis.

The dataset contains 1000 product records with monthly sales data, pricing information, product categories, review scores, and review counts.

Objectives

• Analyze product sales performance.
• Calculate product and category revenue.
• Identify top-performing products.
• Track monthly sales trends.
• Evaluate the relationship between customer reviews and sales.
• Measure product growth over time.
• Generate business insights using SQL queries.

Technologies Used

• SQL (MySQL)
• Data Analysis
• Microsoft PowerPoint
• GitHub

Dataset Information

The dataset includes:

• Product ID
• Product Name
• Category
• Price
• Review Score
• Review Count
• Monthly Sales (January–December)

SQL Analysis Performed

1. Total Sales per Product

Calculated total units sold for each product across all 12 months.

2. Revenue per Product

Computed total revenue generated by each product.

3. Revenue per Category

Analyzed revenue contribution from different product categories.

4. Category-wise Sales Analysis

Compared categories based on:

• Total Units Sold
• Total Revenue

5. Monthly Sales Trend

Analyzed monthly sales performance throughout the year.

Monthly Sales Summary

Month	Sales
Jan	498,306
Feb	507,661
Mar	506,739
Apr	503,823
May	487,194
Jun	491,653
Jul	507,011
Aug	504,569
Sep	491,934
Oct	514,798
Nov	505,838
Dec	500,386

6. Top 10 Best-Selling Products

Identified products generating the highest revenue.

7. Product Review Analysis

Found highly rated products with at least 50 reviews.

8. Product Growth Analysis

Compared Month 1 and Month 12 sales to measure growth.

9. Average Price by Category

Calculated average product prices for each category.

10. Reviews vs Sales Correlation

Studied the relationship between customer reviews and sales performance.

11. Top Rated Products

Ranked products based on review scores and review counts.

12. Weighted Popularity Score

Calculated popularity using:

Popularity Score = Review Score × Review Count

13. Products with No Sales

Identified products that generated zero sales.

14. Increasing Sales Trend

Found products whose sales increased from Month 1 to Month 12.

15. Decreasing Sales Trend

Found products whose sales decreased over the year.

Key Business Insights

• Books and Sports categories generated strong revenue.
• October recorded the highest overall sales.
• Products with higher review scores generally achieved better sales performance.
• Several products demonstrated significant sales growth throughout the year.
• Customer reviews positively influenced purchasing decisions.

Project Outcome

• Analyzed 1000+ product records using SQL.
• Developed 15+ SQL queries to solve business problems.
• Identified top-selling and low-performing products.
• Generated revenue and category performance reports.
• Analyzed sales trends and customer review impact.
• Provided data-driven insights to support business decision-making.

Repository Structure

ecommerce-sales-analysis-sql/
│
├── ecommerce_sales_analysis project.sql
├── Ecommerce Sales Analysis using SQL.pptx
├── README.md
└── dataset.csv

How to Run

1. Import the dataset into MySQL.
2. Open the SQL script.
3. Execute the queries.
4. Analyze the generated results and business insights.

Author

Alekhya Komara

License

This project is created for learning, portfolio, and educational purposes.