Supermarket Sales SQL Project

Overview

This project analyzes supermarket sales data using SQL queries to uncover key business insights such as revenue trends, customer behavior, product performance, and time-based analysis. The results are presented in an interactive Power BI dashboard.

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Project Objectives

• Analyze supermarket sales trends and customer behavior.
• Identify top-selling products and peak sales periods.
• Provide actionable business recommendations.

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Tools & Technologies

• Database: SQLite
• Data Source: Supermarket Sales CSV File
• Query Language: SQL
• Visualization: Power BI

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Key Insights Extracted

1. Sales Performance

• Top-Performing Cities: Cities with the highest total sales.
• Product Insights: Best-selling products and product lines.
• Revenue by Payment Method: Most frequently used payment methods.

2. Customer Insights

• Customer Types: Member vs. Non-Member purchasing behavior.
• Spending by Gender: Comparison of average spending by male vs. female customers.

3. Time-Based Insights

• Monthly Sales Trends: Revenue over time.
• Peak Hours: Hours with the most sales activity.

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Folder Structure

• supermarket_sales_analysis.sql - All SQL Queries
• Cleaned_Sales_Report.csv - Exported Data from SQLite
• Supermarket_Sales_Dashboard.pbix - Power BI Dashboard File

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How to Run the Project

1. Clone this repository to your local machine.
2. Open SupermarketSales.db in SQLite.
3. Execute SQL queries from supermarket_sales_analysis.sql.
4. (Optional) Load Cleaned_Sales_Report.csv into Power BI for visualization.

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Business Recommendations

1. Focus marketing efforts on the top-performing cities and best-selling products.
2. Increase sales promotions during peak sales hours.
3. Offer loyalty programs targeting member and non-member customers.