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Retail Sales SQL Analysis 📊

Project Overview

This project analyzes retail sales data using SQL to uncover meaningful business insights related to sales performance, customer behavior, product performance, and regional trends.

The objective of this project is to demonstrate SQL data analysis skills by exploring a real-world sales dataset and answering business questions using SQL queries.


Dataset Description

The dataset contains retail transaction records with information about:

  • Order ID
  • Customer Name
  • Customer ID
  • Region
  • State
  • City
  • Ship Mode
  • Category
  • Product Name
  • Quantity
  • Sales

The dataset represents customer orders and sales transactions across different regions and product categories.


Tools Used

  • MySQL
  • SQL
  • VS Code
  • Git & GitHub

Project Objectives

The analysis focuses on:

  • Understanding overall sales performance
  • Identifying top-performing categories and products
  • Analyzing customer purchasing patterns
  • Finding high-value customers
  • Comparing regional sales performance
  • Understanding shipping preferences

SQL Concepts Used

The project demonstrates practical usage of:

  • Database creation
  • Data exploration
  • SELECT statements
  • Filtering using WHERE
  • Sorting using ORDER BY
  • Grouping using GROUP BY
  • Aggregate functions:
    • COUNT()
    • SUM()
    • AVG()
    • MAX()
    • MIN()
  • DISTINCT values
  • LIKE operator
  • BETWEEN operator
  • LIMIT clause

Analysis Performed

Sales Analysis

  • Total number of orders
  • Total sales revenue
  • Average sales value
  • Highest and lowest sales transactions
  • Orders above a specific sales value

Category Analysis

  • Number of orders by category
  • Total sales by category
  • Average sales by category

Customer Analysis

  • Top 10 customers based on sales
  • Customer distribution by region
  • Customers whose names start with a specific letter

Product Analysis

  • Top-selling products
  • Lowest-selling products
  • Product-wise sales performance

Regional Analysis

  • Sales performance by region
  • Top-performing states and cities
  • Number of orders by region and state

Shipping Analysis

  • Most commonly used shipping modes

Key Insights

The analysis helps identify:

  • Which categories generate the highest sales
  • Which regions contribute the most revenue
  • Customers with the highest purchase value
  • Products driving business growth
  • Common shipping preferences

Detailed findings are available in:

insights.md


Project Structure

Retail-Sales-SQL-Analysis/

│── README.md
│── insights.md
│── queries.sql
│── retail_sales_dataset.csv


Author

Vrinda Sukumar S

Aspiring Data Analyst
Skills: SQL | Python | Data Analysis

About

SQL-based retail sales analysis project using MySQL to explore sales trends, customer behavior, and business insights.

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