🛒 Superstore Sales Analysis Using SQL

📌 Project Overview

This project leverages a real-world Superstore dataset to perform comprehensive sales analysis using SQL. The primary objective is to uncover patterns in sales, identify profitable products and regions, and generate actionable business insights through structured querying and data modeling.

🎯 Objectives

• Analyze sales performance across different categories, sub-categories, and regions.
• Identify top-performing products and customer segments.
• Discover trends in discounts, profits, and shipping modes.
• Generate insights to support strategic business decisions.

🧰 Tools & Technologies

• SQL (Structured Query Language)
• MySQL / PostgreSQL / SQLite (choose based on your setup)
• DBMS: MySQL Workbench / pgAdmin / DBeaver
• Data Source: Superstore Dataset (CSV format)

📂 Dataset Description

The Superstore dataset contains transactional data including:

• Order ID, Order Date, Ship Date
• Customer Name, Segment, Region
• Product Category, Sub-Category
• Sales, Quantity, Discount, Profit

📊 Key SQL Operations

• Data Cleaning and Filtering
• Aggregations (SUM, AVG, COUNT)
• Joins (INNER, LEFT)
• Grouping and Sorting
• Subqueries and CTEs

📈 Key Insights

• 📍 The West region generates the highest profit despite fewer orders.
• 🛍️ Technology category yields the highest average profit per unit.
• 🚚 Standard Class shipping is most common but not always most profitable.
• 💸 Discounts above 30% often lead to negative profit margins.