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🛒✨ Walmart Sales Data Analysis Using SQL

Welcome to the Walmart Sales Data Analysis project, where we explore and gain insights from Walmart's sales data using Microsoft SQL Server. The dataset is an Excel file that captures various transactional details, including customer behavior, product performance, and more.

📄 Data Summary

The Walmart sales dataset provides detailed information about sales transactions across various branches, cities, and customer demographics. Here’s an overview of the data:

📋 Data Columns and Types

Column Name Data Type Description
Invoice ID varchar(50) Unique identifier for each transaction
Branch varchar(10) Store branch (A, B, or C)
City varchar(50) City where the transaction took place
Customer type varchar(20) Type of customer (e.g., Member, Normal)
Gender varchar(10) Gender of the customer
Product line varchar(50) Category of products purchased
Unit price decimal(10,2) Price per unit of the product
Quantity int Number of units purchased
Tax 5% decimal(10,2) 5% tax applied to the total purchase
Total decimal(10,2) Total amount paid (including tax)
Date date Date of the transaction
Time time Time of the transaction
Payment varchar(20) Payment method used (e.g., Cash, Ewallet)
COGS decimal(10,2) Cost of goods sold
Gross margin percentage decimal(10,2) Gross margin percentage
Gross income decimal(10,2) Gross income from the transaction
Rating decimal(3,1) Customer rating of the transaction (1 to 10)

🛠️ Data Transformation

Several transformations were made on the data to derive meaningful insights:

  • Time of Day: Transactions were classified into 'Morning', 'Afternoon', 'Evening', and 'Night' based on the transaction time.
  • Day of the Week: Extracted the day of the week from the date for each transaction.
  • Month: Transactions were categorized by their respective month for monthly trend analysis.

💡 Insights Derived

The project aims to answer key questions to help understand customer behavior, sales trends, and product performance. Here are some of the insights derived:

  • 📍 Branch Insights:

    • Which branch sold more products than the average?
    • Branch performance comparison across different time periods.

  • 🛒 Product Insights:

    • What are the best-selling product lines?
    • How do product preferences differ based on gender?

  • 👥 Customer Insights:

    • Which customer type generates the most revenue?
    • Gender distribution of customers across different branches.

  • 🕒 Time-Based Insights:

    • What time of day do customers give the highest ratings?
    • Which days of the week generate the most revenue?

📈 Visuals & Reporting

Various SQL queries were used to generate insights for reporting purposes. These include breakdowns by city, branch, product lines, and customer demographics.

🚀 Getting Started

Follow these steps to set up the project and replicate the analysis:

  1. Clone this repository:

    git clone https://github.com/hemilshah99316/Walmart_Sales_Data_Analysis_With_SQL.git

    OR Download ZIP File

  2. SQL Server Setup:

    • Import the Excel file into Microsoft SQL Server.
    • Run the provided SQL scripts to perform data transformations and derive insights.

  3. Explore the data and customize queries to gain additional insights.

💻 Tools Used

  • Microsoft SQL Server: For database management and SQL queries.
  • Excel: Source of the sales data.
  • SQL: To query and analyze the data.

🔑 Key Features

  • 🛍 Product Insights: Learn which product categories are driving the most revenue.
  • 📅 Time-based Analysis: Discover how sales performance varies across different times of the day and days of the week.
  • 💳 Customer Behavior: Understand customer demographics, payment methods, and buying preferences.
  • 📊 Branch Performance: Compare sales performance across different branches and cities.