🛒 BlinkIT Grocery Sales Analysis (MySQL Project)

📌 Project Overview

This project focuses on analyzing BlinkIT grocery sales data using MySQL to extract meaningful business insights.

The dataset was cleaned, transformed, and analyzed to understand:

• Sales performance trends
• Outlet performance comparison
• Product category analysis
• Customer rating behavior
• Inventory and visibility patterns

The goal of this project is to demonstrate real-world SQL analytics skills for business decision-making.

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🎯 Key Objectives

• Clean and standardize raw dataset using SQL
• Perform exploratory data analysis using aggregations
• Build KPIs for business performance tracking
• Identify high-performing outlets and product categories
• Generate actionable business insights

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🛠️ Tools & Technologies Used

• MySQL 8.0
• SQL (Data Cleaning + Analysis)

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📂 Dataset Information

The dataset contains grocery sales information including:

• Item Fat Content
• Item Identifier
• Item Type
• Outlet Type
• Outlet Size
• Outlet Location Type
• Item Weight
• Item Visibility
• Total Sales
• Customer Rating

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🧹 Data Cleaning Process

Key cleaning steps performed:

• Standardized inconsistent values in Item_Fat_Content
• Handled missing values using NULL conversion
• Cleaned numeric fields during data import

UPDATE blinkit_data
SET Item_Fat_Content =
CASE
    WHEN Item_Fat_Content IN ('LF', 'low fat')
    THEN 'Low Fat'
    WHEN Item_Fat_Content = 'reg'
    THEN 'Regular'
    ELSE Item_Fat_Content
END;

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📊 Key Business KPIs

💰 Total Sales

• Overall revenue generated from all items

📦 Total Items Sold

• Total number of transactions/items in dataset

📈 Average Sales

• Average revenue per transaction

⭐ Average Rating

• Customer satisfaction indicator

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🏪 Business Insights Derived

1. Outlet Performance Analysis

• Compared sales across outlet types and sizes
• Identified high-performing outlet categories

2. Product Category Analysis

• Found top-performing item types based on revenue
• Identified low-performing categories

3. Fat Content Analysis

• Compared Low Fat vs Regular product sales behavior

4. Location-based Insights

• Evaluated performance across different outlet locations

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🧠 Key Insights

• Certain outlet types contribute significantly higher revenue
• Medium/Large outlets perform better in overall sales
• Specific product categories dominate total revenue
• Customer ratings help identify high-demand products
• Outlet location plays a key role in performance differences

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📊 Sample SQL Analysis

Example: Sales by Outlet Type

SELECT Outlet_Type,
SUM(Total_Sales) AS Total_Sales,
AVG(Total_Sales) AS Avg_Sales,
COUNT(*) AS No_Of_Items,
AVG(Rating) AS Avg_Rating
FROM blinkit_data
GROUP BY Outlet_Type
ORDER BY Total_Sales DESC;

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🚀 What This Project Demonstrates

✔ SQL Data Cleaning ✔ Data Aggregation & Grouping ✔ Business KPI Development ✔ Analytical Thinking ✔ Real-world Retail Data Analysis

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📈 Future Improvements

• Build Power BI dashboard for visualization
• Add Python-based EDA layer
• Perform predictive sales analysis
• Automate reporting pipeline

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👩‍💻 Author

Rakshita Poojari Aspiring Data Analyst Skills: SQL | Excel | Power BI | Data Visualization