📊 Blinkit Data Analysis Project

📌 Case Study

Blinkit is one of India’s leading quick-commerce grocery delivery companies. This project analyzes sales, product categories, outlet performance, and customer preferences to generate actionable insights.

Objective:

• Identify factors influencing sales
• Provide recommendations to improve revenue, customer satisfaction, and outlet efficiency

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🗄️ Database Description

The dataset BlinkIT Grocery Data.xlsx consists of transactional and categorical information about grocery items sold across different outlets.

Table: Grocery Sales Data

Column Name	Description
Item Identifier	Unique product code for each grocery item
Item Fat Content	Type of fat content (Low Fat, Regular, etc.)
Item Type	Category of product (Fruits, Frozen Foods, Canned, Soft Drinks, etc.)
Item Weight	Weight of the item in kilograms
Item Visibility	Proportion of display area allocated to the item in the store
Outlet Identifier	Unique ID for each store
Outlet Establishment Year	Year when the outlet was established
Outlet Location Type	Tier of the outlet location (Tier 1, Tier 2, Tier 3)
Outlet Size	Size of the outlet (Small, Medium, High)
Outlet Type	Type of supermarket (Supermarket Type1, Type2, Type3, Grocery Store)
Sales	Total sales value of the product
Rating	Customer rating for the product (scale of 1–5)

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🧹 Data Cleaning

• ✅ Handled missing values in Item Weight and Sales
• ✅ Standardized categorical fields (e.g., LF, low fat → Low Fat)
• ✅ Treated outliers in Item Visibility and Sales
• ✅ Encoded categorical variables for analysis

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📈 Data Analysis & Insights

Dataset Summary:

• 📌 Total Records: 8,523
• 📦 Unique Items: 1,559
• 🏬 Unique Outlets: 10
• 💰 Total Sales: ₹1.2M
• 📉 Average Sales per Item: ₹141

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🧮 Measures & DAX Formulas

The following DAX measures were created in Power BI for analysis:

-- Total Sales
Total Sales = SUM('BlinkIT Grocery Data'[Sales])

-- Average Sales
Average Sales = AVERAGE('BlinkIT Grocery Data'[Sales])


-- Average Rating
Average Rating = AVERAGE('BlinkIT Grocery Data'[Rating])

-- No. of Items
No. of Items = COUNTROWS('BlinkIT Grocery Data')


-- Metrics = {
    ("Total Sales", NAMEOF('BlinkIT Grocery Data'[Total Sales]), 0),
    ("Avg Sales", NAMEOF('BlinkIT Grocery Data'[Avg Sales]), 1),
    ("No. of Items", NAMEOF('BlinkIT Grocery Data'[No. of Items]), 2),
    ("Avg Rating", NAMEOF('BlinkIT Grocery Data'[Avg Rating]), 3)
}

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🚀 Tools & Technologies

• Power BI – Dashboarding & Visualization
• Excel – Data source
• Python/Pandas – Data preprocessing (optional)

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📂 How to Use

1. Open the dataset BlinkIT Grocery Data.xlsx for reference.
2. Load the Blinkit data analysis.pbix file in Power BI Desktop.
3. Refresh data if needed and explore interactive dashboards.

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✨ This project provides data-driven insights into Blinkit’s grocery sales and helps in strategic decision-making for product placement, store optimization, and customer satisfaction.