📊 Blinkit Sales Dashboard (Excel)

An interactive Excel Dashboard built to analyze Blinkit grocery sales performance across items, outlets, and customer ratings**.
This project showcases data cleaning with Power Query, analysis with Pivot Tables, KPI generation, and interactive dashboard design.

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🧩 Business Problem

Blinkit, a leading quick-commerce grocery delivery platform, manages a wide variety of products across different outlet types, locations, and establishment years.
However, the company is facing challenges such as:

• Unclear visibility of which outlet types generate the most sales
• Lack of insights on customer preferences (Fat Content, Item Type, Item Weight)
• No consolidated view of ratings, outlet size performance, and category-wise sales
• Difficulty in identifying high-performing vs low-performing product segments
• Absence of a unified dashboard for leadership decision-making

Objective

To build an Excel-based analytical dashboard that helps Blinkit answer:

• Which items and outlet types contribute most to total revenue?
• How do sales vary across different outlet sizes, locations, and item types?
• Are healthier product categories (Low Fat) more popular?
• Which factors influence average ratings?
• How can Blinkit optimize product availability and outlet performance?

The goal is to enable data-driven decisions for improving sales, customer satisfaction, and outlet efficiency.

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📸 Dashboard Preview

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🚀 Key Features

This dashboard provides a comprehensive view of Blinkit sales performance, outlet analysis, and customer insights with interactive visuals and KPIs.

✔ KPI Cards

• 💰 Total Sales
• 📊 Average Sales
• 📦 Number of Items
• ⭐ Average Rating

✔ Charts & Visuals

• 🥤 Sales by Fat Content
• 🏪 Fat Content by Outlet
• 📦 Sales by Item Type
• 🏬 Sales by Outlet Size
• 📈 Sales Trend by Establishment Year
• ⚖️ Item Weight vs Average Sales
• 🏪 Outlet Type Performance
  • 💰 Total Sales
  • 📊 Average Sales
  • ⭐ Average Rating

✔ Interactive Filters (Slicers)

• 🎛️ Outlet Size
• 🎛️ Location Type
• 🎛️ Item Type

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📈 Insights & Findings

Key business insights derived from the dashboard:

1. 🥇 Low Fat items generated the highest total sales, showing customer preference toward healthier items.
2. 🏬 Medium-sized outlets recorded the highest revenue, outperforming small and large outlets.
3. 📦 Fruits & Vegetables and Snack Foods are the top-selling categories.
4. 📊 Outlets established between 1985–1990 maintained consistent sales performance.
5. ⭐ Supermarket Type 1 outlets show higher customer ratings but lower sales compared to Type 2 and Type 3.
6. ⚖️ Heavier items correlate with higher average sales, indicating strong demand for high-weight products.

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📁 Project Structure

├── 📘 README.md 
│
├── 📁 data/
│       └── Blinkit Grocery Data.xlsx
│
├── 📄 Docs/ 
│       ├──  Business Problem.pdf
│       └──  Blinkit Grocery Sales Analysis Report.pdf
│
├── 📊 Excel-Analysis/
│       └──  Blinkit Grocery Dashboard.xlsx
│
└── 🖼️ Images/
        └──  Dashboard_Screenshot.png

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🛠️ Tools Used

Tool	Purpose
Microsoft Excel	Data Cleaning, Processing & Dashboard
Power Query	Data Transformation
Pivot Tables & Charts	Insights & KPI Calculation
Excel Formulas	Data Analysis & Metrics

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📥 How to Use

1. Download the Blinkit Grocery Dashboard.xlsx file.
2. Open it in Excel 2016 or later for best compatibility.
3. Use the slicers to filter data by Outlet Size, Location Type, and Item Type.
4. Explore the interactive charts to analyze sales, ratings, and outlet performance.

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🧑‍💻 Author

👤 Harsh Belekar
📍 Data Analyst | Python | SQL | Power BI | Excel | Data Visualization
📬 LinkedIn | 🔗GitHub

📧 harshbelekar74@gmail.com

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⭐ If you found this project helpful, feel free to star the repo and connect with me for collaboration!