🛒 BlinkIT Grocery Data Analytics Project

Transforming raw grocery sales data into meaningful business insights using Python, SQL, and Power BI.

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Project Overview

This project performs an end-to-end analytics workflow on BlinkIT grocery sales data.

The project simulates a real-world business scenario where raw and inconsistent data is cleaned, analyzed, transformed, and converted into actionable insights for business decision-making.

Goal

• Think and work like a Data Analyst
• Build an end-to-end analytics pipeline
• Create business insights from raw data
• Generate business recommendations using SQL and dashboards

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Business Problem

Businesses generate huge amounts of raw data every day.

Without proper analysis, it becomes difficult to answer questions such as:

• Which products generate the highest sales?
• Which outlet performs best?
• How do product characteristics influence sales?
• Which categories contribute most to revenue?
• Which business factors drive revenue?
• What patterns can improve business decisions?

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Project Progress

Dataset Understanding ✅
Data Profiling ✅
Data Cleaning ✅
Exploratory Data Analysis ✅
Feature Engineering ✅
KPI Creation ✅
SQL Analytics ✅
Power BI Dashboard ✅
Business Insights ✅
Portfolio Optimization ✅

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Dashboard Preview

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Feature Engineering Completed

Created Features:

• Outlet_Age
• Price_Category
• Product_Category
• Visibility_Category
• Fat_Content_Code

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KPI Summary

KPI	Value
Total Sales	₹18.55M
Average Sales	₹2,182
Average MRP	₹140.98
Total Products	1559
Total Outlets	10

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SQL Analytics Summary

Analysis Area	Key Finding
Product Revenue	Fruits & Vegetables generated highest revenue
Outlet Type	Supermarket Type1 generated highest total sales
Average Outlet Performance	Supermarket Type3 generated highest average sales
Location Analysis	Tier 3 generated highest total revenue
Outlet Ranking	OUT027 consistently ranked highest
Bottom Performers	OUT010 and OUT019 consistently ranked lowest
Product Classification	Products categorized using CASE WHEN
Business Recommendation	Fruits & Vegetables, Snack Foods, and Household identified as priority categories

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Key Business Findings

Revenue Drivers

• Fruits & Vegetables generated highest revenue
• Snack Foods and Household products showed strong performance
• Tier 3 locations generated strongest total revenue
• Supermarket Type1 contributed nearly 70% of total sales
• OUT027 repeatedly ranked highest across metrics

Weak Contributors

• Grocery Stores showed weakest performance
• OUT010 and OUT019 consistently ranked low
• Fat Content showed weak influence on sales
• Visibility showed weak relationship with sales

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Business Insights & Recommendations

Key Insights

1. Fruits & Vegetables generated the highest revenue, indicating strong customer demand and consistent sales volume.

2. Supermarket Type1 contributed nearly 70% of total sales, making it the primary revenue driver.

3. Tier 3 locations generated the strongest overall revenue, suggesting higher market potential in these areas.

4. OUT027 consistently ranked as the top-performing outlet across multiple analyses.

5. Medium-sized outlets generated the highest sales, indicating an effective balance between capacity and operations.

6. Grocery Stores showed the weakest performance, suggesting opportunities for strategy improvement.

7. Regular and Low Fat products showed only small sales differences, indicating fat content has limited influence on purchasing behavior.

8. Priority categories identified:

   • Fruits & Vegetables
   • Snack Foods
   • Household

Business Recommendations

• Increase inventory for high-performing categories
• Expand strategies used by OUT027 to similar outlets
• Improve performance strategies for Grocery Stores
• Focus marketing efforts on Tier 3 markets
• Maintain stock availability for top-selling products

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Tech Stack

Programming & Analysis

• Python
• Pandas
• NumPy

Visualization

• Matplotlib
• Seaborn
• Power BI

Database

• SQL (SQLite)

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Project Structure

blinkit_analysis
│
├── Dataset
│   ├── blinkit_dirty_dataset.csv
│   └── blinkit_cleaned_dataset.csv
│
├── Images
│   ├── overview.png
│   └── dashboard.png
│
├── Python
│   ├── data_cleaning.ipynb
│   ├── eda_analysis.ipynb
│   └── feature_engineering.ipynb
│
├── SQL
│   └── blinkit_queries.sql
│
├── BlinkIT_Grocery_Sales_Analytics_Dashboard.pbix
│
├── create_db.py
├── blinkit.db
├── README.md
└── .gitignore

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Future Enhancements

• Advanced business insights
• Predictive analytics
• Time series forecasting
• Sales prediction models

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Author

Saurin Parmar
Aspiring Data Analyst | Python • SQL • Power BI