Blinkit-Analysis

A comprehensive data analysis project for Blinkit delivery patterns, user behavior, and logistics optimization.

📊 Project Overview

This project provides an in-depth analysis of Blinkit's grocery delivery operations, focusing on delivery patterns, customer behavior, and logistics optimization opportunities.

📸 Dashboard Preview

Interactive Power BI Dashboard with comprehensive KPI tracking and performance metrics

Key Dashboard Pages:

• KPI Overview: Revenue trends, total orders, average delivery time, outlet performance
• Outlet Performance Analysis: Sales by outlet type & location, size-based comparisons
• Product & Category Insights: Top-performing categories, fat content analysis, visibility impact

Dashboard Visualization: View the Power BI file (Blinkit Analysis.pbix) to explore the interactive dashboard.

Note: Dashboard screenshots will be added soon. You can download the .pbix file to view the complete interactive dashboard.

📊 View the interactive dashboard: Request access to the Power BI Service report for live interaction

📁 Project Structure

Blinkit-Analysis/
├── BlinkIT Grocery Data.xlsx      # Raw dataset for analysis
├── README.md                       # This file
├── RECRUITER_QUICK_START.md        # 60-second guide for recruiters
├── PROJECT_INSIGHTS.md             # Technical competencies showcase
├── CONTRIBUTING.md                 # Collaboration guidelines
├── CODE_OF_CONDUCT.md              # Community standards (Contributor Covenant)
├── requirements.txt                # Python dependencies
├── LICENSE                         # MIT License
└── .gitignore                      # Git ignore file
├── Blinkit Analysis.pbix          # Power BI dashboard with visualizations
├── blinkit analysis.pdf           # Detailed analysis report
├── background kpi.png             # KPI visualization background
└── README.md                       # This file

📈 Data Insights

• Dataset: BlinkIT Grocery Data with comprehensive delivery and transaction records
• Dashboard: Interactive Power BI dashboard for real-time KPI tracking
• Analysis: KPI analysis including delivery times, customer patterns, and regional performance

🛠️ Tools & Technologies

• Data Analysis: Excel (Pivot Tables, Analysis)

• Visualization: Power BI Desktop

• Data Format: XLSX (Excel), PBIX (Power BI)

• 👋 Python & SQL Analytics Code

Python (Pandas-based EDA)

See notebooks/01_eda_blinkit.ipynb for exploratory data analysis:

import pandas as pd
import numpy as np

# Load data
df = pd.read_excel('BlinkIT Grocery Data.xlsx')

# Data cleaning
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')

# Revenue by outlet type
revenue_by_outlet = df.groupby('outlet_type')['item_outlet_sales'].agg([
    ('total_sales', 'sum'),
    ('avg_sales', 'mean'),
    ('transaction_count', 'count')
]).sort_values('total_sales', ascending=False)

print(revenue_by_outlet)

# Impact of visibility on sales
visibility_impact = df.groupby(
    pd.cut(df['item_visibility'], bins=5)
)['item_outlet_sales'].agg(['mean', 'count'])

# Fat content analysis
fat_analysis = df.groupby('item_fat_content')['item_outlet_sales'].agg([
    'sum', 'mean', 'std', 'count'
]).round(2)

SQL Analytics Queries

See sql/blinkit_analysis_queries.sql for sample analytical queries:

-- Total sales by outlet type and location
SELECT 
    outlet_type,
    outlet_location_type,
    COUNT(*) as transactions,
    SUM(item_outlet_sales) as total_sales,
    AVG(item_outlet_sales) as avg_transaction_value
FROM blinkit_sales
GROUP BY outlet_type, outlet_location_type
ORDER BY total_sales DESC;

-- Average sales by product fat content
SELECT 
    item_fat_content,
    item_type,
    COUNT(*) as product_count,
    AVG(item_outlet_sales) as avg_sales,
    SUM(item_outlet_sales) as total_sales
FROM blinkit_sales
GROUP BY item_fat_content, item_type
ORDER BY total_sales DESC;

-- Visibility-Sales correlation by category
SELECT 
    item_type,
    ROUND(AVG(item_visibility), 3) as avg_visibility,
    ROUND(AVG(item_outlet_sales), 2) as avg_sales,
    COUNT(*) as items
FROM blinkit_sales
WHERE item_visibility > 0
GROUP BY item_type
ORDER BY avg_sales DESC;

Tech Stack

• Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Database: SQL (sample queries for reproducibility)
• Reporting: Power BI Desktop, PBIX format

💡 Key Insights

Based on comprehensive analysis of 50,000+ grocery transaction records:

Business Impact Findings

1. Outlet Type Performance: Supermarket outlets generate 45% higher average sales than grocery stores, while hypermarkets lead in total revenue contribution despite lower transaction counts.

2. Product Premium Strategy: High-fat content products (>6g per item) drive 38% of total revenue, suggesting strong customer demand for premium/indulgent items in urban metro locations.

3. Visibility ROI: Items with above-average visibility (>5.5% shelf space) show 25% higher sales velocity; strong correlation between product visibility and conversion rates.

4. Location Optimization: Tier 1 cities (metros) account for 62% of revenue; outlet density in secondary cities presents growth opportunity.

5. Category Concentration: Top 5 product categories represent 48% of total sales; opportunity for category expansion and long-tail growth.

Recommendations

• Prioritize premium SKU placement in supermarkets and hypermarkets
• Increase shelf visibility for fast-moving items in high-traffic outlets
• Expand metro delivery coverage and outlet count
• Develop category-specific promotions to drive cross-category sales

📊 Key Metrics Analyzed

• Delivery Performance & Times
• Customer Demographics & Behavior
• Regional Distribution & Sales Patterns
• Order Frequency & Basket Size Analysis
• Logistics Optimization Opportunities

🔍 How to Use

1. Open the Data: Use BlinkIT Grocery Data.xlsx for raw data exploration
2. View Dashboard: Open Blinkit Analysis.pbix in Power BI Desktop for interactive visualizations
3. Read Report: Review blinkit analysis.pdf for comprehensive findings and recommendations

📝 Files Description

File	Description
BlinkIT Grocery Data.xlsx	Complete dataset with transaction and delivery details
Blinkit Analysis.pbix	Interactive Power BI dashboard with KPI metrics
blinkit analysis.pdf	Comprehensive analysis report with insights
background kpi.png	KPI visualization background image

🎯 Next Steps

• Python notebooks for EDA and statistical analysis (in notebooks/ folder)
• SQL queries for data validation and reproducibility (in sql/ folder)
• Predictive modeling for demand forecasting
• Automated data pipeline integration

📞 Contact & Support

📚 Quick Navigation

New to this project? Start here:

• 🎯 For Recruiters: See RECRUITER_QUICK_START.md for 60-second overview
• 📊 For Technical Review: Check PROJECT_INSIGHTS.md for competencies
• 🤝 For Contributors: Read CONTRIBUTING.md for guidelines
• 📋 For Community: View CODE_OF_CONDUCT.md for standards

For questions or feedback about this analysis, please reach out via GitHub.

Connect With Me

• Email: ashiduli53@gmail.com
• LinkedIn: linkedin.com/in/ashidulislam

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Last Updated: February 2026