Data Analytics Project | BlinkIT Grocery Sales Analysis
Excited to share my latest Data Analytics project ,where I analyzed BlinkIT Grocery Sales Data and delivery data to uncover meaningful business insights 📈
📊 What I worked on:
Analyzed sales performance across product categories, outlet types, sizes, and locations
Identified top-performing item categories and customer preferences
Studied the impact of outlet size, establishment year, and visibility on sales
Converted raw data into actionable insights and business recommendations
💡 Key Insights:
Fruits & Vegetables, Snack Foods, and Frozen Foods drive maximum sales
FOR FULL PROJECT VIST GIT HUB –( https://lnkd.in/d34sdAPz
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Marketplace Profitability & P&L Engine
Overview
This project is an end-to-end E-commerce Marketplace Analytics solution built using MySQL, SQL, Power BI, and Excel.
The objective of this project is to analyze marketplace profitability across multiple sales channels by calculating revenue, product costs, marketplace commissions, shipping expenses, and net profit.
The project simulates a real-world marketplace operations environment where business teams need visibility into product performance, marketplace profitability, and operational KPIs.
Business Problem
E-commerce businesses often track revenue but fail to understand true profitability.
This project helps answer key business questions:
Which marketplace generates the highest profit?
Which products are most profitable?
How do commissions impact margins?
What is the overall profit margin?
How does shipping cost affect profitability?
Tech Stack
MySQL
SQL
Power BI
Microsoft Excel
Data Sources
Products
Contains SKU-level product information.
Fields:
SKU
Product Name
Category
Cost Price
Selling Price
Orders
Contains marketplace order transactions.
Fields:
Order ID
Order Date
Marketplace
SKU
Quantity
Marketplace Charges
Marketplace commission structure.
Fields:
Marketplace
Commission Percentage
Shipping Cost
Shipping cost by marketplace.
Fields:
Marketplace
Shipping Cost
Returns
Order return information.
Fields:
Order ID
Return Status
SQL Implementation
The project uses multiple SQL tables and joins to create a unified profitability dataset.
Key SQL Concepts Used:
CREATE TABLE
INNER JOIN
SQL Views
Aggregations
Calculated Metrics
A consolidated analytical view was created:
vw_marketplace_pnl
This view combines all business logic and serves as the primary source for Power BI reporting.
Profitability Metrics
Revenue
Revenue = Quantity × Selling Price
Product Cost
Product Cost = Quantity × Cost Price
Commission Cost
Commission Cost = Revenue × Marketplace Commission %
Shipping Cost
Shipping Cost = Quantity × Shipping Cost
Profit
Profit = Revenue − Product Cost − Commission Cost − Shipping Cost
Profit Margin %
Profit Margin % = Profit / Revenue
Power BI Dashboard
Executive Summary
KPIs:
Total Revenue
Total Profit
Total Orders
Profit Margin %
Marketplace Analysis
Revenue by Marketplace
Profit by Marketplace
Marketplace Performance Comparison
Product Analysis
Top Profitable Products
Product Revenue Analysis
Product Profitability Ranking
Trend Analysis
Revenue Trend
Profit Trend
Order Trend
Project Architecture
Excel → MySQL → SQL View → Power BI Dashboard
Key Outcomes
Built a scalable marketplace profitability model.
Centralized business logic using SQL Views.
Automated profitability calculations.
Delivered interactive executive dashboards for decision-making.
Simulated a real-world marketplace operations analytics workflow.
Skills Demonstrated
SQL
Data Modeling
Business Intelligence
Power BI
E-commerce Analytics
Marketplace Operations
Profitability Analysis
Data Visualization
Author
Sunny Mehta
Open to opportunities in:
E-commerce Operations
Marketplace Management
Business Analytics
Operations Analytics
Data Analytics
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Data is useless. Yes, it is.
Because on its own, it doesn’t say much.
It’s just numbers, rows, and columns.
The real value comes when that data is understood —
when it answers a question, shows a pattern, or drives a decision.
That’s what turns data into information.
And that’s what businesses actually need.
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Developed an interactive Amazon Product Analytics Dashboard in Power BI to analyze 200+ product categories, pricing trends, discount effectiveness, customer ratings, and performance drivers. Leveraged Power Query, DAX, and advanced visualizations to transform raw e-commerce data into actionable business insights, enabling data-driven pricing and product strategy decisions.
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