Sunny Mehta
Data Analyst & BI Dev | Dashboards, SQL, Python & ETL
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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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Netflix Movies Analysis Dashboard using Power BI
Project Overview
Interactive Power BI dashboard analyzing Netflix movie trends, popularity, ratings, genres, and language distribution.
KPIs
Total Movies
Total Votes
Total Popularity
Average Rating
Dashboard Features
Top 10 Movies by Popularity
Release Year Trend
Movies Distribution by Language
Genre Analysis
Popularity vs Vote Average
Tools Used
Power BI
Power Query
DAX
Data Modeling
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Banking Data Analysis Dashboard
Develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers
Solution – With our dashboards which are created using Power BI latest tools helps the company to make a decision based on the applicant’s profile like if the applicant is likely to repay the loan then approving the loan otherwise not.
About Dataset – This dataset basically contains information about bank details ,various client details which consists of multiple tables which are interlinked with each other through keys like primary key and foreign key. The various tables are Banking Relationship, Client-Banking, Gender, Investment Advisor and Period.
KPI’S:
In which followings KPIS are present :
Total Clients :
Total Clients KPI represents total number of clients in banking.
Total Clients = DISTINCTCOUNT('Clients - Banking'[Client ID] )
Total Loan :
Total Loan gives you information about the bank loan + Business lending + credit cards balance of particular investor , gender.
Total Loan = [Bank Loan] + [Business Lending] + [Credit Cards Balance]
Bank Loan :
Bank Loan gives you information what is the loan amount of loan to be repaid by the client to bank.
Bank Loan = SUM('Clients - Banking'[Bank Loans] )
Business Lending : Business lending gives you information about the loan amount given to small business. Business Lending = SUM('Clients - Banking'[Business Lending] )
Total Deposit Total Deposit gives you information about the amount deposited by particular investors in bank Total Deposit = [Bank Deposit] + [Savings Account] + [Foreign Currency Account] + [Checking Accounts]
Total Fees : Total Fees is nothing but the amount charged by the bank for account set-up , maintenance charges etc. Total Fees = SUMX('Clients - Banking' , [Total Loan] * 'Clients - Banking'[Processing Fees] )
Bank Deposit : Bank deposit is the money put in the bank. Bank Deposit = SUM('Clients - Banking'[Bank Deposits] )
Checking Account Amount : Checking account amount is nothing but which offers easy access to your money for daily transactional needs. Checking Accounts = SUM('Clients - Banking'[Checking Accounts] )
Total CC Amount : Total CC Amount is a short-term source of financing for a company by a bank. Total CC Amount = SUM('Clients - Banking'[Amount of Credit Cards] )
Saving Account Amount : A savings account is an interest-bearing deposit account held at a bank. Savings Account = SUM('Clients - Banking'[Saving Accounts] )
Foreign Currency Amount : Foreign Currency Account means an account held in a currency that is not the currency of India or Bhutan or Nepal. Foreign Currency Account = SUM('Clients - Banking'[Foreign Currency Account] )
Engagement Account : Engagement Banking is nothing but puts the customer at the center and aims to deliver the digital experiences they expect. Engagment Length = SUM('Clients - Banking'[Engagment Days])
Credit Cards Balance : It is the total amount of money currently owned by a cardholder to their credit card bank. Credit Cards Balance = SUM('Clients - Banking'[Credit Card Balance] )
📊 Tools Used
Excel (Data Cleaning) SQL (Data Processing) Python (EDA) Power BI (Dashboard)
📈 Key Insights Private banks dominate loan distribution European segment shows highest deposits Low-income customers have high loan dependency
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Blinkit-Sales-Dashboard
This project is an interactive Power BI dashboard developed to analyse Blinkit's sales performance, outlet operations, product distribution, and customer-related metrics. The dashboard converts raw retail sales data into meaningful business insights through visualisation and KPI tracking. The objective of this project is to monitor business performance, identify sales trends, evaluate outlet efficiency, and support data-driven decision-making.
Key KPIs
Total Sales: $1.20M
Average Sales: $140.99
Number of Items: 8,523
Average Rating: 3.9
Dashboard Features
Sales Analysis
Tracked total revenue and average sales performance to understand overall business growth.
Outlet Performance
Analysed outlet sales based on the following:
Outlet Size
Outlet Location Type
Outlet Establishment Year
Outlet Type
Product Category Analysis
Identified top-performing product categories contributing the highest revenue.
Customer & Product Insights
Compared low-fat and regular products to understand purchasing trends and monitored customer ratings for performance evaluation.
Interactive Filters
Added slicers for:
Outlet Location Type
Outlet Size
Item Type
Outlet Type
Outlet Identifier
These filters allow users to dynamically explore data and generate business insights.
Project Outcome
This project demonstrates practical skills in business intelligence, KPI reporting, analytical thinking, and dashboard development using Power BI. It highlights the ability to transform raw business data into business insights for decision-making.
Developed By
Sunny Mehta
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