📦 E-Commerce Sales Analytics — Power BI & SQL Project (Data Analyst Portfolio)

An end-to-end Data Analytics project using SQL, Power BI, Power Query, DAX, and Excel to analyze sales performance, customer behavior, and product trends for an e-commerce business.

This project demonstrates complete Data Analyst workflow: data cleaning → SQL queries → data modeling → DAX → dashboard → insights.

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🔧 Tech Stack

• SQL
• Power BI Desktop
• Power Query (ETL)
• DAX (Measures & KPIs)
• Excel
• Data Modeling (Star Schema)

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🚀 Project Overview

This dataset contains e-commerce sales transactions including:

• Customer demographics
• Order details
• Product information
• Regional and city-level sales
• Category insights
• Profit metrics

The goal of this project is to identify:

• Top-performing regions
• Best-selling categories
• Customer segment trends
• Revenue & profit distribution
• Order growth patterns

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🧩 Business Problem

E-commerce businesses face difficulties in:

• Understanding customer behavior
• Identifying profitable categories
• Tracking yearly and monthly sales trends
• Managing regional performance
• Finding repeat customer patterns

This project builds a dashboard that solves these issues.

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🎯 Project Goals / Deliverables

• Build a clean ETL pipeline through Power Query
• Design a star schema model for analytics
• Create custom DAX metrics for KPIs
• Build a 2-page interactive dashboard
• Generate business insights for decision-making

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🔹 Dashboard Pages

Page 1 — Sales Performance

• Total Sales
• Total Profit
• Total Orders
• Profit Margin %
• Sales by Platform (App / Website / Marketplace)
• Sales Trend Over Time
• Sales by Customer Segment
• Category-wise Sales & Profit
• Sales by State (Map Visual)

Page 2 — Customer & Category Insights

• City-wise Sales + Profit
• Customer Segment Analysis
• Category Breakdown
• Top Product Insights
• Custom tooltips for additional detail

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⚙ SQL Queries Used

SQL file included:
Ecommerce_Sales_Queries.sql

Contains:

• Data cleaning queries
• Filtering
• Aggregations (SUM, COUNT, AOV)
• Category-wise analysis
• Segment-level insights
• Trend queries

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🧮 DAX Measures Used

Some important measures:

Total Sales = SUM(Sales[Total_Sales])
Total Profit = SUM(Sales[Total_Profit])
Profit Margin % = DIVIDE([Total Profit], [Total Sales])
Total Orders = DISTINCTCOUNT(Sales[Order_ID])
AOV = DIVIDE([Total Sales], [Total Orders])

(Full DAX list in Power BI file.)

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📁 Project Files

File	Description
Ecommerce_Sales_Analytics_Project.pbix	Power BI dashboard file
Ecommerce_Sales_Dashboard.pdf	Exported dashboard (both pages)
Ecommerce_Sales_Queries.sql	SQL analysis queries
Dataset/	Raw data files used
Images/page1.png	Dashboard Page 1
Images/page2.png	Dashboard Page 2

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📸 Dashboard Preview

Page 1 – Sales Performance

Page 2 – Customer & Category Insights

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📈 Key Insights

• Marketplace platform generates the highest revenue
• Consumer segment dominates sales
• Electronics is the biggest sales category
• West & South regions show strong performance
• Profit margin stabilizes around ~6%
• Repeat customer rate improving steadily
• City-level insights show high performance in Bengaluru, Hyderabad & Mumbai

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▶ How to Explore

1. Download the .pbix file
2. Open it in Power BI Desktop
3. Interact with slicers, tooltips, and visuals
4. Review SQL insights from the .sql file

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👤 Author

G. Shyam Venkat

Data Analyst | SQL | Power BI | DAX | Excel | Data Modeling

🔗 GitHub: https://github.com/shyamcodes-ai
🔗 LinkedIn: https://www.linkedin.com/in/g-shyam-venkat-304ab536b

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