This project analyzes a real-world Brazilian e-commerce dataset to uncover insights into sales performance, customer behavior, and payment patterns. The goal is to simulate an end-to-end data analyst workflow using SQL and Power BI.
- SQL (SQLite via DBeaver)
- Power BI
- Excel (for initial data handling)
- Brazilian E-Commerce Public Dataset by Olist
- Includes data on orders, customers, payments, products, and order items
- Analyze overall sales performance and trends
- Identify top-performing product categories and regions
- Understand customer purchasing behavior
- Evaluate customer retention and repeat purchase patterns
- Examine payment methods and installment usage
- 📈 Sales Trend Over Time
- 🌍 Revenue by State (Top Regions)
- 🛒 Top & Bottom Product Categories
- 💳 Payment Method Distribution
- 👤 Customer Behavior Insights (Orders per Customer, Retention)
- Customer retention is extremely low, with the majority of users making only a single purchase.
- Revenue is highly concentrated, with a few product categories contributing most of the total sales.
- Credit cards dominate transactions, indicating customer preference for this payment method.
- Most purchases are single-installment, suggesting relatively low average transaction values.
- Customer segmentation (One-time vs Repeat users)
- Order frequency distribution
- Cohort analysis to evaluate customer retention trends over time
analysis_queries.sql→ All SQL queries (data exploration + analysis)dashboard.pbix→ Power BI dashboard file (not included due to size)dashboard_page1.png→ Dashboard screenshot (Page 1)dashboard_page2.png→ Dashboard screenshot (Page 2)ecommerce.db→ Database file (not included due to size)
Due to file size limitations, large files such as the database (.db) and Power BI dashboard (.pbix) are not included in this repository.
You can access the dataset from: Brazilian E-Commerce Public Dataset by Olist (Kaggle)
The analysis can be reproduced using the provided SQL queries.
This project demonstrates an end-to-end data analysis workflow from raw data processing and SQL based analysis to building interactive dashboards and deriving actionable business insights.