This project analyzes an e-commerce dataset to uncover key business insights using SQL and Python.
- The United Kingdom is the dominant revenue market
- A small set of products drives a large share of total revenue
- Sales show strong seasonality, with peaks in Q4
- A small number of customers generate a significant portion of revenue
The goal of this project is to explore transaction-level data and answer important business questions related to revenue, customers, and product performance.
- Which countries generate the most revenue?
- Which products generate the most revenue?
- How does revenue evolve over time?
- Who are the top customers?
- Source: Online Retail Dataset
- Contains over 500,000 transactions
- Includes product, customer, and country information
- PostgreSQL
- SQL
- Python
- Pandas
- Matplotlib
- Seaborn
The main queries include:
- Revenue by country
- Top products by revenue
- Monthly revenue trend
- Top customers
📂 See full queries in:
sql/analysis_queries.sql
The dataset was analyzed using Python to:
- clean and prepare the data
- compute revenue metrics
- visualize business insights
- Focus marketing efforts on high-revenue countries
- Optimize inventory for top-performing products
- Prepare for seasonal demand spikes
- Develop retention strategies for high-value customers
sql-ecommerce-analysis
│
├── online_rental.csv
├── ecommerce_sql_analysis.ipynb
├── analysis_queries.sql
├── monthly_revenue_trend.png
└── README.md
This project demonstrates how SQL and Python can be combined to extract meaningful insights from raw transactional data and support data-driven decision making.