SQL Business Analysis: Revenue, Customers & Product Performance

Role: Data Analyst / SQL Analyst
Dataset: Synthetic / anonymized demo data created for portfolio use.
Stack: SQLite, SQL, CTEs, joins, window functions, pandas, matplotlib

Business problem

A retail/e-commerce business needs to understand revenue trends, category performance, repeat customers, top customers and country-level performance from normalized relational data.

What was built

Built a reproducible SQL analysis project using customers, products and orders tables. The project includes database creation, analytical SQL queries, CSV exports and a revenue trend chart.

SQL concepts demonstrated

• JOINs across normalized tables

• GROUP BY aggregations

• CTE-based analysis

• Window functions

• Ranking top customers/products

• Monthly revenue analysis

  Business questions answered

1. How does revenue change by month?

2. Which product categories generate the most revenue?

3. Who are the top customers?

4. What share of customers are repeat buyers?

5. Which countries generate the highest revenue?

   Key outputs

   • sql/analysis_queries.sql — 5 business queries

• results/query_*.csv — query outputs

• results/business_analysis.sqlite — reproducible local database

• results/monthly_revenue_sql.png — revenue visualization

  How to run

  python -m venv .venv
  source .venv/bin/activate  # Windows: .venv\Scripts\activate
  pip install -r requirements.txt
  python src/main.py

  Resume-ready bullets

  • Built SQL analysis across normalized customer, product and order tables using joins, aggregations, CTE logic and window functions.

• Analyzed monthly revenue, repeat customer rate, category performance, top customers and country-level revenue efficiency.