This project provides a data-driven analysis for Monday Coffee, focusing on sales performance and identifying the best cities for new store openings. The analysis integrates population data, rental costs, and transaction history.
The project uses a relational database with four main tables:
- City: Demographics and rental data.
- Customers: Unique customer identifiers and city mapping.
- Products: Coffee product catalog and pricing.
- Sales: Transactional data including ratings and revenue.
Based on the SQL queries in monday_coffee_data_analysis_queries.sql, here are the top findings:
- Identified cities where the estimated coffee consumer base (25% of population) is highest.
- Top Cities by Population: Bangalore, Delhi, and Mumbai.
- The analysis identifies which products drive the most revenue (e.g., Cold Brew vs. Ground Espresso).
- By calculating the Average Rent per Customer, we identified the most "cost-efficient" cities for new store locations.
- Run
monday_coffee_schemas_database_setup.sqlto create the schema. - Import the
.csvfiles from thedata/folder into the respective tables. - Run
monday_coffee_data_analysis_queries.sqlto generate the reports.
City Total Revenue Estimated Coffee Consumers Avg Rent per Customer Pune High 1.87M Low (Recommended) Delhi Highest 7.75M Medium Jaipur Medium 1.00M Very Low (Recommended)
I am a Data Analytics student with a focus on SQL and financial market trends. Check out my other projects or connect with me on LinkedIn:www.linkedin.com/in/sammy-kimaru-da43322