Pizza Sales Analytics

The story behind the work

This Maven Analytics challenge put me in the role of a business intelligence consultant for a fictional Greek-inspired pizza restaurant in New Jersey. The task was not simply to display sales: it was to use transactional data to investigate when orders arrived, which menu items were most popular, and how the business could plan staffing and inventory around demand.

What I set out to do

• Analyze more than 50,000 pizza sales records and distinguish high-volume products from slower sellers.
• Prepare and model the data in a reporting workflow using SQL Server, Power Query and Power BI.
• Build an interactive report with more than 15 KPIs, including order volume, sales and average order value.
• Translate trends in time and product performance into practical restaurant-operating recommendations.

From raw data to a useful result

Used SQL Server, Power Query, relational modeling and DAX measures to analyze sales, orders and product demand.

• Extracted and cleaned order and pizza-level datasets using SQL Server and Power Query, resolving the analytical structure before visual design.
• Created relationships across transaction and product information and defined DAX measures for consistent sales and demand calculations.
• Developed dashboard views that connected time-of-day and day-of-week demand with product-level performance and headline KPIs.
• Investigated busy service periods, popular pizza varieties and low-volume menu items to uncover planning opportunities.
• Translated the analysis into seven proposed changes focused on inventory, staffing and service efficiency.

What I contributed

Independent ETL, data modeling, Power BI dashboard development and seven business recommendations.

• Owned the workflow from data preparation through the Power BI report and interpretation, acting independently within the challenge scenario.
• Created more than 15 reporting KPIs and made the measures comparable across time and menu-item views.
• Presented operational recommendations as a separate judgment informed by the findings, rather than treating them as proven results.

What the analysis revealed

• The strongest order periods in the published analysis were around 12–1 PM and 5–6 PM.
• Thursday, Friday and Saturday appeared among the busiest sales days. The original report noted approximately 2,520 pizzas in the Thursday-noon period across its analyzed data.
• Classic Deluxe was identified as the best-selling pizza; Brie Carre was among the lowest-volume offerings.
• The original project reported an average order value of approximately $38.30.
• Seven operational improvements were proposed, with an estimated 3% revenue opportunity. This was a projection, not measured revenue growth.