Pricing decisions sit at the core of every business, and I’ve always been intrigued by how to get them right — how to set prices that maximise profit, while still attracting enough buyers to make that profit possible. As an entrepreneur, I faced this challenge directly. As a customer, I often wonder whether I’m paying a fair price or overpaying for certain products or experiences.
In this project, I combined my interest in customer behaviour with a relatable scenario: analysing restaurant data to explore how pricing and ordering patterns affect sales performance.
The dataset was downloaded from Maven Analytics and contains restaurant order transactions combined with menu information. The data includes customer orders over a 3-month period, capturing item-level orders, timestamps, prices, and cuisine categories.
| Table | Description |
|---|---|
| menu_items | Contains menu items with item names, categories, and prices |
| order_details | Contains individual orders with order IDs, dates, times, and ordered items |
The objective of this analysis was to examine how pricing and customer behaviour influence revenue performance in a restaurant setting.
The focus was on identifying which items, cuisines, and price tiers drive sales, how demand shifts across time periods, and where pricing or menu structure may be limiting performance.
The ultimate goal was to translate transactional data into actionable insights to support pricing optimisation, menu design, and revenue growth.
The analysis was performed using SQL queries in MySQL Workbench to extract and transform data directly from the source tables. The query outputs were then exported to Excel, where the data was further structured and organised for visual analysis and interpretation.