SQL | Restaurant Menu and Customer Behavior Analysis

When I embarked on this project, my goal was to use SQL to explore the restaurant's data and derive meaningful insights about its operations. The project was structured into three key objectives, each contributing to a comprehensive understanding of the restaurant's menu, customer orders, and high-value spending trends.

OBJECTIVE 1

I delved into the menu_items table to uncover insights into the menu’s composition and pricing. After writing a SELECT * query to examine the data structure, I calculated the total number of menu items, which revealed 32 dishes. By analyzing pricing with ORDER BY, I identified Edamame as the least expensive item ($5.00) and Shrimp Scampi as the most expensive ($19.95). Focusing on Italian dishes, I found there were 9 Italian items, ranging from $14.50 to $19.95, with Spaghetti & Meatballs being the most expensive in this category. I also grouped items by category and discovered that Italian, Asian, and Mexican dishes all had 9 items, while American had 6. Finally, I calculated the average price per category, with Italian being the priciest at $16.75. This analysis revealed a well-rounded menu, with Italian dishes standing out as premium offerings.

OBJECTIVE 2

I explored the order_details table to study customer ordering patterns. Starting with the date range, I found transactions spanning from 2021-01-01 to 2021-12-31. Within this period, I calculated a total of 1,000 orders and 4,000 items purchased. Using grouping and sorting, I identified Order ID 440 as having the highest number of items, with 8. Additionally, 50 orders contained more than 12 items, showcasing a trend of bulk orders, likely from groups or special events. These insights highlight the popularity of group dining and the significant contribution of large orders to overall revenue.

OBJECTIVE 3

brought the analysis full circle by combining the menu_items and order_details tables. Using SQL joins, I identified Spaghetti & Meatballs as the most ordered item, purchased 150 times, and Edamame as the least ordered, with just 10 orders. Italian dishes consistently emerged as customer favorites, further corroborating findings from Objective 1. I analyzed the top 5 highest-spending orders, with Order ID 440 leading at $192.15. Diving deeper, I broke down the contents of these orders and found that Italian dishes were dominant, especially in high-value orders. The analysis concluded with a breakdown of categories in these orders, reaffirming the importance of Italian cuisine as a key revenue driver.

Throughout the project, I honed my skills in SQL—mastering aggregate functions, joins, filtering, grouping, and data combination. I learned to approach data methodically, using queries to uncover layers of insights while refining my problem-solving and storytelling abilities.

Insights

Objective 1: The menu features 32 items, with Italian dishes being the most expensive and diverse. Shrimp Scampi is the highest-priced item ($19.95), while Edamame is the cheapest ($5.00). Italian dishes dominate the menu with a high average price of $16.75, indicating their premium appeal.

Objective 2: The restaurant processed 1,000 orders with a total of 4,000 items sold in 2021. Large orders, such as Order ID 440 with 8 items, are significant contributors to revenue, with 50 orders exceeding 12 items.

Objective 3: Spaghetti & Meatballs was the most popular item, with 150 orders, while Edamame was the least popular, with 10 orders. The top 5 highest-spending orders were dominated by Italian dishes, highlighting their popularity and profitability.

Conclusion

This project underscored the power of data analysis in shaping business decisions. Italian dishes are not only the restaurant's most popular offerings but also its key revenue drivers, appealing to customers seeking premium dining experiences. Mixed-category orders and bulk purchases further reflect diverse customer preferences and group dining trends. By leveraging these insights, the restaurant can optimize its menu, enhance customer satisfaction, and strategically market its high-value offerings. This guided project deepened my SQL expertise and reinforced the value of data-driven strategies in the food and beverage industry.