Using data to save Danny's Diner! This project explores customer behavior and sales trends to help this small restaurant thrive.
See full Case Study Here : - [https://8weeksqlchallenge.com/case-study-1/]
(1). 🔍 Project Overview : -
Danny seriously loves Japanese food so in the beginning of 2021, he decides to embark upon a risky venture and opens up a cute little restaurant that sells his 3 favourite foods: sushi, curry and ramen.
Danny’s Diner is in need of your assistance to help the restaurant stay afloat - the restaurant has captured some very basic data from their few months of operation but have no idea how to use their data to help them run the business.
(2). 🎯 Project Objectives : -
Danny wants to use the data to answer a few simple questions about his customers, especially about their visiting patterns, how much money they’ve spent and also which menu items are their favourite. Having this deeper connection with his customers will help him deliver a better and more personalised experience for his loyal customers.
He plans on using these insights to help him decide whether he should expand the existing customer loyalty program - additionally he needs help to generate some basic datasets so his team can easily inspect the data without needing to use SQL.
Danny has provided you with a sample of his overall customer data due to privacy issues - but he hopes that these examples are enough for you to write fully functioning SQL queries to help him answer his questions!
(3). 💾 Data Description
Table Descriptions :-
👥 members:
customer_id: Unique identifier for each customer.
join_date: Date when the customer first joined Danny Dinner.
🍣 🍜 menu:
product_id: Unique identifier for each product on the menu.
product_name: Name of the product (e.g., "Pizza Margherita", "Pasta Carbonara").
price: Price of the product.
📊 sales:
customer_id: Foreign key referencing the members table.
order_date: Date of the order.
product_id: Foreign key referencing the menu table.
(4). 🔗 Data Relationships:
The sales table links the members and menu tables via customer_id and product_id, respectively, enabling analysis of customer purchases.
Clearly describe the relationships between the tables.
(5). 🛠️ Tools & Technologies:
🛢️ PostgreSQL: Used for writing and executing SQL queries to analyze the dataset.
🗄️ SQL: Core language for querying and generating insights from the restaurant's data.
Thank you for visiting and supporting my project!