Food Delivery Business Analytics using MySQL

Project Overview

This project is a comprehensive SQL-based business analytics solution developed using MySQL. The objective is to analyze food delivery operations and generate meaningful business insights related to customer behavior, restaurant performance, delivery efficiency, and revenue generation.

The project demonstrates real-world SQL skills used by Data Analysts to clean data, perform business analysis, build reusable database objects, and solve business problems using advanced SQL concepts.

Project Objectives

• Analyze customer ordering behavior
• Identify top-performing restaurants
• Measure business revenue and sales trends
• Evaluate delivery partner performance
• Analyze payment preferences
• Study customer ratings and satisfaction
• Generate business KPIs
• Apply advanced SQL concepts to solve business problems

Tools & Technologies

• Database: MySQL
• IDE: MySQL Workbench
• Language: SQL
• Version Control: Git & GitHub

Dataset

The project uses a Food Delivery Orders dataset containing information such as:

• Customer Details
• Restaurant Details
• Order Information
• Delivery Information
• Payment Details
• Ratings
• Weather Conditions
• Festival Information
• Revenue Data

SQL Concepts Covered

Data Cleaning Aggregate Functions GROUP BY HAVING ORDER BY CASE Statements Views Stored Procedures Common Table Expressions (CTEs) Window Functions Triggers Subqueries Ranking Functions Business KPI Analysis

Project Structure

Project Workflow

Part 1 – Data Validation & Cleaning

• Checked duplicate records
• Identified missing values
• Verified dataset quality
• Performed data validation

Part 2 – Business KPI Analysis

• Total Revenue
• Total Customers
• Total Orders
• Average Order Value
• Restaurant Performance

Part 3 – SQL Views

Created reusable business views for:

• Customer Spending
• Restaurant Performance
• City Revenue
• Payment Analysis
• Delivery Partner Performance

Part 4 – Stored Procedures

Developed reusable procedures for:

• Customer Orders
• Restaurant Revenue
• Business Summary
• Orders by City
• Orders by Payment Mode

Part 5 – Business Analysis

Generated business insights including:

• Top Customers
• Top Restaurants
• Revenue by City
• Most Popular Cuisine
• Delivery Performance

Part 6 – Common Table Expressions (CTEs)

Implemented CTEs for:

• Customer Revenue
• Restaurant Revenue
• City Revenue
• Payment Analysis
• Weather Analysis

Part 7 – Window Functions

Applied advanced SQL window functions:

• ROW_NUMBER()
• RANK()
• DENSE_RANK()
• LEAD()
• LAG()
• Running Totals

Part 8 – Database Triggers

Created triggers to validate:

• Customer Ratings
• Restaurant Ratings
• Delivery Fee
• Order Value
• Distance

Part 9 – Advanced SQL

Solved business problems using:

• Subqueries
• HAVING
• Window Functions
• Ranking
• Aggregate Analysis

Business Insights Generated

• Identified highest spending customers
• Ranked top-performing restaurants
• Compared payment methods
• Evaluated delivery partner performance
• Measured city-wise revenue
• Analyzed cuisine popularity
• Studied weather and festival impact
• Identified customer ordering trends

Skills Demonstrated

• SQL Programming
• Data Cleaning
• Business Analytics
• Data Aggregation
• Query Optimization
• KPI Reporting
• Relational Database Management
• Analytical Thinking
• Problem Solving

Future Improvements

• Build an interactive Power BI dashboard using the same dataset.
• Integrate Python for advanced analysis and automation.
• Add monthly and yearly trend analysis.
• Include predictive analytics for customer demand.