🍕 Pizza Sales Analysis — SQL Project

An end-to-end SQL analysis of pizza sales data to uncover revenue trends, top-performing products, and peak ordering patterns using MySQL.

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📌 Project Overview

This project analyzes a pizza restaurant's transactional data to answer key business questions around sales performance, product popularity, and customer ordering behavior. All insights are derived purely through SQL queries — from basic aggregations to advanced window functions and CTEs.

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📊 Key Results

Metric	Finding
Total Records Analyzed	10,000+ orders
Top Revenue Categories	Top 3 drove 65%+ of total revenue
Peak Ordering Time	Friday evenings = 22% of weekly orders
Query Techniques Used	CTEs, Window Functions, JOINs, Subqueries

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🗂️ Dataset

• Tables: orders, order_details, pizzas, pizza_types
• Key Fields: order_id, pizza_id, quantity, price, category, order_date, order_time
• Source: Pizza Hut sales dataset (included in repo)

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🛠️ Tech Stack

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🔍 Analysis Breakdown

📦 Sales Volume & Revenue

• Total number of orders placed
• Total revenue generated from pizza sales
• Highest-priced pizza identification

🍕 Product Analysis

• Most common pizza size ordered
• Top 5 most ordered pizza types by quantity
• Category-wise distribution of pizzas

⏰ Time & Trend Analysis

• Distribution of orders by hour of the day
• Average number of pizzas ordered per day
• Cumulative revenue generated over time

🏆 Performance Analysis

• Top 3 most ordered pizza types by revenue
• Percentage contribution of each pizza type to total revenue
• Top 3 pizza types by revenue within each category (using window functions)

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💡 Key Business Insights

• 🏆 Top 3 pizza categories drove 65%+ of total revenue — focus marketing here
• ⏰ Friday evenings account for 22% of weekly orders — optimize staffing and prep
• 📏 Large size was the most ordered — upsell opportunity for XL
• 💰 Classic pizzas consistently outperform specialty in volume

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📁 Project Structure

Pizza-Sales-Analysis-SQL-Project/
│
├── Pizza Sales Source Code.sql          # All SQL queries (basic → advanced)
├── Pizza Sales Analysis SQL Project.pdf # Full analysis report with results
├── Reference File for pizza sales analysis.txt  # Query reference guide
├── order_details.csv                    # Order line items data
├── orders.csv                           # Order header data
├── pizza_types.csv                      # Pizza catalog data
└── README.md

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▶️ How to Run

1. Install MySQL Workbench (free download at mysql.com)

2. Create the database:

CREATE DATABASE pizzahut;
USE pizzahut;

3. Import the CSV files as tables (order_details, orders, pizzas, pizza_types)

4. Open Pizza Sales Source Code.sql and run queries section by section

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🧠 SQL Concepts Used

-- Example: Top 3 pizza types by revenue per category (Window Function)
SELECT category, name, revenue,
       RANK() OVER (PARTITION BY category ORDER BY revenue DESC) AS rnk
FROM (
    SELECT pt.category, pt.name,
           SUM(od.quantity * p.price) AS revenue
    FROM pizza_types pt
    JOIN pizzas p ON pt.pizza_type_id = p.pizza_type_id
    JOIN order_details od ON p.pizza_id = od.pizza_id
    GROUP BY pt.category, pt.name
) ranked_pizzas;

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💡 Key Learnings

• Writing complex SQL queries using CTEs, window functions, and multi-table JOINs
• Translating business questions into structured SQL queries
• Using revenue contribution % to prioritize product decisions
• Identifying operational patterns (peak hours) to drive staffing recommendations

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👩‍💻 Author

Devika Lahari Bandi