https://github.com/takshshah-16/pizza_sales_sql
SQL-powered pizza sales analytics project using MySQL Workbench to derive business insights through data exploration and queries.
https://github.com/takshshah-16/pizza_sales_sql
business-intelligence data-analysis database-management mysql sql
Last synced: 13 days ago
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SQL-powered pizza sales analytics project using MySQL Workbench to derive business insights through data exploration and queries.
- Host: GitHub
- URL: https://github.com/takshshah-16/pizza_sales_sql
- Owner: Takshshah-16
- Created: 2025-02-18T08:30:00.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-18T10:12:23.000Z (over 1 year ago)
- Last Synced: 2025-10-09T16:49:05.927Z (10 months ago)
- Topics: business-intelligence, data-analysis, database-management, mysql, sql
- Homepage:
- Size: 508 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🍕 Pizza Sales Business Intelligence Analysis
## 📌 Project Overview
Restaurants generate thousands of transactions every day, but raw sales data alone does not provide meaningful insights for decision-making. Businesses often struggle to identify their best-selling products, understand customer ordering behavior, and optimize operations for maximum profitability.
This project uses SQL to analyze a pizza sales dataset and transform transactional data into actionable business insights. Through exploratory analysis and business intelligence techniques, the project identifies revenue drivers, customer preferences, and sales patterns that can support data-driven decision-making.
## 🎯 Business Problem
Imagine managing a restaurant chain with thousands of customer orders being placed every month. While sales data is available, there is limited visibility into:
- Which products generate the highest revenue
- Customer purchasing preferences
- Peak business hours
- Category-wise sales performance
- Long-term revenue trends
Without proper analysis, businesses may struggle to optimize inventory, workforce planning, and revenue generation strategies.
This project addresses these challenges by leveraging SQL to uncover actionable business insights.
## 🗂️ Dataset
The project uses multiple relational datasets containing information about:
- Orders
- Order Details
- Pizzas
- Pizza Types
These tables were joined and analyzed to derive meaningful business insights.
All datasets required for this project are available within this repository.
## 🎯 Project Objectives
The analysis aims to answer the following business questions:
- How many orders were placed?
- How much total revenue was generated?
- Which pizza generates the highest revenue?
- Which pizzas are ordered the most?
- Which pizza categories perform best?
- What pizza size is most preferred by customers?
- During which hours are customers most active?
- Which products contribute most to revenue?
- How does revenue accumulate over time?
- Which pizza categories drive long-term profitability?
## ⚙️ Tools & Technologies Used
- **MySQL Workbench** for writing and executing SQL queries.
- **Data Aggregation** (SUM, COUNT, AVG, GROUP BY)
- **Joins** (INNER JOIN, LEFT JOIN) to combine multiple tables.
- **Window Functions** for advanced analytics.
- **CTEs & Subqueries** for efficient query structuring.
## 📈 Key Insights
- Certain pizza varieties contribute disproportionately to overall revenue.
- Large-sized pizzas are among the most frequently ordered products.
- Customer ordering activity peaks during specific hours of the day.
- A few pizza categories drive the majority of sales performance.
- Revenue demonstrates consistent growth over time.
## 💡 Business Recommendations
- Maintain higher inventory levels for top-selling pizzas to prevent stock shortages.
- Schedule additional staff during peak ordering hours to improve operational efficiency.
- Promote underperforming pizzas through discounts, meal bundles, or combo offers.
- Bundle high-performing pizzas with beverages and side dishes to increase average order value.
- Invest more in categories that consistently contribute higher revenue.