Pizza Sales Analysis with SQL

Introduction

This project is a detailed data analysis of a fictional pizza sales dataset. The primary objective is to leverage SQL to extract meaningful business insights, demonstrate data querying skills, and present the findings in a clear and understandable manner.

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Project Objective & Key Questions

The main goal of this analysis was to answer critical business questions to improve operations and strategy. The analysis sought to answer the following:

• What are the key sales trends (e.g., daily, monthly, and hourly sales)?
• Which pizza types are the most popular?
• What are the total revenue and average order value?
• Which pizza sizes are ordered most frequently?

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Tools & Technologies

• SQL (e.g., MySQL, MS SQL Server): Used for data querying, aggregation, and analysis.
• Microsoft Excel/PowerPoint: Used for preparing the final report and presentation.
• CSV Files: The data was provided in multiple CSV files (orders.csv, order_details.csv, pizzas.csv, pizza_types.csv).

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Methodology

The analysis followed a structured approach:

1. Data Import: All CSV files were imported into a SQL database.
2. Schema Design: The tables were joined to create a unified view of the data.
3. Data Exploration: Various SQL queries were written to clean, explore, and analyze the data. This included using JOIN statements to link different tables and GROUP BY and ORDER BY clauses to aggregate and rank data.
4. Insight Generation: The final queries were used to generate the key findings and answer the business questions.

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Key Findings

The analysis revealed several important insights:

• Total Revenue: A total revenue of [Insert Total Revenue Here] was generated over the period.
• Best-Selling Pizza: The [Insert Best-Selling Pizza Name Here] was the most popular pizza type.
• Peak Hours: The peak order times were [Insert Peak Hours Here], indicating optimal staffing times.
• Top 5 Revenue-Generating Pizzas: The top 5 pizzas by revenue were... [List the pizzas here].