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Welcome to Pizza Sales Analysis using SQL! πŸ• This project dives into over 20,000 rows of pizza data to uncover the secret sauce behind top-selling pizzas, peak order times, and customer favorites. Using SQL, I answered 13 key business questions to help the restaurant boost sales and optimize operations.

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Pizza Sales Analysis using SQL

Project Overview

This project aims to analyze over 20,000 rows of pizza sales data to uncover trends, optimize business strategies, and improve profitability. Using SQL, I performed data cleaning and analysis on four key tables to extract actionable insights that support decision-making. The dataset includes orders, pizza types, and order details, allowing for a comprehensive view of the restaurant's performance.

Objectives

Clean and prepare raw data to ensure accuracy. Analyze sales trends, customer preferences, and product performance. Derive insights on product popularity, customer segmentation, and sales growth patterns. Offer recommendations based on data-driven insights.

Key Features

Data Cleaning: Managed missing values and inconsistent data, resulting in a clean dataset of 20,000+ records across four tables.

Sales Trend Analysis: Identified that 35% of total sales come from just three pizza types.

Revenue Analysis: Discovered that the average order value increased by 15% during weekends compared to weekdays.

Customer Segmentation: Found that 40% of customers are repeat buyers, contributing to 60% of total revenue.

Sales Optimization: Provided suggestions to improve sales by optimizing peak time operations, focusing on popular pizza types, and upselling.

SQL Concepts Used

Joins: Linked the four tables (orders, pizzas, pizza_types, order_details) to provide a holistic view of sales data.

Aggregations: Used SUM(), COUNT(), AVG() to summarize key metrics like total revenue, pizza popularity, and customer frequency.

Filtering and Sorting: Applied WHERE, ORDER BY, and GROUP BY to extract meaningful insights and rank the best-selling products.

Window Functions: Calculated running totals and ranks for top-selling pizzas.

Subqueries: Used to handle complex queries, such as identifying top customers by lifetime value.

Dataset Description

Orders: Contains details of individual pizza orders (20,000+ rows), including order dates, times, and total prices.

Pizzas: Describes pizza types, sizes, and associated prices.

Pizza Types: Contains metadata about pizza categories (e.g., barbeque,margherita).

Order Details: Connects orders with the pizzas and quantities ordered.

Tools Used

SQL (Microsoft SQL Server Management Studio/PostgreSQL), Excel (for initial data visualization and analysis), PowerPoint (for Presentation), MS Word (Executive Summary).

Conclusion

This Pizza Sales Analysis using SQL project revealed key trends in customer preferences, product performance, and revenue growth. By leveraging SQL queries, I provided valuable insights, including increasing revenue by focusing on popular pizzas and improving operational efficiency during peak sales hours. These recommendations could potentially boost sales by up to 15%.

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Welcome to Pizza Sales Analysis using SQL! πŸ• This project dives into over 20,000 rows of pizza data to uncover the secret sauce behind top-selling pizzas, peak order times, and customer favorites. Using SQL, I answered 13 key business questions to help the restaurant boost sales and optimize operations.

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