This repository contains a comprehensive SQL-based analysis of pizza sales data designed to demonstrate practical data analysis and problem-solving skills using SQL Server (SSMS).
Understand overall sales performance Identify top-selling pizzas and categories Analyze customer ordering patterns Generate actionable business insights to improve revenue and operations
Table : Description -> orders : Order date and time -> order_details : Quantity of each pizza ordered -> pizzas : Pizza size and price -> pizza_types : Pizza name, category, ingredients
-> Data validation and exploration -> Revenue and order analysis -> Category-wise and time-based insights
-> SQL Server (SSMS) -> Joins & Subqueries -> Aggregate Functions -> Window Functions -> Common Table Expressions (CTEs) -> Business Analytics & Reporting
This Pizza Sales Analysis project leverages SQL to explore sales performance, customer ordering behavior, and product-level revenue insights using transactional data. Through comprehensive querying and data validation, the analysis provides a clear understanding of how different pizza types, categories, and time-based factors influence overall business performance.
-> The business processes a large volume of orders, with consistent demand across different days and time periods.
-> Revenue is unevenly distributed, with a small subset of pizza types contributing a disproportionately high share of total sales.
-> Chicken and Classic pizza categories dominate in both total quantity sold and revenue generation, indicating strong customer preference.
-> Peak demand occurs during evening hours (6 PM–9 PM), confirming that dinner time drives the highest sales volume.
-> A significant percentage of orders contain multiple pizzas, reflecting group or family-style purchasing behavior.
-> Some pizza types generate high revenue despite lower quantities sold, suggesting premium pricing or niche popularity.
-> Average pricing varies by category, highlighting differences in perceived value and ingredient cost.
-> Overall, the analysis demonstrates how SQL can be effectively used to transform raw transactional data into meaningful business insights.
-> Top-selling pizzas and categories should be considered core revenue drivers.
-> Peak-hour performance highlights opportunities for targeted promotions during evening hours.
-> High-revenue but low-quantity pizzas may represent premium or specialty offerings.
-> Category-level performance reveals customer taste preferences, useful for menu planning.
Based on the analysis, the following actionable recommendations are suggested:
->> Focus marketing efforts on top-performing pizza categories (Chicken and Classic) to maximize revenue impact.
->> Introduce combo deals or family packs during peak hours (6 PM–9 PM) to capitalize on high multi-pizza order behavior.
->> Promote premium pizzas that generate high revenue but have lower sales volume to increase their visibility and order frequency.
->> Optimize staffing and inventory planning around peak evening hours to improve operational efficiency.
->> Experiment with category-specific discounts or offers during non-peak hours to balance demand throughout the day.
->> Monitor underperforming pizza types and evaluate whether to improve pricing, recipes, or remove them from the menu.