Basic: Retrieve the total number of orders placed. Calculate the total revenue generated from pizza sales. Identify the highest-priced pizza. Identify the most common pizza size ordered. List the top 5 most ordered pizza types along with their quantities.

Intermediate: Join the necessary tables to find the total quantity of each pizza category ordered. Determine the distribution of orders by hour of the day. Join relevant tables to find the category-wise distribution of pizzas. Group the orders by date and calculate the average number of pizzas ordered per day. Determine the top 3 most ordered pizza types based on revenue.

Advanced: Calculate the percentage contribution of each pizza type to total revenue. Analyze the cumulative revenue generated over time. Determine the top 3 most ordered pizza types based on revenue for each pizza category.

In the 12 weeks ending July 14, 2024, the dollar sales of frozen pizza in the United States amounted to 1.5 billion U.S. dollars. Refrigerated pizza and pizza kits followed, with a sales value of close to 123 million dollars.

According to the data, for the 12 weeks ending July 14, 2024, Uno was the leading refrigerated pizza and pizza kit brand of the United States, after private labels, with sales amounting to over 340,000 units. Panera Bread followed, with sales reaching close to 289,000 units.

Consumer spending on pizza delivery in the United States amounted to approximately **** billion U.S. dollars in 2024. This shows a slight increase over the previous year's total of **** billion U.S. dollars.

Description:

This dataset contains thoroughly cleaned and transformed data of pizza sales, making it ideal for in-depth analysis and visualization. The data includes key information such as sales dates, pizza types, order quantities, prices, and more. It has been carefully structured to facilitate a wide range of analyses, including sales performance, customer preferences, and seasonal trends.

The dataset allows analysts to explore sales patterns over time, identify best-selling pizza types, and evaluate revenue trends across various periods. Additionally, it can be used to gain insights into customer ordering behavior, peak sales times, and regional preferences.

Features:

Pizza Types: Various types of pizzas sold, including detailed breakdowns of sizes, ingredients, and categories. Sales Data: Information on the number of pizzas sold, revenue generated, and sales by specific periods (days, months, etc.). Order Information: Data on order quantities and combinations, useful for identifying customer preferences and popular menu items. Price Data: Pricing details to evaluate revenue and profit margins. Use Cases:

Performing time-series analysis to discover trends in pizza sales across different seasons and days of the week. Visualizing the distribution of sales across different pizza types and identifying the most and least popular items. Analyzing sales performance to optimize inventory management, marketing strategies, and pricing models. Comparing sales across multiple outlets or regions to identify top-performing locations and customer segments. Whether you're looking to conduct exploratory data analysis, create interactive visualizations, or apply predictive models, this dataset offers a solid foundation for understanding pizza sales dynamics and enhancing business strategies.

In 2024, the pizza restaurant industry's market value in the United States exceeded ** billion U.S. dollars. This showed a decline of *** percent over the previous year.

This dataset is designed for SQL analysis exercises, providing comprehensive data on pizza sales, orders, and customer preferences. It includes details on order quantities, pizza types, and the composition of various pizzas. The dataset is ideal for practicing SQL queries, performing revenue analysis, and understanding customer behavior in the pizza industry.

1. order_details.csv Description: Contains details of each pizza order. Columns: order_details_id: Unique identifier for the order detail. order_id: Identifier for the order. pizza_id: Identifier for the pizza type. quantity: Number of pizzas ordered

2. pizza_types.csv Description: Provides information on different types of pizzas available. Columns: pizza_type_id: Unique identifier for the pizza type. name: Name of the pizza. category: Category of the pizza (e.g., Chicken, Vegetarian). ingredients: List of ingredients used in the pizza.

3. Questions.txt Description: Contains various SQL questions for analyzing the dataset. Contents: Basic: Retrieve the total number of orders placed. Calculate the total revenue generated from pizza sales. Identify the highest-priced pizza. Identify the most common pizza size ordered. List the top 5 most ordered pizza types along with their quantities.

Market Size statistics on the Pizza Restaurants industry in the US

Sales of frozen pizza in the United States amounted to approximately **** billion U.S. dollars in 2023, up from **** billion U.S. dollars the previous year. Frozen pizza - additional informationPizza is an Italian flatbread made with tomato sauce, cheese and an assortment of toppings such as meats, seafood and vegetables. Frozen pizzas, as well as other convenience foods, have become kitchen staples in many households in the United States. Nestlé USA, Schwan Food Co, General Mills and Palermo Villa were the leading frozen pizza vendors in the U.S. in 2019. DiGiorno, a subsidiary of Nestlé, was the nation’s number one frozen pizza brand that year, with sales amounting to over *** billion U.S. dollars. Private labels were also very popular among U.S. consumers, accounting for an ** percent share of the frozen pizza market. Other best-selling frozen pizza brands included Red Baron, Totino’s Party Pizza and Tombstone. DiGiorno frozen pizza was first introduced into the U.S. market in 1995. All DiGiorno pizza products are manufactured in the Midwest. The company sources all of its ingredients in the United States and its pizza bakeries employ more than ***** people. Apart from DiGiorno, Nestlé owns top frozen pizza brands Tombstone, Jack’s and California Pizza Kitchen.

Introduction

Domino's Pizza Statistics: I’ve always been curious about how big Domino’s Pizza is. Like, we all eat it, we’ve all waited for that 30-minute delivery timer, and yeah, most of us probably ordered it while binge-watching something. But how much do we know about their numbers? That’s where Domino’s Pizza statistics get interesting.

I went through all the important information. From how many outlets they’ve got, to what works in India compared to the US, and even how their tech and marketing make them a leader in delivery. I’m breaking it all down here for anyone who just wants to understand how Domino’s is running this global pizza empire. So, let’s get started.

Comprehensive dataset containing 33 verified Pizza businesses in United States with complete contact information, ratings, reviews, and location data.

Pizza restaurants have suffered revenue declines due to growing competition and some economic volatility over the past five years. Still, demand for pizza restaurants has persisted, despite and somewhat because of the high inflation and economic uncertainty. Pizza is a mainstay in many US households, but other restaurants have begun selling it, and various at-home options exist (frozen pizza). Further, rising health consciousness among Americans has lowered demand for pizzas. Pizza restaurant revenue has been falling at a CAGR of 2.9% over the past five years and is expected to reach $49.5 billion in 2025, when revenue will fall an estimated 0.3%. Pizza is generally viewed as a fast-casual food option, with most consumers ordering it as a takeout meal at home. During the pandemic, this trend only increased, providing consumers with a relatively inexpensive dining option that could be obtained through minimal contact with others. Moreover, it is considered a good lunch option for time-crunched families, especially when sold by the slice and as a dinner option. As a result, when economic conditions improve or deteriorate, demand for pizza remains steady. The greatest threat to pizza restaurants tends to be the consumer's penchant for healthier food options. But many owners have combated this by offering health-conscious items like low-fat pizza, salads and others. Other restaurants have opted to boost profit by providing a range of gourmet options and non-pizza food options to varying degrees of success. Growth will return over the next five years, with revenue rising an annualized 1.6% to $53.4 billion. However, saturation will result in greater internal competition, as the number of pizza restaurants rises. Depending on the area of operation, some restaurants will endure a slight slowdown in growth, while others will have to increase marketing campaigns and fight to maintain their customer base. As during previous periods, a rise in health-conscious consumers will continue to threaten potential growth, but restaurants that are able and willing to provide a range of healthier options should fare well.

Comprehensive dataset containing 112,624 verified Pizza restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

Financial overview and grant giving statistics of Pizza Hut Foundation

Employment statistics on the Pizza Restaurant Franchises industry in the US

Dominos Pizza reported $1.15B in Sales Revenues for its fiscal quarter ending in June of 2025. Data for Dominos Pizza | DPZ - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Financial overview and grant giving statistics of American Pizza Community

The market value of the pizza industry in North America was forecast to reach approximately **** billion U.S. dollars in 2022, reflecting the highest projected market value in that year. Following by a small margin was Western Europe, with a projected market value of ** billion U.S. dollars.

1) Data Introduction • The Pizza or Not Pizza? dataset is a computer vision image dataset designed for binary classification to distinguish between images of pizza and non-pizza food items.

2) Data Utilization (1) Characteristics of the Pizza or Not Pizza? dataset: • The dataset is balanced, consisting of an equal number of pizza and non-pizza food images. All images are collected from real user-generated content, enhancing its practical applicability in real-world scenarios. • The images cover a wide range of food types and preparation environments, providing high visual diversity and realism.

(2) Applications of the Pizza or Not Pizza? dataset: • Binary classification model development for food images: The dataset can be used to train deep learning models that automatically classify whether an image contains pizza or not.

Financial overview and grant giving statistics of Pizza Klatch