In 2024, full service restaurant sales in the United States reached *** billion U.S. dollars, showing growth over the previous year. This figure was forecast by the source to grow to *** billion U.S. dollars in 2025.

Description: This dataset offers a granular, item-level view of daily sales from a diverse group of 50 restaurants, spanning a full year from January 2024 to January 2025. It serves as a comprehensive resource for time-series analysis, demand forecasting, and investigating the various factors that influence customer purchasing habits across different restaurant types, including Cafes, Casual Dining, Fine Dining, Food Stalls, and Kopitiams.

Each row represents the total quantity sold of a specific menu item at a single restaurant on a given day. The dataset is enriched with valuable contextual information, such as weather conditions, promotions, and special events. It also includes detailed financial metrics for each menu item, including the typical ingredient cost, the observed market price, and the actual selling price, making it ideal for analyzing pricing strategies and promotional effectiveness.

Potential Uses: This versatile dataset is well-suited for a variety of analytical projects:

1.**Sales Forecasting**: Develop time-series models (e.g., ARIMA, Prophet) to predict daily sales volumes for specific menu items, individual restaurants, or different restaurant types.

2.**Demand Analysis**: Analyze how external factors like weather conditions and special events impact sales across various item categories and restaurant types.

3.**Promotion Effectiveness**: Evaluate the impact of promotions by comparing the quantity sold and profitability on days with and without promotional activities.

4.**Menu Engineering**: Identify the most and least popular menu items to make data-driven decisions regarding menu optimization, inventory management, and marketing focus. By analyzing item popularity against profitability, restaurants can strategically design their menus to maximize revenue.

5.**Customer Behavior Insights**: Uncover patterns in sales data to understand customer preferences. For instance, determine if certain meal types are more popular on weekends versus weekdays or how pricing impacts choice across different dining segments.

6.**Comparative Analysis**: Compare sales performance, pricing strategies, and menu popularity across different restaurants and restaurant types (e.g., Casual Dining vs. Fine Dining) to identify key drivers of success.

Graph and download economic data for Retail Sales: Restaurants and Other Eating Places (MRTSSM7225USN) from Jan 1992 to Aug 2025 about restaurant, retail trade, sales, retail, and USA.

This statistic shows the restaurant industry food and drink sales in the United States from 1970 to 2017. In 2016, food and drink sales of the U.S. restaurant industry amounted to approximately *** billion U.S. dollars.

More statistics and facts on fast food, the U.S. restaurant industry and the pizza (delivery) market.

U.S. restaurant industry - additional information

In 2016, food and drink sales in the United States restaurant industry amounted to *** billion U.S. dollars, up from ***** billion U.S. dollars in the previous year. Restaurants in the United States have created a booming industry that employed more than ** million people nationwide in 2015.

Unsurprisingly, the majority of food and drink sales in the U.S. restaurant industry take place in commercial restaurants. In 2016, full-service restaurant sales amounted to *** billion U.S. dollars and limited-service sales were *** U.S. dollars. The second largest contributor in 2015 was retail, vending, recreation and mobile vendors with sales of ***** billion U.S. dollars. The smallest proportion came from came from bars and taverns.

As of December 2016, things were still looking up for the U.S. restaurant industry: the monthly Restaurant Industry Tracking Survey, conducted by the National Restaurant Association, recorded a performance index score of ***** – any score over 100 indicates a period of expansion. The lowest performance index score between 2011 and 2017, ****, was recorded in August 2011. In November 2016, ** percent of U.S. consumers reported that cheaper restaurants would make them dine out more often.

The Restaurant Revenue Prediction Dataset is a comprehensive collection of simulated data designed for predicting monthly revenue for a set of fictitious restaurants. This dataset was created for educational and illustrative purposes, allowing data enthusiasts to explore and experiment with machine learning algorithms for regression tasks.

Graph and download economic data for Revenue for Limited-Service Restaurants, All Establishments, Employer Firms (LRRAEEF2722513) from 2013 to 2022 about restaurant, employer firms, revenue, establishments, and USA.

In 2024, the month with the highest Quick Service Restaurant (QSR) sales in the United States was May, reaching around ** billion U.S. dollars. Meanwhile, the months of August and July ranked second and third, with QSR sales surpassing **** and **** billion U.S. dollars respectively.

Graph and download economic data for Revenue for Full-Service Restaurants, All Establishments, Employer Firms (FRRAEEF2722511) from 2013 to 2022 about restaurant, employer firms, revenue, establishments, and USA.

Graph and download economic data for Retail Sales: Full Service Restaurants (MRTSSM7221USN) from Jan 1992 to Jan 2020 about restaurant, retail trade, sales, retail, services, and USA.

This dataset presents aggregated transformational (non-operational) revenues for Qatar’s hotels and restaurants sector. It includes interest income, insurance claims, asset and raw material sale profits, and dividend earnings. Data is broken down by revenue item and main activity type. Values are reported in thousands of Qatari Riyals (QR).

In 2023, the quick service restaurant industry's revenue in France increased by roughly ** percent over the previous year. Overall, this industry's revenue amounted to **** billion euros in 2023.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of food services and drinking places (NAICS 722), annual, for five years of data.

Dataset Title: Imperial Dragon Restaurant Sales Data Description: This synthetic dataset contains over 1,000 simulated sales transactions for "Imperial Dragon," a fictional Chinese restaurant. It was meticulously generated for academic purposes. The data is designed to be realistic and contains a mix of numeric and categorical fields, perfect for learning and practicing data analysis, pivot tables, dashboard creation, and business intelligence reporting.

The dataset includes typical Point-of-Sale (POS) information such as transaction details, server performance, menu item sales, and calculated financial metrics like profit. It is intentionally "clean" but contains a few minor inconsistencies to simulate real-world data, making it ideal for teaching data cleansing and preparation techniques.

Acknowledgements: This dataset was generated by an AI assistant. It is intended for educational use only.

Purpose: The primary purpose of this dataset is to serve as a practical tool for students to demonstrate their mastery of advanced Excel functions, including:

Usage: This dataset is perfect for:

Students learning data analytics with spreadsheets.

Educators teaching business intelligence, Excel, or data visualization.

Beginners in data analysis looking for a clean, understandable dataset to practice on.

Creating interactive dashboards, sales performance reports, and inventory analyses.

The dataset includes following fields: (Food) Item, Category, Sub Category, Item Name, Price, Cost. The purpose of this dataset is to practice data visualization in tools like power bi and python.

This dataset presents the overall production value of Qatar's hotels and restaurants sector, aggregated across all establishments. It is categorized by revenue type (e.g., products, other revenues) and main economic activity. Values are in thousands of Qatari Riyals (QR), supporting macro-level output and revenue stream analysis.

In 2024, sales revenue generated in the restaurant industry in Japan amounted to approximately ***** trillion Japanese yen. The restaurant industry is part of the hospitality industry and generates most of its revenue.

This dataset presents transformational (non-operational) revenues in Qatar’s hotels and restaurants sector for establishments with fewer than 10 employees. It includes items such as interests, insurance claims, profits from asset/material sales, and dividend income. All values are in thousands of Qatari Riyals (QR).

Forecast: Full-Service Restaurants Revenue in Canada 2022 - 2026 Discover more data with ReportLinker!

📹 Project Video available on YouTube - https://youtu.be/dQwnyCEZ-UU

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

It is a sales data of a restaurant company operating in multiple cities in the world. It contains information about individual sales transactions, customer demographics, and product details. The data is structured in a tabular format, with each row representing a single record and each column representing a specific attribute. This dataset can be commonly used for business intelligence, sales forecasting, and customer behaviour analysis.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Most Preferred Payment Method ?

Q.2) Most Selling Product - By Quantity & By Revenue ?

Q.3) Which city had maximum revenue , or , Which Manager earned maximum revenue ?

Q.4) Date wise revenue.

Q.5) Average Revenue.

Q.6) Average Revenue of November & December month.

Q.7) Standard Deviation of Revenue and Quantity ?

Q.8) Variance of Revenue and Quantity ?

Q.9) Is revenue increasing or decreasing over time?

Q.10) Average 'Quantity Sold' & 'Average Revenue' for each product ?

These are the main Features/Columns available in the dataset :

1) Order ID: A unique identifier for each sales order. This can be used to track individual transactions.

2) Order Date: The date when the order was placed. This column is essential for time-series analysis, such as identifying sales trends over time or seasonality.

3) Product: The name or type of the product sold. This column is crucial for analyzing sales performance by product category.

4) Price : The unit price of the product. This, along with 'Quantity Ordered', is used to calculate the total price of each order.

5) Quantity : The number of units of the product sold in a single order. This is a key metric for calculating revenue and understanding sales volume.

6) Purchase Type : The order was made online or in-store or drive-thru.

7) Payment Method : How the payment for the order was done.

8) Manager : Name of the manager of the store.

9) City : The location of the store. This can be used for geographical analysis of sales, such as identifying top-performing regions or optimizing logistics.

Graph and download economic data for Restaurant Chains, Sales Per Store for United States (M0607AUSM174NNBR) from Jan 1920 to Dec 1923 about restaurant, sales, retail, and USA.