Restaurant Delivery - United States

United States

Highlights

The Restaurant Delivery market in the United States is forecasted to generate a revenue of US$36.95bn by 2024.
This projection suggests an annual growth rate (CAGR 2024-2029) of 2.70%, leading to an estimated market volume of US$42.21bn by 2029.
Additionally, the number of users in this market is expected to reach 159.3m users by 2029.
In 2024, the user penetration rate is predicted to be 41.3% and is projected to increase to 45.4% by 2029.
The average revenue per user (ARPU) is expected to be US$261.90.
When compared globally, United States is anticipated to generate the highest revenue in the Restaurant Delivery market, with an estimated amount of US$36.95bn in 2024.
South Korea, on the other hand, is expected to have the highest user penetration rate in the Restaurant Delivery market, with a projected rate of 44.8%.
The United States has seen a significant rise in the popularity of third-party restaurant delivery platforms, revolutionizing the way people dine at home.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The popularity of restaurant delivery services in the United States has been on the rise in recent years. With the busy lifestyle of Americans and the convenience of technology, it is no surprise that the restaurant delivery market has become increasingly competitive.

Customer preferences: Customer preferences have shifted towards convenience and speed. With the rise of delivery apps, customers can easily order their favorite meals from the comfort of their homes. Additionally, customers are becoming more health-conscious, leading to an increase in demand for healthy meal options.

Trends in the market: One of the major trends in the restaurant delivery market in the United States is the rise of third-party delivery services. These services partner with restaurants to offer delivery options to customers who may not have previously been able to access them. Another trend is the increasing use of technology, such as mobile apps and online ordering systems, to streamline the delivery process.

Local special circumstances: The United States is a vast country with a diverse population, leading to a variety of local special circumstances. For example, urban areas may have more demand for delivery services due to a higher concentration of busy professionals. On the other hand, rural areas may have limited access to delivery services due to a lack of infrastructure.

Underlying macroeconomic factors: The growth of the restaurant delivery market in the United States can be attributed to several underlying macroeconomic factors. These include the rise of the gig economy, which has led to an increase in the number of delivery drivers available. Additionally, the strong economy has led to increased consumer spending, allowing for more disposable income to be spent on restaurant delivery services. Finally, the COVID-19 pandemic has accelerated the growth of the restaurant delivery market, as more people are staying home and relying on delivery services for their meals.

Data coverage:

The data encompasses B2C enterprises. Figures are based on Gross Merchandise Value (GMV) and represent what consumers pay for these products and services. The user metrics show the number of customers who have made at least one online purchase within the past 12 months.

Modeling approach / Market size:

Market sizes are determined through a bottom-up approach, building on predefined factors for each market. As a basis for evaluating markets, we use annual financial reports of the market-leading companies, third-party studies and reports, as well as survey results from our primary research (e.g., the Statista Global Consumer Survey). In addition, we use relevant key market indicators and data from country-specific associations, such as GDP, GDP per capita, and internet connection speed. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing. The main drivers are internet users, urban population, usage of key players, and attitudes toward online services.

Additional notes:

The market is updated twice a year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. GCS data is reweighted for representativeness.