In-app advertising refers to the promotion of products or services within a mobile application and to ad spending on displaying advertisements within an application. This includes various formats, such as banner ads, interstitial ads, video ads, and native ads, that are integrated into the mobile app's user interface and appear as part of the app's content. The ads are usually shown to target users based on their preferences and online behavior.
In-app advertising consists of 21 app categories, books & reference, business, education, entertainment, finance, food & drink, game, health & fitness, lifestyle, medical, music, navigation, news & magazines, photo & video, productivity, shopping, social networking, sports, travel, utilities, and weather.
In-app advertising comprises advertising spending, users, and average revenue per user. The market only displays B2B spending. Figures are based on in-app advertising spending and exclude agency commissions, rebates, production costs, and taxes. For more information on the data displayed and definition of each category, use the info button right next to the boxes.
Advertising that is displayed within mobile applications includes a variety of ad types, including native ads, interstitial ads, video ads, and banner ads.
Advertising that is displayed within mobile browsers or on mobile webs.
Today, in-app advertising is a thriving market and continues to grow as more businesses recognize the potential of reaching audiences through mobile apps. Advertisers are able to target specific audiences based on their behavior, preferences, and location, which makes in-app advertising a highly effective form of mobile advertising. In-app advertising can also include both programmatic and non-programmatic advertising. In-app non-programmatic advertising, such as direct sales, is when a business directly negotiates the terms of an advertising deal with a specific publisher or app. Meanwhile, programmatic advertising is a way of buying or bidding on ads, which is done automatically by an algorithm. It is also becoming more popular in in-app advertising because it makes buying and targeting ads easier and more efficient.
As more companies become aware of the possibility of reaching audiences through mobile applications, the market for in-app advertising is now booming and is expected to continue expanding. Given that in-app advertising allows advertisers to target particular audiences based on their activity, preferences, and location, it is one of the most successful types of mobile advertising.
According to analysts, in-app advertising will continue expanding as more companies shift their advertising budgets to mobile. As a result of advancements in infrastructure, the in-app advertising market is expected to grow, making it more cost-effective and accessible, with consumers increasingly relying on mobile devices.
The data encompasses B2B enterprises. Figures are based on in-app advertising spending and exclude agency commissions, rebates, production costs, and taxes. The market covers ad spending on advertisements displayed within a mobile application.
The market size is determined through a combined top-down and bottom-up approach. We use market data from independent databases, the number of application downloads from data partners, survey results taken from our primary research (e.g., the Statista Global Consumer Survey), and third-party reports to analyze and estimate global in-app advertising spending. To analyze the markets, we start by researching digital advertising in mobile applications for each advertising format, incidents of in-app and mobile browser usage, as well as the time spent in mobile apps by categories. To estimate the market size for each country individually, we use relevant key market indicators and data from country-specific industry associations, such as GDP, mobile users, and digital consumer spending. Lastly, we benchmark key countries and/or regions (e.g., global, the United States, China) with external sources.
We apply a variety of forecasting techniques, depending on the behavior of the relevant market. For instance, the S-curve function and exponential trend smoothing are well suited for forecasting digital products and services due to the non-linear growth of technology adoption.
The data is modeled using current exchange rates. The market is updated twice a year.