There’s no question that mobile apps have become a fixture of daily life. People spend an average of three hours and 15 minutes a day on their smartphones, and 88% of that time is spent in apps.
The importance of in-app advertising as a way for brands to engage new and potential customers in an increasingly mobile-first world is then clear –and any brand, whether they have an app or not, can include in-app ads in their advertising strategy, with an app-to-web conversion flow.
In an app-to-app conversion flow, traditionally, the in-app ad shown is an incentivised offer that rewards a user for completing a task, such as watching a video or downloading an app. With an app-to-web conversion flow, a user sees an in-app ad, taps it, and is taken to a mobile web page to take a desired action, like completing a registration or a survey.
Because app-to-web does not require a user to complete a desired action to get the reward – like downloading an app – app-to-web presents a low friction experience for performance marketers to scale their campaigns while being cost-efficient.
In as little as three weeks after launching their first mobile in-app campaign, we’ve had clients hit aggressive KPI targets, increase scale 5X and decrease cost-per-registration by up to 16%.
Contextual targeting: the growth engine for the mobile in-app channel
Apple introduced its App Tracking Transparency framework, which prevented the automatic collection of the Identifier for Advertisers (IDFA). Consequently, demand side platforms (DSPs) have increased the use of contextual signals in their ML predictive models to make correlations between privacy-compliant contextual data and what they might indicate about an audience.
Contextual data relays basic information about the environment in which a mobile ad appears, such as the app or mobile web URL. It also provides information about the context of a user’s ad experience, for example, their location, device type, operating system, their device’s battery level, whether the device is plugged in and charging, and even the text size and brightness their screen is set to.
Beyond just using the information provided by ad exchanges, DSPs are developing tools to source and augment new types of contextual data. For example, LifeStreet’s App Mapper feature learns from and uses contextual signals collected from the app store.
We’ve created a database of information complete with screenshots, reviews, version history, and more about every app in the app store. With App Mapper, our ML models ingest and summarise the application content into actionable insights to make our bidding decisions smarter. By boosting our campaign predictions, we can identify new app inventory to invest in and find more better performing apps to increase ROAS.
Generative AI and the new opportunities for contextual data
Technologies like generative AI are also unlocking new opportunities in contextual data augmentation. Using the vast reserves of information available on language learning models like ChatGPT 4, marketers can draw more insights about their audience and app inventory via simple text prompts.
For example, they could automatically generate a list of descriptive tags for specific apps to help models find additional apps to spend against to help broaden inventory sources. Emerging technologies make it possible to advertise efficiently without the need for the user-level data provided through device IDs like the IDFA.
The power of contextual targeting and app-to-web
Globally, there are 6.5 billion smartphone users in the world, making in-app advertising an essential component of any successful marketing strategy.
Data privacy enhancements have certainly complicated the process of in-app advertising, but contextual targeting, combined with app-to-web conversion flows can help performance marketers unlock new customers on mobile channels – and scale faster.