What does increased user privacy mean for mobile advertising?

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Since Apple introduced its ATT privacy framework to give app users more control over their data, ad tech companies have been tasked with making compromises to comply with new data restrictions while still achieving their growth objectives.

Yet while mobile advertisers can no longer use personal identifiers to target the 70% of iOS users who have not consented to being tracked, there are other tools available to them, such as signals contextual and probabilistic attribution, to identify and target quality audiences across the mobile ecosystem.

That being said, in-app advertising may seem less effective with the move away from the Identifier for Advertisers (IDFA). But with the right data, strategies, and partners, it's not just a viable growth strategy, but a vital one.

What changed after iOS 14.5

Under the new privacy restrictions, app advertisers can no longer rely on the IDFA to provide them with device-level data to deliver relevant ads to users on iOS devices. Since advertisers can no longer track a user's activity in apps on iOS, including their clicks, downloads, and conversions, they also have fewer opportunities to measure the effectiveness of their ads and use these information to optimize their campaigns and advertising budgets accordingly.

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This loss in efficiency translates into lower conversion rates, but also lower CPMs (cost per 1,000 impressions). So while scaling campaigns to beat KPIs may be more complex, app growth activity may be less expensive than it used to be.

Performance marketing is different, not worse

As of iOS 14.5, while advertisers may not have access to device credentials, they can still use contextual signals to serve ads to qualified users. What are pop-up signals? These are privacy-compliant data points that convey useful information about an advertising opportunity, such as location, device type, and information about the environment in which an ad is served (i.e. i.e. the characteristics of an application or website).

With this type of data, advertisers can use contextual targeting, or matching an ad to an impression opportunity, to accurately predict the likelihood of a user engaging with an ad. From there, they can determine how much to bid for each impression.

Because users are automatically opted out of IDFA tracking, advertisers can no longer rely on device ID to access data about how a user interacts with an ad, or target individual audiences based on events in the application. Instead, machine learning (ML) models leverage new contextual cues to make efficient predictions.

While this has made in-app advertising less effective, iOS advertising still meets or exceeds advertisers' ROAS goals. For example, at LifeStreet, we're seeing fewer conversions per ad dollar spent on traffic without a device ID, but our CPMs are about 2.1 times lower. This translates to 10% higher ROAS on media spend without a device ID. Although the impression-to-conversion ratio has changed, lower costs have helped iOS advertising improve its efficiency.

New data, new competitive landscape

Context signals can also be combined with other metrics. For example, the number of interactions...