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How Does Increased User Privacy Alter Mobile Advertisement Set-up?


Since Apple came up with its ATT privacy framework in order to garner users' control over their data, tech businesses are facing challenges over making tradeoffs to adapt to the new data restrictions, while still maintaining their growth objectives. 

While mobile advertisements would no longer be able to target iOS users via their personal IDs, who certainly did not consent to be tracked, there are numerous different alternative ways at their disposal - such as contextual signals and probabilistic attribution – to aid in targeting quality potential customers across the mobile ecosystem. 

Given that the Identifier for Advertisers has been deprecated, in-app advertising may appear to be less effective (IDFA). However, with adequate data, tactics, and partners, it is not only still a feasible growth strategy but also a crucial one. 

Changes Made After iOS 14.5 

Under the new privacy restrictions introduced by Apple, app advertisers can no longer rely on the IDFA to provide them with device-level user data in order to pursue iOS device users with relevant advertisements. 

Since advertisers can no longer track users’ activities across apps on iOS, such as clicks, downloads, and conversions, advertisers are less able to measure the efficacy of their ads and use that data to manage their campaigns and ad budgets. 

Performance Marketing is Different, Not Worse 

With iOS 14.5, while advertisers would not be able to access device ID data, they can still utilize contextual signals in order to show ads to a quality audience. 

Contextual signals are the privacy-induced data points that transmit significant information regarding an ad opportunity, such as location device type, and information about the environment in which an ad is shown (i.e. characteristics of an app or website). 

With this kind of data, advertisers may use contextual targeting to precisely estimate the possibility that a user would interact with an advertisement by matching an ad to an impression opportunity. They can then decide how much to bid for each impression. 

Since users are automatically opted out of IDFA tracking, advertisers will no longer be able to access device IDs in order to access data on how a user interacts with the ad, nor target audience one-on-one based on their in-app activities. Instead, machine learning models are utilizing new contextual signals to effectively predict user response. 

New Data, New Competitive Landscape 

Contextual data can further be combined with other metrics. For Example, the number of interactions with a certain ad element reveals which aspect of the creative is most effective. Of course, this is not as accurate as using the IDFA, but thanks to advancements in machine learning (ML) technology, it is now able to absorb these signals and forecast the value of each ad impression in real-time with a level of accuracy that is almost on par with device ID-powered advertising. 

Moreover, the competitive landscape of mobile advertising is more level than it has ever been. In recent times, all tech giants (such as Facebook and Google) have limited information about their users than before. This has eventually compressed the space, and niche players with specialized historical ML models and more active algorithms compete with the tech giants. 

For the given reason, the marketing platforms that continue to make investments in enhancing the effectiveness of their models by including more predictive signals have experienced the most success in the wake of the deprecation of the IDFA. 

Through more effective bidding, lower CPIs, improved user quality, and eventually higher ROAS for their advertisers, it will be possible to continuously train models to boost their prediction accuracy.