AppleAppTransperencyMore than a year ago, Apple introduced new privacy features as part of iOS 14.5, turning the mobile marketing landscape upside down. As part of the changes, users will now have to proactively give consent to sharing device identifiers with apps, such as Apple's Identifier for Advertisers (IDFA).

This is undoubtedly a positive step from a privacy perspective, but for marketers who have relied on access to IDFA to date, measuring the success of their campaigns and understanding the ROI of their marketing spend presents a daunting challenge.

According to a report by, the global mobile advertising market grew by 23% to $295 billion in 2021 – despite Apple's restrictions. In 2022, expects further growth to $350 billion, making it even more urgent to answer the question of how marketers have adapted to implementing the new data protection features after a year.

Mobile marketing can no longer be imagined without predictive measurement

As marketers only have access to a limited data set, predictive measurement is becoming increasingly important. Because it allows the performance of a campaign to be precisely evaluated based on early knowledge. The concept of measurement isn’t new, but with iOS 14.5 and the launch of Apple's own deterministic attribution solution for campaign measurement, SKAdNetwork, it will become crucial.

In short, predictive measurement enables mobile marketers to accurately assess a campaign's performance—before the budget is spent. Even with a limited pool of data, marketers can build predictive models that use data science to estimate which campaign strategies will yield the best results. By deriving behavioral patterns from available data, predictive solutions can identify optimization opportunities that drive user actions and deliver results.

Essentially, this method bridges the data gap created by Apple's privacy updates: predictive measurement provides the information marketing departments need to plan, execute, and improve successful campaigns. Because Apple's SKAdNetwork is based on a timer mechanism and this limits the measurement to user activities that take place within a short time frame, for example 24 hours. Predictive measurement allows marketers to use these early signals to predict long-term campaign performance.

Why data-driven forecasting accelerates marketing

In mobile marketing, the key metrics for ad performance are retention, engagement and monetization. In other words, how long a user interacts with an app, how they interact with it, and most importantly, how much they are likely to spend.

With the help of forecasting functions, the specific behavioral patterns of user groups that have the highest sales potential can be determined. Additionally, marketers can use predictive measurement to learn which tweaks increase engagement, improve customer retention, and maximize results. Or they identify “risk users” and make sure that they return to the app.

By building an extensive database of user behavior over time, marketers can use predictive modeling to gain a more accurate understanding of the patterns that indicate valuable user clusters. With these insights, they quickly optimize their campaigns and save on unnecessary spend to maximize the results of their investments.

Since iOS 14.5, the ecosystem has certainly placed even more emphasis on protecting user privacy. Google recently announced the launch of the Privacy Sandbox for Android. There are also numerous technological innovations that ensure marketers can run and measure their campaigns while respecting privacy.

However, when it comes to running and measuring campaigns in a privacy-friendly world, there is no one-size-fits-all solution. Rather, there are several different approaches that marketers can take, as well as new measurement models, each with their own advantages and disadvantages. Predictive measurement is undoubtedly one of the most important in this discourse. Marketers who understand what works best for them and are ready to adapt to future changes are best positioned and will continue to thrive in data-driven mobile marketing.

By Daniela La Marca