Welcome to the second part of our series on mid-core game forecasting! This time, we’ll turn our attention to iOS. This platform presents unique challenges due to Apple’s privacy-focused changes, including App Tracking Transparency (ATT) and SKAdNetwork. Let’s take a look at how these factors shape the way we calculate and interpret iOS forecasts.

How we calculate forecasts for iOS

With the introduction of iOS 14.5, Apple implemented significant changes to user attribution through the App Tracking Transparency (ATT) framework. Prior to this update, developers could use the IDFA (Identifier for Advertisers) to track users unless they had manually disabled it, resulting in nearly 100% attribution coverage.

Now, each application must request user permission for tracking, necessitating consent in both the device settings and within the app itself. If either condition is not met, attribution is lost, and statistics default to organic traffic for Self Report Network (SRN) channels, as probabilistic or similar attribution methods are unavailable. Consequently, the permission rate has plummeted to around 30-40% of total installations.

Currently, the methods of attribution are distributed as follows:

• Google, Facebook, TikTok on iOS: SKAN attribution.
• Unity, Applovin, Iron Source (amongst others on iOS): probabilistic attribution.

We refer to our forecasts for iOS traffic as "smart forecasts." These are derived by redistributing basic forecasts (for consent traffic) in proportion to the Conversion Value obtained from SKAdNetwork.

Our forecasting process begins with calculating estimates for probabilistic sources, followed by organic traffic, which is modeled separately, and concludes with SRN sources.

However, smart forecasts come with limitations! Since SKAdNetwork attribution does not provide country-specific information, smart metrics should be analyzed at the placement and campaign levels rather than by country.

For SRN sources, the primary attribution tool is the SKAN framework. There are specific nuances in calculating SKAN metrics; by default, the encoding typically represents the revenue generated within the first 24 hours.

Unfortunately, this can lead to a significant underestimation of payments from high-spending players, or "whales." To address this issue, we now offer our clients a customized scheme developed using an ML clustering model, assisting with both calculations and implementation.

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Let’s finish with a recap: forecasting for mid-core games in AdsAdvisor is grounded in the comprehensive analysis of user and payment data, segmented by key indicators such as country and traffic type.

As we’ve seen here, our methodologies differ for Android and iOS: Android forecasts rely on robust data volumes and internal analyses, while iOS employs a flexible smart forecasting approach that accounts for the limitations of SKAdNetwork attribution. This dual methodology enables more accurate revenue predictions, even amidst constrained information.

Ultimately, these insights assist in optimizing traffic purchases and making informed decisions regarding project scaling. Thanks for reading, and stay tuned!