Revenue Attribution on iOS 14 using Conversion Values in F2P Games

TL;DR

This paper addresses the challenge of attributing revenue to advertising campaigns on iOS 14+ by formalizing the problem, deriving the optimal attribution function, and empirically evaluating it using data from a free-to-play game.

Contribution

It formalizes revenue attribution using conversion values, derives the optimal attribution function, and provides empirical analysis on real-world data.

Findings

Optimal attribution function depends on conversion value schema

Empirical results show improved revenue attribution accuracy

Analysis guides effective marketing spend decisions

Abstract

Mobile app developers use paid advertising campaigns to acquire new users. Marketing managers decide where to spend and how much to spend based on the campaigns' performance. Apple's new privacy mechanisms have a profound impact on how performance marketing is measured. Starting iOS 14.5, all apps must get system permission for tracking explicitly via the new App Tracking Transparency Framework, which shows the users a pop-up asking if they give the app permission to track. If a user does not allow tracking, the required identifier to deterministically find the online advertising campaign that brought the user to install the app is not shared. Instead of relying on individual identifiers, Apple proposed a new performance mechanism called conversion value, which is an integer set by the apps for each user, and the developers can get the number of installs per conversion value for each campaign. However, interpreting how conversion values are used to measure the campaigns performance is not obvious because it requires a method to translate the conversion values to revenue. This paper investigates the task of attributing revenue to advertising campaigns using the reported conversion values per campaign. Our contributions are to formalize the problem, find the theoretically optimal revenue attribution function for any conversion value schema, and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.