Mobile app developers use paid advertising campaigns to acquire new users, and they need to know the campaigns' performance to guide their spending. Determining the campaign that led to an install requires that the app and advertising network share an identifier that allows matching ad clicks to installs. Ad networks use the identifier to build user profiles that help with targeting and personalization. Modern mobile operating systems have features to protect the privacy of the user. In Apple's iOS 14 , privacy features are protective to an extent never seen before. Instead of asking the user to opt-out of tracking, all apps need to get system permission for tracking explicitly  . If the user does not allow tracking, the identifier for advertisers (IDFA) required for attributing the installation to the campaign is not shared. The lack of an identifier for the attribution changes profoundly how user acquisition campaigns' performance is measured. For users who do not allow tracking, there is a new feature that still allows following campaign performance. The app can set a conversion value for each user, and the developer can get the number of installs per conversion value for each campaign. This paper investigates the task of distributing revenue to advertising campaigns using the conversion values. Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for anyreasonable conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Mobile app developers use paid advertising campaigns to acquire new users, and they need to know the campaigns' performance to guide their spending. Determining the campaign that led to an install requires that the app and advertising network share an identifier that allows matching ad clicks to installs. Ad networks use the identifier to build user profiles that help with targeting and personalization. Modern mobile operating systems have features to protect the privacy of the user. The privacy features of Apple's iOS 14 enforces all apps to get system permission for tracking explicitly instead of asking the user to opt-out of tracking as before . If the user does not allow tracking, the identifier for advertisers (IDFA) required for attributing the installation to the campaign is not shared. The lack of an identifier for the attribution changes profoundly how user acquisition campaigns' performance is measured. For users who do not allow tracking, there is a new feature that still allows following campaign performance. The app can set an integer, so called conversion value for each user, and the developer can get the number of installs per conversion value for each campaign. This paper investigates the task of distributing revenue to advertising campaigns using the conversion values. 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.
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In
The privacy features of
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, privacy features are protective to an extent never seen before. Instead of asking the user to opt-out of tracking, all apps need
enforces all apps
A
instead of asking the user to opt-out of tracking as before
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a
an integer, so called
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scheme for any
D
reasonable
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conversion value scheme
function for any conversion value schema,