Apple iOS 14: Mobile Gaming Advertising in the post-IDFA world A study of predicting customer lifetime value in an anonymized data landscape
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2021-02-14
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en
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Abstract
In September 2020, Apple Inc. announced that the next update for iOS devices (iOS14)
will include an updated privacy policy regarding user tracking and targeting. Speci cally,
the privacy changes entailed a deprecation of the current Identi ers for Advertisers (IDFA),
which in the future requires mobile advertisers to ask users for tracking permission when
a newly installed app is opened for the rst time. Currently, advertisers rely on real time
tracking, user-level analytics and retargeting of users to measure the success of advertising
campaigns. However, after iOS14 all their incoming data will be anonymized and summarized
through a conversion value if users opt-out of tracking. These conversion values
will then be send through Apple's proposed SKAdNetwork which will send the data to advertisers
after a randomly initiated timer between 24 and 48 hours has passed. Current
estimations state that the opt-out rate will be roughly 90% of users (Koetsier et al., 2021).
Therefore, advertisers and mobile developers need to nd alternative methods to predict
important metrics such as customer lifetime value (LTV) with anonymous data. This thesis
introduces a LTV model that uses forecasted user retention (opt-ins) through a long-short
term memory neural network in combination with simulated average revenue per daily active
user (ARPDAU) data without using user-level IDFA values. The resulting best performing
model has shown to accurately predict LTV values of players in the iOS game Road Crash
(RMSE = 0.052, mean overall prediction error of $0.042) when compared to an existing LTV
model using pre-iOS14 data. The results demonstrate that even in a post-IDFA marketing
world opt-out user data can still be used to predict the lifetime value of users.
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Faculteit der Sociale Wetenschappen