Comparing iOS Campaign Performance on Google and TikTok: Insights and Observations

When comparing the iOS campaign performance between Google and TikTok for a specific product, notable differences were observed in user acquisition costs, overall server-side new user additions, and user retention value.

Key Observations:

  1. Cost and Volume Differences:

When Google had the largest share of the ad budget, the overall cost per acquisition increased, but the total new users and their lifetime value reported by the server were higher.

Conversely, when TikTok took the largest share, the cost per acquisition seemed lower, and the account dashboard showed more conversions. However, server-side data indicated a decrease in total new users and a lower retention value compared to when Google dominated.

  1. Attribution Differences:

Google’s iOS attribution, influenced by SKAN and data modeling, may underreport conversions due to factors like SKAN data delays and privacy threshold restrictions, potentially leading to discrepancies between Google’s self-attributed data and actual server-side new user data.

TikTok, with stronger SKAN capabilities, might provide more accurate attribution in its dashboard, leading to perceived better performance in the short term, but not necessarily translating into higher server-side results.

  1. Assists and Cross-Platform Impact:

Google might provide significant assist value to other channels like Apple Search Ads (ASM) and Facebook, driving indirect conversions that are not immediately credited to Google in last-click attribution models.

When Google’s budget was reduced, natural traffic dropped more significantly compared to reducing TikTok’s budget, suggesting Google’s broader impact on overall brand visibility.

  1. User Demographics and Platform Differences:

The user demographics and behaviors on Google versus TikTok can also contribute to differences in performance, with Google’s search-based platform attracting different types of users compared to TikTok’s short-video-centric audience.

  1. Product and Testing Variations:

The observations are based on ongoing tests with varying levels of rigor, meaning that differences in campaign timing, in-app events, and external factors like holidays may influence the results.

Conclusion: The analysis indicates that relying solely on platform-reported data for iOS campaigns may not provide a complete picture due to differences in attribution models, user demographics, and the inherent impact of iOS 17 policies. A holistic approach that considers server-side data, cross-channel impacts, and continuous testing is essential for accurately evaluating the performance of iOS campaigns on platforms like Google and TikTok.

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