Attribution in Digital Advertising

Abstract

This thesis examined attribution modeling between Google Ads and Meta Ads platforms to understand how advertisers distribute credit for advertising results to improve media efficiency rate (MER). Through qualitative research combining a theoretical framework with three in-depth interviews of advertising professionals across different industries, this study compared theoretical attribution approaches with real-world practices. This study focused specifically on online sales and lead acquisition use cases. The findings revealed that while traditional platform-specific attribution metrics provide valuable insights, advertisers must consider the interconnected nature of these platforms and their collective impact on business outcomes. While this study confirmed that successful attribution requires understanding both platforms' complementary roles, with Meta Ads primarily driving awareness and Google Ads capturing demand, it further demonstrated how and which advertising actions influence and affect four key performance metrics on both platforms, showing that these metrics' true value emerges when viewed as interconnected indicators rather than isolated measures.