Measuring digital advertising in a post-cookie era: A study of marketing-mix models, attribution and incrementality

The implementation of the EU General Data Protection Regulation (GDPR) poses significant challenges to the measurement of advertisement performance, necessitating a shift away from traditional tracking methods such as web beacons, cookies and browser fingerprinting. With the discontinuation of thirdparty cookies and increased privacy standards, it has become harder for marketers to optimise advertising spend and attribution models. This paper explores the GDPR’s impact on the ad tech industry and anticipates challenges in adapting to a cookieless and more regulated data environment. In the wake of iPhone Operating System (iOS) updates and app tracking transparency, direct-to-consumer (D2C) companies have witnessed shifts in advertising spending across different channels. The study investigates three main measurement models: marketing-mix modelling (MMM), multi-touch attribution (MTA) and incrementality for D2C marketers, highlighting incrementality as the most effective method for analysing advertisement impact and optimising spending. The key contributions include a proposed triangulation framework that combines data from MMM, MTA and incrementality to support a datadriven approach, offering insights for tactical and strategic decision-making. To validate the proposed framework, a mixed-methods approach involving qualitative and quantitative surveys is designed. Targeting experienced advertising professionals, the survey evaluates the implementation of MMM and incrementality, assessing the various decision-making attributes of measurement models, such as easeof- use, accuracy, validation, robustness, predictiveness etc. Results align with existing literature and the proposed framework, demonstrating the efficiency of each technique. The paper recommends adoption of the incrementality randomised control trial method and provides a roadmap for further research in this evolving landscape.