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Practice paper

Moving towards inferential attribution modelling in a world without third-party cookies

Roger Kamena
Applied Marketing Analytics: The Peer-Reviewed Journal, 7 (2), 122-130 (2021)
https://doi.org/10.69554/GLTL7716

Abstract

With the gradual disappearance of third-party cookies and Identifier for Advertisers (IDFA) tracking, marketers are becoming more restricted in their capacity to measure the performance of their marketing initiatives. Standard attribution models are currently based on user-level data to establish a one-to-one relationship between customer interactions and conversion goals. However, with user-level data about to become more difficult to access, marketers will need to embrace alternative ways to measure the effectiveness of their marketing efforts. This paper proposes inferential attribution modelling techniques as a potential alternative or complementary approach to user-level attribution techniques, and revisits older marketing techniques, such as media mix models, to address the upcoming changes in the marketing data ecosystem.

Keywords: user data; marketing data; digital advertising; user privacy; attribution; data collection

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Author's Biography

Roger Kamena leads Adviso’s team of data scientists and oversees research and development efforts in marketing artificial intelligence. He has previously led large-scale advertising/marketing technology implementations and data management projects for a number of major companies.

Citation

Kamena, Roger (2021, September 1). Moving towards inferential attribution modelling in a world without third-party cookies. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 2. https://doi.org/10.69554/GLTL7716.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 7 / Issue 2
© Henry Stewart
Publications LLP

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