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

Roger Kamena
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Roger Kamena: Adviso Conseil, Canada

Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. 7, issue 2, 122-130

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 (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2021:v:7:i:2:p:122-130

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