Assessing privacy protected cohort-based market segmentation

Martin P. Block
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Martin P. Block: Professor of Integrated Marketing Communications at the Medill School, Northwestern University, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. 7, issue 2, 131-143

Abstract: Marketers need an efficient way to be able to identify their customers and prospects without contravening any of the various laws or regulations relating to data privacy. There are two solutions to address this issue: reliance on consumer characteristics that cannot be used to identify a particular individual, and the segmentation of individuals into groups or cohorts. With respect to the former, the challenge is to select the appropriate consumer characteristics from the many variables, such as demographic and psychological variables, leisure interests, and behavioural variables such as purchase history or online activities. On the other hand, the size of the cohort or group is also an issue, to ensure that individual consumers cannot be identified. Using fashion brands as an example, this paper demonstrates the efficacy of segmentation variables in the context of leisure activities and online activities. The study finds that although cohortbased segmentation appears to be a reasonable solution to the privacy problem, the utility of the cohort appears to be informed by its size. The findings also point to significant issues regarding the cluster methodology necessary to create the cohorts, the sizes of the cohorts, and perhaps most importantly, the heterogeneity of the cohorts.

Keywords: privacy; cohorts; activities; segmentation (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2021:v:7:i:2:p:131-143

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