Comparing analytical methods of allocating media influence

Martin P. Block
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Martin P. Block: Medill School, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2025, vol. 11, issue 1, 43-60

Abstract: This paper investigates two fundamental problems in marketing mix analysis: (1) the lack of a standardised criterion variable across different media categories and nonmedia categories, and (2) the choice of analytical techniques. Using survey-based media and marketing influence variables, the paper uses multiple analytical models to predict top spenders in the category of women’s clothing. The analytical techniques considered include logistic regression, classification regression trees, cooperative game theory and Bayesian belief network. The Bayesian network is found to be the best-performing technique for delivering overall influence. All the analytical models significantly outperform simple pairwise selection models. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Keywords: Bayesian networks; media influence; media planning (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2025:v:11:i:1:p:43-60

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