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Predicting consumer ad preferences using physiological monitoring and AI

2024-11-01Other Acesso aberto
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dc.contributor.authorMarques, José Alexandre Lobo
dc.contributor.authorNeto, Andreia C.
dc.contributor.authorSilva, Susana C.
dc.contributor.authorBigne, Enrique
dc.date.accessioned2026-02-03T10:38:20Z
dc.date.available2026-02-03T10:38:20Z
dc.date.issued2024-11-01
dc.description.abstractThis policy paper explores how combining neurophysiological tools—Electrodermal Activity (EDA) and Facial Expression Analysis (FEA)—with machine learning (ML) enhances the prediction of consumer preferences in advertising, addressing the biases of traditional self-report methods. Analyzing responses from 37 participants to various cosmetic ads revealed that emotions like joy and disgust significantly influenced ad preference, with the Random Forest ML model achieving high predictive accuracy. Explainable AI (XAI) identified key features such as attention and engagement, offering marketers actionable insights. The findings suggest that integrating neurophysiological data with AI can improve advertising strategies, targeting, and consumer engagement.eng
dc.identifier.citationMarques, J. A. L., Neto, A. C., Silva, S. C., & Bigne, E. (2024, Nov). Predicting consumer ad preferences using physiological monitoring and AI. Universidade Católica Portuguesa. https://doi.org/10.34632/b865755b-88a2-4aeb-a0fb-7ba0df58a1e9
dc.identifier.doi10.34632/b865755b-88a2-4aeb-a0fb-7ba0df58a1e9
dc.identifier.otherb865755b-88a2-4aeb-a0fb-7ba0df58a1e9
dc.identifier.urihttp://hdl.handle.net/10400.14/56900
dc.language.isoeng
dc.peerreviewedno
dc.publisherUniversidade Católica Portuguesa
dc.rights.uriN/A
dc.titlePredicting consumer ad preferences using physiological monitoring and AIeng
dc.typeOther
dspace.entity.typePublication
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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