Predicting consumer ad preferences using physiological monitoring and AI

Marques, José Alexandre Lobo; Neto, Andreia C.; Silva, Susana C.; Bigne, Enrique

http://hdl.handle.net/10400.14/56900

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
140022300.pdf		771.05 KB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Marques, José Alexandre Lobo

Neto, Andreia C.

Silva, Susana C.

Bigne, Enrique

Resumo(s)

This 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.

URI

http://hdl.handle.net/10400.14/56900

Citação

Marques, 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

Editora

Universidade Católica Portuguesa

DOI

10.34632/b865755b-88a2-4aeb-a0fb-7ba0df58a1e9

Coleções

CEGE - Outros / Others

Licença CC

Sem licença CC

Métricas Alternativas

Ver registo completo