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https://hdl.handle.net/11000/34516
An Adaptive Machine Learning Methodology
Applied to Neuromarketing Analysis: Prediction of
Consumer Behaviour Regarding the Key Elements of
the Packaging Design of an Educational Toy
Título : An Adaptive Machine Learning Methodology
Applied to Neuromarketing Analysis: Prediction of
Consumer Behaviour Regarding the Key Elements of
the Packaging Design of an Educational Toy |
Autor : Juárez-Varón, David Tur-Viñes, Victoria Rabasa, Alejandro  Polotskaya, Kristina  |
Editor : MDPI |
Departamento: Departamentos de la UMH::Estadística, Matemáticas e Informática |
Fecha de publicación: 2020-09 |
URI : https://hdl.handle.net/11000/34516 |
Resumen :
This research is in response to the question of which aspects of package design are more
relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used,
and we propose a methodology for predicting which areas attract the attention of potential customers.
The aim of the present study was to propose a model that optimizes the communication design
of educational toys’ packaging. The data extracted from the experiments was studied using new
analytical models, based on machine learning techniques, to predict which area of packaging is
observed in the first instance and which areas are never the focus of attention of potential customers.
The results suggest that the most important elements are the graphic details of the packaging and the
methodology fully analyzes and segments these areas, according to social circumstance and which
consumer type is observing the packaging
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Palabras clave/Materias: packaging design toy neuromarketing eye tracking machine learning predictive models consumers methodology communication |
Área de conocimiento : CDU: Ciencias puras y naturales: Generalidades sobre las ciencias puras |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI : https://doi.org/10.3390/socsci9090162 |
Aparece en las colecciones: Artículos Estadística, Matemáticas e Informática
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La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.