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

David Juárez-Varón, Victoria Tur-Viñes, Alejandro Rabasa-Dolado and Kristina Polotskaya
Additional contact information
David Juárez-Varón: Department of Mechanical and Materials Engineering, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
Victoria Tur-Viñes: Department of Communication and Social Psychology, Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain
Alejandro Rabasa-Dolado: Department of Statistics, Mathematics and Informatics, Miguel Hernández University, Avenida de la Universidad, s/n, 03202 Elche, Spain
Kristina Polotskaya: Department of Statistics, Mathematics and Informatics, Miguel Hernández University, Avenida de la Universidad, s/n, 03202 Elche, Spain

Social Sciences, 2020, vol. 9, issue 9, 1-23

Abstract: 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.

Keywords: packaging; design; toy; neuromarketing; eye tracking; machine learning; predictive models; consumers; methodology; communication (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2076-0760/9/9/162/pdf (application/pdf)
https://www.mdpi.com/2076-0760/9/9/162/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jscscx:v:9:y:2020:i:9:p:162-:d:415989

Access Statistics for this article

Social Sciences is currently edited by Ms. Yvonne Chu

More articles in Social Sciences from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().