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Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants w...

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Autores principales: Guixeres, Jaime, Bigné, Enrique, Ausín Azofra, Jose M., Alcañiz Raya, Mariano, Colomer Granero, Adrián, Fuentes Hurtado, Félix, Naranjo Ornedo, Valery
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671759/
https://www.ncbi.nlm.nih.gov/pubmed/29163251
http://dx.doi.org/10.3389/fpsyg.2017.01808
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author Guixeres, Jaime
Bigné, Enrique
Ausín Azofra, Jose M.
Alcañiz Raya, Mariano
Colomer Granero, Adrián
Fuentes Hurtado, Félix
Naranjo Ornedo, Valery
author_facet Guixeres, Jaime
Bigné, Enrique
Ausín Azofra, Jose M.
Alcañiz Raya, Mariano
Colomer Granero, Adrián
Fuentes Hurtado, Félix
Naranjo Ornedo, Valery
author_sort Guixeres, Jaime
collection PubMed
description The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.
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spelling pubmed-56717592017-11-21 Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising Guixeres, Jaime Bigné, Enrique Ausín Azofra, Jose M. Alcañiz Raya, Mariano Colomer Granero, Adrián Fuentes Hurtado, Félix Naranjo Ornedo, Valery Front Psychol Psychology The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube. Frontiers Media S.A. 2017-10-31 /pmc/articles/PMC5671759/ /pubmed/29163251 http://dx.doi.org/10.3389/fpsyg.2017.01808 Text en Copyright © 2017 Guixeres, Bigné, Ausín Azofra, Alcañiz Raya, Colomer Granero, Fuentes Hurtado and Naranjo Ornedo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Guixeres, Jaime
Bigné, Enrique
Ausín Azofra, Jose M.
Alcañiz Raya, Mariano
Colomer Granero, Adrián
Fuentes Hurtado, Félix
Naranjo Ornedo, Valery
Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title_full Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title_fullStr Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title_full_unstemmed Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title_short Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising
title_sort consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671759/
https://www.ncbi.nlm.nih.gov/pubmed/29163251
http://dx.doi.org/10.3389/fpsyg.2017.01808
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