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

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

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dc.contributor.author Guixeres Provinciale, Jaime es_ES
dc.contributor.author Bigné-Alcañiz, Enrique es_ES
dc.contributor.author Ausin-Azofra, Jose Manuel es_ES
dc.contributor.author Alcañiz Raya, Mariano Luis es_ES
dc.contributor.author Colomer, Adrián es_ES
dc.contributor.author Fuentes-Hurtado, Félix José es_ES
dc.contributor.author Naranjo Ornedo, Valeriana es_ES
dc.date.accessioned 2020-07-18T03:31:31Z
dc.date.available 2020-07-18T03:31:31Z
dc.date.issued 2017-10-31 es_ES
dc.identifier.uri http://hdl.handle.net/10251/148236
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Polytechnic University of Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields. es_ES
dc.language Inglés es_ES
dc.publisher Frontiers Media SA es_ES
dc.relation.ispartof Frontiers in Psychology es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Neuromarketing es_ES
dc.subject YouTube es_ES
dc.subject Artificial neural networks es_ES
dc.subject Eye tracking es_ES
dc.subject Heart rate variability es_ES
dc.subject Brain response es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3389/fpsyg.2017.01808 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.description.bibliographicCitation Guixeres Provinciale, J.; Bigné-Alcañiz, E.; Ausin-Azofra, JM.; Alcañiz Raya, ML.; Colomer, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V. (2017). Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising. Frontiers in Psychology. 8:1-14. https://doi.org/10.3389/fpsyg.2017.01808 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3389/fpsyg.2017.01808 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.identifier.eissn 1664-1078 es_ES
dc.identifier.pmid 29163251 es_ES
dc.identifier.pmcid PMC5671759 es_ES
dc.relation.pasarela S\345791 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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