Abstract
The deep learning neuromarketing (DLN) application algorithm is a digital transformation application, user-first, online, economic, and ecological, able to transform “data” in real time into “knowledge” in order to allow managers of the HORECA channel (hotéis, restaurantes, and cafés) to make agile decisions. To respond to the current challenges of hotels, restaurants, and cafes, a multidisciplinary technological and digital marketing team, in 2019, developed a deep learning neuromarketing solution that allows them to be more effective in promoting the menu meals and services of the establishments of the HORECA channel. The technology used is a deep learning neuromarketing application, already implemented in 20 convenience stores, in an organization that owns its own network with more than 200 points of sale. As a result of this test, data were collected and processed in real time, during 234 calendar days, 3.276.000 visits were recorded, collected through a digital screen placed above the cashier’s window. Through this test, an increase in “impulse sales” of around 30% was observed, by displaying the appropriate “advertisement” for the consumer who sees it, thus allowing the effectiveness and efficiency of the advertising campaigns displayed per establishment to be monitored on a dashboard in terms of units sold and turnover.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Peper, E., Harvey, R.: Digital addiction: increased loneliness, anxiety, and depression. NeuroRegulation, 5(1), 3–8 (2018). https://doi.org/10.15540/nr.5.1.3
Crescitelli, E.: Advertising credibility crisis: considerations about its impact on the effectiveness of the message. Daydreams Lonely Intellect. 12(1st), 51 (2004)
Brown, T.: A lesson in empathy. Des. Thinking: Thoughts by Tim. https://designthinking.ideo.com/blog/a-lesson-in-empathy (12 of April 2020) (2013)
Norman, D.A.: Emotional Design: Why we Love (or hate) Everyday Things. Basic Civitas Books (2004)
Poon, D.T., Prendergast, G.: A new framework for evaluating sponsorship opportunities. Int. J. Advert. 25(4), 471–487 (2006)
Barrett, L.F., Lewis, M., Haviland-Jones, J.M.: Handbook of Emotions. Guilford Publications (2016)
Eysenck, M.W., Brysbaert, M.: Fundamentals of Cognition. Routledge (2018)
Kolb, B., Whishaw, I.: Fundamentals of Human Neuropsychology, 7th edn. Worth Publishers, NY (2015)
Knutson, B., Rick, S., Wimmer, G.E., Prelec, D., Loewenstein, G.: Neural predictors of purchases. Neuron 53(1), 147–156 (2007)
Berns, G.S., Moore, S.E.: A neural predictor of cultural popularity. J. Consum. Psychol. 22(1), 154–160 (2012)
Falk, E.B., Berkman, E.T., Lieberman, M.D.: From neural responses to population behavior: neural focus group predicts population-level media effects. Psychol. Sci. 23(5), 439–445 (2012)
Briesemeister, B.B.: Die Neuro-Perspektive: Neurowissenschaftliche Reply auf die wichtigsten Marketingfragen (2016)
Rodrigues, F.: How we receive and interpret information—Cognition, Motivation and Reward. In: Rodrigues, F.J., Vitorino, L. (coordinator). Consumer Behavior: When Neuroscience, Psychology, Economics and Marketing Meet. Viseu, PsicoSoma (2013)
Rodrigues, F., Oliveira, M., Julien, D.: Principles of Neuromarketing. Cognitive Neuroscience Applied to Consumption, Spaces and Design. Psychosom (2015)
Braidot, N.: Neuromarketing: Why do your clients look out for another self saying that they like you? Generation 2000 (2009)
Chib, V., Rangel, A., Shimojo, S., O’Doherty, J.: Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. J. Neurosci. 29(39), 12315–12320 (2009)
Bischof, N., Bischof-Köhler, D.: Das Zürcher Modell der sozialen Motivation (2013)
Plassmann, H., Venkatraman, V., Huettel, S., Yoon, C.: Consumer neuroscience: applications, challenges, and possible solutions. J. Mark. Res. 52(4), 427–435 (2015)
Schwarzkopf, S.: Measurement devices and the psychophysiology of consumer behavior: a posthuman genealogy of neuromarketing. BioSocieties 10(4), 465–482 (2015)
Plassmann, H., Ramsøy, T.Z., Milosavljevic, M.: Branding the brain: a critical review and outlook. J. Consum. Psychol. Elsevier. 22(1), 18–36 (2012)
Sahu, A., Singh, N.: A practical approach towards neuroscience to understand the consumer behavior in through neuromarketing. ASM’s Int. E-J. Ongoing Res. Manage. IT. e-ISSN-2320-0065 (2013)
Yoon, C., Gonzalez, R., Bechara, A., Berns, G.S., Dagher, A.A., Dubé, L., Huettel, S.A., Kable, J.W., Liberzon, I., Plassmann, H., et al.: Decision neuroscience and consumer decision making. Mark. Lett. 23(2), 473–485 (2012)
Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica: J. Econometric Soc. 263–291 (1979)
Carlzon, J.: Moments of Truth. Paper Moon (2012)
Magalhães, M., Pereira, M., Costa Lobo, C., Cardoso, A., D’Orey, F., Santos Ferreira, T.: Data mining PPV an applied deep leaning neuromarketing tool to the performance of the point-of-sale promotion: a quantitative study. Acad. Strateg. Manage. J. 20(4), 1939-6104-20-4-828 (2021)
Magalhães, M., D’Orey, F., Cardoso, A, Pereira, M., Cairrão, A., Figueiredo, J.: Canvas marketing plan: how to structure a marketing plan with interactive value? Handbook of research on emerging technologies for effective project management. IGI Global. (October 2019) https://doi.org/10.4018/978-1-5225-9993-7.ch009
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Magalhães, M., Rodrigues, M., Pereira, J., Borges, I., Brás, S. (2022). Deep Learning Neuromarketing (DLN) Applied to the HORECA Channel (Hotel, Restaurants, and Cafés). In: Carvalho, J.V.d., Liberato, P., Peña, A. (eds) Advances in Tourism, Technology and Systems. Smart Innovation, Systems and Technologies, vol 284. Springer, Singapore. https://doi.org/10.1007/978-981-16-9701-2_35
Download citation
DOI: https://doi.org/10.1007/978-981-16-9701-2_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9700-5
Online ISBN: 978-981-16-9701-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)