The Measurement of Consumer’s Feel Data Using Neuromarketing and a Scoring Board: Conceptual Model

  • Yahia MouammineEmail author
  • Hassan Azdimousa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1104)


By considering the consumer’s emotion as a separate data, we are now talking about Feel Data, which Neuromarketing provides us with the necessary tools and technologies to observe and measure in real time. It would be interesting to see and measure the emotion caused by the persuasive messages included in digital advertising, even better, to predict it. In this article, and based on the emotion theories with successful use in marketing, we propose a theoretical model of a scoring board, which would allow the analyst to assign a score to the emotion experienced by the consumer, to make a prediction of its nature (positive or negative), before checking its validity by Neuromarketing tools.


Neuromarketing Persuasive messages Feel data Emotions Scoring board 


  1. 1.
    Lee, N., Broderick, A.-J., Chamberlain, L.: What is neuromarketing? A discussion and agenda for future research. Int. J. Psychophysiol. 63, 199–204 (2007)CrossRefGoogle Scholar
  2. 2.
    Chatterjee, S.: Neuromarketing - a path breaking approach to understanding consumer behavior. J. Manag. Res. 9(4/4), 1–10 (2015). ISSN 0974-497Google Scholar
  3. 3.
    Badoc, M., Georges, P.: Le Neuromarketing en action. Eyrolles (2010)Google Scholar
  4. 4.
    Pradeep, A.-K.: The Buying Brain: Secrets for Selling to the Subconscious Mind. Wiley, Hoboken (2010)Google Scholar
  5. 5.
    Rostomyan, A.: The impact of emotions in marketing strategy. In: Ternès, A., Towers, I. (eds.) Internationale Trends in der Markenkommunikation, pp. 119–129. Springer Gabler, Wiesbaden (2014)CrossRefGoogle Scholar
  6. 6.
    Stasi, A., et al.: Food Research International (2017).
  7. 7.
    Zorfas, A., Leemon, D.: An emotional connection matters more than customer satisfaction. Harvard Bus. Rev. (2016)Google Scholar
  8. 8.
    Trespeuch, L.: Un cadre d’analyse pour la «Feel Data» ou Data émotionnelle. Note de recherche, Laboratoire CERAG (2016)Google Scholar
  9. 9.
    Andreassi, J.-L.: Psychophysiology: Human Behavior and Physiological Response, 5th edn. Psychology Press, New York (2007)Google Scholar
  10. 10.
    Griskevicius, V., et al.: Going green to be seen: status, reputation, and conspicuous conservation. J. Pers. Soc. Psychol. 98(3) (2010).
  11. 11.
    Bagozzi, R.-P., et al.: The role of emotions in marketing. J. Acad. Mark. Sci. 27(2), 184–206 (1999)CrossRefGoogle Scholar
  12. 12.
    Huang, M.-H.: The theory of emotions in marketing. J. Bus. Psychol. 16(2), 239–247 (2001)CrossRefGoogle Scholar
  13. 13.
    Plutchik, R.: Emotion: theory, research, and experience. In: Plutchik, R., Kellerman, H. (eds.) Theories of Emotion, vol. 1, 1st edn. Academic, New York (1980)Google Scholar
  14. 14.
    Tromp, E., Pechenizkiy, M.: Rule-based emotion detection on social media: putting tweets on Plutchik’s wheel. arXiv:1412.4682v1 [cs.CL] (2014)
  15. 15.
    Pappas, I.-O., et al.: Sense and sensibility in personalized e-commerce: how emotions rebalance the purchase intentions of persuaded customers. Psychol. Mark. 34, 972–986 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ibn Tofail UniversityKénitraMorocco

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