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Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking.

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Title: Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking.
Authors: Mashrur, Fazla Rabbi (AUTHOR), Rahman, Khandoker Mahmudur (AUTHOR), Miya, Mohammad Tohidul Islam (AUTHOR), Vaidyanathan, Ravi (AUTHOR), Anwar, Syed Ferhat (AUTHOR), Sarker, Farhana (AUTHOR), Mamun, Khondaker A. (AUTHOR)
Source: Journal of Consumer Behaviour. May2024, Vol. 23 Issue 3, p1146-1157. 12p.
Subjects: Neuromarketing, Eye tracking, Consumer preferences, Wrappers, Feature extraction, Electroencephalography, Marketing
Abstract: Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Consumer Behaviour is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking.
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  Data: <searchLink fieldCode="AR" term="%22Mashrur%2C+Fazla+Rabbi%22">Mashrur, Fazla Rabbi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rahman%2C+Khandoker+Mahmudur%22">Rahman, Khandoker Mahmudur</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miya%2C+Mohammad+Tohidul+Islam%22">Miya, Mohammad Tohidul Islam</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vaidyanathan%2C+Ravi%22">Vaidyanathan, Ravi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Anwar%2C+Syed+Ferhat%22">Anwar, Syed Ferhat</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarker%2C+Farhana%22">Sarker, Farhana</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mamun%2C+Khondaker+A%2E%22">Mamun, Khondaker A.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Consumer+Behaviour%22">Journal of Consumer Behaviour</searchLink>. May2024, Vol. 23 Issue 3, p1146-1157. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Neuromarketing%22">Neuromarketing</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink><br /><searchLink fieldCode="DE" term="%22Wrappers%22">Wrappers</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Marketing%22">Marketing</searchLink>
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  Data: Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Consumer Behaviour is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/cb.2253
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        Text: English
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      – SubjectFull: Neuromarketing
        Type: general
      – SubjectFull: Eye tracking
        Type: general
      – SubjectFull: Consumer preferences
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      – SubjectFull: Wrappers
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      – SubjectFull: Feature extraction
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      – SubjectFull: Electroencephalography
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      – SubjectFull: Marketing
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      – TitleFull: Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking.
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            NameFull: Mashrur, Fazla Rabbi
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            – D: 01
              M: 05
              Text: May2024
              Type: published
              Y: 2024
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