Affiliation:
1. Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Institute of Research, Innovation, Incubation & Commercialization (IRIIC) United International University Dhaka Bangladesh
2. Department of Biomedical Engineering University of Rochester Rochester New York USA
3. School of Business and Economics United International University Dhaka Bangladesh
4. Department of Mechanical Engineering and UK Dementia Research Institute Care Research and Technology Centre (DRI‐CRT), Imperial College London London UK
5. Institute of Business Administration University of Dhaka Dhaka Bangladesh
6. Managing Director CMED Health Ltd Dhaka Bangladesh
7. Department of Computer Science & Engineering United International University Dhaka Bangladesh
Abstract
AbstractNeuromarketing 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.
Subject
Applied Psychology,Social Psychology
Cited by
3 articles.
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