Technological advancements and opportunities in Neuromarketing: a systematic review

Brain Informatics - Tập 7 - Trang 1-19 - 2020
Ferdousi Sabera Rawnaque1, Khandoker Mahmudur Rahman2, Syed Ferhat Anwar3, Ravi Vaidyanathan4, Tom Chau5, Farhana Sarker6, Khondaker Abdullah Al Mamun1,7
1Advanced Intelligent Multidisciplinary Systems Lab, Institute of Advanced Research, United International University, Dhaka, Bangladesh
2School of Business and Economics, United International University, Dhaka, Bangladesh
3Institute of Business Administration, University of Dhaka, Dhaka, Bangladesh
4Department of Mechanical Engineering, Imperial College London, London, United Kingdom
5Institute of Biomaterials & Biomedical Engineering, University of Toronto, Toronto, Canada
6Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh
7Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh

Tóm tắt

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

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