Technological advancements and opportunities in Neuromarketing: a systematic review
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Brain Informatics
Open Access
REVIEW
Technological advancements and opportunities in Neuromarketing: a systematic review Ferdousi Sabera Rawnaque1*, Khandoker Mahmudur Rahman2, Syed Ferhat Anwar3, Ravi Vaidyanathan4, Tom Chau5, Farhana Sarker6 and Khondaker Abdullah Al Mamun1,7
Abstract 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. Keywords: Neuromarketing, Neural recording, Machine learning algorithm, Brain computer interface, Marketing 1 Introduction Neuromarketing, an application of the non-invasive brain–computer interface (BCI) technology, has emerged as an interdisciplinary bridge between neuroscience and marketing that has changed the perception of marketing research. Marketing is the channel between product and consumers which determines the ultimate sale. *Correspondence: [email protected] 1 Advanced Intelligent Multidisciplinary Systems Lab, Institute of Advanced Research, United International University, Dhaka, Bangladesh Full list of author information is available at the end of the article
Without effective marketing, a good product fails to inform, engage and sustain its targeted audiences [1]. The expa
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