Technological advancements and opportunities in Neuromarketing: a systematic review
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 fi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-09-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40708-020-00109-x |
| _version_ | 1818261400054661120 |
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| author | Ferdousi Sabera Rawnaque Khandoker Mahmudur Rahman Syed Ferhat Anwar Ravi Vaidyanathan Tom Chau Farhana Sarker Khondaker Abdullah Al Mamun |
| author_facet | Ferdousi Sabera Rawnaque Khandoker Mahmudur Rahman Syed Ferhat Anwar Ravi Vaidyanathan Tom Chau Farhana Sarker Khondaker Abdullah Al Mamun |
| author_sort | Ferdousi Sabera Rawnaque |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T18:46:37Z |
| format | Article |
| id | doaj.art-d9300b9c7fd0489e919ae535d7d89fb6 |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-12-12T18:46:37Z |
| publishDate | 2020-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-d9300b9c7fd0489e919ae535d7d89fb62022-12-22T00:15:31ZengSpringerOpenBrain Informatics2198-40182198-40262020-09-017111910.1186/s40708-020-00109-xTechnological advancements and opportunities in Neuromarketing: a systematic reviewFerdousi Sabera Rawnaque0Khandoker Mahmudur Rahman1Syed Ferhat Anwar2Ravi Vaidyanathan3Tom Chau4Farhana Sarker5Khondaker Abdullah Al Mamun6Advanced Intelligent Multidisciplinary Systems Lab, Institute of Advanced Research, United International UniversitySchool of Business and Economics, United International UniversityInstitute of Business Administration, University of DhakaDepartment of Mechanical Engineering, Imperial College LondonInstitute of Biomaterials & Biomedical Engineering, University of TorontoDepartment of Computer Science and Engineering, University of Liberal Arts BangladeshAdvanced Intelligent Multidisciplinary Systems Lab, Institute of Advanced Research, United International UniversityAbstract 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.http://link.springer.com/article/10.1186/s40708-020-00109-xNeuromarketingNeural recordingMachine learning algorithmBrain computer interfaceMarketing |
| spellingShingle | Ferdousi Sabera Rawnaque Khandoker Mahmudur Rahman Syed Ferhat Anwar Ravi Vaidyanathan Tom Chau Farhana Sarker Khondaker Abdullah Al Mamun Technological advancements and opportunities in Neuromarketing: a systematic review Brain Informatics Neuromarketing Neural recording Machine learning algorithm Brain computer interface Marketing |
| title | Technological advancements and opportunities in Neuromarketing: a systematic review |
| title_full | Technological advancements and opportunities in Neuromarketing: a systematic review |
| title_fullStr | Technological advancements and opportunities in Neuromarketing: a systematic review |
| title_full_unstemmed | Technological advancements and opportunities in Neuromarketing: a systematic review |
| title_short | Technological advancements and opportunities in Neuromarketing: a systematic review |
| title_sort | technological advancements and opportunities in neuromarketing a systematic review |
| topic | Neuromarketing Neural recording Machine learning algorithm Brain computer interface Marketing |
| url | http://link.springer.com/article/10.1186/s40708-020-00109-x |
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