Implementation of Naïve Bayes method for product purchasing decision using neural impulse actuator in neuromarketing

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.

show abstract

“…Naïve Bayes Purchase decision prediction using Neural Impulse Actuator (NIA) device (Taqwa et al) [73] 48.5%…”

Section: %mentioning

confidence: 99%

Technological advancements and opportunities in Neuromarketing: a systematic review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The literature offers examples of very different analytical techniques. It is common to find classical statistical techniques such as component analysis from ANOVA for the prediction of consumption preferences (Goto et al 2019), or the use of Naive Bayes to predict the acquisition of products (Taqwa et al 2015). Since 2016, it has become increasingly frequent that studies on EEG data on neuromarketing (in general) and on advertisement scoring (in particular) have begun to incorporate predictive machine learning techniques, such as SVM in combination with random forest classifiers (Libert and Hulle 2019), C4.5 classifier together with ANN (Morillo et al 2016) or SVM (Wei et al 2018).…”

Section: Research Instrumentmentioning

confidence: 99%

An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

et al. 2020

View full text Add to dashboard Cite

This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.

show abstract

AI in Consumer Behavior

Gkikas

Theodoridis

2021

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

Implementation of Naïve Bayes method for product purchasing decision using neural impulse actuator in neuromarketing

Cited by 5 publications

References 5 publications

Technological advancements and opportunities in Neuromarketing: a systematic review

Technological advancements and opportunities in Neuromarketing: a systematic review

An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

AI in Consumer Behavior

Contact Info

Product

Resources

About