| Summary: | Cognitive neuroscience is useful for understanding human behaviors related to marketing and adapting to consumer preferences. By analyzing consumers' brain responses to marketing stimuli, researchers seek to discover the reasons for decision-making. This study proposes a framework for participants' decision-making processes in terms of liking and disliking when viewing and selecting the products of an online store. To this end, the participants' brain signal (EEG) is used when displaying different products. Estimation of power spectrum density by Welch method, detrended fluctuation analysis (DFA), and recurrence quantification analysis (RQA) were used to extract the feature vector. The results show that the two categories of liking or disliking a product can be classified with 73.5% accuracy using a support vector machine (SVM), which compared to the previous study, there is a 3.5% improvement in results. By better understanding consumer behavior and mastery of consumer demands, market strategies can be determined in a way that in addition to customer satisfaction, increase sales and profits. The results are promising and the proposed method can be used for a better electronic commerce model.
|
|---|