Browsing by Author "Kemerci, Emre"

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Predicting Customer Perfection on Brands Functional Near-Infrared Spectroscopy Measurements
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kemerci, Emre; Koç, Utku
Customer perception on the brands have importance to give strategic decisions by marketing professionals. In classical ways, customer perception on brands are researched through conducting field surveys. Similarly, neuromarketing discipline have studies on customer behaviors, their perceptions, communication techniques etc. under the frame of decision-making process of human. In neuromarketing, functional near-infrared spectroscopy (fNIRS) is a technology used to measure oxy and deoxy hemoglobin concentration in the tissues in order to enable to analyze hemodynamic responses of the brain activities. In this study, a group of participants’ activations of prefrontal cortex so the hemodynamic responses that were collected against a set of stimuli, which is a brand logo and adjective associated with the brand is used as dataset. Measured hemodynamic response metrics are oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), total hemoglobin (HbT) and Oxygenation (Oxy) and the dataset includes 168 participants’ measurements for 30 stimuli. In addition, the information regarding the responses of the participants and common perception of stimuli (field study results for same stimuli) are also exists in dataset. The aim of the project is to predict through machine learning algorithms whether relation between brand and the relevant adjective is Positive, Negative or Neutral using these feature set. As methodology of this study, fNIRS measurements in the data is cleaned and Null values are handled, measurements are consolidated per participant and stimuli with two different method as feature creation and classification algorithms are used as supervised learning to predict brand perception. In conclusion, performance of support vector classifier and XGBoosting algorithms are become very low, slightly over 50% accuracy despite the optimization with different classifier parameters. Further studies are addressed as performing feature engineering studies with different options.