Neuromarketing Insights: Using Eye-Tracking and Machine Learning to Understand Consumer Preferences for University Promotional Products

Abstract

This research applies neuromarketing approaches along with traditional marketing tool like self-report to study how design factors, such as visual appeal, quality and pricing, affect consumer engagement with promotional products at Almaty Management University. It explores how eye-tracking measures affect consumer preferences and how machine learning can be used to predict consumer behavior. 16 university students and young professionals participated in this quantitative study and interacted with a range of promotional items, including clothing, stationery and dishware. Gaze patterns and fixation durations were recorded using eye-tracker technology, and data including survey results were analyzed using machine learning model such as random forest classifiers to find patterns in consumer preferences. The results suggest that visually appealing designs increase consumer attraction and perceived attractiveness, especially in clothing. Eye-tracking metrics, such as size (cm2) and dwell time, were strongly correlated with attractiveness ratings and were identified by machine learning models as key features in predicting. The study emphasizes the importance of visual appealing products in increasing consumer engagement and brand loyalty. This study offers insightful information about how eye-tracking and machine learning can be used to predict consumer behavior in the context of promotional products. The results highlight how important visual design is in attracting consumers, by offering practical implications for marketers to improve promotional products.