NEUROMARKETING AND AI: PREDICTING CONSUMER EMOTIONS AND BRAND PREFERENCES USING MACHINE LEARNING

Abstract

Artificial Intelligence (AI) and neuromarketing are transforming the way organizations understand consumer behavior in the digital marketplace. Traditional marketing approaches often fail to capture the subconscious emotional responses that strongly influence consumer decision-making and brand preference. This study explores the integration of neuromarketing techniques and machine learning algorithms to predict consumer emotions and identify brand preferences with greater accuracy and efficiency. The research examines how AI-driven analytical models can interpret emotional and behavioral data collected through consumer interactions, facial expressions, sentiment analysis, and cognitive responses. The study adopts a quantitative research approach by collecting data from consumers through structured questionnaires and digital behavioral observations. Machine learning techniques such as Random Forest, Support Vector Machine (SVM), and Neural Networks are applied to analyze emotional patterns and predict consumer preferences toward brands. The findings indicate that AI-based neuromarketing models significantly improve the prediction of consumer emotional engagement, purchase intention, and brand loyalty compared to conventional marketing analytics methods. The research further highlights the growing strategic importance of predictive analytics in developing personalized marketing campaigns, improving customer experience, and enhancing competitive advantage in dynamic business environments. The study contributes to the emerging literature on AI-enabled consumer analytics by providing a conceptual and empirical understanding of how machine learning can support emotionally intelligent marketing strategies. The results offer valuable implications for marketers, researchers, and organizations seeking to leverage advanced technologies for data-driven decision-making and customer-centric brand management.