Neuromarketing as a Tool for Predicting Consumer Behavior in an Environment of Information Overload

Abstract

The relevance of the study is driven by the rapid increase in informational load on consumers amidst the digital transformation of markets and the proliferation of advertising channels. Traditional marketing methods based on declared preferences fail to accurately predict consumer behavior, as the majority of decisions are made at an unconscious level. The purpose of the study is to evaluate the effectiveness of neuromarketing tools in predicting consumer buying behavior under conditions of information overload and to substantiate practical guidelines for adapting these technologies in Ukraine.

In the course of the research, general scientific methods were applied: analysis and synthesis to generalize theoretical approaches; systematization to structure neuromarketing technologies by the object of measurement; comparative analysis to evaluate the predictive accuracy of neuromethods relative to traditional surveys.

The results of the study confirm that neuromarketing tools provide higher reliability in predicting purchasing actions due to the registration of unconscious neural responses. The synthesis of empirical data demonstrated a 20–30% increase in accuracy compared to traditional surveys. In the Ukrainian context, neuromarketing reveals a 15–25% growth in impulse buying, a 20% improvement in ad recall, and an 18–22% increase in conversion rates. The main implementation challenges were identified: the lack of unified protocols, a limited number of long-term studies, and ambiguity regarding the ethical boundaries of using biometric data.

The conclusions substantiate the expediency of harmonizing procedural standards, combining multiple measurement approaches within a single experiment, and adhering to the principles of informed consent and participant anonymity protection. Prospects for further research are associated with the development of unified protocols, conducting long-term panel studies, and analyzing the impact of digital transformation on the reliability of neuromarketing data.