Intelligent neuromarketing framework for consumers' preference prediction from electroencephalography signals and eye tracking

Abstract

Neuromarketing uses brain‐computer interface technology to understand customer preferences in response to marketing stimuli. Every year, marketing professionals spend over $750 Billion (US dollars) on traditional marketing, which is usually behavioral and subjective, focusing on self‐reports acquired via questionnaires, focus groups, and depth interviews. Neuromarketing, on the other hand, promises to overcome such limitations. This work proposes a machine learning framework that incorporates multiple components (endorsement, offer, and slogan) in real advertisement to predict consumer preference from electroencephalography (EEG) signals. In addition, we also use eye‐tracking data to visualize consumer viewing patterns according to both advertisement type and preference. EEG signals are collected from 22 healthy volunteers while viewing the real ads as stimuli. After preprocessing the signals, three‐domain features are extracted (time, frequency, and time‐frequency). Then, using wrapper‐based approaches we choose best features which are later classified into strong and weak preferences using the support vector machine. The experimental results demonstrate the best performance using all the frontal channels with an accuracy of 96.97%, sensitivity of 96.30%, and specificity of 97.44%. Additionally, eye tracking data reveals that subjects substantially prefer an ad, when they first glance at the endorsement. In addition, people tend to blink their eyes less frequently while viewing ads with endorsements and strongly prefer these commercials too. Additionally, our work lays the door for deploying such a neuromarketing framework in a real‐world context by employing consumer‐grade EEG equipment. Therefore, it is evident that neuromarketing technology may assist brands and companies in accurately predicting future customer preferences.

A method for synchronized use of EEG and eye tracking in fully immersive VR

“…In engineering science, it measures cognitive load across tasks and participant knowledge levels (Keskin et al, 2020 ), identifies optimal learning approaches and efficiency (Baceviciute et al, 2022 ). Beyond engineering, applications include neuromarketing (Mashrur et al, 2023 ; Zhu and Lv, 2023 ), and safety training utilizing VR, eye tracking, and EEG to evaluate attitudes and learning abilities (Katona, 2014 ; Comu et al, 2021 ; Huang et al, 2022 ).…”