An SVM-based machine learning framework analyzing EEG signals predicted consumers' purchase intention with 84% accuracy and affective attitude with 87% accuracy.
Cross-Sectional (n=20)
No
A machine learning framework using EEG signals can predict consumers' purchase intention and affective attitude with 84% and 87% accuracy, respectively.
Effect estimate: 84% accuracy for PI and 87% accuracy for AA
750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.
Mashrur et al. (Thu,) conducted a cross-sectional in Healthy participants (Neuromarketing) (n=20). Advertising stimuli (product, endorsement, promotion) was evaluated on Prediction accuracy for purchase intention (PI) and affective attitude (AA) (84% accuracy for PI and 87% accuracy for AA). An SVM-based machine learning framework analyzing EEG signals predicted consumers' purchase intention with 84% accuracy and affective attitude with 87% accuracy.