Predicting User Satisfaction and Recommendation Intentions: A Machine Learning Approach Using Psychophysiological and Self-Reported Data

The finance sector, just like e-commerce, utilizes online platforms (websites or mobile apps) to deliver its services or products, making usability and user experience one of the key concerns of digital banking. Having identified a research gap in using psychophysiological data to understand the determinants of customer satisfaction on digital platforms, this study focuses on predicting factors influencing users’ satisfaction and intention to recommend a banking website using both self-reported and psychophysiological data. With a within-subject study design, we collected data on 100 participants. Our research-in-progress aims to develop a machine learning model capable of predicting real-time user satisfaction and the likelihood of a user recommending a digital banking experience to friends or colleagues. Results showed that psychophysiological metrics improved the prediction of users’ intention to recommend. Similar features such as Phasic EDA, pupil size, time-to-first-mouse-click, k-coefficient, emotional valence, and subjective success were found to be good predictors of both intention to recommend and customer satisfaction.