Eye-Tracking Data Predict Importance of Product Features and Saliency of Size Change

Abstract

Features, or visible product attributes, are indispensable product components that influence customer evaluations of functionality, usability, symbolic impressions, and other qualities. Two basic components of features are visual appearance and size. This work tests whether or not eye-tracking data can (1) predict the relative importances between features, with respect to their visual design, in overall customer preference and (2) identify how much a feature must change in size in order to be noticeable by the viewer. The results demonstrate that feature importance is significantly correlated with a variety of gaze data. Results also show that there are significant differences in fixation time and count for noticeable versus unnoticeable size changes. Statistical models of gaze data can predict feature importance and saliency of size change.