Eye-tracking-based methods are generating a growing interest in marketing research. Nevertheless, most of the studies are focusing on intention, emotion or the evaluation of the products by the costumer. The work that is presented here investigates two of the main purchasing scenarios: the routine purchasing act and the impulse purchasing act. The purpose is to propose a predictive model that best distinguishes the first scenario from the second scenario. To reach this goal, we extracted statistically relevant eye-tracking descriptors. We use a supervised learning algorithm, Support Vector Machines (SVM), to build the model and reach performances of 82.5% of good identification.
Identification of purchasing scenarios through eye-tracking features
