The dimension of age and gender as user model demographic factors for automatic personalization in e-commerce sites

Abstract Personalization in e-commerce increases sales by improving customer perception of site quality. However, some demographic data about customers (crucial for the success of the personalization process) not always can be obtained explicitly, as is the case of anonymous web site visitors. The paper describes a user study focused on determining whether it would be possible to categorize the age and gender of individual visitors of a web site through the automatic analysis of their behavior. Three tasks commonly found in e-commerce sites ( Point & Click, Drag & Drop and Item Selection ) were tested by 592 volunteers and their performance was analyzed using several different statistical methods. The study found consistencies in the execution times of individuals across the different tasks and revealed that age and gender are sufficiently determining factors to support an automatic profiling. Results also showed that relevant information about gender and age can be extracted separately through the individual analysis of each one of the mentioned interaction tasks.