An Exploration of Cursor tracking Data

Cursor tracking data contains information about website visitors which may provide new ways to understand visitors and their needs. This paper presents an Amazon Mechanical Turk study where participants were tracked as they used modified variants of the Wikipedia and BBC News websites. Participants were asked to complete reading and information-finding tasks. The results showed that it was possible to differentiate between users reading content and users looking for information based on cursor data. The effects of website aesthetics, user interest and cursor hardware were also analysed which showed it was possible to identify hardware from cursor data, but no relationship between cursor data and engagement was found. The implications of these results for web analytics and the design of user engagement experiments are discussed.