Assessing Ad Attention through Clustering Viewport Trajectories.

Advertisers have to pay publishers for “viewable” ads, irrespective of whether the users paid active attention. In this paper, we suggest that a granular analysis of users’ viewing patterns can help us to progress beyond mere “viewability” and toward actual differentiation of whether a user has paid attention to an ad or not. To this end, we use individual viewport trajectories, which measures the sequence of locations and times an object (e.g., an ad) is visible on the display of a device (desktop or mobile). To validate our model and benchmark it against the extant models, such as the “viewability” policy (50% threshold) model, we use data from an eye-tracking experiment. Findings confirm the improved model fit, highlight distinct viewing patterns in the data, and inform information processing on mobile phones. Consequently, implications are relevant to publishers, advertisers, and consumer researchers. [ABSTRACT FROM AUTHOR]