A MILP approach to the optimization of banner display strategy to tackle banner blindness.

The paper addresses the issue of online advertising efficiency in the context of the 'banner blindness' phenomenon. We extend the traditional exposure metric of banner efficiency by allowing the effect of a banner display (exposure effect, EE) to vary depending on the sequence of pages that lead to the display. Within a user session on the given website, we assume that EE differs for a banner carried over from the previous page (low EE), from the case where the banner suddenly appears in a previously unoccupied place (high EE). This assumption implies that a banner's efficiency can be increased by optimising its placement throughout the website's structure; to this end, we develop a simple model framework. First, we formalize the efficiency metric based on expected total EE for a representative user, using a Markov chain model estimable from the website's clickstream data. Next, we formulate the selection of efficient banner placement as a mixed integer linear programming problem. We consider two scenarios: (i) a static one, where banner placement is fixed throughout the day, and (ii) a dynamic one, where banner placement is allowed to vary in different time intervals in order to respond to changes in browsing patterns. Finally, we demonstrate the use of our model on an empiric case study, and analyse the effect of different parameter settings. [ABSTRACT FROM AUTHOR]