In this paper we address the problem of finding the most suitable banner display advertisement option for a user given his/her current browsing session. Using the historical browsing session infor- mation for an advertisement campaign, we mine the association of different ad views, engagements and clicks. Using a probabilistic model, we find the likelihood of an ad to be clicked given a specific set of events that describe the user session. The major challenge in training the model for optimum precision is the sparsity of data (< 0.5% Click through rate) and we propose the use of ad engage- ment as a success event like ad click to train the model more effectively. Our results show a good click through conversion probability on test data.