With the increasing share of online advertising, the amount of highly detailed data available on an in-dividual consumer level has substantially increased over the last few years. Because there are costsassociated with collecting, analyzing, and storing this type of data, it is important to understand thevalue of this specific type of personal data. In this paper, we illustrate the economic value of clickstreamdata by highlighting the direct and interaction effects of multiple types of online advertising on an in-dividual user-level. By developing a binary logit model with Bayesian estimation techniques to predictconsumer behavior and by analyzing clickstream data from a large financial service provider, we findstrong differences in the effects of repeated ad exposures. Along with uncovering important insights,such as the positive cross-channel effects of display and paid search advertising, which are importantwhen managing online advertising campaigns, we demonstrate how the value of clickstream data can beestimated to support decision-making in the emerging context of real-time bidding (RTB