von Florian Nottorf und Burkhardt Funk (2013), ECIS 2013 Proceedings Utrecht University.


With the increasing share of online advertising, the amount of highly detailed data available on an individual consumer level has substantially increased over the last few years. Because there are costs associated with collecting, analyzing, and storing this type of data, it is important to understand the value of this specific type of personal data. In this paper, we illustrate the economic value of clickstream data by highlighting the direct and interaction effects of multiple types of online advertising on an individual user-level. By developing a binary logit model with Bayesian estimation techniques to predict consumer behavior and by analyzing clickstream data from a large financial service provider, we find strong 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 important when managing online advertising campaigns, we demonstrate how the value of clickstream data can be estimated to support decision-making in the emerging context of real-time bidding (RTB).

Keywords: personal data valuation, clickstream analysis, online advertising efficiency, consumer behavior, Bayesian estimation

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