ACM SIGIR Forum
Bayesian Statistics and Marketing
Marketing Science
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
INFORMS Journal on Computing
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
Ex Ante Information and the Design of Keyword Auctions
Information Systems Research
Online Display Advertising: Targeting and Obtrusiveness
Marketing Science
Editorial: Business and data analytics: New innovations for the management of e-commerce
Electronic Commerce Research and Applications
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The evaluation of online marketing activities using standalone metrics does not explain the development of consumer behavior over time, although it is of primary importance to allocate and optimize financial resources among multiple advertising channels. We develop a binary logit model with a Bayesian mixture approach to demonstrate consumer clickstreams across multiple online advertising channels. Therefore, a detailed user-level dataset from a large financial service provider is analyzed. We find both differences in the effects of repeated advertisement exposure across multiple types of display advertising as well as positive effects of interaction between display and paid search advertising influencing consumer click probabilities. We identify two consumer types with different levels of susceptibility to online advertising (resistant vs. susceptible consumers) and show that the knowledge of consumers individual click probabilities can support companies in managing display advertising campaigns.