Measuring consumer response to online advertising: the case of mobile applications

To research will help Tap for Tap to generate and collect information on user behavior that can help improve their matching algorithms. The core of the work consists in analyzing a large dataset of app users’ responses to advertising. Clearly, much of this search for determinants of successful matching will be statistical in nature. Still, this search will also be guided by an alert eye on the mechanisms that may lie behind the relationships uncovered in the data. Many mechanisms have been documented in the computer science, economics and marketing literatures and could prove to be useful in the interpretation of the evidence. At this early point in the research, it is not clear what mechanisms will prove relevant. Still, to make the connections with the academic literature clear, we briefly discuss three examples. The intern will dedicate most of her time analyzing this information using statistical models that leverage the random variations that have been introduced in the matching generation process. Finally, the intern will make recommendations to improve the matching algorithm.

Faculty Supervisor:

Dr. Pascal Courty


Matt Agbay


Tap for Tap Promotions Inc.




Information and communications technologies


University of Victoria



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