Scalable TargetingFeb 09, 2017
In this talk, Sanjog Misra will present a framework that brings together experimental methods, machine learning and causal inference, as well as marketing and economic theory, to achieve this promise, and will illustrate implementations using multiple real-world case studies.
Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber PlatformFeb 09, 2017 Keith Chen, University of California, Los Angeles and Chief Economist, Uber
In many markets, new technologies allow traditional jobs to be divided into discrete tasks that are widely distributed across workers and dynamically priced given prevailing supply and demand conditions. This “sharing” or “gig” economy represents a more flexible work system, and is most common in two-sided markets in which a firm acts as a platform to connect service providers and consumers.
Causality, Optimality, and Marketing ModelingFeb 09, 2017 Peter E. Rossi, University of California, Los Angeles
The role of causal inference is to establish the true effect sizes for various marketing actions. As such, causal inference relies heavily on counterfactual reasoning. Peter Rossi will review the basics of causal inference and provide examples of valid reasoning and challenges for existing practices.
TV Ads and Search Spikes: Toward a Deeper UnderstandingFeb 09, 2017 Kenneth C. Wilbur, University of California, San Diego
Mobile devices and media multi-tasking have become so common that Google search spikes can now be detected in response to "mundane" TV ads. The question is, does search reliably predict intent?
Advertising Spending in the Digital AgeFeb 09, 2017 Shuba Srinivasan, Boston University
Much has been learned about the impact of advertising on business performance. But do these lessons still hold in the digital age?