|Individuals and organizations interact through many channels, on multiple screens and devices, across a myriad of touchpoints and over time. Given the ubiquity of data, there is a new opportunity for firms to more fully understand the effect and value of their marketing actions. Big data, experimentation, and new models offer new insight into the causal levers and influences in this complex, extended world.|
There’s No Free Lunch Conversation: The Effect of Brand Advertising on Word of MouthFeb 28, 2017 Mitchell J. Lovett, Renana Peres, and Linli Xu, 2017, 17-103
There's a widely held belief that brand advertising can create big consumer brand buzz. This study suggests otherwise.
Closing Thoughts: Harnessing Marketing Analytics for Business ImpactFeb 09, 2017 Randolph E. Bucklin
Concluding remarks from MSI February 2017 conference, "Harnessing Marketing Analytics for Business Impact"
Scalable TargetingFeb 09, 2017 Sanjog Misra, University of Chicago
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.
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?
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