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Marketing Research Seminars

Details of our upcoming seminars are posted here:


Date: Friday, September 29, 2017

Speaker: Ryan Dew, Columbia Business School
Topic: Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  Marketing managers are responsible for understanding and predicting customer purchasing activity, a task that is complicated by a lack of knowledge of all of the calendar time events that influence purchase timing. Yet, isolating calendar time variability from the natural ebb and flow of purchasing is important, both for accurately assessing the influence of calendar time shocks to the spending process, and for uncovering the customer-level patterns of purchasing that robustly predict future spending. A comprehensive understanding of purchasing dynamics therefore requires a model that flexibly integrates both known and unknown calendar time determinants of purchasing with individual-level predictors such as interpurchase time, customer lifetime, and number of past purchases. In this paper, we develop a Bayesian nonparametric framework based on Gaussian process priors, which integrates these two sets of predictors by modeling both through latent functions that jointly determine purchase propensity. The estimates of these latent functions yield a visual representation of purchasing dynamics, which we call the model-based dashboard, that provides a nuanced decomposition of spending patterns. We show the utility of this framework through an application to purchasing in free-to-play mobile video games. Moreover, we show that in forecasting future spending, our model outperforms existing benchmarks.


Date: Friday, October 6, 2017

Speaker: Andrey Fradkin, MIT Sloan School of Mangement
Topic: The Welfare Effects of Peer Entry in the Accommodation Market: The Case of Airbnb
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  We study the entry of Airbnb in the accommodation industry and its effects on travelers, hosts, and hotels. We first document the heterogeneity of Airbnb’s penetration across 50 major US cities and demonstrate that much of this heterogeneity can be explained by proxies for the costs of hotels, the costs of peer hosts, and demand fluctuations. Next, we document that Airbnb has an effect on hotel revenues. This effect is mostly due to a reduction in hotel prices rather than occupancy and is greatest in cities with low hotel capacity relative to the size of demand. Third, we estimate a structural model of competition between peer hosts and hotels and use it to study the effects of Airbnb on the distribution of surplus across consumers, peer hosts, and incumbent hotels. We find an average consumer surplus of $63 per night from Airbnb. This surplus is disproportionately concentrated in locations (New York) and times (New Year’s Eve) when hotels have high occupancy. Because Airbnb bookings occur when hotels are sold out, most of these bookings would not have resulted in hotel bookings.


Date: Friday, October 13, 2017

Speaker: Shervin Shahrokhi-Tehrani, Rotman School of Mangement, University of Toronto
Topic: A Heuristic Approach to Explore: Value of Perfect Information
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  How do consumers choose in a dynamic stochastic environment when they face uncertainty about the return of their choice? The classical solution to this problem is to assume consumers use dynamic programming to obtain the optimal decision rule. However, this approach has two drawbacks. First, it is computationally very expensive to implement because it requires solving a dynamic programming problem with a continuous state space. Second, a decision maker is assumed to behave “as if” she optimally processes information regardless of its cognitive tractability. To address these two issues, we propose a new heuristic decision process called Value of Perfect Information (VPI), which extends the idea first proposed by Howard (1966) in the Engineering literature. This approach provides an intuitive and computationally tractable way to capture the value of exploring uncertain alternatives. Intuitively, in VPI, a decision maker investigates a subset of information which is expected by her to improve her myopic decision outcome. We argue that our VPI approach provides a “fast and frugal” way to balance the tradeoffs between exploration versus exploitation. More specifically, the VPI approach only involves ranking the alternatives and computing a one-dimensional integration to obtain the expected future value counterpart. In terms of computational costs, we show that the VPI approach is significantly simpler than the standard dynamic programming approach, making it a very practical learning model for consumers to employ. Moreover, the VPI approach provides switching patterns which differ from the independence of irrelevant alternatives (IIA) property, and generates potentially more realistic asymmetric switching reactions to price promotions. Using individual level scanner data, we find evidence that our VPI approach is able to capture consumers’ choice well.


Date: Friday, November 10, 2017

Speaker: Ye Li, University of California, Riverside
Topic: TBA
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  TBA


Date: Friday, November 10, 2017

Speaker: Romain Cadario , IESEG, France
Topic: TBA
Time: 3:00PM - 4:30PM
Place: Henry Angus 969

Abstract:  TBA


Date: Friday, December 1, 2017

Speaker: Stephanie Tully, Marshall School of Business, USC
Topic: TBA
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  TBA


Date: Friday, April 13, 2018

Speaker: Andrea Morales, W.P. Carey School of Business, Arizona State University
Topic: TBA
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  TBA


Date: Friday, May 18, 2018

Speaker: Juanjuan Zhang, MIT Sloan School of Management
Topic: TBA
Time: 10:00AM - 11:30AM
Place: Henry Angus 966

Abstract:  TBA