IEOR-DRO Seminar: Srikanth Jagabathula (New York University)
Tuesday, October 1, 2013
12:30pm – 2:00pm EST
Uris 3rd Floor - 333
3022 Broadway, New York, NY 10027
Event Details
Title: A Two-Stage Model of Consideration Set and Choice: Learning, Revenue Prediction, and Applications Tuesday, October 1 – Srikanth Jagabathula, New YorkUniversity Time: 1:10-2pm Room: 333 Uris Hall Abstract: A common operational problem in many business applications is the accurate prediction of demand shares of differentiated products in response to variations in the offer set and prices. The main challenge in predicting demand shares accurately is to isolate substitution, due to price variation from substitution, due to stock-outs, (absence of a product from the offer set), from the observed demand. Existing approaches either offer flexibility in the price-elasticity patterns they can capture or the ability to simultaneously account for substitution, due to price variation and stock-outs, but not both. Motivated by this limitation, this paper proposes a general two-stage model class of consideration set and choice. Under this model, each consumer first evaluates the products to form a smaller subset for consideration, and then makes a purchase decision from the consideration set. The model simultaneously captures the effect of substitution, due to stock-outs and price variation. We propose techniques to estimate our models from historical transaction data, which comprise observed sales for each of the products when offered at different price vectors. We study estimation procedures for two model instances in detail: a parametric instance and a semi-parametric instance. For both the model instances, we derive sample complexity bounds and prove that our estimation technique is computationally efficient. In addition to providing an appealing combination of model flexibility and analytical tractability, we demonstrate through numerical experiments, based on real-world transaction data on sales of television sets from a retailer and synthetic transaction data, that our semi-parametric method obtains a close to 30% improvement in prediction accuracy over the existing methods. This is joint work with Paat Rusmevichientong, USC Marshall. Bio: Srikanth Jagabathula is Assistant Professor of Information, Operations, and Management Sciences at Leonard N. Stern School of Business. His research interests involve understanding how to handle and extract useful insights from the large quantities of data being generated by businesses. His current work is focused on using transaction data to understand the choice behavior of customers. Prof. Jagabathula has received a number of awards recognizing his work. They include first place in the MSOM Student Competition in 2010, the Best Student Paper Award at NIPS in 2008, the Ernst Guillemin Award for the best Master's thesis in 2009 and the President of India Gold Medal in 2006. Prof. Jagabathula received a B.Tech. degree in Electrical Engineering from IIT Bombay, and an S.M. degree and Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.
Where & When

Uris 3rd Floor - 333, 3022 Broadway, New York, NY 10027

Tuesday, October 1, 2013, 12:30pm – 2:00pm

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Decision, Risk, and Operations

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