A Model to Estimate Individual Preferences Using Panel Data
Speaker: Gustavo Vulcano, Stern School of Business, New York UniversityAbstract:
In a retail operation, customer choices may be affected by stock out and promotion events. Given panel data with the transaction history of each customer, our goal is to predict future purchases. We use a general nonparametric framework in which we represent customers by partial orders of preferences. In each store visit, each customer samples a full preference list of the products, consistent with her partial order, forms a consideration set, and then chooses to purchase the most preferred product among the considered ones. Our approach involves: (a) defining behavioral models to build consideration sets, (b) a clustering algorithm for determining customer segments in the market, and (c) the derivation of marginal distributions for general partial preferences under the multinomial logit (MNL) and the Mallows models. Numerical experiments on real-world panel data show that our approach allows more accurate, fine-grained predictions for individual purchase behavior compared to state-of-the-art existing methods.
Joint work with Srikanth Jagabathula, NYU
Friday, December 11th, 11:00 am – 12:00 pm