A Statistical Learning Approach to Personalization in Revenue Management
37 Pages Posted: 18 Mar 2015 Last revised: 9 Jan 2020
Date Written: March 15, 2015
Abstract
We consider a logit model based framework for modeling joint pricing and assortment decisions that take into account customer features. This model provides a significant advantage when one has insufficient data for any one customer and wishes to generalize learning about one customer's preferences to the population. Under the multinomial model, we establish finite-sample convergence guarantees on the model parameters. The parameter convergence guarantees are then extended to out-of-sample performance guarantees in terms of revenue, in the form of a high-probability bound on the gap between the expected revenue of the best action taken under the estimated parameters and the revenue generated by a decision-maker with full knowledge of the choice model.
Keywords: revenue management, statistical learning
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