Estimating Personalized Demand with Unobserved No-purchases using a Mixture Model: An Application in the Hotel Industry
44 Pages Posted: 14 Nov 2020 Last revised: 19 Oct 2021
Date Written: September 11, 2021
Estimating customer demand for revenue management solutions faces two main hurdles: unobservable no-purchases and non-homogenous customer populations with varying preferences. We propose a novel and practical estimation and segmentation methodology that overcomes both challenges simultaneously. We combine the estimation of discrete choice modeling under unobservable no-purchases with a data-driven identification of customer segments. In collaboration with our industry partner, Oracle Hospitality Global Business Unit, we demonstrate our methodology in the hotel industry setting, where increased competition has driven hoteliers to look for more innovative revenue management practices such as personalized offers for their guests. Our methodology predicts demand for multiple types of hotel rooms based on guest characteristics, travel attributes, and room features. Our framework combines clustering techniques with choice modeling to develop a mixture of multinomial logit discrete choice models and uses Bayesian inference to estimate model parameters. In addition to predicting the probability of an individual guest's room type choice, our model delivers additional insights on segmentation with its capability to classify each guest into segments (or a mixture of segments) based on their characteristics. We first show using Monte-Carlo simulations that our method outperforms several benchmark methods in prediction accuracy, with nearly unbiased estimates of the choice model parameters and the size of the no-purchase incidents. We then demonstrate our method on a real hotel dataset and illustrate how the model results can be used to drive insights for personalized offers and pricing. Our proposed framework provides a practical approach for a complicated demand estimation problem, and can help hoteliers segment their guests based on their preferences, which can serve as a valuable input for personalized offer selection and pricing decisions.
Keywords: personalized demand, mixture model, soft clustering, censored demand, hotel industry
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