Estimating Personalized Demand with Unobserved No-Purchases Using a Mixture Model: An Application in the Hotel Industry

Abstract

Problem definition: Estimating customer demand for revenue management solutions faces two main hurdles: unobservable no-purchases and nonhomogenous customer populations with varying preferences. We propose a novel and practical estimation and segmentation methodology that overcomes both challenges simultaneously. Academic/practical relevance: 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. Methodology: 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. Results: 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 data set and illustrate how the model results can be used to drive insights for personalized offers and pricing. Managerial implications: 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.

A reinforcement learning approach for hotel revenue management with evidence from field experiments

“…Given the recommended average discount levels, we then solve the linear program separately for each type of rooms to obtain capacity allocations. When there are multiple room types, hotels may offer standby upgrades to increase revenue (Cho et al, 2022; Cui et al, 2018; Yılmaz et al, 2017). It would be interesting to study how to incorporate the upsell process into a reinforcement learning framework.…”

“…Current hotel revenue management literature focuses on sophisticated issues such as optimizing standby upgrades (Yılmaz et al, 2017), pricing of conditional upgrades (Cui et al, 2018), estimating strategic customer behavior in the context of standby upgrades (Yılmaz, Ferguson, et al, 2022), predicting personalized demand with unobserved no-purchases (Cho et al, 2022), and identifying demand elasticities by leveraging the ability of observing delayed price adjustment to demand shocks (Garcia et al, 2022). However, the aforementioned challenges that budget hotels face are often ignored.…”