No 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.

An increasingly studied auction format is the asymmetric procurement auction, which features an advantaged bidder selling a good or service to a buyer. Such asymmetric setups, although generally believed to be more realistic, are also more complex. We investigate one such setting where one bidder has a cost advantage. Our central question is whether bidders who are cost disadvantaged can overcome their inferior cost position. In a design that mirrors real construction procurement auctions, our laboratory experiment tests whether two information asymmetries (more precise cost estimates and knowledge about the cost estimating abilities of a competitor) allow cost-disadvantaged sellers to better compete. We begin by testing bidder performance in a procurement auction where the only asymmetry is due to a cost. In this setting, analytical benchmarks are known. We find-consistent with other studies-that all bidder types submit aggressive bids well below the Nash equilibrium predictions. Over time, subjects submit less aggressive bids, moving closer to what theory would predict. We then extend our experiments to include our novel, multiple asymmetry setup. We find that endowing cost-disadvantaged bidders with higher cost estimate precision benefits the bidder, as one might expect. Notably, providing market knowledge about a rival's ability to estimate costs may not provide a benefit; in fact, it seems bidders are not able to use this information effectively and performance suffers. Finally, we show how bidders behave myopically when making these decisions. These implications raise important questions about how asymmetries interact in complex auction settings. [

No abstract

No abstract