Data-Driven Pricing for Availability-Based Upgrades Under a Multiple Binary Choice Model with Copula

Problem definition: Intense competition in the travel industry has increasingly shifted focus toward ancillary services, particularly seat upgrades in airlines and room upgrades in hotels. In response to this trend, several innovative solutions have emerged, among which Nor1’s eStandby Upgrade program stands out by offering discounted, availability-based room upgrades. Revenue management for these upgrades is complex because customers may request multiple upgrades, whereas hotels allocate them based on availability and typically grant at most one upgrade per customer. Methodology/results: Partnering with Oracle, which acquired Nor1, we develop a state-of-the-art framework for prediction, pricing, and allocation to maximize total revenue from eStandby upgrades. We first model customer decision making using a novel copula-based multivariate choice model that captures complex dependencies among multiple decisions made by the same customer. Next, we develop efficient pricing and allocation algorithms to address the challenges associated with offering multiple availability-based upgrades and tracking customer requests. Managerial implications: Validated with real-world data and data-driven numerical experiments, our choice model for upgrade requests and algorithms for pricing and allocation demonstrate significant revenue potential by capturing dependencies across customers’ multiple decisions. History: This paper has been accepted as part of the 2025 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by Oracle Research [Grant CR 4223]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0328 .