Context-based Pricing for Revenue Optimization with Applications to the Airline Industry

dc.contributor.author Ettl, Markus
dc.contributor.author Subramanian, Shiva
dc.contributor.author Drissi, Youssef
dc.contributor.author Sun, Wei
dc.contributor.author Biggs, Max
dc.date.accessioned 2022-12-27T18:58:39Z
dc.date.available 2022-12-27T18:58:39Z
dc.date.issued 2023-01-03
dc.description.abstract Most airlines use dynamic pricing to optimize the price of their base economy product by maximizing the expected revenue. However, when it comes to pricing of premium products, airlines often uses a static price increments that are applied to the best available economy fare based on simple business rules for adjusting the price based on supply. In this paper, we present a suite of machine learning algorithms that take advantage of the rich booking session context available at the time of the booking to make its predictions. The challenge is to accurately predict bookings for new combinations of attributes by market and segment (departure time, length of stay, advance purchase, length of haul, …) while accounting for cross-product price effects in a scalable manner. To generate practical pricing policies, the approach accommodates a variety of real-world business requirements into the decision optimization problem. We present a scalable approach based on a novel path-based mixed-integer program (MIP) reformulation that can efficiently recover near-optimal pricing policies. We demonstrate the efficacy of our model with extensive experiments on synthetic and real-life data. Finally, we present an airline case study on deriving profitable prescriptive policies for premium cabin tickets based on easily interpretable pricing rules.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.171
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102801
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject case studies
dc.subject machine learning
dc.subject pricing
dc.subject revenue optimization
dc.title Context-based Pricing for Revenue Optimization with Applications to the Airline Industry
dc.type.dcmi text
prism.startingpage 1366
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