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

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dc.contributor.authorEttl, Markus
dc.contributor.authorSubramanian, Shiva
dc.contributor.authorDrissi, Youssef
dc.contributor.authorSun, Wei
dc.contributor.authorBiggs, Max
dc.date.accessioned2022-12-27T18:58:39Z
dc.date.available2022-12-27T18:58:39Z
dc.date.issued2023-01-03
dc.description.abstractMost 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.extent10
dc.identifier.doi10.24251/HICSS.2023.171
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other1505f78a-c8ea-48f7-85b7-aa9719e91bb5
dc.identifier.urihttps://hdl.handle.net/10125/102801
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectcase studies
dc.subjectmachine learning
dc.subjectpricing
dc.subjectrevenue optimization
dc.titleContext-based Pricing for Revenue Optimization with Applications to the Airline Industry
dc.type.dcmitext
prism.startingpage1366

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