Competitive revenue management models with loyal and fully flexible customers

Ravi Kumar (), Wei Wang, Ahmed Simrin, Sivarama Krishnan Arunachalam, Bhaskara Rao Guntreddy and Darius Walczak
Additional contact information
Ravi Kumar: PROS Inc, Suite 600, 3200 Kirby Dr
Wei Wang: PROS Inc, Suite 600, 3200 Kirby Dr
Ahmed Simrin: Etihad Airways, Khalifa city A
Sivarama Krishnan Arunachalam: Etihad Airways, Khalifa city A
Bhaskara Rao Guntreddy: Etihad Airways, Khalifa city A
Darius Walczak: PROS Inc, Suite 600, 3200 Kirby Dr

A chapter in Artificial Intelligence and Machine Learning in the Travel Industry, 2023, pp 47-66 from Springer

Abstract: Abstract Developing practical models for capturing competitive effects in revenue management and pricing systems has been a significant challenge for airlines and other industries. The prevalent mechanisms of accounting for competitive effects rely on changing the price structure and making manual adjustments to respond to dynamically evolving competitive scenarios. Furthermore, micro-economic models have also not become popular in practice primarily because of the simplistic mechanisms proposed for modeling consumer behavior in a competitive setting. In particular, many of these models assume that the customers always seek the lowest price in the market, that is they are fully flexible. In practice, customers may display some degree of affinity or loyalty to an airline and may pay a premium for their preferred choice. On the other hand, almost all early revenue management models did not explicitly consider competitive effects and assumed that an airline’s demand only depends on their prices i.e., demand is fully dedicated to an airline (loyal). This paper develops a model to capture more realistic competitive dynamics by including both these types of customer behavior.We also develop a Bayesian machine learning based demand forecasting methodology for such models with explicit competitive considerations and show the benefit of this approach over traditional models on a real airline data set.

Keywords: Bayesian inference; Dynamic pricing; Competitive modelling; Continuous pricing (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-25456-7_5

Ordering information: This item can be ordered from
http://www.springer.com/9783031254567

DOI: 10.1007/978-3-031-25456-7_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().