Corporate booking recommendation using a machine learning approach

Prabhupad Bharadwaj, Sunitha Chandrasekaran and Sanjeeb Patel

International Journal of Revenue Management, 2023, vol. 13, issue 4, 297-316

Abstract: Corporate globalisation has led to a significant increase in business travel in recent years, and large corporations use travel management agencies to provide a hassle-free experience for their employees. Using historical airline corporate booking data, this study examines the travel patterns of corporate clients. In the research, one year of corporate travel booking data is evaluated, and prediction algorithms are utilised to recommend itineraries to business travellers for a specific origin-destination pair with numerous combinations. A boosting method is used to anticipate booking and non-booking events based on a prediction probability threshold value. The results indicate that the origin-destination (OD) pair and the airline of travel are the most important aspects of corporate booking selection. The choice probability is further used for recommending a few combinations for a selected OD pair which is a practical application of this analysis.

Keywords: corporate booking; travel management; prediction algorithms; boosting; recommendation; choice probability. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=134682 (text/html)
Access to full text is restricted to subscribers.

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:ids:ijrevm:v:13:y:2023:i:4:p:297-316

Access Statistics for this article

More articles in International Journal of Revenue Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().