Title: Corporate booking recommendation using a machine learning approach

Authors: Prabhupad Bharadwaj; Sunitha Chandrasekaran; Sanjeeb Patel

Addresses: Decision Engineering and Pricing Lab, Department of Management Studies, Indian Institute of Technology Madras, Tamil Nadu, India ' Software Engineering, Sabre Corporation, International Technology Park, Whitefield, Bengaluru, Karnataka, India ' Software Engineering, Sabre Corporation, International Technology Park, Whitefield, Bengaluru, Karnataka, India

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.

DOI: 10.1504/IJRM.2023.134682

International Journal of Revenue Management, 2023 Vol.13 No.4, pp.297 - 316

Received: 07 Mar 2023
Accepted: 01 Aug 2023
Published online: 03 Nov 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article