Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue management

Alberto Guerrini (), Gabriele Ferri (), Stefano Rocchi (), Marcelo Cirelli (), Vicente Piña () and Antoine Grieszmann ()
Additional contact information
Alberto Guerrini: Boston Consulting Group
Gabriele Ferri: Boston Consulting Group
Stefano Rocchi: Boston Consulting Group
Marcelo Cirelli: Boston Consulting Group
Vicente Piña: Boston Consulting Group
Antoine Grieszmann: Boston Consulting Group

Journal of Revenue and Pricing Management, 2023, vol. 22, issue 2, No 8, 180 pages

Abstract: Abstract Recently, several macro trends have converged to provide airlines new opportunities for one-to-one digital customer engagement and personalization. Airlines have more types and volumes of data available than ever before: shopping-behavior data, data providing context on booking decisions, social media data enriching the information available on travel trends, and more. All of these can play a critical role in defining the right offers and setting the right prices for each shopping request. A plethora of advanced AI and ML techniques have become available on open-source platforms, letting players generate actionable customer insights and leverage vast amounts of existing data. New distribution technology is being deployed to allow airlines to implement real-time retailing capabilities. Consumers have been trained by the likes of Amazon, Netflix, Alibaba, and Starbucks to expect products and services tailored to their individual needs along with superior and engaging content. This paper presents different approaches to price-product personalization that have been tested in airline cases globally. It also explores how the concept of experiential learning is nicely suited to tackling scenarios in which the purchaser is well-identified as well as cases in which not much is known about the visitor except the context of the shopping session.

Keywords: Revenue management; Experiential learning; Price personalization; Machine learning; Customer segmentation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1057/s41272-022-00404-8

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