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Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

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submitted on 2024-01-29, 06:24 and posted on 2024-01-29, 06:25 authored by Saravanan Thirumuruganathan, Noora Al Emadi, Soon-gyo Jung, Joni Salminen, Dianne Ramirez Robillos, Bernard J. Jansen

Employing customer information from one of the world's largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer's acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries.

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Published in: Information & Management
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

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