Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing

Arinbjörn Kolbeinsson (), Naman Shukla (), Akhil Gupta (), Lavanya Marla () and Kartik Yellepeddi ()
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Arinbjörn Kolbeinsson: Imperial College London, London SW7 2AZ, United Kingdom
Naman Shukla: Deepair Solutions, London W1K 5AY, United Kingdom
Akhil Gupta: Industrial and Enterprise Systems Engineering, Grainger College of Engineering, University of Illinois at Urbana–Champaign, Champaign, Illinois 61801
Lavanya Marla: Industrial and Enterprise Systems Engineering, Grainger College of Engineering, University of Illinois at Urbana–Champaign, Champaign, Illinois 61801
Kartik Yellepeddi: Deepair Solutions, London W1K 5AY, United Kingdom

Interfaces, 2022, vol. 52, issue 3, 233-249

Abstract: Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules of thumb and are matched only to the average customer or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a start-up (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air (pseudonym), to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context ) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best-performing ones live on the airline’s booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased the ancillary revenue per offer by 25% and conversion rate by 15% compared with the industry standard of human-curated rule-based prices.

Keywords: airline ancillary pricing; dynamic pricing; customized pricing; deep learning (search for similar items in EconPapers)
Date: 2022
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