Galactic Air Improves Airline Ancillary Revenues with Dynamic Personalized Pricing

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 startup (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, 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 ancillary revenue per offer by 25% and conversion rate by 15% compared to the industry standard of human-curated rule-based prices.