Offer management is the process of selling the right bundle – airfare and air ancillaries – to the right customer at the right price at the right time. It is an extension of the well-established revenue management process of optimizing inventory controls for airfares to also include airline ancillary products. This is the first of three articles describing how machine learning methods can generate targeted, ancillary bundle offers by customer segment to delight customers and maximize revenue for airlines.
Retail merchandising of branded fares (pre-defined, static bundles) and ancillaries is growing in importance for airlines. Average airfare in the airline industry has declined annually by -0.9% over the past decade (IATA 2018 [1]), but sales of air ancillaries, like checked bags, extra leg room, wifi and food and beverage, have grown +40%. In 2018, the total sale of ancillaries was $93 billion (US) (Ideaworks and Cartrawler 2018 [2]) and expected to cross the $100 billion threshold in 2019. That’s a lot of in-flight cocktails!
Effective airline offer management requires an understanding of customer preferences and purchase-behavior patterns across all channels of distribution, from online travel agencies, corporate booking tools or directly from the airline’s website. Thanks to Amazon.com, Netflix, iPhones and other high-quality consumer applications, traveler demands are changing, and they now expect simplification and personalized services throughout the travel planning process. Sabre’s framework for offer management decision support can elevate both simplification and personalization for travelers. See this earlier Sabre posting about our dynamic ancillary bundles prototype [3]). The core components of an offer management solution are:
- Part 1: data collection and customer segmentation based on context for their travel (trip purpose segmentation),
- Part 2: a recommendation engine to recommend bundles to customers based on such segments or persona, and an offer engine to customize and price the offer for a segment of ONE (1:1 personalization) and
- Part 3: a test-and-learn experimentation engine using reinforcement learning to continuously adapt the product recommendations to changing consumer behaviors and new competitor products.
- Airlines Financial Monitor. https://www.iata.org/publications/economics/Reports/afm/Airlines-Financial-Monitor-Jan-2019.pdf, December 2018–January 2019.
- Ideaworks and Cartrawler. Airline Ancillary Revenue Projected to Be $92.9 Billion Worldwide in 2018. https://www.ideaworkscompany.com/wp-content/uploads/2018/11/Press-Release-133-Global-Estimate-2018.pdf.
- Airline Offer Management – Sabre Insights https://www.sabre.com/insights/innovation-hub/prototypes/airline-offer-management/