Airlines are, more than ever, focused on capturing revenue in a highly-competitive marketplace – offering ancillary “add-ons” like extra legroom seats, additional baggage allowance and trip insurance. A year into the global Covid-19 crisis, this incremental revenue is even more critical to airlines’ financial sustainability in a reduced and uncertain market.
These product bundles, a la carte items or even third-party ancillaries mean that the number of offers available to travelers has grown exponentially, making air shopping increasingly complex.
When creating offers, airlines traditionally rely on rules-based systems that follow an if-then logic and require manual calibration for best results. While they have served airlines well, their limitations are becoming more apparent today and are preventing airlines to make the most of the limited customer demand.
In addition, Covid-19 has accelerated the pace of change: border controls, health guidelines and travel protocols are evolving rapidly, requiring real-time adjustments. As a result, historical data patterns may not be as reliable as they used to be.
It is becoming increasingly clear that to respond to the personalized service and enhanced flexibility travelers expect and drive airline revenue, a new approach to offer management needs to be developed.
Going beyond the rules with Sabre Travel AI ™
Moving beyond rules-based systems, companies like Netflix or Spotify have been utilizing machine learning (ML) to help them handle large datasets, tailor their offering and improve user retention by recommending content to users they might not have initially chosen on their own.
ML models can process vast amounts of data instantly and perform tasks without being pre-programmed to do so. This application of Artificial Intelligence (AI) begins with data observations and looks for patterns within the data to make better recommendations in the future. The success or failure of the new recommendations becomes insights that feed the continuous learning loop, without the need for human intervention.
While ML and AI are not new, their broad rollout to travel is the next evolution. New models will understand traveler choice and will be market adaptive, meaning travel suppliers will be able to improve future offers at scale. Sabre and Google Cloud recently announced Sabre Travel AI ™ . Created as part of their strategic innovation framework, Sabre Travel AI ™ combines Sabre’s travel expertise with Google Cloud’s infrastructure and AI capabilities, allowing machine learning models to be integrated into existing and future Sabre products faster, and with more scalability than ever before.
Application to airline offers with Sabre Smart Retail Engine ™
Imagine an airline continuously running thousands of experiments to determine the best offer combination to put in front of one specific traveler – an offer with pre-reserved seats, a lounge pass for a socially distanced airport experience, and even hotel accommodation is created based on its optimal chance of being selected by the customer.
One of the first applications of Sabre Travel AI ™ technology is in airline retailing, using machine learning models to infuse intelligence in offer creation. Sabre Smart Retail Engine ™, launching this year, will make this a reality by enabling dynamic bundling of air and ancillary offers. It continuously learns which offer is most successful and airlines will be able to test and compare multiple retailing strategies in real time.
Initially, Sabre Smart Retail Engine will be available for airline direct channel. In the future, travel agencies will have access to these dynamic offers via Sabre Red 360 and Sabre APIs, enabling the presentation of more relevant offers to travelers and in turn, optimizing their revenue and increase customer satisfaction.
Moving towards a personalized experience
Technology continues to get smarter, evolving the air trip from a commodity transaction to eventually, a personalized experience across the entire customer journey, something that the entire travel ecosystem has been trying to unlock for years.
The industry’s adoption of ML and AI will support this evolution towards personalized offers, by helping present the right offer, at the right time, to the right customer.
Getting smart just got personal.