This is the second in a four-part series looking into how airlines manage the application of artificial intelligence in commercial functions.

Read part one: The Commercial Role in Artificial Intelligence – Lessons from Airlines

New applications for artificial intelligence and machine learning offer tremendous opportunities for airlines in operations, customer service, and revenue. Of course, for decades airlines have applied Big Data and complex analytics to revenue, developing sophisticated pricing systems that automatically set fares across broad networks.

Modern Technology Offers New Opportunities

But modern AI/ML technology now offers further improvements in airline revenue management systems. New applications could help improve demand forecasts, introduce new customer micro-segmentation, implement “continuous pricing” and more. There is a danger, however, with this pursuit of more revenue.  

Over the past decades as the industry has gotten more and more sophisticated, and more and more technical in optimizing revenue, increased revenue has been accompanied by greater customer dissatisfaction.

There is now more customer confusion, more complexity, less transparency, and, ultimately, more unhappy customers. Can the industry move forward without the baggage that has accompanied such “revenue improvements” in airline systems in the past?

The objective for systems in the past has been flight revenue (or revenue across a complex of connecting flights) from base fares on each specific day. The objective is to fill seats at the highest fare possible and to turn away lower fare travelers.  

"New applications could help improve demand forecasts, introduce new customer micro-segmentation, implement “continuous pricing” and more."

There is no notion of customer satisfaction except that it is assumed that the customers who pay the going rate are “satisfied.”  There is no attempt to “engage” customers; the sales pitch is closer to “take it or leave it.”  

The entire process is complex and opaque: when is the best time to buy? Will the fare go up tomorrow? If the fare increases, will it go up by $10 or $100? Will there be hidden add-ons?

There can be considerable customer frustration in trying to find the fare and product that best meets their needs (the average traveler is said to spend hours, visiting multiple sites multiple times, before he books). Many customers who don’t buy are simply frustrated in the process. Some customers who do buy are dissatisfied even before the flight begins!

AI/ML Can Help Improve the Customer Experience

AI/ML can help. AI/ML has the opportunity to dramatically transform customer engagement. On the other hand, if AI/ML is strictly seen as a short-term, transaction-based, flight-revenue-maximizing system, it risks driving even more customer dissatisfaction.  

"AI/ML offer new tools for airline revenue management but they can also be seen as a way to move airline pricing to a new level, one that is more customer-centric."

In an effort to find more revenue, AI/ML may introduce new, more complex dimensions to pricing, making it harder not easier for travelers to find what they want. Rather than appending AI/ML to the existing process, a more fundamental change needs to take place. Besides “revenue” per se as the only objective, alternative metrics include:

  • Traditional e-commerce metrics: abandonment rates; look-to-book; number of searches.
  • Customer booking experience: length of time to book; number of clicks; satisfaction measured during searches and booking.
  • Repeat buyers (even if not frequent flyers).
  • Capture (ability to engage with a customer and help her find something that meets her needs; a customer who expresses interest in a product on the airline site should be cultivated, not just dismissed).
  • Direct sales (a proxy for both trust and assessment of website utility).

As airlines seek a fundamental re-positioning, such customer-oriented metrics can offer a path forward. The re-orientation to the needs of the Customer can add longer-term value, more revenue in the long-term:

  • Simplicity and transparency may replace some complexity.
  • A focus on repeat buyers may change how current processes turn away “unworthy” customers and increase loyalty.
  • Customer engagement and capture rates may drive some discounts or may override standard RM system recommendations as a way to better meet individual customer needs.

AI/ML offer new tools for airline revenue management but they can also be seen as a way to move airline pricing to a new level, one that is more customer-centric.

Tom Bacon is an airline industry consultant based in Denver, Colorado. With 30 years experience in a variety of travel companies and business situations, he has a track record of dramatically improving profitability through innovative revenue strategies.