Airlines around the world are implementing AI applications to drive value in their business. But what does that mean and how does it look in the airlines industry?
While there is a lot of buzz around AI recently, AI has been around for a long time. What’s changed recently is the amount of data airlines are collecting and the scalability of computing that data. Regardless of the application of the technology, the key component of AI is a learning loop to drive value. “Learning loop” simply means that an AI application can receive more data and learn from it to refine its function.
AI in the Airlines Industry
Specifically, the airlines industry is using AI to personalize offers and create a better customer experience. Airlines have the opportunity to use AI to learn about the behavior of passengers, see how it changes over time, and then create offers that make the most sense. But, airline applications for AI go far beyond customer experience. Airlines can also leverage AI in operations and revenue management.
For airline revenue management, the goal is to accurately predict who will show up to buy a ticket and what the passenger is willing to pay if they do show up. With AI, airlines can more accurately forecast this while automating processes around data crunching and adjustments. This enables analysts at airlines to focus on other, value driving tasks.
In fact, revenue management really couldn’t be done without science or AI. Airline seats are perishable goods, there is fixed capacity in the short term, volatile demand, and very seasonal demand. Science allows airlines to manage those factors to stay in business and offer their products to so many passengers.
Common Questions About AI in Airline Revenue Management
We hear a lot of questions about AI in revenue management, and we’ve compiled them for you here:
What challenges/opportunities do you see with scaling airlines revenue management focusing on the user profile across the distribution channels (eg. GDS, aggregators)?
The distribution channel question can present some challenges given the restrictions that some channels have. However, the channels also offer potentially more information. The real opportunity comes from being able to leverage the information well and being able to create the right price for the passenger.
With regards to business travel, how does corporate policy play into personalization?
This would need to become an attribute of the passenger, understanding their channel. Meaning if it is a corporate travel agency, can we understand the buying behavior differently?
Are you using simple linear models or complex models more often?
The models used vary based on the application. It is all about what makes the most sense for the business application and the data available.
Do we think that AI can play a role in bridging the gap between Revenue Optimization and Airline Distribution Strategies to create a win-win situation for both airline and end customer?
Yes, this is exactly one of the ways we see this. Taking the answer from RM and using the distribution more effectively will only make the answer provided by RM better.
When collecting the data, seems like a large part of the data is more passive data we are collecting in the background (implicit data around history or behavior but also explicit based perhaps on search, filter inputs, etc.). Do you explore and use any data where there is more of an obvious dialogue with the user (e.g., in the form of asking questions, chat, setting up a profile)? If so, can you give us an example? How useful is it?
Customers generally do not appreciate active interruptions to their visit around websites. However, a rigorous randomized A/B testing can provide useful data on user reaction to different website flows, layouts/color schemes and types of content based on fluctuation on the conversion rate, session interrupt and navigation patterns. Similar testing can be performed on email & sales campaigns.
Any direct user feedback can be requested during or after the experience should the user agree. Fruitful applications of this can include testing website design/layout preferences and measuring the “intuitiveness” of navigations across specific screens. In terms of personal experience, I have seen websites being able to ask “is there something I can do to help?” when a sell page is not being clicked on.
Airlines Revenue Management at PROS
Built on industry leading AI, the PROS Platform for Travel connects the critical pieces of revenue management, offer creation, distribution and digital retailing – driving collaboration and coordination – in order to reduce inefficiency and create consistent and revenue optimal offers across all the channels your airline operates in. The PROS Platform for Travel is the only solution of its kind that sits at the center of every customer offer—the intersection of product, price, and place—enabling your airline to define, implement, and optimize its selling efforts.
PROS is serious about our science: we invest 30% of annual revenue in research and development, we have more than 20 patented algorithms, employ more than 2 dozen PhD data scientists. PROS software processes more than a trillion price recommendations every year.
If you want to talk with us about Artificial Intelligence applied to revenue management for airlines, please reach out to us!