What do air travelers consider before booking a flight? Airlines understand there are many factors at play in each purchasing decision — including price, comfort, brand loyalty, safety, flight schedules and more.
Gaining the performance edge in such a competitive market requires understanding how these factors influence consumers’ perceptions and behaviors — and acting on those insights to make each and every customer experience as great as it can be. And, of course, there’s the ongoing goal of keeping operating costs efficient at the same time.
It makes sense, then, that airlines are using artificial intelligence-driven data analytics to better serve customers and streamline expenditures.
Here are three examples of how the leaders in the industry are doing so.
Optimizing Ticket Pricing & Revenue
Seasoned travelers know the prices of plane tickets aren’t fixed. Rather, pricing depends on when you book, how you book and who the airline thinks you are — among other factors. This personalized approach to cost setting is known as dynamic pricing, and it’s something airlines are keen on mastering so they can optimize pricing across their flights.
Travel Weekly outlines a few airline data- and AI-driven dynamic pricing strategies:
- A passenger’s booking history is used to generate a personalized offer — which can differ from another person searching the same flight at the same time.
- Frequent passengers receive tailored offerings based on their existing profile.
- Enticing new fliers with a particularly affordable first-time offer.
The overall goal of dynamic pricing is, of course, to raise conversion rates and boost revenue. As dynamic pricing is data-driven, how well a company is able to engage in this strategy depends greatly on its approach to AI analytics.
Predicting Maintenance to Reduce Downtime
Mechanical issues can lead to costly downtime and diminish passenger trust. In fact, airlines globally collectively spend more than $40 billion annually on aircraft maintenance — meaning maintenance accounts to 10-20 percent of direct operating costs.
AI analytics are helping airlines proactively predict maintenance issues based on patterns in data pulled from various sensors, touchpoints and systems. Better predictive maintenance can help raise fleet reliability so airlines can avoid the ill effects of unanticipated maintenance.
Maximizing Customer Satisfaction & Loyalty
The expectations of customers across every sector are rising. People want increasingly quick, convenient and cost-effective buying experiences — and airline services are no exception.
This means airlines need to deeply understand their customers — from potential buyers comparing ticket prices to passengers currently on board a flight to past travelers who may be interested in choosing an airline again.
Consider the importance of in-flight experiences to travelers today and how they contribute to people’s perceptions of airlines and their willingness to build loyalty for a certain carrier. According to one expert for ZDNet, analytics can help airlines understand what travelers truly want and expect during their journey by answering questions like:
- What in-flight products are people most interested in?
- What devices do people tend to use while traveling?
- How long do people spend on the internet or engaging with entertainment during flights?
- What perks, entertainment choices and comfort options do people prefer?
AI analytics can examine all these facets of passenger behaviors on a massive scale to help airlines make better decisions regarding what services to offer before, during and after flights.
These use cases are just the tip of the iceberg in terms of how airlines are harnessing AI analytics — and will continue to do so more in the months to come.
