In June, Aviation Today published a great article on the state of machine learning and AI in the airline industry. The article showed that machine learning and AI are helping the industry become more lucrative in the 21st Century.
The airline industry has started relying more on machine learning technology as new challenges threaten to cripple its business. They will need it to survive if things go further south.
Machine Learning is the Key to Saving the Ailing Airline Industry
Common wisdom in the world of commerce dictates that the airline industry does not make money. Sure, there was a time in between the inception of manned flight and the mid-1930s when airlines were seen primarily to deliver mail and pesticide and one could argue that airlines had some financial success. However, passenger airliners have been notoriously unprofitable. In fact, many of them have been kept aloft by legislation subsidizing flights by as much as $800 per passenger per flight.
The good news is that there are a number of ways that machine learning is helping them turn things around. Some of the benefits of machine learning in the airline industry are listed below.
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Increasing Operational Efficiency
There are a lot of great ways that airlines can use machine learning to get more value. One of the biggest benefits of this advanced form of AI technology is boosting operational efficiency. GroundLink AID+ is one of the AI tools that airlines are using to leverage the benefits of various devices.
What Is Airline Revenue Management?
Airline revenue management can be summed up in one word, alignment. It is a tactical yet essential tool that allows vendors to create off-the-shelf management solutions. Machine learning is making airline revenue management easier than ever.
Today, airlines differentiate themselves from each other in three basic categories:
- Scheduling
• Product
• Pricing/Ancillary
For example, one airline has the largest schedule in DFW, thereby setting itself apart from its competitors. Others may offer low-frequency fares to smaller communities that their competition is not serving. Competitive fares and bookings are monitored by the airlines, which allows revenue management to help airlines determine what strategy their schedule should take with the goal of driving demand. It can be difficult to use an older form of technology to manage all of these issues. New machine learning technology is making things much easier.
Other airlines focus on ancillary. They offer cheap prices for flight and focus on selling additional bags, meals, complete trip packages, and flight insurance as a way of making money. Machine learning makes it easier for them to cut costs.
In each of these instances, revenue management is designed to create a demand for travel that harmonizes with the company’s corporate goals. These are influenced by other sources that affect demand, including customer experience, marketing, sales, schedules, etc.
Helping with Customer Outreach
One poll of 40 experts showed that machine learning is essential to helping airlines with their marketing. Machine learning tools are helping them better identify their market and tailor their strategies to various customer groups. Predictive analytics will be used much more in airline marketing in the months to come.
Is Machine Learning Truly Helping Airlines?
The answer to that question is not straightforward. Airlines have invested heavily in creating sophisticated machine learning systems, taking advantage of revenue management software. These systems have been heralded by many for their ability to forecast demand, allowing airlines to manage the availability of inventory, a.k.a. seats on planes. they are designed to allow airlines to react to competitor’s prices in their market. The idea is that it will create higher yields. To some degree, this has been true.
The success of airline revenue management is dependent on the airline’s understanding that technology, no matter how intuitive it is, can only be one part of a comprehensive strategy. Airline revenue management should allow flight analysts, commercial executives, and pricing teams to create circumstances that allow them to deliver revenue outcomes.
If revenue management is not seen from that optic, the technology that enables airlines and their pricing teams to respond more quickly will be used wrong or will be underutilized, leading to missed revenue opportunities.
When it comes to airline revenue, airlines have to approach it by first understanding their overarching commercial strategy. Then they have to ask themselves what role this strategy plays in framing the decisions needed to either sustain or improve revenue and yields. When airline revenue management is seen within this context, it can and has been shown to produce some powerful results.
What Is the Future of AI and Machine Learning for Struggling Airlines?
Air travel has gone from being a luxury afforded to only a small group of people to almost a public utility that people require. Still, from late 2018 on through 2019, many commercial airlines have ceased operations. The ones that want to survive will invest more in machine learning in the future.
In part, poor revenue management is to blame. For example, a number of low-cost carriers built a strategy around providing bare-bones flights within a limited geographic area. In 2018 a few of these decided to expand by offering long-distance routes. Larger operations mean new costs, new competition, unforeseen logistical risks, and new pricing strategies. For some low-cost airlines, keeping up with their overseas flights meant needing to cancel some of the local flights that were at the backbone of their operation.
Fuel prices account for 22% of a carrier’s costs. However, fuel prices have gone up, increasing more than 50 percent between 2018 and 2018. Delays cost airlines billions of dollars annually. This leads to shareholder disputes and in some cases, airlines filing for bankruptcy.
Will Machine Learning Save the Airline Industry?
Machine learning is making a big difference in the way that airlines operate. All is not gloom and doom for airlines. The years 2018 to 2020 are expected to show increases in global revenue, as they rely more heavily on advanced machine learning tools. When applied correctly, revenue management may help airlines create a consistent environment where competitive costs, on-time scheduling, and customer satisfaction are well accounted for.
