Fed by data on everything from internet searches and Covid outbreaks to weather forecasts and football results, computers are learning how everyday life influences demand for flights.
With access to mountains of data, artificial intelligence is quickly becoming a crucial tool for airlines to determine the best rates to charge travellers, enabling them to maximise revenue as the sector recovers from its worst crisis.
Main points
Computers are learning how daily living affects demand for flights using data on everything from internet searches and Covid outbreaks to weather predictions and football outcomes. The mysterious airfare codes and pricing bands that have restricted ticket sales for decades are destroyed by AI in its most advanced form.
Technology providers can assess how much passengers are willing to pay for tickets by weighing the data, and they may then continuously reprice seats.
Other’s statement
“We are able to determine at every price point how many people will buy a ticket,” said Roy Cohen, chief executive officer and co-founder of Fetcherr, whose directors include Alex Cruz, a former CEO of British Airways Plc. “It’s very hard to hide from a system like us.”
The first open testing of the demand prediction and pricing system from Fetcherr was announced last month by the Brazilian carrier Azul SA. Emails sent to Azul requesting more details about the study received no response.
Cohen claims that Fetcherr’s demand simulations are so precise that by the time the flight actually departs, the prices set by algorithms for flights six months in the future have hardly changed. “Almost exactly,” he remarked. “Occasionally to the cent.”
Facts
Any assistance is really needed in aviation. In 2020, travel virtually disappeared as governments all around the world implemented Covid-19 regulations and locked borders. According to the International Air Transport Association, a recovery from the epidemic should increase global airline revenue to $782 billion this year, still short of the $838 billion in 2019.
Although airlines have long employed algorithms to regulate pricing, to some extent what consumers actually pay is determined by the availability of seats in different price ranges. After two years of lockdowns, it has grown harder to determine how closely to match fares to riders’ inclination to pay.
Reports
According to Amanda Campbell, director of solutions marketing at global travel technology company Accelya, “conventional tactics are actually more like blunt instruments to deliver particular things at certain price points to the market.”
Although AI’s impact on aviation is still in its infancy, the information flows are already too enormous to comprehend in a practical manner. Cohen estimates that Fetcherr alone analyses many petabytes of data every second to estimate global travel demand. According to estimates, 500 billion normal printed pages of text fit within one petabyte. He declared, “The bigger we get, the better we get.”
Conor O’Sullivan, chief product officer at Datalex Plc, a company that offers real-time pricing, claimed that there is a limitless amount of data. The Dublin-based business revealed a trial with IAG SA-owned Aer Lingus, an Irish airline, last year. An email asking Aer Lingus for information on the tests received no response.
Conclusion
According to O’Sullivan, Datalex still relies significantly on historical data to anticipate current and future flight demand, such as airline reservations and timetables. But as well as hotel reservations and airport lines, computers are increasingly taking into account one-time events like concerts and sporting events. The market can be impacted by changes in governments, policies, or even the removal of a minister. The algorithm’s task is to assess the relative value of each byte.
“All of these things have effects,” O’Sullivan said. “Then you get down to all sorts of behavioral psychology. If it’s raining outside, you’re more likely to book to a sunny destination than if it’s sunny.”
While giant AI-powered retailers like Amazon.com Inc. clearly show the benefits of machine learning, aviation’s in-built aversion to risk means it is likely to embrace the technology at a far slower pace. Change in the industry moves at a glacial pace, hamstrung by legacy network systems and aging ticket distribution partnerships.
“A lot of trust needs to be built up before they go full on into something like this,” O’Sullivan said. “They see this as really high potential value, but high risk as well.”
