What happens when a route that has never been flown before is launched? Common questions that an airline might ask itself include, can it be flown daily? How should the Revenue Management system be set up initially? What should marketing budgets be optimised for?
Typically, when an airline is planning for these eventualities a lot of "gut feeling" comes into the mix. And when things don't work, it’s easy to put it down to competition, macroeconomics, or various other factors. Airlines tend to plan new routes based on countries and often use destinations in that same country to predict the behaviour of a new route. This method falls short, however, when you consider two cities in the same country grouping such as Moscow and St Petersburg, or Lisbon and Faro, which, though close geographically, cannot be treated similarly when it comes to route planning.
Yet, what happens if the industry could apply a more scientific approach to predicting how new O&D routes are likely to behave? This would surely help Marketeers to guide when and where they should concentrate their efforts – especially for new routes, assist Revenue Managers in developing the initial pricing, and support Schedulers in decisions around time and day to schedule flights.
Machine Learning can provide a more scientific solution to address these questions, and we'll demonstrate how it can be applied to solve complex scenarios just like the above, where multiple variables come into decision-making. In many industries, Artificial Intelligence (AI) is being rolled out to help improve efficiency in various business areas, yet its use is not commonplace in airline strategies. In this piece, we demonstrate how Artificial Intelligence - in this case a fairly simple formula called the K-means algorithm*, can be applied to flight search data to help airlines gain a significant advantage in how they evaluate routes in order to make better informed decisions.