By recognizing user habits, the platform is able to influence resulting (often addicting) behavior – a tactic airlines could employ to entice travelers to book more and fill empty seats, so argues a new whitepaper from marketing technology company Yieldr.
With commercial airlines carrying more than 4.1 billion passengers in 2017, massive amounts of data – including date of birth, payment method, flight history and trip extras – is collected, but it’s not fully utilized.
To improve personalization, airlines need to understand more about travelers, particularly distinguishing between those who book the flight they originally searched and those that are flexible to change their initial search at the right price.
According to Yieldr, 45% of travelers will book a flight different to their original search. For departures, 38% are open to flying on another day. Of that percentage, about 1/3 will limit their new flight to within four days of the original date.
Travelers are less flexible – 18% - about changing destinations.
Knowing these statistics, airlines can use a recommendation algorithm - similar to Netflix - to encourage flexible travelers to book some of their less popular flights, Yieldr argues.
Like the streaming service, airlines can leverage the data at their fingertips and machine learning platforms to automate demand, which combined with insights into traveler behavior, can help fill seats and increase revenue.
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