How Machine Learning Boosts Travel Personalization
She opens the browser, puts the cursor in the search bar, types “cheap flights from Boston to London,” and up pops the first ten links from Google's results page. After some surfing, she lands on Skyscanner filtering the flights by date and cost, and selects the cheapest deal from Norwegian Airlines. As Skyscanner aggregates the offers from providers, she again bulk opens multiple links leading to deals by online travel agencies. She glances over the pages, never staying more than 30 seconds on any of them. At one of the online travel agencies, she opens flight details, hastily closes a pop-up window without reading its contents, and continues searching. In two days, she returns there closing the top deal from the search feed.
The behavior of this imaginary user is quite common. The data scientists at AltexSoft, a travel tech provider, call this type of ticket surfer a “cheap buyer”. Cheap buyers account for about a half of airfare searches. They look for the most affordable deals, don't spend too much time exploring flight details, don't care about long layovers or seating.
Back in 2012, Amadeus published a research called Who Travel with You. The study outlined five main segments of travelers: digital natives, young adults, family travelers, empty nesters, and golden oldies. While digital natives and young adults combined are only 22 percent of the entire travel market, they are the most active web users who prefer booking flights separately from accommodation and leisure activities. But there's a tangible difference even between digital natives and young adults in their behavior. For instance, digital natives usually belong to the cheap buyers group, while young adults, aged 25-44, with no kids can afford a more preferential manner of choosing their travel services.
The data science team from AltexSoft has proved that by actively gathering user interaction data working with their OTA clients. The team tracks basically everything: destination searches, clicks, dates, hover moves, and even the ways users examine travel service providers.
To collect all records linked to user behavior, the team devised a user behavior tracking engine (UBT). It consolidates data and allows data scientists to build prediction models around it. Today, the machine learning model trained on this data starts predicting the likelihood of a visitor's conversion after just a couple of clicks. Cheap buyers belong to the main category assigned to a new user by default. Once they begin checking amenities and looking at other details, the model gains confidence about whether the user is likely to buy and learns their value needs.
The spectrum of this AI application is broad. “For example, we can easily distinguish between business and leisure travelers,” says Alexander Konduforov, head of data science at AltexSoft. “Now, it's a matter of value difference that we can suggest to these two groups of visitors.”
Cheap buyers comprise half of the audience. The other half, are more sophisticated in their travel preferences and you can't just suggest the lowest cost to them. Here's when the real personalization starts as the system must account for things this person values most. Some don't like long layovers when choosing flights, some are picky about meal options, and some are fans of particular hotel chains. These insights lay grounds for making a customized search engine that will filter travel alternatives considering cost and value priorities for everyone typing in their destinations.
While 79 percent of business executives surveyed by Forrester believe that personalization will help them achieve marketing and customer experience goals, the practice is still an investment in data science and the underlying technology itself. “The two main challenges we see today are data related,” according to Alexander. “As we collect more data, we have to figure out how to efficiently store and further process it.”
Another problem is the lack of individual user data. Although the dataset has enough records to build accurate predictions about incoming visitors, the machine still needs users to stay on a website a bit to define their group and tailor the offerings. A long-term user interaction history can provide better personalization opportunities. Saving cookies allows the algorithm to recognize visitors who have been visited before, and that simplifies things. But people tend to block cookies. Even if a user had purchased travel services before and can be qualified as promising during the second or third visit, one browser cleanup rolls this person back to the unknown state. Now the system is dealing with a clean slate and is at square one in data collection on this individual.
A registered customer who regularly uses the account on all devices allowing the system to provide the best value options based on long-term and consistent data is the best-case scenario. But that's not the reality. Just a fraction of users is registered. Some login only on desktops, and most don't have accounts at all.
There are some ways to partially sidestep this problem. Although you may not have the behavior data, you can make assumptions about users solely relying on metadata: Referral websites that people came from, devices, and browsers give some insight. For instance, the users coming from Skyscanner are more likely to buy than those coming directly from Google. But the tricky balance between user privacy and data collection has yet to be found.
Today, Alexander Medovoi, AltexSoft CEO, is busy preparing for the EyeforTravel North America 2017 conference. It will be held in Las Vegas October 19-20. Alexander Konduforov, head of AltexSoft's data science team, will accompany him. Medovoi will discuss personalization opportunities from the business perspective, while Konduforov will speak to the data science side of the topic. As consumers ask for deeper personalization, opportunities abound.