The travel industry is not short of data. Travelport processes 10-12 billion searches a day from travelers researching or booking trips. It makes the term ‘big data’ seem like an understatement, doesn’t it?
In our recently-published Mobile Travel Trends report, our experts predict that one of the biggest trends to impact travel in 2019 is ‘datafication’—in other words, the transforming of our digital lives into valuable, computerized data that can be mined and used.
When we talk about ‘datafication’, there are two kinds of data that are relevant: big data and small data. Big data involves large volumes that can be processed and analyzed to produce large-scale insights (e.g. fare predictions, trip booking patterns). Small data provides insights into an individual traveler’s behavior, which enables personalization across various stages of the travel journey. The opportunities that both of these areas offer travel brands is enormous.
Over the last few years we’ve seen travel brands starting to use the wealth of data now available, combined with AI and machine learning, to create great customer experiences. From the release of fare prediction apps, to airlines and OTAs presenting personalized offers based on traveler behaviors, data has been the driver behind some of the biggest success stories in our industry.
If your travel brand is looking for inspiration on how to use data and predictive analytics to enhance your customer journey, have a look at what some travel brands in the corporate travel, airline and OTA space are doing today.
No article about predictive analytics in travel would be complete without mentioning Hopper, the rapidly-growing fare prediction app.
According to Travelport’s 2018 Digital Traveler Survey, 50% of US travelers and 51% of Canadian travelers identified the time spent trying to find the best price as a top pain point for searching and booking leisure trips.
Hopper saves users time, money, and anxiety in their quest to book the perfect trip by offering travelers recommendations and alerts based on highly accurate pricing predictions. The innovative app accesses bookable travel options using Travelport’s next-generation APIs, then uses powerful machine learning to uncover price drops and exclusive deals for a personalized search and booking experience on mobile devices.
Hopper is one of the biggest success stories to hit the travel tech space and is currently on-track to process over $1 billion in sales this year, according to recent reports from the company.
Image source: Hopper.com
Booking.com uses predictive analytics not only in product development, but also across multiple departments, including customer service. Lukas Vermeer, Data Scientist at Booking.com, describes the importance of predictive analytics to the company in an interview with Predictive Analytics World:
“Booking uses predictive analytics for lots of different things! In web marketing, attribution models and ROI predictions help bring customers to our site. On the product side, recommendation systems help us show more relevant destinations, hotels and content to our users. In customer service, call volume predictions and scheduling algorithms help staff our call centers and connect customers to the right agent as quickly as possible. I could go on. In fact, I honestly struggle to think of a single department that is not using predictive analytics in one way or another.”
Airlines have a vast amount of customer information at their disposal. United Airlines has seen the benefits of using individual data to improve customer experience over the last few years, which has resulted in an increase in year-over-year revenue by more than 15%.
Its ‘collect, detect, act’ system analyses 150 variables in a customer’s profile, like previous purchases to customer priorities, and presents an offer tailored to the individual. Its terms, on-screen layout, copy, and other elements will vary based on an individual’s collected data.
IBM and Travelport
When it comes to corporate travel management, controlling the budget is one of the biggest challenges facing travel managers. In August 2018, IBM introduced its AI platform, the IBM Travel Manager, to help businesses manage corporate travel spend.
The platform allows organizations to track, manage, predict and analyze travel costs in one place, so they can optimize their travel program, control spend and enhance the traveler experience.
The IBM Travel Manager gives users complete access to previously siloed information from travel agencies, cards, expense systems and suppliers. When combined with travel data from the Travelport GDS, this information is then used to create real-time predictive analytics recommending how adjustments in travel booking behavior patterns can positively impact a company’s travel budget.
The platform features advanced AI and provides cognitive computing, predictive data analytics using “what-if” type scenarios, and integrated travel and expense data to help travel management teams optimize their travel program, control spend and enhance the end-traveler experience.
HRS Innovation Hub
HRS established an Innovation Hub to gather data and develop tools that understand traveler preferences, so it can optimize recommendations for those travelers and offer enhanced sourcing of hotel content for corporates. This means that travelers are being presented with the hotels that are most likely to hit their preferences, making subsequent searching unnecessary. It also enables organizations to ensure that their travelers book hotels within policy.
Martin Biermann, vice present of product development and chief technology officer for HRS, predicts that this type of technology will negate the need for business travelers to search at all: “You will just make the appointment in your calendar and you’ll get the hotel recommendation right into it, maybe even the reservation if the system is confident this is exactly what you want.”
Mike Mulligan, Product Director at Travelport, is one of the expert contributors to our Mobile Travel Trends 2019 report and forecasts that AI predictive technologies such as the HRS Innovation Hub and IBM Travel Manager could spell the end of search for business travelers. Find out more by downloading the Mobile Travel Trends 2019 report.
JetBlue uses AI to improve and de-stress the day of travel experience for customers. By live streaming security camera footage, the airline tracks people on the TSA line to determine how quickly its moving. It then combines this information with traffic data to predict how long it will take a traveler to get from their house to the gate.
“If I can tell you before you leave your house that your flight is two hours delayed, you can spend those two extra hours with your family… or at the bar,” says Ramki Ramaswamy, the airline’s VP of IT, Technology and Integrations. “It’s the same with how long you’ll have to wait at the airport.”
In January 2018, TUI Group partnered with discovery platform Utrip to help personalize the traveler planning experience through AI.
TUI customers can enter their preferences in various categories like arts and culture, food or shopping in Utrip’s platform. They then receive a personalized itinerary for their trip, including excursions and tips for restaurants or attractions. To create this customized travel program, Utrip’s AI algorithm sorts through millions of potential combinations within a matter of seconds. The platform enables customers to save time in planning their trip and offers them more relevant recommendations.
The Google Flights search engine is using predictive analytics not only for fare predictions, but also to make judgments on the likelihood of flight delays—even before the delay has been flagged by the airlines themselves.
By using historical data and machine learning algorithms, Google Flights can predict delays, and provide reasons for the delay, like weather or the late arrival of an aircraft. As the predictions are not confirmed by the airlines themselves, it won’t flag these in the app unless it’s 80% confident in the prediction.
There is no one technology enabling predictive analytics and personalization; it is a range of technologies, but at its heart is data. Using the wealth of data now available will allow travel brands to know so much about individuals that they can present offers based on their preferences. In addition, analyzing big data like historical flight itinerary data and weather patterns enables brands to make more general predictions on areas like flight delays and fares.
Our end traveler research found that 65% of travelers would provide personal details if it resulted in a more personalized travel experience. This shows how customers are increasingly looking to technology to take the stress out of travel and enhance the journey—whether that’s being presented with more relevant offers at the booking stage, being able to track fare patterns to ensure the best deal or being informed as early as possible how long it will take to get to the airport.
Of course as an industry, we need to ensure we use personal data ethically, with integrity and responsibility and this is where a big focus is likely to lie in 2019 and beyond. Read more on ‘datafication’ in our Mobile Travel Trends 2019 report.