Picture this: an event that you attend regularly is approaching. An email alert reminds you that you’ll get cheaper tickets if you book now and that Bristol Airport will be your best departure point. When you book, your company discount is automatically applied, and your usual seating preferences accommodated.
When you step aboard the plane, the entertainment system is packed with choices that match your taste and, when you reach your destination, there are plenty of taxis waiting to accommodate all passengers. It’s as if they knew you were coming.
This is the future of travel. Seamless, well organised and thoughtfully personalised for each customer. Different sectors of the travel industry have all been using big data in different ways to help them improve their service offerings.
Take airlines, for instance. Many are starting to use a ticketing system called New Distribution Capacity, or NDC. It relies on a combination of mass data collected on previous purchasing preferences and routes travelled with real-time routing information.
Once a destination is searched, artificial Intelligence is used to sift through possible flight options and extras, then present the customer with one combined price for their tailor-made package. This means no more nasty price hikes on selecting a meal or adding an extra bag, and less time spent laboriously tacking together the individual elements of the journey.
Of course, that doesn’t mean the customer will be obliged to accept everything on offer. Henry Harteveldt  at Atmosphere Research says, “It will be essential for airlines to be clear and unambiguous about what’s included in each package offered, so the consumer can make a fully informed decision.”
Ticketing isn’t the only aspect of air travel that is set to improve, the in-flight experience will soon include smarter Inflight Entertainment Systems (IFEs). Thales announced this year that it will soon be deploying two new systems of IFE, powered by the InFlyt 360 digital platform.
It’s all about educating the customer through the use of their own data and providing a better end-to-end experience.
As the customer interacts with the system, machine learning will register their choices and use them to optimise the entertainment experience. The high-end system will present a tailored interface, the lower end system will use tailored ads to boost revenue. Both will enable the customer to spend less time selecting and more time enjoying their favourite content.
Panasonic is also developing a new IFE system, as well as a digital retail platform that can predict what type of meal passengers might like. It does this based on past preferences and social media information, provided by Black Swan Data. The latter input helps the airline to predict the latest food trends before they occur.
Jon Norris , Panasonic’s senior director of marketing, says “Preferences from passengers can steer what gets loaded. From a sustainability point of view, that is very positive.”
Also collecting information on customer selections is Trainline, the UK’s most popular online train ticket retailer. Around 6-7 million searches for journeys and tickets are made using their platforms each day, and the company uses the information to make recommendations and price predictions.
Fergus Weldon , head of data science at Trainline, says their main focus is to drive value for customers from the data collected. They do this by creating smart, customer-facing innovations, such as a price prediction tool that spurs users on to buy tickets early to get the best deals. Trainline has also recently introduced the BusyBot tool, an app that can be used to predict where free seats on the train will be located.
Fergus explains, “We asked customers if their train was delayed or on time, if they got a seat or not, where they were based in the train. So then we could build this feature where customers are best informed where they might be able to get on; what part of the platform to stand at, what carriage might have free space.
“It’s all about educating the customer through the use of their own data and providing a better end-to-end experience.”
Another way the rail industry is using prediction is to keep customers safe. The Institute of Railway Research and the Railway Safety and Standards Board have teamed up with the University of Huddersfield to formulate a new system of Big Data Risk Analysis (BDRA) to predict trouble hot-spots.
The technology, which is already used in the oil and nuclear industries, uses algorithms to identify incidences of SPAD (Signals Passed at Danger) and RAATS (Red Aspect Approaches to Signals). This allows risks to be dealt with before any ‘close calls’ or accidents have occurred.
Text analysis (NoSQL) can also be used to highlight important warnings received from concerned members of the public via a free helpline, even when words are spelt wrong. The new Safety Management Intelligence System (SMIS+) is being rolled out now.
SMIS+ isn’t the only safety innovation big data has brought to rail travel. “Big problems can be solved by big data,” says John Voppen , the chief operations officer of Netherlands service ProRail. “We have developed an algorithm to solve the problem of predicting where trespassers will be, (which is) even able to predict where animals close to the track could be.”
Conductors are also using smart watches to gain information on the status of approaching services or to signal when trains are ready to depart. There is talk of using sales data from such watches to optimise ticket sales.
Irish taxi-booking service iCabbie has also found that the new ways of processing journey data points are a great improvement on the traditional methods. Business development executive Ian McDonald  admits that manipulating data using Excel spreadsheets, to try and gain insights into how the business and its services could be improved, wasn’t always easy.
“(Data analysis) was absolutely there.” he says, “but we found that difficult because number one, it’s time consuming, number two, you’re only scratching the surface.” Instead, iCabbie now analyses data on cab locations, journey times, jobs accepted and declined, and popular versus unpopular pickup areas using Oracle Analytics cloud.
The basic information from 60,000 taxis and 100,000 drivers is collected through an app connecting taxi companies to the drivers. Passengers data is also collected through a special booking app.
The company now not only stores the data but disseminates it to customers through the provision of self-service analytics dashboards. This gives individual taxi companies insights into how their service is performing, which accounts are making most money, and where improvements can be made.
Ian says, “For us, it’s a more rounded tool. It satisfies our immediate requirements, with lots of room to grow into it. We’re very much at the start of our journey, both internally and for our customers, which is a great position to be in, and the product will allow us to grow into it as we grow as a business.”
It seems the possibilities for using big data to improve the customer experience are endless, right up to helping them to buy items they might need at their destinations or book extras such as hotels and concert tickets.
One thing is for sure, analytics technology will only improve in the future. Travel industry analyst Henry Harteveldt  says: “As with any new tech, once you get through the early teething pains, follow-on updates are going to be easier to add. Ten years from now, it’ll be in widespread use.” With such great results already being delivered using big data, is it any wonder that travel providers are realising they’ll need to get on board or risk losing out?