Booking holidays used to be a simple affair – you went to a travel agent in the High Street, told an adviser where you wanted to go and when, and then spent a couple of hours going through the various options that were available before finally booking what you wanted.
Now of course, most of us do everything online and this is where predictive analytics - the science of extracting information from existing data sets in order to determine patterns and predict outcomes and trends in the future - comes in. It offers travellers the chance to personalise their future experiences, based on their past preferences and other data, and get exactly what they’re looking for right down to the last detail.
Predictive analytics can be used on travel websites to present ‘next best’ actions based on the customer’s past history. Additionally, if they’ve been browsing and booking across several devices, context can be preserved across those channels through channel orchestration. Of course nobody can predict the future with complete certainty, but it’s possible to make a pretty good guess by analysing data.
Not only does this put customers in control of their choices but, from the vendor’s point of view, it’s also a way of increasing incremental revenue by approaching prospective customers at the right time and with the right offer.
Today’s travel companies, armed with the ability to crunch Big Data can analyse mountains of consumer data (for example, locations, hotels or tours most often browsed or booked, their age, gender, geographic location etc) to create the perfect product (holiday destination, hotel recommendation, direct flight routes) for each one of its millions of customers within milliseconds and then turn it into a sale.
So, for example, say you were flying to a three day conference in Geneva and you decided to stay on for a few days for some sightseeing afterwards; then you might search online for flights into Geneva on Monday and back out again on Saturday and you might search for interesting places to visit in your free time and events that were taking place at the end of that week.
Then thanks to predictive analytics which anticipates what you want to do, you might receive a discounted offer from an airline you have booked with before for your flight, receive the name of a car firm to book a collection from the airport, with an option to book a hotel with your favourite hotel chain, specifying a room on one of the upper floors for a good view of the city, perhaps with an upgrade option for a small fee.
There might also be some suggestions for some of the best Michelin-starred restaurants in Geneva (for those with an expense account) – Il Lago or the Domaine de ChateauVieux perhaps – then a trip on Lake Geneva, or a tour round Patek Phillipe, the famous clock and watch museum, or perhaps a trip to a spa. All of these offers would be tailored to preferences you had demonstrated in the past.
But not just individuals benefit; added together, the preferences of millions of travellers can be used to evaluate what destinations were popular last year and what destinations might be popular again this year and then ensure that extra flights or hotel places are made available.
Some destinations will be well sign-posted, for example Rio De Janeiro will be a popular destination for the Olympics in 2016 or Russia for the World Cup in 2018, but predictive analytics might also suggest other destinations that you might like – for example, Malaga in Spain. Malaga’s qualifications as a centre of culture will get a strong boost in 2015, when the Pompidou Centre opens its first extension outside of France in the Spanish city. Or Cuba, where President Obama’s recent normalisation of diplomatic relations means that change is sure to follow once the country is opened up to the outside world.
Revealing trends like this have important implications as travel companies try and match their product offerings, prices and promotions, with their customers’ anticipated wishes and optimise their revenue.