Where are you? If you don’t know, it’s likely that the artificial intelligence (AI) in your pocket does. From Google Maps and voice assistants to the scores of travel apps that make online booking easy and instant, AI is grabbing hold of the travel industry – and it’s not letting go. There is a simple reason for this; the internet has made booking flights, hotels and rental cars a wholly online experience. So there’s now big data on all of our travel habits, and it’s allowing AI algorithms, customization and chatbots to spread.
However, the travel industry does need to be careful not to go overboard with AI and chatbots.
“As we operate in an industry that is incredibly personal, emotional and complex, maintaining the right balance between genuine human interaction and efficient automation is something we’re always trying to fine-tune and optimize throughout every stage of the consumer journey, ” says James Waters, Global Director of Customer Service at Booking.com. His company found that 80% of customers prefer to self-serve in order to get the information that they need. So, with the help of AI, why not let them?
Travel is stressful, especially for railway passengers. So why not use machine learning to analyze the vast quantities of data now available to create a contextually rich, highly personalized, and fully predictive journey? In the UK, online rail booking service Trainline has use crowdsourced data to create a bot that advises passengers where they’re most likely to find a seat, depending on the location and direction of their specific journey. One of the key features is price prediction, which tries to pre-empt a passenger’s demand for specific tickets, and shows how long a ticket is going to be a certain price for, how many tickets are left at the price, and what the cheapest available ticket is each day. Meanwhile, BusyBot crowdsources data from passengers to notify others about how busy a specific section of a train is.
AI is also being used by airlines to plan routes. Instead of using instinct (okay, and quantitative analysis) to predict the demand for a new airline route, Skyscanner thinks that machine learning is better. It used a Python-based K-means algorithm on vast datasets within its Travel Insight data platform, analyzing the search patterns of over 50,000 origins and destinations during 2016. An unsupervised machine learning algorithm – one of the new breed of AI techniques that doesn’t require much involvement of computer scientists – K-means discovered that the largest category is summer family holidays leaving on weekend departures, and that ‘romantic’ couples destinations like Venice and Paris are just as popular with solo travelers. AI like this looks set to help airlines decide not only where to fly, but on what day of the week, and even what time of the day.
Read full article at How AI is changing how we travel
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