How machine learning is changing the travel industry
Machine learning’s growth continues as it permeates into unrelated industries. Travel booking might not seem like a good fit at first, but Wilco van Duinkerken of trivago explains how ML is innovating the way you find and book your next holiday.
Everyone’s heard how machine learning has huge potential, how it could upend existing systems and change the world. But that only tells us so much — to really understand the potential of machine learning, you’ve got to focus on applications and outcomes. Travel isn’t the first industry that comes to mind when you think about machine learning. However, there are impressive innovations coming out of the travel sector with a foundation in machine learning and AI technologies, which should be an inspiration to other sectors in the future.
The travel industry has changed a lot thanks to the internet. Where we once went to brick-and-mortar travel agents to book a holiday, we now book our flights and accommodation online. Or so you’d think; as recently as 2016, only 33% of people actually book their hotels online. This is a stunning figure when you consider how much of our work and personal lives has been digitized.
Travel is a deeply personal choice. Where you choose to go on holiday, where you stay, and even what airline you fly with are all choices that say something about you and your personal preferences. For many, the experience of looking at a list of hotels in a web browser traditionally hasn’t always been as good a user experience as speaking with a real person in a travel agency or speaking to someone on the phone.
Making improvements to user experience and offering enhanced personalization are two key ways of improving customers’ online travel buying experience. Machine learning presents an exciting opportunity to accelerate this change.
Bringing hotel search to life with personalization
Today’s online consumers are producing unprecedented amounts of data. This ‘data exhaust’ is increasingly being used in innovative new ways to provide personalized services for customers. Companies like Amazon and Netflix have already shown how effective product personalization can be in driving engagement and return visits, and the travel sector is moving in the same direction.
The goal is always to offer the traveler the best possible experience. At a company like trivago, this means optimizing the number of steps (i.e. clicks) it takes for a customer to get to what they’re looking for. Machine learning technologies can achieve this by helping to personalize what the customer sees. Natural language processing can be used on the hotel side to analyze hotel descriptions and customer reviews, as well as isolate the most popular features and key points of feedback. This data can then be fed into a database where it can be matched with existing customer preference data.
The information that hotels input to our platform is only part of what can be used to personalize results. Images accompanying listings can also be analyzed using neural networks, a subset of machine learning. For hotels that don’t have the time or the technical know-how to input all the relevant data, analysis of the images that accompany the listing can also yield valuable data around amenities, ambience, and scenery, all of which can be matched with user preferences to develop a more tailored results page.
Let’s have an example of these technologies in action: say a customer wants to see hotels with family-friendly pools. Presenting the customer with hotels that have pools is relatively straightforward, but pools that are specifically family-friendly? That’s much more challenging. To start to narrow down the list, natural language processing can be deployed on hundreds of user reviews, measuring the proximity of words like “clean”, “quiet”, “family” or “safety”. But often the words we’re looking for are not posted immediately next to each other, so it becomes more important to understand syntactic relationships and understanding how terms relate to each other. This is something that can only be done through advanced semantic technologies and specialized databases.
The end goal is to make the experience of searching for travel products more a search for an exciting experience, rather than a technical process of selecting features and on/off toggles. Machine learning is critical in helping platforms like trivago isolate the most unique and attractive aspects of a hotel and suggesting those experiences to customers who have already signaled their interest.
Build teams made for machine learning
The conversation around machine learning often focuses on raw computing power, but not enough attention is paid to the significant ways in which we need to change our working patterns. Things that were manual processes not very long ago are now automated. Machine learning systems can generate sophisticated suggestions that were previously not available to teams.
This presents unique challenges and requires new specializations within teams to make the most out of machine learning. It’s not enough to keep going in the way of working that you’re used to, such as Agile or Scrum. It’s important to distribute your machine learning resource throughout your teams, making sure there is shared understanding of how that resource is going to be used, and that there is a shared understanding among different product teams around what the goals are with your machine learning implementation.
SEE ALSO: A basic introduction to Machine Learning
Look after your data
Finally, a word on user data. Machine learning needs user data. There’s no getting around it and it’s important to be honest and upfront with your users about it. Your customers’ data is currency: it’s valuable and it should be respected. It’s important to be upfront and transparent with customers about what data they are providing and what their data will be used for. If you present your customers with a clear and transparent choice over what to share and what they stand to gain if they do share, then they will be more open-minded.