More and more, the travel industry is finding deep learning to be an indispensable ingredient for success. Deep learning allows brands to find new customers looking to take advantage of travel deals, optimize advertising spend in real time, and predict which places will be the next to open or close their borders, thus enabling them to respond more readily to market trends.
There is no question that the travel industry has been hit hard by the current pandemic. Places once packed to the gills with tourists now lie empty, and people are increasingly reluctant to travel given the risk of infection as well as quarantine requirements. However, all indications point to this travel freeze being temporary: the number of people traveling in the United States this August was higher than it has been compared to the past four months, and analysts predict that pent up demand for travel will provide a further boost to the hard-hit industry as restrictions lift and people’s anxieties ease. This is a crucial moment for the industry as travel brands work to overcome the challenges imposed by COVID-19. Now more than ever, travel companies need the advanced capabilities of deep learning to unlock new opportunities and find innovative ways to reach new customers.
For those who are unfamiliar with the technology, deep learning is a form of machine learning that relies on neural networks – a system that consists of layers of interconnected nodes – to take in data sets and make predictions. Facebook’s image recognition abilities, Alexa’s voice recognition, Google’s translation capabilities – all of these innovations are made possible by deep learning. Systems that utilize deep learning are able to quickly analyze large amounts of data, determine the probability of a certain outcome, and adapt as new information comes in. They are able to find patterns that would otherwise have remained unnoticed (and thus unacted upon) by humans.
Deep learning’s analytical abilities make it a perfect technological fit for the travel industry, for several reasons. First, deep learning algorithms work best when trained on as much data as possible. Given that there are usually, at any given time, millions of people either traveling or planning their next trip, this gives brands the ability to identify potential customers and the best way to reach them with even greater accuracy. In 2019 alone, there were 1.5 billion international tourist arrivals recorded worldwide, 4% more than the total for the previous year. Travel brands have the opportunity to leverage a huge amount of user data, data that can be used to identify up and coming travel destinations, dynamically price airline tickets and hotel rooms, in additional to offering personalized packages.
Deep learning can also help solve some of the biggest roadblocks that both travelers and travel companies currently have to deal with: the everchanging quarantine regulations and travel restrictions enacted by local and national governments. Airlines and other major hotel and travel brands can find it difficult to keep on top of all of these new requirements, which in turn impinges on the level of service that they can provide to prospective customers. With deep learning, however, travel brands can rely on superior data processing and pattern identification abilities to help them predict which areas are undergoing a surge in coronavirus cases, and are thus likely to go under lockdown, as well as which places have the virus under control enough to begin opening up to visitors. The same information can be used to predict whether visitors from a particular location will be forced to quarantine, which would then allow travel companies to tailor travel packages to visitors that take into account those restrictions.
Major players in the travel industry are already deploying some forms of machine and deep learning on a day-to-day basis. For instance, chatbots that utilize text and voice recognition technologies are frequently incorporated onto travel websites to assist with customer service and provide responses to basic queries. Airbnb has credited the implementation of neural networks into the company’s site and mobile app with improving the relevancy of search results and increasing bookings. Hotels.com uses machine learning algorithms to suggest accommodation to customers based on their previous behavior, and personalize recommendations based on which listings customers choose to click on.
Deep learning can help travel companies achieve three objectives: first, to identify the best possible candidates for conversion; second, to gain a better understanding of user behavior and the factors that motivate someone to choose a particular destination or brand over another; and third, to optimize the advertising and product selection seen by that particular user and improve conversions. With this level of adaptive algorithmic advertising, travel companies can focus on providing customers with a fast, simple and seamless experience, all while increasing conversion rates and fostering brand loyalty.
More and more, the travel industry is finding deep learning to be an indispensable ingredient for success. Deep learning allows brands to find new customers looking to take advantage of travel deals, optimize advertising spend in real time, and predict which places will be the next to open or close their borders, thus enabling them to respond more readily to market trends. Brands who are able to leverage their data to create sophisticated algorithms have a significant advantage over those who do not, especially in times like these where adapting to new behavioral patterns is more important than ever.
Jeremy Fain is the co-founder and CEO of Cognitiv. With over 20 years of interactive experience, Jeremy has worked across the agency and publisher landscape in roles ranging from marketing, to industry standards, to revenue leadership. While at CBS Interactive, he launched and oversaw one of the first premium programmatic exchanges, which eventually led to his role managing all North American accounts at Rubicon Project. Jeremy founded Cognitiv in 2015 with two childhood friends who shared an interest in making Deep Learning accessible to business. He holds a BS in Electrical Engineering from Yale University and an MBA from Columbia Business School.