If you want to keep up with today’s travelers, you need to move fast. AI fast.

AI driving travel research
According to the U.S. Travel Answer Sheet, travel spending in the US averaged $3 billion a day or over $1 trillion a year in 2018. Of this, leisure travel accounted for $761 billion. US residents alone log 1.8 billion person-trips for leisure travel. And this is by no means an isolated phenomenon; the World Travel and Tourism Council’s 2019 Economic Impact Report shows that travel performance figures are up across the globe. While this is undoubtedly good news for the travel and hospitality industry, it comes with a caveat:
It’s time to take research technology very seriously. The way we travel has changed.

In this article, we’ll look at some trending ways retailers are using AI to prepare for the changes a new decade will bring.


Remember Life Before Airbnb?

Prior to the 2000s, people who wanted to book an international leisure trip usually went to travel agents to handle the complex planning involved. Now, thanks to websites like Expedia and Travelocity, many people are comfortable with making their own travel arrangements. And who could have predicted the rapid popularity of Airbnb and other DIY vacation-rental-by-owner sites?
Then came the travel apps, which allowed people to book hotels or flights on the go. Lately, these apps and sites are becoming more like full-service travel agents, helping people find deals, planning routes and connections, and even suggesting restaurants and activities. What’s behind this trend? Well, such “smart” apps wouldn’t be possible without AI and machine learning (ML).


AI is Massively Changing Travel Research

Data is flowing into the travel and hospitality industry – from websites, apps, partners, third parties, and from companies’ own records. If tech has changed how people research, find, and book their experiences, it has also changed how the industry consumes data. AI and ML are being integrated into the industry, helping companies:

  1. See how consumers conduct research prior to booking. By seeing which websites customers visit before a purchase, what keywords they search for, and other pre-buying behaviors, travel and hospitality companies can better tailor their online content and offers. This information helps marketers understand the “why” and “how” of travel plans across industry silos, not just the static “what”.
  2. Create and track hyper-personalized, targeted marketing campaigns. In addition to creating what we might call “planned” offers, which are generated in advance, travel companies can use AI to create “on-the-fly” offers. For example, travel companies could send special real-time lodging offers to customers whose flights have been cancelled. If the customer has an app, the company could use that to send location-based push notifications offering deals on nearby experiences that the customer might enjoy. On the website, a shopper looking at airfare prices could also receive information on car and hotel deals, alternative travel dates, and other specifically curated information as they browse.
  3. Understand customer purchase patterns, including emerging patterns. Rather than presenting activity by each isolated channel or touchpoint, AI allows organizations to see the entire purchase journey as it moves from channel to channel or device to device. Since today’s consumers expect an omnichannel experience, this information is vital. This whole-entity view allows them to learn what key factors influence customers’ decisions.
  4. Analyze customer sentiment and feedback relevant to the company’s properties, services, etc. Review sites like Yelp and TripAdvisor provide a fascinating window into how customers perceive an experience. But these sites have hundreds of millions of reviewers leaving hundreds of millions of reviews per minute. It’s not possible for humans to keep up with that amount of information, let alone analyze each review for positive/negative sentiment, tone, aggregate similar reviews, etc. ML techniques allow travel companies to analyze this data and use it to improve their offerings.
  5. Boost customer loyalty and lifetime value. The four previous points are all strongly customer focused. And maintaining the focus on customers – even going as far as meeting the preferences, budget, and needs of the individual customer – will naturally improve the brand relationship. Even though travel and hospitality companies often have years of historical customer data to mine, it’s always preferable to add in real-time data and other current, enriched information sources. This develops a complete and rounded profile.
  6. Predict future prices and occupancy rates. By combing through many years of travel information (using an AI-powered algorithm), travel and hotel companies can learn what factors affect bookings – including seasonality, special occasions (e.g. a big annual sporting event), holidays, recurring marketing campaigns, etc. Even partner deals can affect bookings. In the case of hotels, this can predict how soon and how many of the rooms are likely to be booked and help companies set prices accordingly.


What’s Next for Travel Tech?

As the above list demonstrated, there’s more information coming at travel and hospitality companies than ever before. Conventional research methods are too ponderous and time-consuming to keep up; today’s market demands speed and flexibility in the decision-making process, and traditional research struggles to supply the above insights in anything like the timeframe AI delivers.
In the next few years, look for the travel and hospitality industry to deepen their commitment to AI and ML. With the potential rewards of realistic customer profiles, detailed price predictions, and increased customer loyalty, it simply makes sense to utilize this remarkable technology.

Authored by: Dr. Anil KaulCo-founder and CEO of Absolutdata