• Machine Learning
  • Vanhishikha Bhargava
  • APR 18, 2018

How Machine Learning And Predictive Analytics Are Redefining The Travel Industry

Travel and tourism is on the rise globally. The industry now accounts for more than one-tenth of the world’s GDP. Interestingly, the target market is not only from developed nations but also from the emerging parts of the world that boast of increasing disposable incomes and a strong desire to explore cultures outside their own.

No wonder, travel and tourism have managed to scale steadily every year. Take US for example. It is considered to be one of the most popular tourism destinations in the world. Travel offers a large source of economic source of employment for US residents with the industry contributing $2.3 trillion to the economy in 2016.

Digital travel sales have grown rapidly in the last few years, touching nearly $500 billion in 2015; the figure is predicted to cross $800 by 2020. Technological advances such as machine learning and predictive analytics have fueled the growth of travel and tourism.

Is the travel industry competitive?

Yes, and the competitiveness is on the rise. We live in an era where a travel business is now capable of converting a consumer on their first interaction itself. Sadly, there are numerous competitors vying to do the same.

Businesses ought to explore new possibilities around the travel experience because at the end of day, marketing about travel takes a little more than only posting picturesque imagery on social media and talking about how to have fun in the sun.

There is a need to understand the changing market dynamics. And 76% of consumers demand personalization. This means not everyone wants to hear about the ultimate beach holiday that you’ve been trying to sell. 

That’s where using data science to understand consumer psychology, have meaningful omnichannel conversations and predicting future behaviour comes in. 

Machine learning for travel industry

1) Intelligent AI bots

Gartner reports, a customer will manage 85% of its relationship with a business without any human interaction. But how will this happen? With AI powered chatbots for instance, and their usage is fast extending to the travel industry.

A travel bot can act as a personal assistant to the consumer and help him solve his queries, give flight and hotel recommendations and send price alerts from time to time. Skyscanner.net, Booking.com, Kayak are already using this technology via Facebook Messenger to assist their customers.

machine learning for travel - bots


Alternatively, there are intelligent, device-oriented assistants like Apple’s Siri, Google Assistant and Amazon Echo that also help in solving queries related to travel.

2) UX personalization

79% of businesses believe personalization helps them better achieve marketing and customer experience goals. Big brands in the industry, like, United Airlines agree. It follows a data-driven strategy that tracks customer behaviour and historical data (previous search destinations and purchases) to create detailed customer segments.

The airline then makes adjustments to specific landing pages, on-screen layouts and website content to increase the likelihood of conversion. McKinsey reports travel businesses and airlines have 23x higher chance of customer acquisition if their strategy is data-driven.

machine learning for travel - user experience

The hyper-personalized approach has helped United Airlines increase its year-over-year ancillary revenue by about 15%.

3) Recommendations

Amazon was a pioneer of product recommendations and first introduced this feature back in 2006. In fact, this feature is responsible for 1-30% of eCommerce revenues, reports Forrester. Netflix is yet another example of product recommendations.

Recommendations rule the travel industry. For example, when searching on Skyscanner for flights to Bali, the consumer will be offered hotel options for the trip. Even if you don’t convert on your first visit, your second session on the site is sure to be personalized. 


For instance, it displays ‘popular destinations’ that you can fly to from your preferred city - all based on what you previous search was for. 

With enough consumer data in hand, it is possible to create effective recommendation algorithms that can help a travel business offer valuable suggestions based on the behavior of a certain consumer.

4) Customer service

In the last few years the focus of businesses has shifted to customer experience as it is set to become the key brand differentiator, surpassing price and product, by 2020. Moreover, 50% of consumers believe speed of inquiry is an important factor of a success customer experience.

A Qantas experiment on travel disruption system concludes what takes an experienced customer support professional 20 minutes can be finished by an algorithm in just one minute. Yes - technology is that fast-paced!

With this kind of tech, airlines can breathe a sigh of relief and solve on-the-ground complaints such as loss of passenger luggage quickly if a virtual assistant can conduct an automated search and speed up the process of tracking the misplaced goods.

5) Social media analysis

90% of US travelers share photos of their holiday and overall travel experience on social media and review portals via smartphones. This subjective data, if properly studied, can help businesses in making significant changes in their services to offer a better customer experience.

TripAdvisor is an interactive travel forum with a count of 455 million unique visitors. As of March 2018, there are 600 reviews and opinions on it. The portal is a huge platform for travel businesses to understand the sentiments behind the reviews, both good and bad.

The study can be done with tools like Google Cloud Natural Language API that reveals the structure and meaning of text, and define the intent behind a review.

6) Flight and hotel price forecasting

Perhaps, the biggest factor for choosing a holiday destination is the flight fare. And since these costs vary a lot, consumers don’t have the time to keep an eye on the best deals. Fortunately, there are intelligent tools like Skyscanner that will track the flight fare drops for consumers and send hot deals to them on time. The same technology of predictive analytics is applied in the case of hotel rates.

7) Hotel price optimization

Renowned hotel chains such as Marriott and Hilton have been changing their room rates one to two times a day for the last 14 years. This is called dynamic pricing.

On the basis of that technology, Starwood Hotels developed a predictive analytics tool in 2015 to display the most efficient hotel price after considering factors like daily rates, consumer’s booking pattern, weather and more.

The tool allowed human operators to see the data and adjust prices manually. 

8) Fraud Detection and Fraud Prevention Analytics 

With the rise in different channels and online booking, fraud is becoming increasingly common in the travel industry. Digital travel sales combined with focus on customer experience and expanding customer base is increasing the likelyhood of fraud. The universal problem is how to quickly determine the root cause of incidents and then contain and remediate them. Once that is done Machine learning and AI can help with proactive diagnostics and mitigation for continuous cyber security improvement.

Wrapping up

Data science is cleverly changing the face of the travel industry. It is helping travel businesses create authentic experiences without losing human connection since travel is very much a people-to people experience. 

Need help with your project or simply need data scientists to augment your existing team? Post your project in the Experfy Marketplace for on-demand help!

Still not sure how data science and machine learning is empowering the travel industry to personalize their customer experience? Learn with our expert, Larry Kim  how different industries are leveraging the power of machine learning.  machine learning and predictive analytics for businesses

The Harvard Innovation Lab

Made in Boston @

The Harvard Innovation Lab


Matching Providers

Matching providers 2
comments powered by Disqus.