Machine Learning, AI & Big Data Analytics in the Travel & Hospitality Industry: Applications, Scopes, and Impact on the Job Market

Travel & tourism is on its rise nowadays. This may be explained by the fact that it has become more affordable to a broader audience. But, in today’s fast-paced world, finding time to travel to a ticket office and get your tickets is a luxury few can afford. Like any other industry, machine learning, AI and big data analytics have changed the travel & hospitality industry as well. In this post, we will discuss the major applications and future scopes of machine learning, AI & big data analytics in the travel & hospitality industryacross the globe and India. Additionally, we will also have a look at how machine learning, AI and big data analytics are reshaping the hospitality job market.


Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Industry
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Due to rapid digital transformation, over 500 billion dollars ($564.87 billion) was made in the travel & hospitality sector in the year 2016 alone. The number is expected to reach $817.54 billion by 2020. Travelport processes 10-12 billion searches a day from travelers researching or booking trips. So, in order to handle such a massive amount of data, edging out competitors, and providing a good customer experience, machine learning, AI, and big data analytics are extremely critical.


Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Industry


Co-authored by Parinita Gupta



In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI), data analysis are very critical than ever before.


Applications of Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Industry
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In this article, we will explain in detail about Machine Learning and how Data Analytics can be a disruptive technology in the near future for the Travel and Hospitality Sector. Let’s look into the scopes of Data Analytics and Machine Learning in Travel & Hospitality sector.


A Brief Introduction to Machine Learning and Data Analytics


Machine Learning


Machine learning is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.


The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.


Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away. Tech giants like Google, Facebook, Amazon, Flipkart are already using machine learning to enhance the customer experience and to strengthen data security.


Data Analytics


Data Analytics which is interlinked with Machine learning is the discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making. In other words, analytics can be understood as the connection between data and effective decision making, within an organization. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.


Both real-time analytics and predictive analytics have many applications in the travel & hospitality industry.


10 Major Applications of Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Sector Worldwide


1. Recommendation Engines


One of the most mainstream use cases for data science, some recommendation solution is currently incorporated in 99% of all successful products. Similar to personalized content suggestions based on neural networks on Netflix or the “Featured Recommendations” box on Amazon, online travel booking providers often provide tailored suggestions, based on your recent searches and bookings.


Related Article: Differentiating AI, Machine Learning, Deep Learning, and Neural Networks


For example, when searching on MakeMyTrip or Expedia for flights to London, you will be offered several accommodation options for your trip. Similarly, offers alternative destinations you might like for your next trip.


Automated recommendations based on customer data works well to increase sales, upsell, and keep loyal customers coming back for more.


2. Flight Fare and Hotel Price Forecasting


Flight fares and hotel prices are ever-changing and vary greatly depending on the provider. No one has time to track all those changes manually. Thus, smart tools which monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.


According to Travelport’s 2018 Digital Traveler Survey, 50% of US travelers and 51% of Canadian travelers identified the time spent trying to find the best price as a top pain point for searching and booking leisure trips.


Sites like Hopper are great examples of a service like this, helping its users to book cheap flights using analytics. Adding a tool like this to an online travel agency portal is a smart way to hook customers in and entice them to book more trips.


Scopes of Big Data Analytics & AI in the Travel & Tourism Industry
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Hopper saves users time, money, and anxiety in their quest to book the perfect trip by offering travelers recommendations and alerts based on highly accurate pricing predictions. The app also leverages machine learning to uncover price drops and exclusive deals for a personalized search and booking experience on mobile devices.


Another great example is the innovative fare predictor tool by, which accesses and collects historical data about millions of fare searches going back several years. With such rich data and machine learning algorithms, the platform predicts the future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers, and deals.


A great deal of this application is dependent on Predictive Analytics and Machine Learning.


3. Intelligent Travel Assistants


As convenience is the king in today’s world, smart concierge services, powered by AI are gaining momentum in various industries. Travel booking is only one of the areas being heavily automated by machine learning algorithms.


Intelligent programs (bots) are trained to perform certain tasks on users’ requests. Instant messaging platforms like WhatsApp are also becoming very popular for customer service. With the top four chat apps having over 4 millions of monthly active users just for April 2018, instant messaging platforms are widely adopted by some prominent brands as a great way to reach out to the clients and build better customer relations.


They can be further used as mobile travel companions, solving several problems on the go, such as:


  • What’s the baggage allowance for my flight?
  • Where is the nearest business lounge?
  • What’s my boarding gate number?
  • How long will it take to get to the airport?


4. Optimized Disruption Management


What is automated disruption management? It basically means resolving roadblocks that a traveler may face on their way to the destination. As the name suggests, it’s a way to automatically handle disruptions to the plan. This aims at resolving actual problems a traveler might face on his/her way to a destination point, and particularly applicable for business and corporate travel.


Disruption management is always a time-sensitive task, requiring an instant response. While the chances to get impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of canceled flights every day.


With the recent advances in technology, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveler and the carrier. This is where Real-Time Analytics plays a vital role.


The opportunity for data science here lies in predicting travel disruptions based on available information about weather, current delays, and other airport service data. Thus, an algorithm trained to monitor this data can send out timely notifications, alerting the users and their travel managers about upcoming disruptions, and automatically put a contingency plan into action.


The 4site tool, built by Cornerstone Information Systems, aims at enhancing the efficiency of enterprise travel. The product caters to travelers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management.


5. Customer Support


Like personal travel assistants and intelligent disruption management, airlines can utilize the power of artificial intelligence to streamline the customer support process. Especially now, when almost half of all consumers agree that the speed of response to an inquiry is the most important component of successful customer service.


AI and chat-bot are a great way to streamline certain aspects of customer service and support. Basic informational and transactional services can be offered through a custom programmed chatbot. Combining virtual assistants with human ones, not only can help businesses grow their brand loyalty, but also optimize business performance.


6. Personalized Offers for Most Valuable Customers (MVCs)


The importance of loyalty programs for the travel and hospitality industry continues to grow. In 2016, the number of loyalty program members for major hotels chains increased by 13.1%.


The members of loyalty programs, i.e. most valuable customers, are those users that the travel industry players should focus on first to avoid churn. And this is the low-hanging fruit for the machine learning application.


Using legacy data and customer purchases, you can develop a solid model for offering special deals to your most loyal clients.


7. Sentiment Analysis in Social Media


According to Amadeus, 90% of US travelers with a smartphone share their experiences and photos in social media and reviews services. TripAdvisor has 390 million unique visitors and 435 million reviews. Every minute, about 280 traveler reviews are submitted to the site.


This is a large pool of valuable data that brands can analyze to improve their services. While conventional statistical analysis of reviews subsets is possible, the computing power and underlying machine learning techniques allow for analyzing all brand-related reviews.


Sentiment analysis is the branch of supervised learning that aims at exploring textual data to define and rate emotional and factual qualities of it. For instance, Google Cloud Natural Language API is an off-the-shelf application programming interface that can be tweaked and integrated with analytical tools to provide real-time analysis of all brand-related reviews.


This can help you identify issues and resolve them to improve customer goodwill. Using supervised learning and natural language recognition, data tools can tap into the great wilderness of social media conversation to identify opportunities for intervention.


8. Dynamic Pricing in the Hospitality Industry


Dynamic pricing is based on the idea of changing room prices depending on various market circumstances. This isn’t something new for the travel & hospitality industry.


Many properties like Hilton and Marriot have been changing their room rates once or twice a day since 2004. In 2015, Starwood Hotels started developing a predictive analytics tool that accounts for hundreds of factors to display the most efficient price for the moment. These include competitive pricing data, weather, a user’s booking pattern, occupancy data, room types, daily rates, and other variables. This is another classic application of Predictive Analytics.


While the system can work in a fully automatic manner, it also allows human operators to see the data dashboard and manually adjust rates if needed. Using machine learning and data analytics for dynamic pricing can enhance the effectiveness and profitability of such schemes.


9. In-Stay Experience


Customer experience matters a lot. AI solutions can assist a traveler not just en route to a destination, but also during a hotel stay. With voice-enabled virtual assistants in rooms, guests can make themselves more comfortable. For example, they can set a temperature in a room, adjust the light, switch the TV on and off. With facial recognition, hotels can speed up check-ins, and make stay more secure.


According to the Oracle report Hotel 2025, 78% of hotels will upgrade suites with voice-controlled gadgets, and 68 percent of them will use robots for check-in and check-out by 2025.


10. Fraud Detection


According to the Juniper Research report, airlines and travel industries suffer from eCommerce fraud the most. The travel industry & hospitality industry loses billions of dollars every year having to refund stolen money to customers.


Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Industry
Source: Juniper Research


Payment fraud is one of the most popular types of scams in this industry that entails using a stolen credit card for booking flights or accommodation. Another popular type of fraud is a friendly fraud, when a customer pays for a purchase, and then claims that the card was stolen, demanding a chargeback.


Customer behavior analysis using profiling and machine learning technologies can help prevent and detect illegal transactions from happening. Italian online-booking platform Wanderio cooperated with Pi School that applied an AI-technology of fraud detection. Mobile booking app HotelTonight also applied a customized machine learning model to predict and detect fraud that allowed them to reduce chargebacks to 50%.


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How Machine Learning, AI and Big Data Analytics are Reshaping the Travel & Hospitality Sector in India


India’s Travel sector has expanded in recent years, driven by the increase in domestic spend, internet penetration, and availability of smartphones. Technology today plays a major role in shaping the travel industry. In this competitive industry cheap flight, affordable hotel prices, and lots of travel apps that help travelers plan and navigate their trip enabled and encouraged people to travel more. No wonder digital travel sales are predicted to cross $800 billion by 2020. Such apps as OYO, MakeMyTrip, Airbnb, and Expedia disrupted the industry of travel agents and now are recreating that full-service experience by using machine learning for travel service development.


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Technological Disruption in the Travel & Hospitality Industry in India


The travel industry has always been at the forefront of technology adoption. Travelers have been equally enthusiastic about adopting technological changes to make travel simpler and more enjoyable. This has given rise to remarkable innovation in products and business models such as Airbnb, OYO and many others.


The travel and hospitality industry welcomed the era of mobile applications and websites with an online presence quickly becoming the primary channel to reach customers. The Android/IOS Apps are a true companion today for travelers which can be accessed anywhere and everywhere. Budding popularity of such applications backed by user-generated content and sharing economy services has opened the door for many new travel start-ups, offering choices to tech-friendly travelers.


Data Analytics in the Travel & Hospitality Industry in India


There are three primary buckets for data analytics — reactive, predictive and proactive. An organization ought to formulate its goals and expectations from data prior to venturing into the applications its analysis can serve.


Reactive – Based on the study of historical data to identify issues, preferences, and patterns.

Predictive – Based on historical, current and forecast data to predict outcomes and set future expectations.

Proactive – Forward-looking like predictive analytics, but based more on qualitative than quantitative data.


Related Article: Careers in Analytics


Big Data Analytics and Machine Learning Together


An overview, how analytics and machine learning works: A traveler creates an immense amount of data throughout the journey from search and booking, to travel, check-in and check-out, and arrival at the origin. This data is crucial to players — especially hotels, Online Travel Agencies (OTA), Airlines and metasearch engines — that are looking to improve and personalize customer experience, employ dynamic pricing for value maximization, predict future demand and prepare accordingly, optimize operations through better visibility into internal data, and channelize marketing efforts to achieve targeted responses.


Essentially, it is the key to making informed decisions. Technology-driven players such as OTAs, metasearch engines and online hotel aggregators are typically more invested in Big Data analytics than hotels or airlines. However, over the past few years, many large hotel chains have started using Big Data.


A Few India-Specific Case Studies


If we see business models of the Hospitality sector in India, it is simple as they connect the guest with the hotel by listing hotels on their website and take a commission as their revenue. The way OYO Rooms operates is different from other hotel and OTA models since they focus on co-branding. They partner with up to 3-star hotels and even guest houses, ‘Standardize’ them and bring them customers through their site and app. Along with a clean room, they offer quality and standardized services like in-room WiFi, complimentary Breakfast, AC, power back, etc.


Travel companies are actively implementing AI & Machine Learning to dig deep in the available data and optimize the flow on their websites and apps and deliver truly superior experiences. There was a survey done by and it was established that almost a third (29%) of global travelers say they are comfortable letting a computer plan an upcoming trip based on data from their previous travel history, and half (50%) don’t mind if they deal with a real person or computer.


Currently, booking flights, hotels, and rental cars have entirely turned into an online experience. Therefore, there is a lot of data on all our travel habits, which allows AI algorithms to pull out lots of insights and transform them into customized offerings and a new kind of experience.


Future Scopes of Machine Learning, AI & Data Analytics in the Travel & Hospitality Sector


There is no one technology enabling predictive analytics and personalization; it is a range of technologies, but at its heart is data. Using the wealth of data now available will allow travel brands to know so much about individuals that they can present offers based on their preferences. In addition, analyzing big data like historical flight itinerary data and weather patterns enables brands to make more general predictions on areas like flight delays and fares.


Our end traveler research found that 65% of travelers would provide personal details if it resulted in a more personalized travel experience. This shows how customers are increasingly looking to technology to take the stress out of travel and enhance the journey—whether that’s being presented with more relevant offers at the booking stage, being able to track fare patterns to ensure the best deal or being informed as early as possible how long it will take to get to the airport.


The future travel brand isn’t therefore just about moving people from A to B, unveiling new destinations, or organizing trips. Instead, it is about a thoroughly progressive, completely 360-degree view of the traveler and everything that goes into creating special, unique, memorable experiences.” – Defining the Future of Travel through Intelligence.


In the near future, there is going to be a mass-market conversion to semantic, location-aware and Big Data [data sets that are beyond our reasonable abilities to manage or comprehend so that more imaginative methods and ways to visualize them are required] applications, which will be of transformative use to travelers.” – Filip Filipov, Head of B2B, Skyscanner.


Machine Learning, AI and Big Data Analytics in the Travel & Hospitality Industry


Luxury Trips – A Big Opportunity for the Travel & Hospitality Sector


ML, AI and Analytics in Travel and Hospitality Sector
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Business and corporate travel are anyway on the rise. However, the global luxury travel market size is expected to reach $2.5 trillion by 2025, expanding at a CAGR of 4.6% over the forecast period, according to a new report by Grand View Research, Inc. The average anticipated outlay on vacations this summer (2019) is $2,037, topping $2,000 for the first time since 2010.


Increasing disposable income and consumer spending of the middle and upper class, growing demand for the accumulation of travel memories, increase in micro-trips, and emerging tourism and corporate industries all across the globe are expected to drive the market.


Key Opportunities to Leverage Technology within the Travel & Hospitality Industry


The majority of travel platforms and hospitality properties are leveraging AI, ML and Big Data Analytics. Below are two additional scopes.


Digital interactions that are conversational and voice-based assistants that are personal


Today, everything a traveler needs to do is available on a website. Using a website, travelers can plan where they want to go, compare options, weigh budgets and make bookings and cancellations. So, an intuitive UX/UI design and NLP-based bots are must to have for any travel website.


The experience will likely look something like this: When you want to book a trip, you will call upon your favorite AI agent — Siri, Alexa, Google, Cortana, Facebook “M”, or some yet to be created AI assistant — and tell it the origin, destination, dates and price point. With that one request, the AI agent will search all of the existing travel content, or data, across the globe.

This includes flights, ground transportation, lodging – including the ancillaries and extras such as seat upgrades or baggage insurance. Then, the AI agent, knowing your personal preferences, will quickly book the best possible solution based on your dates, budget, and personal preferences. Done. 

– Levi Brackman, Principal Data Scientist, Travelport.


Related Article: Hospitality Becoming More Experiential Than Transactional


Facial Recognition with additional heft from Blockchain Technology


Travel requires repeated scrutiny of travel documents by different sets of people. Facial recognition technology promises to bring an end to these tiresome paper-bound processes. With facial recognition, travelers can seamlessly move through airports, immigration, customs, and board aircraft without the need for having travel documents scrutinized at each step.


When combined with blockchain, it becomes easier for customers to visit restaurants, duty-free stores or access entertainment with a simple facial scan. The blockchain technology ensures that reliable and trustworthy traveler data is made available to complete the transactions.


Big Data Challenges and Job Market Scenario within the Travel & Hospitality Sector


Tech Challenges within the Travel & Hospitality Sector


With so much data available (and much of it complex in nature), there are also challenges facing companies in the travel sector when it comes to establishing insights from the datasets. While having data professionals in your company is a step in the right direction, ultimately ensuring big data is used effectively will require cross-departmental collaboration and data-lead company culture.


Economic Impact of the Travel & Hospitality Sector


The Travel & Hospitality sector is responsible for generating about 10.4% of the world’s GDP and 319 million jobs. In today’s competitive landscape, travelers are becoming more and more “loyal to themselves,” to their own needs, and they want to live tailor-made experiences, to be treated as “individuals,” according to their own individual needs and characteristics, and no longer as one of many.


Hybrid Skills are Required in the Current Travel & Hospitality Job Market 


Within this industry, soft skills are of paramount importance. But, with such technological disruption, tech skills cannot be ignored. So, the new job categories within the travel & hospitality industry need the workforce with “hybrid” skills.


Not only will staff have to know how to do their traditional job as in the past, but they will also be expected to have knowledge of the technological world that is evolving around them.  The combination of the two creating hybrid positions that are in great demand but for which there is little supply.


An example of this hybridization is marketing roles that require an understanding of sophisticated statistical analysis tools, data science, and AI.

Retraining current employees will be essential as the pure data scientists and engineers rarely have the soft skills, especially communication skills that are essential to succeed in the hospitality world.

– Julia Aymonier, Chief Digital Officer, EHL Group.


Jobs and careers within the travel and hospitality sector will no doubt be greatly affected by new technologies – data science, machine learning, NLP, predictive analytics. But, the future is bright if hospitality institutes and the organizations ensure the correct education for hospitality employees.  The industry is more than capable of creating its own hybrids.



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Co-author Bio: Parinita Gupta is a full-time banking professional. Additionally, she is also a passionate blogger and digital marketer.

She mostly writes about the Banking & Finance, Technology, and FinTech sector. But, she also enjoys writing on other topics as well.  You can follow her on Twitter.




Sources: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10.

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