Machine learning (ML) in the travel industry is opening new frontiers for personalized, data-driven experiences. Where customers once had to navigate time-consuming booking systems and generic services, AI-powered travel apps predict customer preferences and provide tailored recommendations.
In recent years, the rise of large language models (LLMs) and generative AI has expanded use cases even further. Customers can now interact with AI chatbots that understand their needs, remember previous conversations, and provide relevant tips, suggestions, and even entire itineraries.
As a trusted technology partner, Intellias has helped travel businesses worldwide harness the power of AI and ML. With our help, these businesses have turned raw data into powerful insights and incredible services that boost revenue and growth.
In this article, we’ll explore everything you need to know about machine learning in the hospitality industry. Read on to explore:
- How travel businesses are applying machine learning to deliver incredible results
- The three key machine learning technologies used in the travel industry
- Key benefits and challenges of implementing ML in the hospitality industry
- How Intellias can help you maximize the opportunity that ML offers
The growing role of ML in the travel industry
Travel is deeply personal, with each traveler seeking a unique experience. Some people want to be pampered in luxury five-star hotels. Others want to backpack from hostel to hostel. For some, travel is about exploring the world and expanding horizons. For others, it’s purely business.
Traditionally, travel businesses struggled to understand the needs of their customers beyond broad demographic indicators and trends. As a result, the onus was on customers to search for the perfect locations, the best deals, or the most suitable hotels.
With the advent of AI and ML, travel companies can provide a highly engaging customer experience. Instead of sifting through endless travel options, customers can receive personalized recommendations that align with their interests, travel history, and requirements. Instead of trawling information online, they can receive instant advice and answers via AI-powered chatbots.
Machine learning in the travel industry doesn’t just improve the customer experience, however. It also helps streamline internal operations. This combination of changing customer expectations and business benefits has led to an explosion in demand for AI solutions. In the coming years, the global AI market for the tourism industry is predicted to grow from around $888 million in 2025 to almost $10 billion by 2033. That’s an impressive compound annual growth rate (CAGR) of 35%.
Global AI in tourism market
Source: Market.us
Source: Market.us
While machine learning for hospitality has a broad range of use cases, personalization is currently the most widely adopted application, making up over 30% of AI adoption across the industry. Today, 60% of travel businesses have either implemented AI-powered personalized recommendations or are considering it.
Global AI in travel market
The benefits of machine learning in the travel industry
Machine learning in the travel industry isn’t a technological gimmick, nor a passing trend. It’s an evolutionary jump that enables travel businesses to provide a level of customer service and personalization that was impossible a few years ago.
By leveraging data-driven insights, ML empowers travel companies to thrive in a competitive landscape. If implemented effectively, machine learning in the hotel industry offers the following advantages:
Increased customer satisfaction
Machine learning in the travel industry redefines the customer experience. By combining ML techniques such as deep learning with advanced data science, travel businesses can offer highly personalized deals, recommendations, and interactions. Each customer feels like a VIP, leading to greater satisfaction and loyalty.
The flip side is that if your travel business isn’t able to offer slick, personalized experiences, your customers will find a competitor that can. Indeed, research by PwC found that one in three US customers will walk away from a brand they love after just one poor experience. The number is significantly higher for LATAM customers. The key takeaway is this: travel companies that implement ML and AI solutions effectively will gain market share over those that don’t.
When do consumers stop interacting with a brand they love
Source: PwC
Higher revenue potential
Implementing machine learning infrastructure can help increase revenue in several ways. By enabling dynamic pricing, for example, travel businesses can optimize fares and rates in real time, balancing competitiveness with profitability. This approach has enabled airlines to increase revenue by up to 5%.
But that’s not all. AI-powered virtual assistants are helping travel agencies boost cross-selling opportunities by 10%. At the same time, AI customer segmentation can potentially lead to a 30% increase in conversion rates.
Cost efficiency
Machine learning in the travel industry reduces operational costs by automating key processes. Chatbots, for example, can handle routine inquiries, reducing the need for human support staff. This allows businesses to allocate resources more efficiently, cutting down on manual processes while maintaining high service standards.
Other AI-driven processes, such as price optimization and personalized recommendations, are fully automated and highly scalable. At the same time, ML solutions help travel businesses cut costs through predictive maintenance. Machine learning for hotels has enabled industry-wide savings of up to $800 million by anticipating equipment failure before it happens.
Improved operational agility
Machine learning enhances forecasting accuracy, enabling businesses to predict customer demand and allocate resources like staff and inventory with precision. Picture a hotel that’s able to understand exactly what demand might look like in one, three, or six months from now. That hotel can then plan key maintenance, make recruitment decisions, and stock key items proactively rather than reactively.
This approach minimizes waste, reduces overbooking risks, and ensures operations run smoothly, even during peak travel periods.
Service differentiation
AI and ML solutions have opened new horizons for travel businesses looking to offer something different. Despite their transformative impact, these technologies are still relatively nascent. Use cases and capabilities are expanding all the time. By staying at the forefront of innovation, travel companies can differentiate themselves from competitors through AI adoption.
Top four applications of machine learning in travel and hospitality
1. Dynamic pricing for airlines and hotels
Once a plane takes off, all empty seats are lost revenue. Dynamic pricing strategies help solve the issue of unsold seats (and unbooked rooms). Machine learning algorithms can evaluate various parameters, such as demand patterns, seasonality, competitor prices, and upcoming events or holidays — and then optimize prices in real time.
For instance, setting a lower price on a room or ticket when demand is anticipated to be low can attract more bookings. When demand is predicted to be high, travel and hospitality firms can use this opportunity to increase revenue.
The vast majority of airlines and hotels now use dynamic pricing strategies to optimize pricing. Yet, not all of them use ML algorithms to define prices. Some still rely on simple rule-based systems. For example, if a flight is 80% booked 30 days before departure, increase the price by 15%. Rule-based systems are far more time-consuming and resource-intensive than using ML.
2. Destination discovery
Travel and hospitality businesses, as well as private entrepreneurs, are under intense pressure when they consider locations in which to open new ventures such as resorts, theme parks, hotels, and restaurants. Choosing poorly can result in financial losses or overlooked opportunities.
Machine learning can be used to determine places that will become tourist hotspots before they grow into widely popular locations. ML algorithms can identify patterns of tourist demand and interest using data from social media posts, blogs, and other content. This data can be clustered and segmented, allowing businesses to create targeted marketing campaigns for the right audience.
An example of a company that uses machine learning to help investors choose the most attractive investment locations is AirDNA. The company compiles reports and market rankings so that private entrepreneurs and businesses can find the most profitable locations for renting houses and apartments.
3. Smart booking assistants and chatbots
ML and large language models (LLMs) have transformed chatbots from tools that provide predefined prompts and mechanical responses to dynamic assistants that recognize the intent and context behind a question.
For example, Expedia has integrated ChatGPT into their app, enabling travelers to chat with Expedia’s AI-powered virtual agent 24/7 to find answers to any travel-related questions. The ML algorithm can pre-select the most relevant trip options out of 1.26 quadrillion variables such as property location, room type, pricing, and length of stay.
Travelers can not only manually type requests to ChatGPT and Bard but can also engage in a voice conversation. For instance, they can take a picture of a landmark and ask questions about it to get instant answers. The majority of travel agencies have already integrated voice bots into their services. According to Statista, 72% of customers who use voice bots share their positive experiences with friends and family.
4. Personalized travel experiences
With 76% of customers becoming frustrated when they do not experience personalized interactions. It’s clear that the future of travel and hospitality lies in hyper-personalization — and machine learning is the passcode to unlock it.
As the Twilio State of Personalization Report 2023 outlines, 92% of companies use machine learning to deliver personalized customer experiences and grow their business. What’s more, the same report mentions that customers are willing to spend 38% more when their travel experience is personalized. These numbers highlight that tailored hospitality experiences, powered by machine learning, help to increase ROI.
Three key machine learning technologies used in travel and hospitality
1. Attribute-based filtering
Attribute-based filtering is an important ML technique that helps provide personalized travel experiences based on the interests and behaviors of similar users.
Big online travel agencies like Booking.com and Airbnb leverage attribute-based filtering to recommend properties. If a traveler likes specific categories of accommodation, such as mountain chalets, the platform will suggest similar accommodation options chosen by other travelers with similar interests. The same applies to travel destinations.
2. Large language models (LLMs)
Source: Google Blog
LLMs redefine traditional applications of machine learning in travel and hospitality. They go further than basic analysis of keywords in travelers’ searches to understand the context, intent, and sentiment behind each question.
For instance, if a user is looking for a special experience, LLMs like ChatGPT and Bard can analyze the request and suggest a personalized travel itinerary filled with exciting activities and destinations.
LLMs are great not only for visitors but for travel and hospitality firms as well. They help to decrease the operational workload by automating answers to queries that once required professional support.
“LLMs are like supercharged tour guides with an encyclopedic brain and a knack for personalization.”
Since 2014, Intellias has been working on enhancing language accessibility and learning. For example, we worked with a client to develop an NLP-based language learning app that allows users to get instant feedback on grammar, spelling, meaning, and pronunciation.
This app is a great example of how NLP and LLMs can be leveraged to create practical solutions that address everyday language challenges, making learning and communication easier. It helps to fill in the gaps learners normally have: getting conversational practice and prompt feedback on their mistakes.
3. Predictive analytics
Predictive analytics helps to anticipate future demand for accommodation, flights, trains, and bus rides based on historical data, market trends, and information about upcoming events and holidays. In particular, time-series analysis and regression models trained on high-quality data can generate accurate forecasts on anticipated revenue based on data on how many bookings have been made in the past.
This information can be used to customize pricing for rooms and flights, helping travel and hospitality firms improve their revenue and at the same time remain competitive. McKinsey estimates that travel and hospitality businesses that harness the power of predictive analytics will see a 15% to 25% revenue increase.
Moreover, predictive analytics can be used to personalize accommodation and flight recommendations for travelers with similar interests, directly addressing expectations for tailored travel experiences. This resonates with the findings of Oracle Hospitality research, which indicates that 68% of travelers want to experience a personalized journey by pre-screening rooms and amenities and paying only for the options they have chosen.
Roadblocks of machine learning implementation for travel and hospitality
Integration of machine learning in the travel and hospitality sector is not without roadblocks. Obstacles range from technical and financial to regulatory and operational. Understanding and addressing these challenges is not just about foreseeing upcoming problems. It’s about developing efficient solutions that realize and extend the potential of machine learning. Let’s dive in:
- Insufficient data quality. Travel data is usually scattered across various booking services, systems, and platforms. Often, data is formatted and structured in different ways, and it becomes challenging to unify, standardize, and normalize data sets. Fragmented, incomplete, and absent data limits the power of machine learning.
- High initial investment. Machine learning projects require powerful infrastructure to run on. Apart from cloud infrastructure, investment in machine learning software solutions is required.
- Hard-to-measure ROI. Implementing machine learning in travel and hospitality involves data collection, algorithm training, and iterative software engineering before the system produces tangible benefits and ROI.
- Integration issues. Resolving compatibility challenges may involve considerable refactoring of existing systems, which can be costly and time-consuming.
- Data bias. When there is not enough data, or when available data is not representative, the machine learning model might produce biased predictions. For example, if the algorithm for travel packages is trained on data from couples or solo travelers, it may suggest hotels or activities that are not suitable for families or big groups.
- Algorithmic bias. The machine model can itself introduce bias if it is based on assumptions. For example, if a machine learning algorithm ranks more convenient yet expensive accommodation options higher, it may unintentionally favor travelers that have higher spending habits.
- Talent gaps. Machine learning integration in hospitality and travel requires professional expertise and knowledge. In many cases, travel companies will need to work with a technology partner like Intellias to implement ML effectively.
- Privacy and security concerns. Machine learning models may process and analyze huge amounts of personal information, such as contact information, payment details, travel choices, and booking history. If compromised, this can lead to privacy breaches.
Reasons preventing widespread adoption of AI in travel
Source: McKinsey
To overcome the roadblocks, travel and hospitality firms have to adhere to a well-thought-out strategy and choose a dedicated technology partner with deep expertise in machine learning solutions for the travel and hospitality sectors, like Intellias.
Intellias’ expertise in machine learning solutions for the travel industry
If you’re considering implementing ML solutions in your travel business, having access to the right expertise and technologies is crucial. This is where Intellias can help.
As a leading technology partner for more than two decades, we help businesses like yours implement transformative digital solutions that drive success. With our deep expertise in the travel industry, we understand the challenges you face in a highly competitive market — and how ML technologies can give you an edge. We can help you at every stage of your ML journey, including:
- Cleaning, structuring, and integrating data from a range of different sources
- Developing and training custom ML models aligned with your goals
- Integrating ML solutions with your existing systems with minimal disruption
- Building a cloud-based ML architecture that’s scalable and secure
- Refining and optimizing ML solutions post-deployment to deliver sustainable results
Here are a few examples of projects we’ve delivered for hospitality and travel companies:
- Travel booking solution that connects different countries and enables travelers to search and book different types of transportation for their trips
- Smart city solution for tourism, outdoor entertainment, environmental protection, digital identity access, and more
- Shared trips platform that connects bus providers and their end customers for shared journeys and rent-a-bus services
- Mobility as a service system to deliver rich navigation data to a variety of transportation services, including public transport, bike sharing, car sharing, taxis, and car rentals
Interested in working with us? Contact Intellias today to explore tailored ML solutions for your travel business.
Machine learning trends in the travel industry
ML adoption in the travel industry is an ongoing journey, not a one-off event. To stay ahead of the game, your business must adapt to new use cases and capabilities as they arise. Below, we’ll look at some of the major ML trends to look out for in the travel sector now and in the future.
Hyper-personalization through advanced analytics
ML is meeting travelers’ growing expectations of tailored experiences by leveraging advanced analytics. By processing real-time data from sources like social media, geolocation, and booking patterns, ML algorithms can deliver hyper-personalized recommendations. For example, a traveler can receive not just personalized recommendations but also curated travel packages that align with their preferences.
Predictive maintenance for operational reliability
When combined with IoT devices, ML enables airlines and transportation providers to anticipate mechanical issues in aircraft, vehicles, and other equipment before they occur. The result is less downtime, fewer delays, and reduced maintenance costs. Hotels can implement the same approach with equipment such as lifts, HVAC systems, and smart room technologies.
Sustainability through optimized resource management
Machine learning is supporting the travel industry’s push toward more sustainable practices. By analyzing consumption-related data, ML models can provide insights that help companies reduce the amount of resources they use. For example:
- Airlines can cut fuel waste
- Hotels can optimize energy usage
- Tour operators can streamline logistics
AI-driven sentiment analysis for customer insights
Customer reviews and feedback have always been a powerful source of data for travel businesses. With ML, companies can analyze customer reviews, social media posts, and survey responses in real time using sentiment analysis. This enables them to gauge traveler satisfaction, identify pain points, and adapt services quickly. The result is a more customer-centric approach that drives loyalty.
Voice and visual search integration
In addition to AI-powered chatbots, forward-thinking travel companies are looking to voice assistants to enhance customer interactions. Instead of booking trips or creating itineraries using traditional website navigation, customers simply talk to the app and explain what they want.
Likewise, visual search integration enables customers to find destinations or accommodation by uploading images. If a customer finds a picture of a place they’d love to visit, the ML model will provide similar alternatives that better align with the customer’s preferences and budget.
Both voice and visual search offer a convenient, user-friendly alternative to traditional search and chatbot interactions.
Reasons for using voice search
Source: Contentstack
Conclusion
The path to outpacing the competition in travel and hospitality lies in incredible customer experiences. Using machine learning technology, you can provide highly personalized interactions and recommendations that will keep customers coming back.
Yet machine learning is a highly complex field. To harness its power, it pays to work with a technology expert like Intellias. We offer work with travel businesses worldwide to deliver not only data-driven solutions but also transformative experiences.
Ready to transform your hospitality services? Connect with machine learning experts from Intellias.
