Jetpacking into Machine Learning for Travel & Hospitality

Machine learning and artificial intelligence (AI) are catalyzing a huge shift in the travel and hospitality sectors. This is far more than a simple update; it’s a powerful overhaul of how the travel industry operates, interacts with customers, and delivers hospitality experiences. The introduction of new technologies — such as explainable and generative AI along with reinforcement, federated, and multimodal learning — is providing opportunities that were previously unimaginable, transforming the very definition of travel and hospitality experiences.

At the heart of this transformation is predictive analytics. It equips travel and hospitality businesses with comprehensive information about demand for rooms, flights, and hospitality experiences. Yet the true game-changer has been large language models (LLMs), which can understand and generate text fluently. The widely known GPT-4 LLM has 1 trillion parameters, according to The Decoder, and supports 85 languages as per OpenAI. LLMs like GPT-4 and PaLM 2 are powering chatbots and virtual travel assistants, enabling them to handle complicated and ambiguous requests. Moreover, LLMs remember what has been talked about during a conversation and can provide highly relevant responses. These capabilities not only improve the customer service of travel and hospitality companies but also streamline internal operations.

In this article, you will learn about innovative machine learning applications in the travel and hospitality industry. Let’s get started!

Top four applications of machine learning in travel and hospitality

Dynamic pricing for airlines and hotels

Once a plane takes off, all empty seats are lost revenue. Dynamic pricing strategies help to 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, so that prices can be modified 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. And when demand is predicted to be high, travel and hospitality firms can use this opportunity to increase revenue.

The vast majority of airlines, including Qatar Airways, British Airways, Air France, Ryanair, Lufthansa, EasyJet, and American Airlines, use dynamic pricing strategies to optimize ticket pricing. Yet, not all airlines use machine learning algorithms to define prices. Some still rely on rule-based systems: for instance, if a flight is 80% booked 30 days before departure, increase the price by 15%. In this case, historical data and statistical analysis are still used to define the price, but using a rule-based system is far more time-consuming than using machine learning and requires constant effort. Can ticket pricing be automated with machine learning, making the pricing process faster, more efficient, and more accurate?

Huge hotel chains like InterContinental, Hilton, Marriott, and Hyatt, as well as online travel agencies like Expedia, Booking.com, Agoda, Airbnb, and Kayak, are already using machine learning algorithms to dynamically determine prices. However, some midsize and boutique hotels aren’t using dynamic pricing strategies yet due to budget constraints or restricted access to the requisite technology.

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. In particular, growth in mentions of specific locations on social media or travel blogs may signify increased tourist interest. Machine learning algorithms can identify such patterns of tourist demand. Afterwards, this data can be clustered and segmented, determining groups of tourists that have similar interests and 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.

Smart booking assistants and chatbots

Machine learning and large language models (LLMs) have considerably transformed chatbots from tools that merely give automated prompts and mechanical responses to tools that recognize the intent and context behind a question. Today’s chatbots are much better than chatbots of past years at responding to travelers’ requests.

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. In particular, the machine learning 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. What is more, machine learning algorithms are embedded in a price tracking feature that enables travelers to compare flight prices with historical data and choose the most suitable time to book a flight.

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. Or they can ask any other questions they have about vacation, transportation, or things to do. Indeed, the majority of travel agencies like Expedia, Kayak, SkyScanner, TripAdvisor, Kiwi, and EaseMyTrip have already integrated voice bots into their services. According to Statista, 72% of customers that use voice bots share their positive experience with friends and family and have greater trust in a company as a result of voice bot use. This is brilliant evidence that hands-free conversations are becoming a new standard of excellence and an accurate, fast, and reliable way to get support and information.

However, the LLM transformation of the travel and hospitality sector doesn’t stop here. According to WhatPlugin.AI, 54 travel agencies have already implemented an LLM GPT in web plugins. is a huge leap forward and strong evidence of how quickly machine learning and LLMs are getting to be the new normal for travel and hospitality.

Personalized travel experiences

As indicated by McKinsey, 76% of customers become frustrated when they do not experience personalized interactions indicatesexpectations among travelers, the one-size-fits-all approach. expect not just basic customer service but a personalized and unique journey offering, tailored travel itineraries, and round-the-clock support.

Fulfilling such a multitude expectations personalization with operational effectiveness. Personalization requires especially challenging for smaller businesses that have limited resources. How can travel and hospitality firms get better at personalization while at the same time remaining cost- and labor-efficient? The answer is on the surface: with machine learning technology.

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 businesses’ ROI. Companies that are the early adopters of machine learning technology are very successful and become an example for others to follow.

Three key machine learning technologies used in travel and hospitality

Attribute-based filtering

Attribute-based filtering is an important machine learning technique that helps to provide personalized travel experiences based on 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 like interests.

The same applies to travel destinations. Services with embedded attribute-based filtering can suggest visiting Bruges after Ghent, for instance, as other like-minded travelers to Ghent have also enjoyed visiting Bruges.

Attribute-based filtering can be leveraged in the hospitality sector. Based on a traveler’s choices during a previous trip, the system can suggest similar types of restaurants, cafés, or activities at other destinations.

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 earlier required professional help. LLMs can also customize itineraries and improve marketing materials, freeing up professionals for other important tasks.

Since 2014, Intellias has been working on enhancing language accessibility and learning. Intellias specialists have worked with one of our clients 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.

Predictive analytics

Predictive analytics helps to precisely estimate 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 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 to machine learning adoption range from technical and financial to regulatory and operational. Understanding and addressing these challenges is not just about foreseeing upcoming problems; it is 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 through 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. Fragmentary, incomplete, and absent data might set back the full 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 needed.
• Hard-to-measure ROI. Implementing machine learning in travel and hospitality necessitates a considerable period of data collection, algorithm training, and iterative software engineering before the system produces tangible benefits. It might take a while to realize the ROI, especially for smaller firms and those that may have less financial readiness for high upfront expenditures.
• Integration issues. Resolving compatibility challenges may involve considerable refactoring of existing systems, which is costly and takes time to implement.
• Data bias. When there is not enough data, or 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 more expensive accommodation options higher, it may unintentionally favor travelers that have higher spending habits.
• Talent gap. Machine learning integration in hospitality and travel requires professional expertise and knowledge and may involve working with technology partners like Intellias to make things run.
• 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.

To overcome the roadblocks, travel and hospitality firms have to adhere to a well-thought-out strategy and choose a dedicated technology partner like Intellias who is skilled in providing cutting-edge, customized machine learning solutions for the travel and hospitality sectors. Here are a few examples of projects we have 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 for public transport, bikesharing, carsharing, taxis, and car rentals

Conclusion

Are you ready to jet into the future of travel and hospitality? The road ahead is thrilling, but at the same time, it is challenging. Machine learning tools are in the hands of travel and hospitality companies, and their impact is impressive. Not only large corporations but also medium-sized businesses can turn insights from machine learning algorithms into opportunities.

For instance, using machine learning analysis, travel and hospitality companies can discover prospective locations for building hotels and infrastructure and get reliable returns on their investment.

LLMs enable accurate and context-aware replies to any traveler’s question, easing up the operational workload for travel and hospitality companies.

And with dynamic pricing, defining prices on tickets and rooms becomes an easy and seamless process that is quick to set up and manage.

The path to outpacing the competition in travel and hospitality lies in personalizing experiences. Using machine learning technology — and particularly its subfields such as large language models and attribute-based filtering — businesses can get to know the intent behind travelers’ searches, tailor their offerings accordingly, and pave the way for higher customer retention. The journey towards an intuitive and seamless travel experience starts today. Will you take the leap?

Ready to transform your hospitality services? Connect with machine learning experts from Intellias.