{"id":71032,"date":"2024-03-01T17:30:25","date_gmt":"2024-03-01T16:30:25","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=71032"},"modified":"2024-07-16T11:57:02","modified_gmt":"2024-07-16T09:57:02","slug":"jetpacking-into-machine-learning-for-travel-hospitality","status":"publish","type":"blog","link":"https:\/\/intellias.com\/machine-learning-in-travel-hospitality\/","title":{"rendered":"Jetpacking into Machine Learning for Travel & Hospitality"},"content":{"rendered":"

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\u2019s a powerful overhaul of how the travel industry operates, interacts with customers, and delivers hospitality experiences. The introduction of new technologies \u2014 such as explainable and generative AI along with reinforcement, federated, and multimodal learning \u2014 is providing opportunities that were previously unimaginable, transforming the very definition of travel and hospitality experiences.<\/p>\n

At the heart of this transformation is predictive analytics<\/a>. 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<\/a>, and supports 85 languages as per OpenAI<\/a>. 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.<\/p>\n

In this article, you will learn about innovative machine learning applications in the travel and hospitality industry. Let\u2019s get started!
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Top four applications of machine learning in travel and hospitality<\/h2>\n

Dynamic pricing for airlines and hotels<\/h3>\n

\"applications<\/p>\n

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.<\/p>\n

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?<\/p>\n

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\u2019t using dynamic pricing strategies yet due to budget constraints or restricted access to the requisite technology.<\/p>\n

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Discover how Intellias helped a travel booking company increase the number of transportation connections<\/p>\n

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Destination discovery<\/h3>\n
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Traditionally, selecting a location for a new venture is a bit like throwing a dart at a map. With machine learning, we turn this process into a precise science, allowing us to target future tourist hotspots with laser-like accuracy and confidence.<\/p>\n <\/div> \n <\/div>\n

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.<\/p>\n

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.<\/p>\n

An example of a company that uses machine learning to help investors choose the most attractive investment locations is AirDNA<\/a>. The company compiles reports and market rankings so that private entrepreneurs and businesses can find the most profitable locations for renting houses and apartments.<\/p>\n

Smart booking assistants and chatbots<\/h3>\n

\"Smart<\/p>\n

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\u2019s chatbots are much better than chatbots of past years at responding to travelers\u2019 requests.<\/p>\n

For example, Expedia<\/a> has integrated ChatGPT into their app, enabling travelers to chat with Expedia\u2019s 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<\/a> that enables travelers to compare flight prices with historical data and choose the most suitable time to book a flight.<\/p>\n

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<\/a>, 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.<\/p>\n

However, the LLM transformation of the travel and hospitality sector doesn\u2019t stop here. According to WhatPlugin.AI<\/a>, 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.<\/p>\n

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Learn how travel companies use AI to provide better customer service visualization system<\/p>\n

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Personalized travel experiences<\/h3>\n
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Forget one-size-fits-all. The future of travel and hospitality lies in hyper-<\/span>personalization<\/span>. And machine learning is the passcode to unlock it.<\/span><\/span>\u00a0<\/span><\/p>\n <\/div> \n <\/div>\n

As indicated by McKinsey<\/a>, 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.<\/p>\n

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.<\/p>\n

As the Twilio State of Personalization Report 2023<\/a> outlines, 92% of companies use machine learning to deliver personalized customer experiences and grow their business. What\u2019s 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\u2019 ROI. Companies that are the early adopters of machine learning technology are very successful and become an example for others to follow.<\/p>\n

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Personalize travel experiences for your customers with Intellias<\/p>\n

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Three key machine learning technologies used in travel and hospitality<\/h2>\n

\"machine<\/p>\n

Attribute-based filtering<\/p>\n

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.<\/p>\n

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.<\/p>\n

Attribute-based filtering can be leveraged in the hospitality sector. Based on a traveler\u2019s choices during a previous trip, the system can suggest similar types of restaurants, caf\u00e9s, or activities at other destinations.<\/p>\n

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Explore digitalization trends in travel and hospitality<\/p>\n

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Large language models (LLMs)<\/h3>\n

\"Large<\/p>\n

Source: Google Blog<\/a><\/em><\/p>\n

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Data used to be a grimy archive; now, it\u2019s the most important ingredient in customizing the travel experience.<\/p>\n <\/div> \n <\/div>\n

LLMs redefine traditional applications of machine learning in travel and hospitality. They go further than basic analysis of keywords in travelers\u2019 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.<\/p>\n

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.<\/p>\n

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LLMs are like supercharged tour guides with an encyclopedic brain and a knack for personalization.<\/p>\n <\/div> \n <\/div>\n

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<\/a> 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<\/a> practice and prompt feedback on their mistakes.<\/p>\n

Predictive analytics<\/h3>\n

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.<\/p>\n

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.<\/p>\n

Roadblocks of machine learning implementation for travel and hospitality<\/h2>\n

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\u2019s dive in:<\/p>\n