Quick Summary:
New techs are making travel better for everyone. AI and data science in travel and tourism make trip planning, customer service, pricing, and marketing better by analyzing voluminous data in real time. AI and data science are improving pricing, personalization, and operational efficiency. Key use cases of data science and AI include dynamic pricing, competitor analysis, hyper-personalized recommendations, improved multi-modal mobility services, targeted marketing, etc.
AI and data science are creating waves in all industries. Some of their interventions are disruptive and scary too. However, wherever they are together, they are bringing massive changes in the way business conduct their operations. The travel industry is not an exception and is currently under the effect of the AI and data science revolution.
The competition that travel businesses are facing demands immediate implementation of AI and data science to counter the same.
These two advanced technologies are assisting travel companies, travel aggregators, tourism businesses, airlines, travel search engines, and more in perfecting their operations, improving traveler experiences, and boosting bottom lines.
This post discusses at length the top use cases of AI and data science in the travel industry.
Top Use Cases of Data Science and AI in the Travel Industry
Travel businesses face cutthroat competition that is more driven by personalized experiences. However, challenges such as fluctuating demands, operational inefficiencies, and recklessly evolving customer preferences stand in the path to success. Due to these problems, travel agencies and restaurant businesses mostly lose revenues, suffer poor resource allocation, and have dissatisfied travelers.
Thanks to AI and data science they offer transformative solutions to the travel sector, such as predictive analytics, dynamic pricing, and tailored customer journeys.
Let’s check out their top use cases:
#1 Exceptional Edge in Competitive Pricing
The first of its kind as a travel metasearch engine, Kayak frequently receives recognition for innovation. The company offers exceptional services of competitive flight pricing and tailored travel plans.
What’s the secret behind its success? It’s the unmatched use of data science and AI.
KAYAK scans hundreds of travel sites instantly to find prices for flights, hotels, rental cars, and more. Prices are shown directly from travel sites with no additional KAYAK fees. Kayak uses AI-based recommendation systems to show relevant content based on factors like location, search criteria, pricing, and destination popularity. By analyzing user search patterns and booking behaviors, Kayak shows pricing in real-time, with competitive rates and personalized deals.
Moreover, understanding demand trends allows Kayak to prioritize and display the most relevant travel options. Kayak provides fare predictions helping travelers save more by recommending the best booking times and prices.
Kayak’s Flight Deals in Real-Time
#2 Optimizing Seat Allocation and Dynamic Pricing in the Airline Industry
Travel patterns are not the same throughout the year. During holiday seasons, demand rises, while during off-seasons, flight seats often go vacant. Therefore, airline companies adjust the fares according to the prevailing occupancy conditions to maximize revenues. This is called dynamic pricing.
Pricing and Demand Management
Delta Airlines uses AI and machine learning to refine dynamic pricing through data analytics. This approach optimizes their seat allocation and pricing throughout the booking process. It aids the airline in reducing the risk of empty seats while maximizing fares for high-demand flights. A well-implemented dynamic pricing strategy has been shown to increase airline revenues.
“You’re on hold for five minutes waiting for an answer, they should be on hold for five seconds getting an answer,” Bastian said at the Morgan Stanley investor conference. “That’s one of the first applications that we’re deploying and we’re using AI already to build.”
Delta CEO Ed Bastian said the airline wants to use AI to cut down the time customers wait to get answers about their reservations.
In the long run, Bastian pointed out, the company will gain significantly “if AI can boost the value of that by 2%” by applying this tech to tackle complex problems involving massive amounts of data.
#3 Hyper-segmentation for personalization in Travel Marketing
Hyper-segmentation means the classification of customers as per certain criteria such as booking frequency, booking amount, travel frequency, and types of hotels/flights chosen, etc. Travel businesses are using AI and data analytics to segment their customer base. This helps in strategizing marketing and also reducing marketing expenses.
For instance, travel companies can spend more marketing or personalized deals money on customers who have high booking frequency and generate more revenues for the travel businesses.
Major OTAs like Booking.com and Expedia use AI and data analytics to fuel their hyper-segmentation strategy. (source)
Booking.com, the top digital travel company worldwide, uses data science and AI to power its personalized marketing approach. The company creates, expands, and tweaks its creative campaigns using insights from AI and data analysis. Booking.com creates particular customer groups using advanced data science techniques. These groups are based on real-time and tailored data points and demographic and psychographic details.
Next, marketers developed AI-powered user journeys for tailored product suggestions, email content, and ads that addressed a particular customer need.
The outcome?
Booking.com beats AirBnB, its closest competitor.
#4 AI and Data-Driven Travel Recommendation Engine
Travel search engines like Expedia and its data-driven recommendations (using ChatGPT integration) make it the top choice for travelers worldwide. The company’s recommendation engine utilizes machine learning algorithms including collaborative and content-based filtering to personalize travel suggestions.
Expedia trains its recommendation models using customer data, including search history, hotel clicks, booking information, and preferred locations.
Further, it classifies hotels according to price, star rating, location, and facilities. This makes it simpler to predict consumer preferences. Moreover, the company actively seeks to accommodate various user needs and promote discovery by offering a range of possibilities within a suggestion set.
#5 Optimized Travel Web Experiences with Customer Journey Analytics
Booking.com uses customer journey analytics to track how travelers interact with different steps on its website, like searching for hotels, filtering prices, and booking rooms. This process is called touchpoint mapping and helps find which parts of the journey need improvement.
Booking.com uses conversion rate optimization (CRO) by testing features such as showing ‘urgency’ messages like “Only 1 room left!” or displaying prices more clearly.
With a churn prediction model, it identifies users likely to leave without booking and sends them reminders or special discounts. These smart strategies make the website better and keep customers coming back.
#6 Multi-Modal Travel Integration
Multi-modal travel integration is rapidly growing, especially in eco-friendly transport ecosystems. Research shows that about 70% of people use multiple transport modes during their journey, creating opportunities for travel businesses to innovate.
For instance, Sixt combines different travel data streams into one platform. This allows for the smooth integration of multiple travel modes. Sixt gathers and processes data from car rentals, ride-hailing services public transport networks, and even micro-mobility options like e-scooters.
This lets Sixt give users custom and improved travel plans.
- Sixt applies data science to collect up-to-date information such as available vehicles, transit times, and traffic status.
- AI models then predict demand and suggest efficient travel mixes.
- This integration ensures live updates and cost-effective trip planning.
- The complete view from data aggregation makes things easier for customers and improves operations. This strengthens Sixt’s position as a leader in the mobility world.
#7 Improved Rental Forecasting
During the pandemic, Airbnb was on the verge of shutting down, rebounding back, in 2024, the company had more than 5 million hosts with more than 8 million property listings.
How?
By employing the powerful capabilities of data science and AI in the travel space.
- Airbnb used historical rental data analysis to identify seasonal trends and track occupancy rates across various locations.
- By leveraging location analytics, they predict demand fluctuations in specific areas and adjust pricing strategies accordingly.
- Time series forecasting models allow Airbnb to forecast demand spikes, ensuring that hosts are prepared for high-traffic periods.
What’s more, Airbnb influences its models by including outside factors such as weather, events, and holidays. For instance, a major festival or holiday season might cause a jump in demand, and precise predictions can help property owners adjust prices and availability on the spot.
This approach based on data helped Airbnb bounce back by taking advantage of demand spikes, boosted customer satisfaction, and in the end increased revenues.
#8 Location-Based Marketing
Travel companies are turning to location-based marketing to differentiate in a heavily crowded industry. This tactic enables them to increase ROI on their ad spends, generate high-quality leads, and drive conversions. Allegiant Air invests radically in location-based marketing.
Imagine you’re planning a family vacation, and an airline starts sending you ads for exciting destinations nearby, just when you’re thinking of traveling. That’s how Aviation companies use location-based marketing! They track geospatial data to understand where people are and suggest places within proximity to their location, like sunny beaches or ski resorts.
They also use geo-conquesting, a strategy to attract travelers considering other airlines, by showing them better deals. With sentiment analysis, they check how customers feel about these offers and tweak them for better responses. This clever use of technology helps Allegiant stand out, making vacations irresistible!
#9 Mobility-as-a-Service (MaaS)
Built on the concept of travel integration, Mobility as a Service (MaaS), is now becoming on-trend in the travel sector. People can use trains, buses, bike rentals, or car-sharing services through one app. It covers people’s entire journey planning from booking to payments in one place, making it super convenient and eco-friendly.
Moovel, a mobility as a service app lets users book and pay for public transport, taxis, car rentals, or bikes—all within a single platform. Behind the scenes, data science makes this possible. The app uses real-time data to check where vehicles are, predictive analytics to suggest the fastest routes, and time-series models to forecast busy times. It’s smarter, faster, and better for everyone!
#10 Personalized Guest Experiences
Marriott is a fantastic example of how AI and data science are transforming the travel business. Think of a hotel that knows your preferences—like your favorite room temperature or the activities you enjoy—even before you arrive. This isn’t magic; it’s predictive analytics at work. Marriott uses machine learning to study guest preferences, booking habits, and feedback, so every stay feels tailored just for you.
Their Bonvoy App takes it a step further. It offers mobile check-in, digital room keys, and real-time chatbots. These features are powered by technologies like customer behavior modeling, making the experience smooth and personalized. With over 8,800 properties, Marriott uses these innovations to ensure guests have memorable stays, while also keeping operations efficient.
| “The challenge for us is, how do we take the data that we have, make it accessible, fast, accurate and feed that data with new information that we gain so that we can then apply these tools in a way that allows us to deliver value.”- Arne Sorenson, CEO of Marriott. |
#11 Fraud Detection
Fraud is a growing challenge in the travel industry, but companies are stepping up with some impressive tech solutions. OTA platforms are using machine learning to spot unusual booking patterns, like mismatched payment details or suspicious locations, stopping fraudulent transactions in their tracks. Even British Airways is ahead of the game, using biometric check-ins to tackle identity theft. These measures have reduced fraud rates within months.
#12 Customer Sentiment Analysis
As customer tastes keep changing, sentiment analysis has become a key part of data science and AI in travel. TripAdvisor’s sentiment analysis dashboard is a prime example. This tool helps the company get clear insights into what people think about certain hotels, resorts, restaurants, and attractions.
When people post reviews, TripAdvisor’s system scans for specific words and phrases such as “amazing view” or “poor service,” to determine if the review is positive, negative, or neutral. It employs intelligent tools like Natural Language Processing (NLP) with methods including tokenization (splitting sentences into smaller units) and stemming (identifying the base of words) to interpret the feedback.
Other Use Cases:
AI Chatbots and Interactive Avatars for Travel Assistance
AI virtual assistants have transformed travel booking into a seamless process. Navan’s Ava uses NLP to effortlessly book flights, hotels, and cars through natural conversations. Amadeus and Expedia also offer chatbots for travel changes and recommendations.
Major airlines use WhatsApp bots to deliver boarding passes and updates directly to travelers.
Real-time Translation
Translation technology helps overcome language barriers abroad. AI translation systems like Microsoft Azure and iFLYTEK are integrated into travel platforms, offering text and speech conversion at airports. New devices use AI to translate both written and spoken content into local languages.
Social Media Analytics for Trends
Travel companies use AI to analyze trends and optimize marketing. Agoda analyzed social media insights to target eco-conscious travelers. It boosted green bookings. Travel Companies monitor social conversations to identify emerging preferences and adapt services accordingly.
Final Word
AI and data science have a huge influence on tourism, and most companies in the field now use these advanced techs.
In the past, planning a trip was a hassle. People had to search for flights, hotels, and things to do on their own. Now, AI has changed the game. Sites like Skyscanner, Airbnb, and Tripadvisor give you suggestions based on what you like. Indeed, in just a short time, AI and data science have become game-changers in the travel industry, reshaping how businesses operate and personalize experiences. The examples shared above represent only the beginning.
As the volume of travel data continues to grow, the key question remains: Will you use the potential of AI and data science for 5X growth, beating the competition and improving the travel experiences of your customers?
Let X-Byte help you in powering your travel business with our travel data scraping and analysis services.
