How to Implement Artificial Intelligence (AI) and Data Science in the Travel Industry?

I guess you have bought your tickets online for the last trip you went on. I am right, am I not? Because in today’s busy world, it is difficult for someone to find time to go to the ticket office and buy the tickets. Other than that, why should someone bother to go to the ticket office, if he or she can buy them via his or her laptop or even the smartphone with just several clicks?

Digital travel sales rapidly increased over the last few years and it is still being increased due to the recent technological developments, one of the most important is data science. Here are some new ideas to implement AI and data science by using the data for the development of the travel industry.

Recommendation Engines

Personalized suggestions are the most common implementation of data science. The best examples of these personalized suggestions are Netflix and Amazon. Netflix provides personalized content suggestions for users while the Amazon provides a “Featured Recommendations” box. Like this way, most of the online travel booking services also provide personalized suggestions for users using the data of their recent bookings, searches, and real-time data.

For example, Expedia suggests multiple accommodation options when you search for flights for your trip. Likewise, Booking.com recommends alternative locations for you to travel in next time based on your preferences.

Moreover, online travel companies can provide alternative travel dates or routes and car rental deals using data-powered recommendation engines. Fareboom.com is the best example of recommending alternative travel dates for a trip.

A powerful recommendation algorithm can be built if you have enough data on preferred deals or typical searches. Then the developed algorithm can learn more itself from a specific user’s personal browsing history and provide highly personalized recommendations for that user.

A personalized user interface will enhance user engagement so that it will positively affect the revenue. Moreover, personalized suggestions will upsell users to have more trips so that the revenue will be increased in that way also. Apart from that, providing the best recommendations at the best time will make the customers’ loyalty stronger.

Forecasting Price Movements in Flight Fares and Hotel Prices

It is difficult to manually track all the price changes in flights and hotels because they are constantly changing. Due to this, there is a high demand for the smart tools that can track and timely notify users of hot deals. ML can find hidden patterns in price changes that humans might not notice. For example, when a price is increased in a huge amount, ML algorithms can find the anomalies after that price change considering different parameters that caused that change. So ML can identify the best time to buy a flight ticket or book a hotel for the cheapest price and then an app notifies the user when such kind of hot deals are available.

AlexSoft has developed such a fare predictor tool for Fareboom.com. They built a self-learning algorithm using the data on fare searches done in several past years. That algorithm has the capability of forecasting future price movements. For these predictions, the algorithm uses some factors, such as airlines’ special offers and deals, seasonal trends, demand growth, etc. This tool can make short-term and long-term predictions with a 75% average confidence rate. It will help price-sensitive customers to buy airline tickets at the best possible time. In this way, customers can make better purchasing decisions and save their money.

There is another mobile app called Hopper for helping travelers to track the best hotel and flight deals. It offers many features to users such as best dates to travel, when to buy tickets and book a hotel, flights that serve passengers the best and alternate airport choices. With the feature “when to buy”, users can buy tickets at the cheapest price at the right time. AI and predictive analytics were used for developing this app.

These analytics-based predictive tools can be used not only in flight booking but also in other areas of the travel and hospitality industry. Movements in hotel pricing and the time that it takes to be booked all the rooms in a particular hotel can be predicted using these tools. Moreover, alternative itineraries based on the weather forecast or estimated airport load on a given day also can be suggested using these tools. So these tools will help users to travel more efficiently and enjoy the journey while saving the money. Integrating such tools to your travel agency’s website will attract more customers to your company too.

Intelligent Travel Assistants

Smart concierge services that are powered by AI are growing day by day in different industries due to the convenience. Travel booking is a great example of such smart services that are automated by algorithms to a great extent.

Bots or Chatbots are heavily used in travel companies to provide 24/7 customer support, minimizing the workload of personnel. Natural Language Processing (NLP) and Machine Learning (ML) technologies are used in developing Chatbots. These chatbots analyze data such as previous related queries and find patterns in them using data analytics and AI algorithms. Then they respond to users based on these analyses and calculations faster than the human customer care assistant does.

Most of the travel companies use instant messaging apps such as Whatsapp and Facebook Messenger to reach out to their customers and maintain good relationships with them. But this kind of 24/7 mobile support needs both humans as well as financial resources. This is where virtual assistants that are powered by AI are useful.

By integrating a virtual assistant to any instant messaging apps, a company can enhance its customer relationships without any human interventions and with less financial resources by automating various tasks such as finding the cheapest deals, planning the entire trip, booking flights, reserving hotels and providing useful and valuable information and suggestions on well-known tourist attractions, great restaurants to eat and much more. Travel agencies can save a lot of time by assigning easily automated tasks to the virtual assistant and complicated queries that need human interaction to employees.

“Hello Hipmunk” is such a personalized virtual assistant that helps you to plan your travel. It is totally free. Kayak is another example of such personalized virtual assistants. It will provide information on flights, rental vehicles, and much more and help you to directly plan your next travel through Facebook Messenger. Apart from these two, there are a lot of chatbots that are used for various purposes in the travel industry such as “Lola”, “Mezi”, “Sam” and Watson-powered “Connie”. Connie, Hilton Hotel’s newly deployed robot concierge uses AI and speech recognition techniques to answer hotel guests’ questions face-to-face and assist them as a concierge. The robot can learn itself through each human interaction. Due to that, it can provide quality service to the customers in the future.

Moreover, without limiting them to booking and research, these AI-based virtual travel assistants can be used as mobile travel companions for solving various problems while traveling. For example,

  • How many hours will it take to reach the airport?
  • What is the number of your boarding gate?
  • What is the location of the nearest business lounge?

Further, these chatbots can be also used to reach travelers with voice-activated devices. For example, Amazon’s Alexa which is an AI-based virtual assistant developed by Amazon has already integrated with the above mentioned third-party service-Kayak. When a user activates the Kayak skills on his or her Amazon’s Echo device that is powered by Alexa, he or she is able to track flights in real-time, book hotel rooms, search and discover travel options, and much more using his or her Echo.

In this way, your company can enhance your brand loyalty as well as optimize business performance by combining human assistants with virtual assistants. For example, assume that the luggage of a passenger is lost. Then it might speed up the process of finding it by reporting the loss or doing an automated search through the virtual assistant. Moreover, this will also improve the customer experience by removing the paperwork and bureaucracy. Additionally, you can retain the customer by giving a considerable amount of money back for any inconvenience occurring.

Content Optimization

Quality content is the main thing to build a strong bond between the brand and the customer. It encourages customers to engage more on the website. Though content curation is a manual process, ML can be used to personalize and automate routine tasks.

For example, Trip Advisor used a Deep Learning (DL) approach when redesigning their website. They wanted to improve the way their photos are shown on the website because there were over 110 million great photos in the database. Initially, they did it manually. But that process was really slow and expensive. Then their engineering team invented a model based on DL. It selects relevant and attractive photos and then displays them in priority on the website.

Booking.com’s translation management system is another example of implementing AI in content optimization. They have used Neural Machine Translation (NMT) which uses Neural Network (NN) to translate content between 43 languages.

Optimized Disruption Management

A traveler might face various problems before reaching his or her destination. Solving such actual problems is the main objective of disruption management.

Instant response is the most essential feature in disruption management. Though the chance of getting caught in a storm or volcano eruption is very low, the chance of occurring any other travel disruption is really high. Because a lot of flight delays and cancellations happen every day. Imagine you are on business travel and stranded somewhere before reaching the destination. This might cause significant losses and have a bad impact on your business.

Now you can predict disruptions as well as reduce the loss for both parties- the traveler and aircraft carrier thanks to AI and data science. The 4site tool is a great example of this. It was built by Cornerstone Information Systems for improving the efficiency of business travel. It offers a collection of great features for real-time disruption management that is valuable for travelers, enterprise clients as well as travel management companies.

Data science comes in handy when predicting the travel disruptions. These predictions are made using the available information about operational delays, weather conditions, and other airport service information. The algorithm that was trained to track this data will timely alert the users as well as their travel managers about forthcoming disruptions and automatically implement a contingency plan.

Not only the passengers but also the airlines have to tolerate the great losses when a flight is delayed or canceled. As the solution for this problem, Amadeus has developed a Schedule Recovery System for minimizing the loss by instantly giving attention and efficiently handling disruptions. In this way, it helps airlines to make informed decisions as well as optimize their operations.

Personalized offers for Most Valuable Customers (MVCs)

Focusing on the most valuable customers is important for a travel company to avoid churn. This can be easily achieved using ML applications.

Predicting using general history data such as purchase history, travel history, previous behavioral patterns, and metadata will not be highly accurate for new customers. To get higher accuracy in predictions real-time data should be combined with the historic data. Now it can be easily done and make personalized suggestions and personalized travel packages for your most valuable customers based on that data using AI. It will make the bond between brands and loyal customers stronger.

Further, these personalized suggestions can also provide up-selling as well as cross-selling opportunities. For example, when a customer books a flight, personalized suggestions on car rental offers or hotel rooms can be provided to him or her, using data of the Online Travel Agency (OTA) and booking APIs from service providers through the website or email campaigns.

Personalization of User Experience (UX)

User engagement with the website is critical to the success of the travel business. If the content or layout is not pleasing, then the customer’s engagement with the website will be decreased.

United Airlines is a great example of implementing data science in UX personalization. Though they have used the “collect and analyze” method to their data before 2014, from 2014 onwards, they have started to use the “collect, detect, act” method when working with the landing pages.

To track customer behavior, the company uses more than 150 variables for collecting data including individual and general historical data. Examples for these individual data are search destinations and prior purchases. Using this large dataset, they segment their customers in a detailed manner and customize UX in real-time based on that customer segmentation. This dynamic UX personalization has increased the revenue in a considerable amount, according to United Airlines.

Sentiment Analysis in Social Media

Most of the travelers share their travel experiences with others through social media as well as reviews services. For example, people submit about 280 reviews per minute to TripAdvisor.

So these websites consist of a large amount of valuable data. By analyzing these data companies can identify problems, fix them, and improve the quality of their services. Though you can use the conventional statistical analysis for analyzing reviews, using ML techniques for that is more powerful than the conventional one and it allows you to analyze reviews on all the brands.

Sentiment analysis which is a subset of supervised learning is used to interpret and classify the emotional qualities of textual data. Google Cloud Natural Language API is a great example of that. You can customize it and integrate it with your analytical tools to analyze reviews on all brands in real-time.

In this way, sentiment analysis and NLP can be used for accurate analysis without wasting time for data gathering in a wide range. Examples of such implementations are dynamically tracking the overall brand image and ad hoc analysis done after the product of service updates.

When it comes to real-world experience, Dorchester Collections which is a luxury hotel operator modified its breakfast menu by analyzing the guest reviews using AI.

Mindtree’s PaxPulse which is an AI-based listening tool tracks the real-time social media posts by customers. It analyzes the post and if the post expresses negative emotions, then it will reach out to the customer. Assume a customer posts something expressing frustration on social media due to a delayed flight. Then PaxPulse will analyze the post, reach out to the customer, help him or her to understand the situation, and even offer a discount for his or her next trip. This will help to win back the customer’s loyalty after something bad happened.

Dynamic pricing forecast in the hospitality industry

Hotel room prices are often changed due to different circumstances. Now, predictive analytics is used to forecast the dynamic pricing in the hotel industry to improve effectiveness and profitability.

For Example, Starwood Hotels has developed its own predictive analytics tool to forecast the best price for the moment. Hundreds of variables including a customer’s booking pattern, room types, competitive pricing data, daily rates, occupancy data, and weather are used for forecasting. Though the system can operate automatically, it also allows the manual adjustment of rates.

Get the advantages by addressing travel disruptions

When travel disruptions like flight cancellations occur, they give unexpected chances for hotels. For example, Red Roof Inn which is an American hotel chain started an event-based campaign to address the flight cancellation, in 2014.

The hotel gathered public data on flight cancellations as well as weather conditions via an API. Then they used a conditional algorithm to process these data. Thanks to this data processing, they were able to forecast the canceled flights and launch an advertising campaign for the targeted group of travelers.

They gained quite impressive results from this campaign and the bookings were increased by a greater percentage.

Fraud detection

eCommerce fraud is the most common fraud that the travel industry is suffering today. Their revenue has decreased to a great extent due to refunding stolen money to their customers.

Someone can book a flight with a stolen credit card. This type of scam is called payment fraud. On the other hand, someone can book a flight with a credit card and then he or she can claim that the used card was stolen and that booking was not done by him or her, demanding a refund. This type of fraud is known as friendly fraud.

Such fraud can be detected and prevented by analyzing customer behavior. Here, profiling and ML come in handy. Pi School has developed an AI system for Wanderio which is an online travel booking platform to detect such frauds efficiently. Due to this implementation, their final average cost per transaction was improved.

Another example of AI implementation for fraud detection is HotelTonight, a mobile booking app. This app also has implemented a customized ML model with it to predict and detect such frauds. Due to this implementation, they were able to reduce the chargebacks to 50%.

In-stay experience

AI solutions can be used to assist travelers while they are staying in hotels too. By implementing voice-enabled virtual assistants in hotel rooms, travelers can easily turn on and off the TV, change the temperature in the room, adjust the light, and much more with their voice. Apart from that, facial recognition techniques can be used to speed up check-ins by reducing the paper-bound processes and enhance security.

Most of the hotels already use these technologies. Wynn Las Vegas has implemented the Amazon Echo speaker in all the hotel rooms while Safeco Field Suits uses it in their rooms as well as for suggesting activities for travelers to do in the city while staying in the hotel. Radisson Blu Edwardian and Cosmopolitan of Las Vegas Hotels have their own chatbots named “Edward” and “Rose” respectively for guest self-service.

Further, the face-recognition system in Lemon Tree Hotel, Delhi identifies facial images from CCTV cameras and then compares them with the images in its database. Moreover, all the operations of the Japanese Henn na Hotel are entirely done by robots. It is amazing, isn’t it?

Some hotels use sensors for food and beverage. AI is used to analyze data that gather from these sensors. The primary goals of this analysis are improving the prevention of contamination as well as tracking the temperature control.

Opportunities for Marketing

Thanks to the AI, marketers can reach the exact group of company’s clients at the right time with granular targeting by analyzing the cookies and IDs of their devices.

Conclusion

Above mentioned cases are just simple examples of implementing AI and data science in the travel industry. There are more things to do.

AI and data science save time and improve the accuracy of tasks in the travel industry where time is a critical factor for the success of the business as well as its data is rapidly changing. Keep in mind that the accuracy of the output of the model directly depends on the quality of the data that you gather.

In the near future, the travel industry will be hugely changed due to new technological improvements in AI and data science.

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.

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