Data Science and AI in the Travel Industry: 5 Real-Life Use Cases
“The future travel brand isn’t therefore just about moving people from A to B, unveiling new destinations, or organising trips. Instead it is about a thoroughly progressive, completely 360 degree view of the traveller and everything that goes into creating special, unique, memorable experiences.”
Defining the Future of Travel through Intelligence,
discussion paper by Amadeus Travel Intelligence
Remember your last trip? Did you get your tickets directly from the ticket office? I doubt it. In today’s fast-paced world, finding time to travel to a ticket office and get your tickets is a luxury few can afford. Besides, why bother if you can get your tickets in just a couple of clicks via your laptop or even your smartphone?
Indeed, digital travel sales grew rapidly over the last several years, totaling $496.21 billion in 2015. And the number is expected to reach $817.54 billion by 2020. Such explosive growth is fueled by recent technology advances, not the least of which is data science.
We at AltexSoft are no strangers to successfully applying data science and machine learning technologies to the field of custom travel software development. So, if you are searching for some fresh ideas on how to put your data to good use, here are 5 application scenarios for data analytics and machine learning in the travel domain.
1. Recommendation engines
Possibly the most mainstream use case for data science, some recommendation solution is currently incorporated in 99% of all successful products. Similar to personalized content suggestions on Netflix or the “Featured Recommendations” box on Amazon, online travel booking providers often provide tailored suggestions, based on your recent searches and bookings.
For example, when searching on Expedia for flights to London, you will be offered several accommodation options for your trip.
Expedia recommended hotels when searching for flight deals
Similarly, Booking.com offers alternative destinations you might like for your next trip.
Booking.com recommended destination based on previous searches
These are only two of the ways to use data-powered recommendation engines in the travel industry. Following this pattern, online travel agencies can offer car rental deals, alternative travel dates (see example below) or routes, new travel destinations based on the user’s preferences or even some recommended local attractions.
Fareboom.com travel agency suggesting alternative dates for a trip
With enough data about typical searches or preferred deals at hand, you can build a powerful recommendation algorithm. It can learn even more from the personal browsing behavior of a certain user to deliver highly tailored and more valuable suggestions.
For instance, if 8 out of 10 families book a trip to Disneyland in July, offering a single business traveler such a deal in January makes little sense. But if you show a one-day trip to Las Vegas to such a user instead, the chances for success are quite high.
Accordingly, investing in data science consulting might not only have a positive impact on your revenue through upsells, it may also improve user engagement by contributing a personalized, efficient UX.
2. Dynamic pricing and fare forecasting
Flight fares and hotel prices are ever-changing and vary greatly depending on the provider. No one has time to track all those changes manually. Thus, smart tools which monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.
The AltexSoft data science team has built such an innovative fare predictor tool for one of our clients, a global online travel agency, Fareboom.com. Working on its core product, a digital travel booking website, we could access and collect historical data about millions of fare searches going back several years. Armed with such information, we created a self-learning algorithm, capable of predicting the future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers and deals.
Fareboom Price Predictor, short-term forecast example
With the average confidence rate at 75 percent, the tool can make short-term (several days) as well as long-term (a couple of months) forecasts.
Fareboom Price Predictor, long-term forecast example
For example, if during the last several holiday seasons all the prices of flights from San Francisco to Dallas grew significantly a couple of weeks before Christmas, the same tendency might occur this year as well. In this case, the algorithm will say that waiting might be too risky and prompt you to book your flight now.
Similarly, if the price for flights to Las Vegas usually drops below average a week before Christmas, you will be offered to wait and book your flight closer to the date.
Hopper is another prominent startup, using data science to help people book the cheapest flights using applied predictive analytics. A company that raised total $37.72 million to date, has built an intuitive mobile application for airfare prediction.
Named one of the best apps of 2015 by Apple, Buzzfeed, Vogue, Tech Insider, New York Times, and TIME, the tool analyzes billions of data sets daily in order to provide accurate price movement predictions. There is a detailed description of how the system works, which can be found at the company website.
Hopper fare forecast app
Therefore, analytics-based forecast tools, such as mentioned above, have proven to be a valuable addition to an existing travel booking portal or agency website. While being applied to flight booking mostly, such tools could be used in other niche areas of the travel and hospitality industry.
They can be used to predict the changes in hotel pricing, tell you how soon all the rooms in a certain hotel will be booked or even suggest alternative itineraries for your trips according to the weather forecasts or projected airport load on a given day, to help users not only save money, but make traveling more efficient and enjoyable.
3. Intelligent travel assistants
As convenience is the king in today’s world, smart concierge services, powered by artificial intelligence (AI) are gaining momentum in various industries. Travel booking is only one of the areas being heavily automated by algorithms.
Intelligent programs, trained to perform a certain task on a user’s request are usually referred to as “bots.” With the top four chat apps having 3 billion monthly active users, instant messaging platforms are widely adopted by some prominent brands as a great way to reach out to the clients and build better customer relations.
For example, Hyatt, a global hospitality leader, has been using social platforms to connect with its customers since 2009. Recently, the company announced its intentions to expand its customer support toolset with Facebook Messenger. Yet such 24/7 mobile support requires significant resources, both human and financial. This is where AI-powered virtual assistants come in handy.
Most often integrating with popular instant messaging apps, such as Facebook Messenger, Slack, Telegram or Skype, virtual travel assistants are trained to perform various tasks. From searching for the cheapest deals, booking flights and making hotel reservations, to planning whole trips and enhancing your overall travel experience through useful information and valuable suggestions about popular tourist destinations, places to eat or local attractions — these are the most popular ways to use AI bots.
By simply typing “/hipmunk I need to get from London to New York on November 5” on Slack, you can get the most up-to-date flight options, including prices.
Using Hello Hipmunk on Slack to search for flights from London to New York
Kayak, another popular travel booking service allows you to plan your next trip directly from your Facebook Messenger app. This chatbot is also more human-like. It doesn’t need special commands, understands simple questions and responds in a casual, conversational style.
Searching for flights and hotels with Kayak travel assistant on Facebook Messenger
Aside from large travel agencies, such as Hipmunk or Kayak, the list of travel chatbots is growing instantly. So does the number of possible use cases for the technology. The application of chatbots does not end with research and booking. They can be further used as mobile travel companions, solving several problems on the go, such as:
- What’s the baggage allowance for my flight?
- Where is the nearest business lounge?
- What’s my boarding gate number?
- How long will it take to get to the airport?
4. Optimized disruption management
While the previous case is focused mostly on planning trips and helping users navigate most common issues while traveling, automated disruption management is somewhat different. It aims at resolving actual problems a traveler might face on his/her way to a destination point.
Mostly applied to business and corporate travel, disruption management is always a time-sensitive task, requiring instant response. While the chances to get impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of canceled flights every day.
Regardless of the reasons, being stranded somewhere in Europe late at night when you need to be in Tokyo by noon tomorrow can cause huge inconveniences. Moreover, in business travel, this might result in significant losses and have serious implications for your company.
With the recent advances in technology, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveler and the carrier. The 4site tool, built by Cornerstone Information Systems, aims at enhancing the efficiency of enterprise travel. The product caters to travelers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management.
The opportunity for data science here lies in predicting travel disruptions based on available information about weather, current delays, and other airport service data. Thus, an algorithm trained to monitor this data can send out timely notifications, alerting the users and their travel managers about upcoming disruptions, and automatically put a contingency plan into action.
For example, if there is a heavy snowfall at your destination point and all flights are redirected to another airport, a smart assistant can check for available hotels there or book a transfer from your actual place of arrival to your initial destination.
Not only passengers are affected by travel disruptions; airlines bear significant losses every time a flight is canceled or delayed. Thus, Amadeus, one of the leading global distribution systems (GDS), has introduced Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption. A data science-powered recommendation engine, the tool helps airlines instantly address and efficiently handle any threats and disruptions in their operations.
Taking the manual work out of airline management, it helps carriers make informed decisions and optimize the operations for better efficiency. Qantas, Australia’s largest airline, was the first to apply the system to improve its operations. As a result, the company reported: “The Amadeus solution helps reduce the number of and length of delays, whether due to excessive traffic, operational delays, or weather conditions, leading to an overall improved experience for travelers.”
The system underwent a serious test this year. During heavy storms that caused delays across Australia’s east coast, just 15 out of 436 Qantas flights (about 3.4 percent) were canceled, as compared to 70 out of 320 (22 percent) flights by Virgin Australia, which uses the old manual system to manage disruption. The company’s on-time performance also remained high: 86 percent on Saturday and 62 percent on Sunday, while Virgin’s performance was at 74 and 48 percent respectively.
The described opportunities are a great example of how data can add value, reduce cost and improve efficiency of travel disruption management. Being a new and unsaturated market, it offers relatively low barriers to entry.
5. Customer support and loyalty programs
Not unlike personal travel assistants and intelligent disruption management, airlines can utilize the power of artificial intelligence to streamline the customer support process. Especially now, when almost half of all consumers agree that the speed of response to an inquiry is the most important component of successful customer service.
Based on the experiment conducted by Qantas to test the efficiency of their travel disruption system, what takes an experienced professional about 15-20 minutes can be done by an algorithm in under one minute.
That said, Gartner predicts that 25 percent of customer service and support operations will rely on virtual assistant technology by 2018.
At the same time, the importance of loyalty programs for the travel and hospitality industry continues to grow. Last year, the number of loyalty program members for major hotels chains increased by 13.1 percent (estimated 344 million members).
Combining the two might not only help you grow your brand loyalty, but also optimize your business performance. For example, if a passenger’s luggage is lost, reporting the loss or even conducting an automated search through a virtual assistant might significantly speed up the process of finding it. This approach eliminates the bureaucracy and paperwork, which is a great way to rehabilitate yourself in terms of customer experience. Moreover, offering a free bonus for any inconvenience caused is an even better way to retain your customers.
Onwards and upwards: shaping the future of travel tech
In its report The Future of Travel 2024, Skyscanner reveals the most promising technology trends shaping the travel experience in the upcoming decade. As stated by the company’s head of B2B, Filip Filipov, “In the near future, there is going to be a mass-market conversion to semantic, location-aware and Big Data [data sets that are beyond our reasonable abilities to manage or comprehend so that more imaginative methods and ways to visualize them are required] applications, which will be of transformative use to travelers.”
Indeed, in only two years since the report was released, data science is clearly one of the most promising technology fields that is changing the way we travel. The above listed fields of its application are only the tip of the iceberg.
With Amadeus alone having a data center with over 37 petabytes of storage, the amount of available travel data is staggering. So, the main question is: Will you be able to turn it to your advantage before your competitors do?