Four Ways AI is Re-imagining the Future of Travel
The travel industry has always been at the forefront of technology adoption, especially with digital technology trends. Travelers have been equally enthusiastic about adopting the technological changes to make travel simpler and more enjoyable. This has given rise to tremendous innovation in products and business models such as Airbnb and Uber. The travel industry comfortably welcomed the era of websites with an online presence quickly becoming the primary channel to reach customers. Then it gradually advanced into the era of mobile, catalyzed by the social media craze. The industry has quickly taken a mobile-first approach in a bid to be everywhere, all the time.
We are now at the cusp of the third era – that of Artificial Intelligence (AI) in travel industry. The industry is adopting an AI-first approach, banking on “relevance” becoming the winning factor. AI in travel and tourism is being used to predict travel choices, personalize services, complete bookings and manage in-trip and post-trip needs.
- Airlines like KLM have begun to deploy AI to deal with social media inquiries. The KLM system, by end of 2017, was dealing with 50% of all inquiries.[i]
- Hotel operator ‘Dorchester Collections’ altered its breakfast menu after AI analyzed guest reviews and came up with customizing options[ii].
- Lola, a travel app for iPhones, combined AI with human agents to provide assistance for hotel bookings, flight schedules and advice on restaurants[iii].
This brings us to the important question of whether AI can bring about any substantial changes to the way travel is managed and delivered. There are four key areas where AI can strongly impact travel to provide better assistance and elevate customer experience. Read on:
1. Digital interactions that are conversational and voice-based assistants that are personal
Today, everything a traveler needs to do is available on a website. Using a website, travelers can plan where they want to go, compare options, weigh budgets and make bookings and cancellations. Doing this involves reading copious amounts of descriptions, instructions, terms and conditions and user comments before arriving at decisions. The alternative is to provide conversational apps that reduce the amount of interaction required by factoring intent and context into the conversation.
For example, just a line of text to a chatbot saying, “show me flight options for Christmas from NY to London”, or further “use my frequent flyer miles for the purchase” accomplishes the task. Bots using Natural Language Processing (NLP) can be deployed to accomplish more complex personalization using AI for context: You could be in the lobby of a hotel and say in conversational English (or your language of preference that the bot is configured to understand), “I am hungry. Get me something”; the bot would check the hotel menus, cross it with your preferences and help place an order. Now assume that you moved from the lobby to your room while the order was being placed. The bot would automatically discover the context and location and ensure the order is delivered to your room.
NLP and AI have the potential to add considerable weightage to all types of travel-related activities. NLP-based bots could, for example, help Chinese language travelers negotiate their way around a European airport with ease. And the most attractive feature of these bots? They can scale almost infinitely! The ability to scale is critical in the travel industry. An AI and NLP-based bot that scales during an emergency, say a storm, can be invaluable. In such situations, passengers want instant answers to all kinds of queries. The limited staff cannot handle the queries fast enough. Assistance from AI would go a long way in easing the pain of travelers in such disruption-led scenarios.
2. Facial recognition with additional heft from blockchain
Travel requires repeated scrutiny of travel documents by different sets of people. There are complex embarkation and disembarkation processes (especially for cruise liners). Facial recognition technology promises to bring an end to these tiresome paper-bound processes. With facial recognition, travelers can seamlessly move through airports, immigration, customs and board aircrafts without the need for having travel documents scrutinized at each step. When combined with blockchain, it becomes easier for customers to visit restaurants, duty free stores or access entertainment with a simple facial scan. The blockchain technology ensures that reliable and trustworthy traveler data is made available to complete the transactions.
3. Machine learning, the (new) hidden persuader
Airlines and airports are starting to mimic mega retail outlets, selling everything from seats to blankets and hotel rooms. Machine Learning is fast rising as the hidden persuader to assist in the sales. Using big data and machine learning, airlines are able to build recommendation engines that help personalize offers around products from their inventory and partner catalogues.
Applying machine learning in travel industry provides powerful messaging and product bundling capabilities based on context and traveler propensity. This is important for travel brands since travelers are expecting travel providers to know them better and offer them deals and services based on their past preferences. But is personalization as good as it could be? The span and quality of personalization in travel offers is, in fact, a key improvement area, particularly given its growing importance and the loyalty that it can inspire in customers. According to a recent survey by Mindtree, “Expectations vs. Reality: How to Better Serve the Connected Traveler”, of those who receive offers from travel providers, only 23% rate them as excellent, in terms of being based on the traveler’s specified preferences, leaving a lot of scope to grow. Only a third (31% of respondents) report using them every time or most of the time. Also, of those who do not always use the offers that they receive, the most common reasons are that they don’t arrive at the right time (45% respondents say so), expire too soon or don’t offer enough saving (35% respondents say so).
Machine Learning can also use external data to proactively assist travelers in making quick decisions (such as a change in travel plans triggered by storm forecasts). Mindtree’s framework ‘Connected Traveler’, for example, uses Machine Learning to understand the traveler. It integrates traveler data from various functional applications and creates a 360-degree view of behavior and trends that ultimately helps drive higher conversion and improves loyalty.
4. Social media to uncover sentiment
There are scores of social media listening tools. Of interest to the travel industry is a subset specifically engineered for travel applications. These tools decipher social sentiment and co-relate it to the traveler’s journey, whether before the actual travel, during travel or post travel. Is a travel customer frustrated because of a delayed flight or by a hotel room that is less-than-perfect? If the customer makes a social media post expressing the frustration, the listening tool (like Mindtree’s PaxPulse) analyzes the customer’s intent and the context to automatically reach out with real time interventions that are most likely to deliver a positive impact. The interventions could range from providing additional information, helping the customer understand the situation to more options that can meet the customer’s requirements or offer him/her a discount on the next purchase. According to the Mindtree survey cited above, More than three quarters (77%) of respondents have had a bad experience with a travel provider. As a result, over a quarter (27%) never booked with that travel provider again. Of those who have had a bad experience with a travel provider, the majority (74%) report that the travel provider tried to redeem themselves. Providers that try to redeem themselves after a bad experience win back customer trust and repeat business. This can be augmented by AI-powered bots, which can parse through unstructured data and use natural language processing to respond appropriately to customer concerns on digital channels.
The above four applications of AI in travel industry have one thing in common: they reduce the time taken to complete tasks while improving the accuracy of processes and outcomes. In an industry where time is critical, and information is constantly changing, these are invaluable capabilities.
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