NB: This is a viewpoint by Matt Zito, managing partner at Travel Startups Incubator.
Since online travel agencies (OTAs) first appeared around 15 years ago, few tears have been shed in memory of having to visit local travel agencies.
But while browsing deals online is undoubtedly easier than flicking through holiday brochures, one element is missing: personalized customer interaction with a representative who can answer questions and alleviate concerns.
A new wave of startup companies have the answer. By applying artificial intelligence to the travel tech sector, they aim to edge out OTAs and offer consumers a more convenient, personalized experience from their desktops and mobile devices.
In this article I will outline the benefits that mobile travel chat (MTC) offer to businesses and consumers, and how artificial intelligence could bring the human element back into making holiday reservations.
In recent years many OTAs have struggled to stay relevant due to a push back from leading airlines, such as Southwest and Delta, who have removed flights and schedules from OTAs and price comparison websites and begun intense marketing campaigns to win back customers for themselves.
To stay in the running, OTAs need to show consumers that they can do things better, faster, and cheaper than a supplier can directly.
Another challenge has been providing a service that offers users a range of affordable options, while maintaining a level of personalization and customer care.
OTAs use dropdowns and widgets for users to select flight times or hotel stay dates. They have uniform blank boxes and drop-down calendars within the interface that help the system users identify the correct flight, based on the information they input.
By all intents and purposes, it is a very efficient and effective way to find the best flight and hotel options. But it lacks a personal touch that was once afforded by working directly with a travel agent, or dealing directly with an airline or hotel via phone.
The majority of these platforms have no communication or customer service tools available, which means that to address common queries, potential customers are often forced to visit the official sites of the airlines or hotels themselves, thus effectively making the OTAs redundant.
Mobile Travel Chat – also known as Mobile Travel Concierge, Mobile Travel Agent, Artificial Travel Intelligence, Travel AI — is a new mobile travel application that is making waves in the consumer travel industry. Harnessing the power of machine learning and natural language recognition MTC offers a more personal and free flowing experience than OTAs because of the way users interact with it.
As the world has moved its internet usage to mobile, personalization has become a key selling point. For years marketing professionals have stressed the importance of personalization within the mobile experience. Even mega-OTAs such as Booking.com have acknowledged personalization’s importance, and the company’s CEO, Darren Huston, told the Wall Street Journal last year that younger travelers don’t want to be bombarded with choices, but rather prefer hotel searches closely tailored to their profiles.
New MTCs – such as HYPER, Lola, ETA, HelloGbye, 30secondstofly, GoHeroGo, Pana and Hello Scout* use mobile and desktop apps that work with SMS and other messaging services, offering users a direct line of communication with a travel agent, automated or not. The machine learning that these new MTC startups use personalizes the experience by learning user preferences and remembering them to make suggestions for the next booking.
(*Name corrected, post-publication.)
Instead of filling in a blank box, users can answer natural language questions prompted by the MTC like “where would you like to fly?” or send a request such as “Need to fly to LA from JFK Wednesday morning for three days with three star accommodation and transport.” Reminiscent of personal concierge apps, which are gaining in popularity, the new tech utilizes AI technology to act as a pocket-sized personal travel agent offering suggestions based on the quickest to the most affordable, including options selected by the user in the past.