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Using Data Analytics and AI to Disrupt the Business Travel Market
By Dr. Sebastian Mika, Vice President Data, Comtravo
Typical shortcomings include tools and processes that are based on an ancient tech stack. However, business travelers nowadays expect an experience that is as seamless as the B2C experience. Bookers and travelers produce a lot of data, and it should be possible to use that data to improve bookings and managing travel. Using data-driven solutions to automate the booking process offers tremendous potential to reduce friction and costs for both the customers and the agencies.
As an example, imagine a system that enables customers to book a trip with one simple message that adapts to customer preferences and behavior. Such a system would be able to understand the text-based request and make individually meaningful choices from a plethora of travel options. Would that be possible, given the tremendous advancements in AI and machine learning in the last few years?
There is reason to be skeptical. First of all, there are many examples of companies that put big bets on AI-driven assistants – but failed or fell short on delivering the promises they made. This is not only limited to startups like Go Butler, but big-name solutions (Alexa, Siri, etc.) are also not really intelligent. They often fail because they basically target an unlimited variety of use cases – from ordering a pizza to, well, booking a business trip.
Using data-driven solutions to automate the booking process offers tremendous potential to reduce friction and costs for both the customers and the agencies
I would however, argue that the picture changes drastically if we focus on solving a single, isolated problem, e.g. only booking business trips, no pizzas. To begin with, understanding requests from a single domain is an easier problem to solve. Although very impressive language models like Open AI’s GPT-2 have recently been released, enormous domain-specific amounts of data are required to train these models. Data for the travel domain – unlike unspecific text – is not easily available. Limiting yourself to a narrow domain means these demands can be reduced, given the fact that typical business travel requests are fairly repetitive. Another important aspect is the fact that users are not very tolerant of errors produced by machines. Human communication uses very subtle details. In order to have a good user experience, it is important to get these details right. While it may be acceptable for a machine translation to be slightly off (human intelligence is able to fill in the gaps or interpolate the errors), there is no tolerance toward these details in an automatically generated trip booking. If it does not match what has been asked for, the experience is painful or useless. Finally, it also makes a difference whether you put AI directly into the face of the customer or use AI to supercharge humans in performing their tasks.
Based on our experience at Comtravo, AI is an extremely valuable tool to improve the first 80% of our tasks, which is then combined with humans that can take care of the remaining 20%. While this is still far from being trivial, limiting the application of AI tech to a narrow domain increases the chance of coming up with a solution that actually works.
Over the next few years, I expect to see many new impulses in the travel industry that will leverage data, use analytics and machine learning and that will ultimately make booking a business trip as simple and convenient as ordering a pizza.
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