Machine learning is fast changing the business landscape, and disrupting the way business is done. But it’s not just that – one really need to understand the might of machine learning in terms of applications and outcomes.  Machine learning has penetrated the travel industry deeply such that several impressive innovations have surfaced that have inspired several other sectors. Here’s how Machine Learning is turning the tables on radical innovation.

Why travel?
The number of business travellers shifting gears from booking from brick-and-mortal travel agents to travel booking sites is growing exponentially with time. Because travel is purely a personal choice, most travel booking sites or apps online do not appeal much in terms of user experience and personal touch. This presents enormous opportunities where user experience (UX) and personalized can be enhanced to improve buying and traveller experience – Machine learning is the step forward.

Bring search to life with personalization:
In today’s world where online buyers consume more than what they can digest, biggies like Netflix and Amazon have shown the way how personalization can bring customer engagement and retention to life. Though there are several ways to achieve this, one of which is to reduce the number of steps or clicks required for a user or customer (business traveller, in our case) to look what he/she is searching for, Machine learning technology helps personalize what customers normally view. Natural Language Processing (NLP) is used where description and customer reviews are to be analysed and popular features and feedback points are to be isolated. This data when fed into a database can be matched with the existing set of customer preference data.

What we use to personalize results is only part of information that is fed into a website or app. Images that follow listings can also be analyzed with neural networks, subfield of machine learning. If travel service providers such as hotels do not have the required tech skill set or the time to feed all the relevant data, they can display valuable data around facilities, scenery and ambience, all of which can be combined with user preference data set for a more tailored search results page. Note that this also requires the use of specialized databases and the latest semantic technologies.

The ultimate goal is to ensure the search experience of travel product more personalised than just the technical process of choosing on/off toggles and features. Machine learning can prove crucial in isolating the most attractive and unique features of a hotel and recommending those to customers who’ve already signed up for a particular preference or service. The focus should shift from raw computing power to change of working patterns.  Mind you, many things were manual not long ago.

Implementing Machine Learning is more than just distributing your machine learning resources across your teams, it requires a pooled understanding of how the resources is utilized across different product teams and what goals are awaiting to be unlocked.

A brief note on user data: Note that machine learning requires user data, and you somehow have to derive it as it is everything to what machine learning does. It is important to be transparent and upfront when it comes to customer data collection and utilization. How you ask them to share their data and how they benefit by this needs to be clear and consistent with your goals.

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