What makes a holiday wonderful is those family trips that can help you refresh and spend time with your loved ones, while for travel businesses, this means Gold!

With the market flooded by millennials today, customer tours are on the high. Custom tours are those tour packages that are specifically designed for consumers according to their preferences and schedule. That is the sole reason, consumers search for experiences than the actual hotels.

Today, travel businesses around the world are building experiences rather than tours and travel packages. This has led to the acceptance of intelligent technologies like Artificial Intelligence into the design structure of these experiences.Intelligent Travel Agents(ITA):

Intelligent Travel Agents or ITA is the amalgamation of ML(Machine Learning) and the travel-purchase behavior of the consumers. This is considered as a radical innovation as it ensures consumer engagement through “Smart Services”. 

For example, you are at the airport, waiting for your flight and suddenly your entry gate to the flight is changed, you are instantly notified through an ITA regarding the same, without you even leaving your Social Media site. 

ITAs uses machine learning techniques to identify the optimum pricing, comfort, and relevance to any particular traveling activity through real-time data analysis and processing through an algorithm trained for the same.

Take an example of the “Virgin Trains Alexa Skill”, launched in 2018, it allowed the passengers to book their tickets directly from Amazon’s Alexa device. This is a prime example of an ITA operating through an intelligent voice bot.Travel Intelligence-The Personalized Learning:


Artificial Intelligence technologies have a wide acceptance in the business of tours and travels, due to their ability to learn from the real-time data and come up with unique and relevant solutions for the consumers.

There are three different learning models in the Artificial Intelligence that needs to be explored for designing algorithms that can use real-time data to learn specific travel knowledge and use the same to provide more personalized experiences to the consumers.These three models are:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Supervised learning uses the data available to train the machine under human supervision. The unsupervised learning model helps machines to learn on their own without guidance or human intervention. But, reinforcement learning targets a particular dataset for training the machine for specific scenarios.

When it comes to travel intelligence use of any of the above machine learning algorithms can be used based on the type of personalization required.Real-Time Preferences: