The cancellation rate for travel bookings in the online travel industry is significantly high. Many people book hotel rooms, flight tickets, or honeymoon packages in advance and most of the time, they cancel it. The reasons behind the cancellation can be many. But once the reservations are canceled especially during the last minute, there is nothing to be done. These sudden cancellations can create discomfort for many travel companies. That is why more and more companies want to take precautions. Predicting the probability of travel booking cancellation will create a surplus value to the travel businesses and help them establish appropriate business strategies.

With the advent of artificial intelligence, the travel industry has modified its products and services. Many travel companies use machine learning algorithms to predict the probability of travel booking cancellation. This way, companies can be saved from facing a big loss in the future.

In artificial intelligence language, booking cancellation can be explained by the term “customer churn”. Let’s understand what a customer churn exactly means.

What a Customer Churn Exactly Means?

Customer churn is a tendency of customers to abandon a business or stop being a paying customer of a specific brand. The percentage of customers that are most likely to discontinue using a product or service of the company during a particular time period is known as a customer churn rate. Having a high customer churn figure can indicate that a business is probably doing something wrong. Some natural customer churn is inevitable, and the figure may vary from one industry to another.

There are many reasons for businesses that may go wrong, from complex onboarding when clients are not given relevant or simple-to-understand information about any product to its poor communication with the customers. The truth is that even loyal clients won’t accept a brand if they have had one or more issues with it.

Since the impact of customer churn on the travel industry can be extremely bad, predicting travel booking cancellation seems the only solution. Especially when done with the precision of machine learning, the companies can expect a better productivity in the future. In the following section, we will be explaining about predicting customer churn with machine learning.

Predicting Customer Churn with Machine Learning

Are you worried about having high possibility of customer churn in your travel business? What is the perfect solution to prevent such cases? How to begin with churn rate prediction? Do we need data? What will the basic steps? Let’s discover!

As with any machine learning task, you would first need data to work with. Without data, nothing can be predicted. If you have your customer’s data related to their booking information, customer’s feedback, date of the booking, and time, you would be able to use it for prediction. Depending on your goal, machine learning experts define what information they must need.

In the next step, they prepare the selected data, preprocess it, and then transform it in a form that is suitable for building machine learning models. Once the model predicts with high accuracy is selected, it can be put for getting solutions (prediction probably).

Basic steps that any machine learning expert would use to forecast customer churn may look like the following:

1.      Understanding the issue and finalize goal

2.      Data collection from analytics services, feedback on social media, CRM systems.

3.      Preparation of data

4.      Preprocessing of data

5.      Modeling and testing

6.      Model deployment and monitoring

Why Predicting booking Cancellation Using Machine Learning is Important?

To predicting potential churners, machine learning algorithms can do an excellent job. They uncover some shared behavior patterns of existing customers who have already canceled the booking. Then, machine learning algorithms verify the behavior of existing customers against such patterns and indicate if they discover potential booking cancellations.

Subscription-based travel agencies also leverage machine learning for predictive analytics to determine which existing users are not completely happy with their services. They address the major problems when it is not too late.

Identifying customers at the highest risk of canceling the bookings in advance enables the customer success team to attract these customers, understand their core issues, and with them, put together a long marketing plan on helping the customers realize value from the service they booked.

TRAVELYTIX ENGINE helps in determining the factors that impact churn, fuelling businesses to plan strategies to improve retention and increase upsell

1.      Geographical factors

2.      Customer Experience

3.      Personalisation

4.      Value for Money

5.      Brand awareness

6.      Price

7.      Rewards Program

8.      Service Commitment

9.      Customer Satisfaction

10.  Responsiveness

11.  Empathy

12.  Reliability

Final Thoughts

In the travel industry, booking cancellations can contribute to the decline in the production rate of the business. According to recent research, computational power and machine learning algorithms can be used to build models to predict booking cancellation. Nonetheless, the accuracy of such models is not evaluated in a real environment. That is why machine learning experts have to build a prototype in order to fill this gap and check the implementation of these models. If you don’t want your travel business’ productivity to get hampered due to booking cancellations, you can also use machine learning algorithms.