Machine Learning and Big Data in tourism and hospitality

In terms of services, this allows companies to offer their customers more personalized offers, thus ensuring that more value and engagement will be generated. 

Some of the most common applications of Machine learning in companies in the tourism sector are:

• Cancellation prediction: applies to reservations of rooms, flights, activities, and tables in restaurants. In short, knowing in advance if a reservation is going to be canceled significantly improves management.

• Prediction of reservations: the planning capacity is significantly increased when it is known with precise data which resources (rooms, seats, tables) are going to be used in a specific time frame.

• Prediction of flight delays: not all delays can be predicted, but there are patterns that enable you to determine in a large majority of cases if a flight is going to be delayed.

• Personalization of the experience: discovering behavior patterns allows you to know and understand customers’ needs and therefore offer services and products that fit their profile.

• Prediction of guest needs: Segmentation is a powerful strategy that enables you to understand the specific needs of each client, based on the segment they’re grouped into.

• Optimization of customer segmentation: The traditional statistical approach finds linear relationships between a limited number of variables. However, using Machine Learning it is possible to discover patterns in a sea of data with a virtually unlimited number of variables.

Maximize the efficiency of promotional campaigns: Allows the prediction of target audience segments for each type of campaign, the means or time of communication to increase the efficiency of campaigns, both online and offline.

Hospitality and Predictive Personalization

Predictive personalization is a two-step process where machine learning techniques are used to understand user behavior and then personalize their experience by presenting the best content and offers tailored for each person. However, you may ask yourself: isn’t this done already? The big leap here is that the prediction occurs in real-time and is much more sophisticated, using not only basic rules but hundreds of variables that interact with each other.

Hoteliers can quickly adopt Predictive Personalization because it will simplify their work very fast. Prediction is the key because, in the first place, there are many tourists (customers) and each one is different. It is impossible to guess what they are looking for based on general attributes such as gender or nationality. Also, it’s necessary to anticipate their behavior.

People are not exactly patient today and if we have to wait for their preferences to be revealed to us, they might give up on your services. You have to act quickly and accurately to boost sales.

Personalization comes into play because predictive analytics is only interesting if it allows us to modify behaviors.

The data that you can't help us make better decisions regarding actions is useless, it's just a game that gives you an illusion of control. Just as each customer is unique and requires a specific approach, through personalization we can ensure that each visitor to our website has an exclusive experience.

Because nobody can know all the variables that configure a user's behavior and how they interact with each other, we need machine learning techniques to automate this process and obtain a level of precision and speed that can never be achieved by a human.

Personalization is a buzzword in the online travel industry. All CEOs are thinking about it and all marketing managers are trying to find ways to apply it. OTA and hotels are similar for AI and ML but there is an important difference - the problem they are trying to solve. 

An OTA has a “discovery” problem: which hotels should we put first, among the hundreds of thousands that are on the platform? A hotel, on the other hand, has a “supply” problem: what combination of room, services, and price is ideal for each user?

Until now, the supply problem has been resolved using conventional revenue management: finding the price per room that maximizes hotel income, using demand analysis and competitor prices. However, there is one fundamental piece missing in this approach: the customer, or to be more specific - the visitor.

How can you talk about personalization if the price, the most important variable is the same for every user, regardless of their preferences? We need to add the user to the equation and start thinking in terms of "complete offer" and not just price. It is the way to dynamically change the offer to adjust it to the characteristics of each person.

In the end, perfect revenue management implies a personalized price. Current methods allow hoteliers to optimize, so to say their side of the deal, but with the new technology, we can also optimize things to reflect the user’s side of things.

Undoubtedly, Big Data and Machine learning in tourism and hospitality will be a competitive advantage for the companies that implement them in their strategies. 

If you want to ask a question or discuss the impact of machine learning on hotels or OTAs’ operations, feel free to contact us.