Revenue Managers make use of Machine Learning to get the best results in their work.
The concept of Machine Learning (ML), which translates into English as Machine Learning, is a relatively new term that emerged with the rise of Artificial Intelligence.
Artificial Intelligence is a field of computer science that creates machines that can perform tasks that previously required human intelligence and now dispense with it. These tasks are not only of a mechanical or rational nature, but encompass areas that are often associated with capabilities not present in machines but in people, such as perception, learning and decision making.
Within Artificial Intelligence there is an area called Machine Learning, which is dedicated to the creation of systems that learn, perform tasks and improve autonomously from the development of algorithms and without the need to be programmed.
Autonomous learning is achieved by using large data sets to generate models from which the system makes predictions or decisions based on patterns or features present in the data.
If we compare it with human learning, we could say that Machine Learning is a process similar to the one we do when we can predict, for example, the weight of a glass of water before lifting it to use the appropriate force, or the reaction that other people will have from our behavior. We have patterns stored in our memory that were formed with large amounts of data received and processed throughout our lives. And these allow us to perform instant and imperceptible predictive analysis.
In the case of Machine Learning, it is algorithms that give computers the ability to identify patterns in massive data, perform autonomous predictive analysis and thus make predictions.
Artificial Intelligence simulates human intelligence to be applied in robotics, pattern generation and human language processing. So, the goal of Machine Learning is to focus on the development of algorithms that allow machines to learn by themselves and make predictions.
The great advantage that Machine Learning provides to industries and business activities is to make possible a task that would take a very large amount of human resources and time and impossible to carry out in the dimensions needed. Another unparalleled benefit is that computer systems continuously and autonomously adjust and optimize themselves as they accumulate more experience. Therefore, its performance improves as it receives and processes larger and more varied data sets.
What is Machine Learning used for in Revenue Management?
In the world of STRs, large Revenue Management companies such as ListingOK monitor in real time all the variables that affect the optimal pricing and availability of each of our clients’ temporary rental properties.
Here at ListingOK we update prices on a daily basis (Daily Pricing). We apply Dynamic Prices , which means that the variables we monitor are the current occupancy of the vacation rental properties in the area where an accommodation is located, the prices of the properties that have been rented up to that moment in that same geographic area, and the prices at which vacation rentals of similar properties are offered in that area at all times of the year.
This daily monitoring makes it possible to forecast demand and occupancy for each zone at any given time. How? With Machine Learning.
The large amount of data collected daily is processed with Azure Machine Learning to build models and make projections, allowing you to react quickly to make informed decisions and take advantage of market opportunities. In this way, the objective of achieving the best possible performance for each accommodation is achieved.
In short, at ListingOK we apply Machine Learning tools to make Occupancy Forecasts, projections, build models, draw statistics, make reports and define the best strategy for each client’s accommodation.
