Revenue Mangers work with Machine Learning to achieve the best results.
The concept of Machine Learning (ML) is a relatively new term that emerged with the rise of Artificial Intelligence.
Artificial Intelligence is a field of computing that creates machines that can perform tasks that previously required human intelligence and now do without it. These tasks are not only mechanical or rational in nature, but also cover areas that are usually 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 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 thanks to the use of large data sets that allow the generation of models with which the system makes predictions or makes decisions based on patterns or characteristics present in the data.
If we compare it with human learning, we could say that in Machine Learning a process is carried out similar to what people 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 based on our behavior. We have patterns stored in memory that were formed with large amounts of data received and processed throughout our lives. And these allow us to perform instantaneous and imperceptible predictive analysis.
In the case of Machine Learning, it is the 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. Therefore, the objective 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 commercial activities is to make possible a task that would take a very large amount of human resources and time and would be impossible to carry out in the dimensions that are needed. Another unmatched benefit is that computer systems continually and autonomously adjust and optimize themselves as they accumulate more experiences. So, its performance improves when receiving and processing larger and more varied data sets.
In the Short Term Rental landscape, large Revenue Management companies like ListingOK monitor in real time all the variables that affect the optimal formation of prices and availability of each temporary rental property of their clients.
Here at ListingOk we update the prices every day (Daily Pricing). We apply Dinamic Pricings, which means that the variables we monitor are the current occupancy of the properties for Short Term Rentals that are located in the area, the prices of the properties that have been rented up to that moment in that same geographical area, and the prices at which temporary rentals of similar properties in that area is offered at all times of the year.
This daily monitoring allows us to predict demand and occupancy for each area and for each moment. How? With Machine Learning.
The large amount of data collected daily is processed with Azure Machine Learning to build models and make projections, allowing us to react quickly to make informed decisions and take advantage of market opportunities. In this way, we achive the goal to have the best performance for each STR.
In summary, at ListingOK we apply Machine Learning tools to make occupancy forecasts and pricing projections, and, in general, to build models, draw statistics, create reports and define the best strategy for each client's properties.