Alongside with dedicated clients of Maxxton, we are working on a new software feature through which machine learning algorithms help dynamic pricing of the available accommodations. The algorithms take into account the important parameters. This way the revenue management departments gain clear suggestions for dynamic pricing.
In a dynamic and data-driven world sometimes it is difficult to keep an overview. That is precisely why intuitive and clear dashboards are being created to have direct access to the most relevant information, such as occupancy and revenue. Furthermore, see immediately whether specific locations and clusters of accommodations are doing better than expected and proactively optimise.
Data comes forth from our data warehouse. The great advantage is that also external data, such as Google Analytics 360 can be easily connected. Moreover, it is possible to make predictions from this entire data pool and in which you can go through various scenarios. For instance, sometimes it is wiser to focus on higher unit prices, where the occupancy is not maximised, however the revenue is.
In general, guests do not search for a specific unit. However, they do search for a region, a location, or a holiday park. Hence, the specific amenities are important here, e.g. a swimming pool, a child and pet friendly accommodation. When the price of a specific unit, such as a 4-person cottage is increased by 15%, there is a good chance that a 6-person cottage will quickly become more appealing. This is why it is smart to create clusters of accommodations, all of which adapt to price changes. You even receive recommendations for this within the new Revenue Management software.
When 60% of the accommodations are rented out, it is important to figure out how the remaining 40% of the accommodations can be rented out as favorably as possible. Maxxton works towards a recommendation engine. The recommendation engine will help by means of forecasting to enter the prices most advantageous as possible, where different goals can be the starting point.
