The hospitality sector, and the travel sector in general, has seen an exponential increase in its data in recent decades. This sector is among those that generate the most data of all: just think of the information on guests in terms of personal data required for bookings, general preferences on types of travel, purchasing habits and decisions relating to the Food & Beverage sector, information that concerns multiple sectors linked to the tourism industry of a destination.
Revenue Management, the growth of data
The granularity of this information, its breadth and heterogeneity, implies the need to adopt technological tools capable of helping the sector’s activities to maximize the potential of this data. The risk one runs otherwise is that of taking decisions based on strategic approximations and consequently losing profits, business opportunities and competitiveness.
Revenue Management, the tools to support hotel management
The main technological tools supporting such activities in the hotel business are PMS, CRM and RMS. The PMS (Property Management System) is a management system for organizing and collecting booking data, invoicing and often also inventory management. The CRM (Customer Relationship Management) allows information to be collected and relationships with actual and potential customers to be managed. Finally, RMS (Revenue Management System) is a tool that enables the management of availability and revenue through the analysis of available data.
Revenue Management, what is an RMS?
This last tool, and consequently revenue management in its entirety, is the area most strongly impacted by the evolution of data in the hotellerie. The number of variables and constants that can be used to correctly determine the cost of a room or service has increased exponentially, to the point where man is no longer able to manage them simultaneously.
An RMS based on AI has two fundamental characteristics, the first relating to the computational domain and the second to the economic aspect.
Concerning computational capabilities, an advanced RMS is capable of analysing a wide variety of variables more accurately and faster than a human being. For example, it is capable of processing an output based on the joint analysis of data on the pace of bookings, historical employment values, cancellation rates, the presence of events and holidays, weather forecasts for the destination, competitors’ sales strategies, aggregate demand for the destination, air flows, macroeconomic variables, political and social situations between states, etc.
Revenue Management, the advantages of AI
Performing the same activity without technological support would involve greater use of resources, time and money. Suffice it to say that the human brain is capable of making around 35,000 decisions per day, while an AI algorithm in the same time frame can make millions. Hence, to have the same accuracy and calculation capacity as an AI-based RMS, a hotel would have to hire about 3,000 revenue managers per year, costing at least EUR 200 million. The superiority in terms of computational efficiency and cost-effectiveness that an RMS can bring today is therefore evident.
Moreover, entrusting this processing to humans, for example through an excel spreadsheet, also implies accepting a significant margin of error, which can have a significant impact on the structure’s revenue. If AI-based revenue management systems need human support and knowledge to set up the logic and in the activity of output control, in day-to-day operations the machine is decidedly more efficient. These constatations are not valid for every reality in the hospitality world, nor do they imply the complete replacement of human activity. But in the future, the role of the human being in this field will increasingly be one of control and supervision of algorithms, rather than calculation and analysis.
Revenue Management, Premoneo’s solution
This stems from the fact that fields such as Revenue Management, characterised by a strong logical relationship between variables and a large amount of data, are ideal for the application of machine learning algorithms. If the goal, on a theoretical level, is to achieve one-to-one price discrimination, artificial intelligence can identify similar patterns in large datasets and provide an optimized output in a shorter time and at a lower cost.
In Premoneo, we have developed an RMS that offers predictive occupancy analysis accurate to 95 per cent, price suggestions and competitive monitoring directly in the cloud and integrated with the company’s IT environment. Want to get more information? Download the flyer here or schedule a demo with our team here.
