The travel industry is composed of a set of different stakeholders directly affected by tourist arrival flows. Many accommodation and food-beverage companies have developed strategies to increase their income by trying to predict these flows at a daily rate, or even for circumscribed periods, like summer. These strategies fall into the discipline of Revenue Management. However, nowadays static accommodation pricing is no longer ensuring a viable growth. In order to maximize their income, hoteliers and food-beverage managers need to know how to sell the right product to the right customer, at the right time.
ROSIE aims to build an AI-based Revenue Optimizing System which will leverage Big Data and Machine Learning technologies to build forecasting models taking into account the entire demand/supply spectrum. The solution will provide meaningful insights to the personnel of travel SMEs regarding the optimal prices throughout the year, without the need for advanced statistical training. The system will study dynamic pricing as price discrimination for different customer segments, thus maximizing occupancy rate, as well as other hotel Revenue Management KPIs. ROSIE aspires to transform raw data into business value propositions with a clear focus on efficiency, sustainability and experience enrichment.