Hospitality professionals use big data and artificial intelligence to learn future demand of destinations, sentiment around travelers and prices in order to sustainably grow.

As the idea of big data begins to transform the sector, hospitality executives are starting to reconsider the long-standing measures of RevPAR, ADR, occupancy and direct bookings. Carlos Cendra, partner of tourism data intelligence company Mabrian Technologies, in a recent podcast series, Insights, noted the strategic usefulness of examining macro trends, which affect the whole destination, rather than in relation to individual hotels.

In this discussion, it was highlighted that hotels can never flourish in environments devoted to solitude, but they require the perception of the destination to be successful. Hoteliers will be able to predict traveler concerns, adjust their marketingdata, and determine sustainable pricing that aligns with the global tendencies by utilizing big data, especially a real-time sentiment analysis.

Unlike conventional surveys, big data is based on millions of organic interactions in social media and reviews that provide a more realistic perspective of the motivations of travelers. These huge quantities of data can be converted into actionable stories with the help of AI, e.g., flight search numbers decrease, and users become sensitive about the costs of flights in the major markets.

Geopolitical events or airfare changes also were identified, by means of case studies, to affect demand while the change is not reflected in the data of the bookings. This demonstrates the significance of combining the destination-level of intelligence with the hotel-level takeaways.

The implementation of big data leads, ultimately, to a combined approach, i.e., maximizing the use of the experience of guests while, at the same time, adapting to external forces that influence demand. Travelers, according to Cendra, select destinations first; hotels that integrate their strategies with this fact will be in a better position to grow over time.