On the prestigious B2B platform CTA – Change Tourism Austria – the official hub launched by Österreich Werbung (Austrian National Tourist Office) together with the Ministry of Economy and the Chamber of Commerce (WKÖ) – an in-depth contribution signed by Khrisna Canares, Senior Business Development & Hospitality Expert and Head of Revenue, Sales & Strategy at Zell am See-Kaprun Tourism.

The article traces the balance of an innovative Six-month pilot project, conducted by HBenchmark in close synergy with the DMO Zell am See-Kaprun Tourism Ltd, in the renowned alpine resort in the Salzburg district.

Testing has shown how HBenchmark's data intelligence technology can guide destinations towards building an innovative data-driven model that overcomes the limitations of historical analysis alone.

Here are the key points of the pilot project described in the article:

• Real-time, real-world data: Analysis of actual booking dataon-the-book) extracted directly from the partner hotels’ property management systems (PMS), providing a realistic overview of future occupancy levels and going beyond a simple review of public price lists.
• Predictive capability Using future data to anticipate seasonal trends, identify the most active source markets and implement proactive marketing initiatives before the start of the tourist season.
• Event measurement Early monitoring of the economic impact of major events (such as the Grossglockner Ultra-Trail and the’IRONMAN 70.3measuring the increase in employment, average price, and revenue several months in advance.

The success of this trial in Austria confirms the international scalability of the HBenchmark model and its ability to support DMOs in transitioning towards fully managed tourism data-driven.

Per approfondire i dettagli del progetto pilota è possibile consultare la Following further details of the pilot project can be found Specific page dedicated to the Zell am See project on HBenchmark.

The original blog post is available in German, but easily accessible and usable through common machine translation systems.