Der weltweite Verband der Messewirtschaft ufi hat den Global Exhibition Day ins Leben gerufen, um die Bedeutung der Messewirtschaft sichtbarer zu machen. Der Global Exhibitions Day, GED, findet jährlich am ersten Mittwoch im Juni statt. In diesem Jahr wird daher #GED19 am 5. Juni 2019 gefeiert!
The digital transformation doesn’t stop short of trade fairs and exhibitors. The trade fairs are reacting and are already using technologies such as blogs, social media, apps, and analog and digital screens in their communication. And they have digitalized visitor management and thus collect enormous amounts of data.
Evaluating and visually displaying data is the task of so-called business intelligence (BI) in order to gain insights that can be used to support the company when taking operative and strategic decisions. Using BI, trade fairs are able to answer economic and trade fair-specific questions by systematically linking, evaluating and displaying trade fair data. Key figures and evaluations at specific points of time, during and after the trade fair, in conjunction with target/actual comparisons provide better support for the management in order to improve the quality of operative or strategic decisions.
Trade fairs gather millions of items of data
Trade fairs possess an unbelievable amount of data, which can be considered a hardly exploited treasure. That is why trade fairs are predestined to use big-data analyses and can profit from these in order to better plan their business. The data comes from the registration of the visitors, from shop data, admissions and departures, no-show rates, sociodemographic structural questions (position, company size, business sector, interests), ticket sales, and support requests.
At the trade fair itself the visitors also generate data. Movement data is also available in anonymous form and in compliance with data privacy regulations, not only by accurately counting admissions and departures but also through heat maps of the premises. Furthermore, trade fairs also collect data from exhibitors and can thus provide matchmaking between exhibitors and visitors.
This data can be used more comprehensively. The trade fair company can, for example, search for correlations. Thus, using the anonymized data on visitor movements, it is possible to analyze the probability that a visitor will visit another exhibitor. Ticket and visitor data can be combined and used to produce forecasts. In future, it should be possible to anticipate potential industry-specific developments using predictive analytics.