Archiv für den Monat: November 2018

Big data gives the trade fair business a boost

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.

Exciting questions

This throws up exciting questions. For example: how can trade fairs create visitor profiles not just for one trade fair but in future create profiles that cover several fairs and also several trade fair companies. To achieve this, it is necessary to merge the data pools of several trade fair customers. This would enable trade fairs to identify new and unexpected target groups for individual events with much greater ease and achieve better cross-marketing. To do this, it is necessary to record the visitor pattern of the individual profile in keeping with data protection requirements – how regularly does a person visit a trade fair over the years? Is there a correlation between the visit to a trade fair and another one in a completely different industry? Is there a correlation between the point of time at which the ticket is purchased or the visitor’s country of origin and the probability that the trade fair visitor does not turn up? All this data can be collated on dashboards by means of reports. In the field of business intelligence one refers to central KPIs.

What advantages does big data offer trade fairs?

The trade fair business is in a period of transition. The sale of floor space to exhibitors as the core business model is no longer sufficient. Due to the changing expectations of the exhibitors, it is becoming necessary to expand the business model by offering additional services. For the exhibitors the purpose of a trade fair is matchmaking. Thanks to digitalization and the available data, in future the trade fair will act as the broker of contacts and trends and will support the exhibitors’ marketing activities.

Big data offers trade fairs additional practical benefits:

  • Improved planning of ticket office utilization (by evaluating the cashier statistics in real time).
  • Improved capacity utilization of the halls through live data on admissions and departures.
  • Anonymized movement profiles in keeping with data protection requirements

This enables trade fairs to be more agile and better plan their resources. The data also allows forecasts to be made:

  • Early detection of changes to the visitor structure.
  • Discovery of new patterns and correlations in the data (data mining). For example: Which exhibitors need to attend the trade fair in order to increase the total number of visitors to the fair? Which market players who attract a particularly large number of interested parties should be won for the trade fair? This allows network effects to be achieved by offering the right combination of diverse exhibitors.
  • Which target groups come increasingly often? On which days? This makes it possible for the trade fair to greatly improve the orientation of its framework program (conferences, special tours, meetings with VIP purchasers).
  • Forecasts via ticket sales.
  • Forecasts of exhibitor behavior.
  • Improved matchmaking between supply (exhibitors) and demand (visitors).
  • Vision for the future: Better overview of how the market is developing in an industry (predictions based on the previous data).

How to go about implementing this?

As is the case in all industries, trade fairs also face typical challenges when using and evaluating data. First of all, the data originates from different sources and needs to be collated. This includes data on the number of visitors entering the grounds, data from the ticket office, on the number of admissions, support requests, telephone calls, reactions to newsletters, activity on trade fair websites and from lead-tracking. All this data requires a common key (ID of the ticket with corresponding link to the profile) in order to link up the various data and correlate it almost in real time.
Before a trade fair company evaluates its data, it should seek professional advice in order to formulate meaningful questions. It should bring data analysis experts with a deep understanding of the industry on board.
In order to analyze the comprehensive and varied data, it is necessary to create interfaces to analysis tools such as Google Analytics, in which data is collected such as the reactions to newsletters and visits to the websites. This data is linked to the overall ticketing and visitor management system and, if required, supplemented with data from support requests.

Conclusion

Trade fairs possess a fantastic wealth of data and this needs to be exploited. This not only allows individual trade fair events be optimized, but also enables a great many new insights to be gained in order to:

  • Be the industry barometer.
  • Expand the business model based on data.
  • Become the central platform for data-driven matchmaking between supply and demand.

Learn more about FairMate Business Intelligence and how it helps you to boost your trade fair!

Lunch & Learn bei dimedis – Als in Kalk noch die Traktoren rollten

Egal ob Wissensmanagement, neueste Trends im Bereich Social Media oder die richtige Bewegung am Arbeitsplatz: die regelmäßigen Lunch & Learn-Veranstaltungen bei dimedis werden immer gerne angenommen, um während der Mittagspause nebenher noch Nützliches und Interessantes zu lernen. Dieses Mal war Karl-Heinz Fuchs von der Geschichtswerkstatt Kalk e.V. zu Gast, um uns aus der Zeit zu erzählen, als in den heutigen Räumen von dimedis noch die Traktoren rollten. Für die Mitarbeiter gab es dann eine lebendige Geschichtsstunde, in der sie mehr über die Gebäude erfahren konnten, in denen sie nun Software entwickeln. Die Geschichtswerkstatt Kalk ist übrigens durch unseren vorherigen Blogbeitrag über genau dieses Thema auf uns aufmerksam geworden.

Karl-Heinz Fuchs von der Geschichtswerkstatt Kalk referierte beim Lunch & Learn über die Geschichte von Kalk und der Dillenburgerstraße. (Foto: dimedis)

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