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Driving exhibitor sales through data

Overview

The biggest decision a tradeshow sales team has is where to dedicate their effort. Detailed information on prospective exhibitors and how similar they are to your existing exhibitors can help shape the decisions of where to allocate resource. To maximise the benefit an organiser needs to embrace the tools of big data and build internal processes which effectively focus sales effort on those exhibitors which are most likely to buy.

Detail

The internet has changed the way in which data is created and consumed, as tradeshow organisers there has never been such an ideal opportunity to prospect for new exhibitors. Data on the exhibitors of competing shows are readily available, however traditional methods of building or buying a mailing list and then ringing down this to find interested exhibitors has diminishing returns when applied at scale.

Readily available access to information at scale creates a new set of challenges for organisers which require new skills, with the ability to create a list of all exhibitors at competing shows, with a new set of challenges which arise from doing this at scale:

  • Dealing with ambiguity – Information on exhibitors varies between tradeshows, and a robust method to identify the same exhibitor at different tradeshows is critical. Simple issues such as variations in spelling or company abbreviations (Ltd vs Limited), and different addresses mean that probabilistic matching is required to link exhibitors between shows.
  • Dealing with scale – To prospect for exhibitors an organiser needs to have the tools and processes to gather, refine and process data at scale. Historical approaches of using a spreadsheets rapidly become unwieldy as the time involved in transforming 1,000 of rows and deduplication becomes an impractical use of time.

 

  • Collecting timely data – one of the most valuable times for an organiser is to understand those exhibitors which have not rebooked at a competitors show. When an organiser launches their current year exhibitor directory there is a valuable window to understand those exhibitors which have yet to rebook. So rather than one instance of exhibitors per show per year, the most value is generated by understanding the flow of exhibitors.

 

  • Understanding relationships – historical approaches such as flat files and most CRM systems are based on a concept that there is a single version of the truth, often with a relational database behind the surface. The reality is that despite their name relational databases are pretty poor at understanding the underlying relationships between data, graph representation and the tools of network analytics offer a richer perspective on the relationships between exhibitors.
  • Enriching data – alongside matching exhibitors we have found that is valuable to understand more about individual exhibitors. How they describe themselves, where they have offices, who they compete with, what sectors they serve etc. Only by understanding this and building models based on natural language processing can you begin rank potential exhibitors as being the most attractive leads.

The required data management, processing and analysis skills to effectively deliver a data led sales approach to exhibitor prospecting are somewhat uncommon within the world of exhibition organisers, however the benefits in allocating time more effectively to focus on those exhibitors is clear. The ability to move beyond lists and sales funnels and target those exhibitors which represent the most effective and efficient use of time exists for the organiser who uses data to drive their exhibitor sales.

Next steps

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About Us

Events Intelligence is a data business that helps organisers attract the right exhibitors to their tradeshows. Founded by an exhibitions industry expert and a banking CTO we help our clients understand the global tradeshow landscape, provide a basis for data led decisions, and help focus sales and marketing efforts on those exhibitors most suited to their shows. Using advanced data science tools and machine learning, we radically transform the traditional method of selling exhibition space.

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