A compilation of records according to a classification system, such as your attendee file, exhibitor file or sponsors file. Integrated database: Several files using the same design and platform to accommodate searching and using information that aids in decision making. Many shows have parallel databases that aren’t integrated, such as the attendee files accumulated through registration (and each show’s may be separate), prospect files housed in marketing or exhibitor files in the sales team’s contact management program. Algorithms: Formulas used to produce the results of data mining. Simple formulas, such as regression analysis, result in reports on quantitative data such as average attendance or age trends based on numbers and history. More complex algorithms use qualitative analysis, models and decision trees to produce classifications and clusters, such as more sophisticated information about buying habits or a profile of a typical customer.

 

DATA MINING DEFINED

Typical show reports are focused backward, looking at past attendance and participation. The purpose of data mining is to use the historical information to look forward and make predictions about what may occur or what attendees and exhibitors may want; anticipate ways to cross-sell; or rate attendees/buyers, exhibitors or sales prospects based on certain algorithms.

 

HOW IT WORKS

Data mining amasses credible data that helps organizers make decisions to avoid taking the show in the wrong direction or remaining stagnant. Research (from Web site searches, clicks on exhibitors, survey data and even chat sessions) might identify an interest in a certain new product category. Organizers may discover that the category was under-represented in the exhibit hall at the last show. Sales can use the interest data to convince more exhibitors in that category to join the show, while marketing can promote meeting the latest needs and encourage prospective attendees to see the popular product category.

 

Qualitative analysis:

Studying non-statistical information. For instance, quantitative factors about Web site use may include the number of visitors, how long they stay on your site and how many click-throughs are performed. Qualitative analysis looks at how people use the site, such as which white-papers they download, comments they make to a blog site, which exhibitor profiles they examine or questions they might send to the contact e-mail. Profiling: Using both quantitative and qualitative factors to create a picture of a customer that may be predictive of what that customer may respond to, view or buy. Using profiling to better match a customer to a marketing message or offer avoids the “shotgun” approach and can increase ROI in marketing efforts.