Trade show organizers collect mountains of data through registration, attendee surveys, session surveys, exhibitor surveys, lead retrieval systems, social media, etc.

The question is, how is all that data being used? Is the data being used effectively?

The problem most shows face is not how to get more data, but how to make sense of what they already have.

Jeremy Figoten, senior vice president of Meetings, Sales and Communications for the National Apartment Association is taking a close look at his show data to better understand exactly why their show has been growing.

Figoten classifies his show as a mid-size event, with about 7,000 people in attendance at their annual meeting. He’s lucky in that the NAA is seeing tremendous growth in both membership and meeting attendance over the last few years.

“We wanted to understand why that was happening,” Figoten said. “We do a good job of collecting data. We don’t do a good job of looking at the data and analyzing it due to a lack of time and resources.”

If he could understand his audience better, he would be able to focus his marketing and messaging to specific demographics. But where to begin?

Figoten turned to Bear Analytics to sort through all NAA’s data and present it in a way that he could make proper use of it.

One thing that surprised him was the number of loyalists that came to their event every year. “We had about sixty percent. It was encouraging. That’s a good number that we don’t have to convince to come,” he said.

Bear Analytics likes to use the example of Netflix when explaining the value in analyzing the data you’ve been collecting.

 “Netflix is using data to customize content to their audience and engineering shows/episodes based on their audience behaviors and demographics,” said Eric Misic, co-Founder and vice president of Business Development for Bear Analytics.

He added, “The days of running a test pilot TV show and seeing how the audience receives it are going by the wayside. Media companies are leveraging the data they are capturing to create content that will immediately become “sticky” and using social data to increase that audience base.”

“The event industry has tons of data on the participation at their events that they can use to create programming content, increase engagement, strengthen networking pre, onsite and post event,” Misic said.

Robbi Lycett, senior vice president of Conventions & Conferences, Biotechnology Industry Organization (BIO) also turned to Bear Analytics for help with their data.

“We wanted to better understand some of the things we were hearing in our surveys to attendees and exhibitors,” Lycett said.

She added, “To drill down into specific issues so that we could make improvements; lower registrations fees where possible, lower costs and increase revenue.”

As a result of their analysis, BIO made several changes to their convention, including lowering the price of their Convention Access registration, while increasing the price of their Partnering Registration.

They even changed their event from four days to just three days.

BIO also discovered that attendees had trouble finding like-minded people at BIO’s big 3000-plus receptions, so they replaced one of their big receptions with seven smaller receptions on the same night/time in the same area of town. These smaller events were targeted to attendees with specific interests.

“We added new exhibiting zones on the exhibit floor with more innovations and cutting-edge technology,” Lycett said.

With the help of Bear Analytics, the National Apartment Association and the Biotechnology Industry Organization were able to uncover valuable information in their mountains of data.

Figoten said he was impressed by how Bear was able to break down the data in so many different levels.

“When you see it literally broken down you say wow this is what you are looking for, this is the justification you need to go to your board to ask for what you need,” he added.

If budget is a concern, Figoten’s advice to show organizers is to start small. The information you get will help you justify your next step.

What kind of problems are you looking to solve? What insights are you looking to gain? Is it possible the answers are all right there in the data you already have?