Clemson cross-referenced data from licensed merchandise partner Fanatics with its own to produce new insights. (Getty Images)
Data experts say taking the first step can lead to big discoveries
Data can provide a ticketing operation innumerable ways to make itself better. Perhaps that’s what makes data scary to some.
“For me, a few years ago, it seemed like this stuff was really, really overwhelming,” said Mario Morris, chief financial officer and associate athletic director for the University of Wisconsin. “I love data; I’ve loved data since I was a kid; I like to process information, but there’s so many solutions out there,” citing all the companies and products in the field to choose from.
But Morris and two other data pros from university athletic departments said taking that deep dive into numbers had improved both their ticketing operations and other parts of their businesses.
“I think we’re in a transformational time in college athletics with regards to data,” Morris said.
For Ryan Gottlieb, associate athletic director for sales strategy and business intelligence at Rutgers University, using data more effectively started with implementing an advanced analytics platform as part of a recent renewal with Paciolan.
“To say this has been a game changer for us would probably be an understatement,” Gottlieb said.
“Our data wasn’t clean. It was all over the place,” he said. “We had no real strategy, we had no real systems, and our thought process was we need to develop a road map.”
That map led officials to the idea of re-examining how they look at potential football season-ticket renewals. “We identified 25-30 variables that we thought could affect the likelihood of somebody renewing their football season ticket,” Gottlieb said, but they found they needed to rein in the process.
“We realized we were overcomplicating the hell out of this,” Gottlieb said. Instead, “we came up with five categories that we wanted to do a deep dive in to see how it was affecting renewal.” The categories they chose were number of season tickets bought, scan data, consecutive years of purchase, year-over-year difference in season ticket purchases and priority points.
It led to a renewal scoring platform, available to sales reps, showing how likely an account was to renew. It also made Rutgers officials rethink their compensation for reps, after numbers showed some reps with lower renewal percentages had also been renewing larger numbers of the accounts least likely to renew.
With the shift, “We’re celebrating renewing somebody that had a 1 score on our 1 to 5 scale, has been a one-year season-ticket holder (and) scanned half of their tickets the previous year, and that’s a renewal worth celebrating,” Gottlieb said.
Gottlieb said Rutgers plans to add data from surveys it does to its database. At Clemson University, where Matthew Cobb is director of business intelligence, data analytics and special projects for athletics, “We decided to look externally for data to bring in and compare to our ticketing and our donation data, to help enrich the data that we already have.”
Incorporating three years of information from Fanatics, Clemson’s merchandise-side provider, led to some interesting discoveries.
Clemson takes the Fanatics data, which includes physical and email addresses of buyers and how much they spent on Tigers gear, “and we cross-reference it with Paciolan information.”
They found that the average Fanatics buyer lived 421 miles from Clemson. More important, the share of those buyers who had donated to Clemson’s athletic fund, were football season-ticket holders or had bought a single-game ticket in the last three seasons was below 10 percent in each case. They then could dial down to a 250-mile “driving distance” radius.
“That’s a lot of potential leads for IPTAY (the athletic foundation) and our ticket office to act upon,” Cobb said. “And that’s kind of the whole purpose of this is that we wanted to bring in all this information to enrich the information we already had.”
Cobb said the benefit of cross-referencing the data reached into other areas, including showing retail partners why it made sense to carry more product. He described how, using the data, the school could tell retailers how many fans lived near a particular store. “Look, there are almost 4,700 customers that live within 15 miles of this store,” he gave as a possible example of that conversation, “and you’re only carrying two hats. So, you need to carry more product.”
Once organizations have tasted success with data analysis, the new ideas come quickly. Gottlieb said his school was looking at why the athletic department’s marketing emails were opened more often but clicked on less often when the user was on a mobile device. Morris is curious to see if scanned attendance is a leading indicator for paid attendance.
For those who are just starting their “data journey” as Morris called it, one key is to find people who will champion data in the organization.
Another is finding the right target for early efforts. “One thing we focused on was starting small and trying to get easy wins,” Morris said.
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