After 19 consecutive losing seasons, the Pittsburgh Pirates need a little extra help in its effort to fill the nearly 40,000 seats at its home field, PNC Park.
For instance, the team is now turning to predictive analytics technology identify customer patterns and trends to help it better retain existing season ticket holders and and attract new ones each season.
The Pirates say the SAS Institute tools can predict which fans are likely to either renew or buy new season tickets by analyzing their self-professed avidity for the team, their previous purchase patterns, their social media interactions and other demographic factors.
The goal is to provide the club's sales and marketing personnel with information that can be used to deliver targeted sales, promotional and advertising campaigns, said Jim Alexander, senior director of business analytics for the Pirates. "We interact with fans in several ways, and [the] SAS [tools] analyze data from all those sources."
The Pirates are among a small but growing number of organizations using predictive data modeling tools to improve operational efficiencies and find new ways to generate revenue.
Rita Sallam, an analyst at Gartner, said that about 10% of enterprises, including multiple professional sports teams, are successfully leveraging predictive analytic approaches to drive strategic benefits for their organizations.
Moneyball, a recently released movie based on a book of the same name written by Michael Lewis, tells the story of how the Oakland Athletics built a winning team on a shoestring budget by applying data mining and statistical analysis approaches to find strengths in little-known -- and low-salaried -- players.
Rather than looking at just batting averages, home runs and the like, the Oakland A's considered other so-called sabremetric measures such as walks plus hits per inning pitched (WHIP) and value over replacement player (VORP) when selecting players.
"In the case of baseball, the batting average was the gold standard for measuring the value of a player," Sallam said. What the Oakland A's did was to "throw away conventional wisdom and look at other measures that were equally valuable in predicting performance."
Like the Oakland A's, many other teams have started measuring previously ignored and undervalued statistics when selecting players, Sallam said.
The Pirates, she noted, are extending that approach to improving operations and marketing capabilities, she said.
The team has found that fans who profess the most fervor for the team, and those who already hold tickets for best seats in PNC Park are most likely to renew season ticket packages. The fan's age and the team's record are other factors, Alexander said, noting that when the Pirates were on a winning streak earlier this season, attendance shot through the roof.
The goal is to combine such information with other data the club already has on its fans to identify those it has the most chance of persuading to buy tickets. Alexander said.
The team is currently using SAS to forecast its 2012 attendance, he added.
"Analytics applications are the wave of the future," Sallam said. "This is an area where there will major growth as long as we can overcome the skills gap and the ease-of-use gap," that she said has long stunted adoption of predictive modeling approaches for so long.
Jaikumar Vijayan covers data security and privacy issues, financial services security and e-voting for Computerworld. Follow Jaikumar on Twitter at @jaivijayan, or subscribe to Jaikumar's RSS feed. His email address is firstname.lastname@example.org.
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