The ultimate challenge for any professional sports team is filling seats and building a fan base that consistently contributes to bottom-line results. But understanding fans—and what motivates them to attend games and spend their hard-earned dollars on everything from television subscriptions to souvenirs—has traditionally proved elusive. Most teams take a swing at broad advertising and marketing campaigns, pitch emails to previous ticket buyers and hope for a good result at the cash register.
However, the New York Mets are now attempting to hit a home run with by applying data analytics to fan relationships. The franchise has entered an agreement with software firm SAS “to better understand who the fans are on more of an individual level,” states Lou DePaoli, chief revenue officer for the team.
Among the things he and other executives at the Mets are interested in: What specific factors impact overall satisfaction? What motivates fans to interact with the brand and attend games, watch or listen to games, follow the team on social media, and purchase Mets merchandise.
“Being able to drill down to the individual level to understand who these fans are and what motivates them will allow us to accomplish a couple of key things,” DePaoli points out. It helps the Mets boost engagement levels and, by using predictive modeling, it enables management to identify similar groups of fans so that the team can market to them more narrowly and, in the end, better engage them with the brand.
“Understanding fan behavior and motivations is crucial to creating and maintaining long-term loyalty,” he adds.
DePaoli believes this reflects an evolution in thinking for sports teams and other businesses. “It is important to understand that you should not build your business plan around selling wins and losses,” he says. That’s because “team performance is an unstable and unsustainable business model” to build the business on.
Instead, the Mets are tuning into big data and analytics to identify the factors that maximize loyalty and build better relationships.
The task isn’t without challenges. For example, if a person purchases tickets for a game with cash at the box office, the team currently doesn’t ask for contact information for fear of slowing down the line. The downside is that the team doesn’t know whether the person is a season ticket holder who purchased extra tickets for a game, or is someone attending a game for the first time in years.
“We need to know exactly who is interacting with our brand and at what level,” DePaoli explains.
In order to get the most from the analytics software, the team will revamp sales and marketing practices to create new data points, match the data with historical records and current patterns, and shape it into new marketing and sales practices. The goal is to expand the database, but also use the data in new and innovative ways.
“The benefits are higher levels of engagement today, which will lead to longer-term fandom down the road,” DePaoli concludes.