Scoring higher revenue with analytics
Orlando Magic customizes fan experience, sells more tickets
Professional sports teams in smaller markets often struggle to build a big enough revenue base to compete against their larger market rivals. Using SAS, the Orlando Magic is among the top revenue earners in the NBA, despite being in the 20th-largest market.
The Orlando Magic accomplishes this feat by studying the resale ticket market to price tickets better, to predict season ticket holders at risk of defection (and lure them back), and to analyze concession and product merchandise sales to make sure the organization has what the fans want every time they enter the arena. The club has even used SAS to help coaches put together the best lineup.
"Our biggest challenge is to customize the fan experience, and SAS helps us manage all that in a very robust way," says Alex Martins, CEO of the Orlando Magic.
The Orlando Magic uses SAS to help manage the basketball team by analyzing the efficiency of certain lineups. "The 'Moneyball' approach, if you will," says CEO Alex Martins.
The challenge: Filling every seat
Like all professional sports teams, the Orlando Magic is constantly looking for new strategies that will keep the seats filled at each of the 41 yearly home games. “Generating new revenue streams in this day of escalating player salaries and escalating expenses is important,” says Anthony Perez, Director of Business Strategy. But with the advent of a robust online secondary market for tickets, reaching the industry benchmark of 90 percent renewal of season tickets has become more difficult.
Perez' group takes a holistic approach by flowing data from all revenue streams (concession, merchandise and ticket sales) and outside data (secondary ticket market) to develop models that benefit the whole enterprise. "We're like an in-house consulting group," explains Perez.
We have the seventh-largest revenue stream in terms of ticket sales of all 30 teams and we're the 20th largest market in the NBA. It's because we can take data and analyze it so effectively.
In the case of season ticket-holders, the team uses historic purchasing data and renewal patterns to build decision tree models that bucket subscribers into three categories: most likely to renew, least likely and fence sitters. The fence sitters then get the customer service department's attention come renewal time, Perez explains.
Ease of use helps spread analytics message
Perez likes how easy it is to use SAS – it was a factor in opting to do the work in-house rather than outsourcing it. Perez' team has set up recurring processes and automated them. Data manipulation is minimal, "allowing us more time to interpret rather than just manually crunching the numbers." Business users throughout the organization, including the C-suite, have instant access to information via portals. "It's not just that we're using the tools daily, we are using them throughout the day to make decisions," Perez says.