One of the best assets a professional basketball organization can have these days is big data analytics. Sure, much of today’s chatter about it focuses on what granular information teams can learn about their players, prospects and competitors – 15 NBA teams are using STATS’s SportVU, for instance, which captures stats like how fast a player runs, his number of sprints per game, and where on the court he gets his rebounds. But analytics tools can be used to score in other ways, too.
The Orlando Magic, in fact, is gaining a reputation for being at the forefront of using data analytics to retain season ticket holders. It’s studying the behavior of these valuable customers this year – based largely on their ticket usage – to determine who is likely to renew for next year, who is not, and who could go either way. The Magic can then tailor its communications to help retain those it projects might leave.
The team began using a predictive renewal model during the 2010-2011 season, but it significantly ramped up those efforts the next season when it consolidated its customer data in a data warehouse, began using advanced analytics tools from SAS, and started issuing digital tickets instead of mailing physical ones.
“We’ve always had a lot of data available to us through our ticketing system and in the past we haven’t had a great way of leveraging it,” says Anthony Perez, the Orlando Magic’s vice president of business strategy. “Now we’re using a lot more sophisticated tools to do that and one of the things we’re trying to do is use that data to really identify the different segments of our customers that need a certain type of attention or a certain type of interaction from a service rep.”
Delving into Customers’ Motivations
Perez says the team doesn’t need to treat every customer the exact same way, and that’s useful because its season ticket holders have different reasons for purchasing tickets for 45 home games. Some may buy them for their personal use, for instance, while others intend to resell them on the secondary market.
The team can create action plans for accounts based on how they’re using their tickets and how often. That information can guide conversations from service representatives. If someone hasn’t had much luck reselling the tickets, for instance, the rep can offer tips for being more successful, Perez says.
Utilization of tickets is just one variable in the predictive model the Magic is using; it also considers factors like tenure, feedback from customer satisfaction surveys, and whether or not the account holder personally attended the game.
Digital ticketing has helped give the Magic visibility on that personal utilization piece. Now plan holders can currently get into Amway Center by using a special card with the tickets loaded on or by printing the digital tickets. “If you didn’t forward your tickets and you didn’t print them, we feel like that means the primary account holder came to the game and that’s been a significant variable in our model that we didn’t have until last season,” Perez says.
The new investments are producing new efficiencies. Compiling datasets used to take two to three weeks and involved tasks like exporting data from different areas into Excel and combining five spreadsheets into one.
“Now with some of the tools we use, with the data warehouse we have where we’ve consolidated all this data into one place, it takes two minutes to generate a dataset,” Perez says. “We’ve essentially automated the process, and that’s why I think we’ve seen huge benefits. Now this information can be actionable a lot closer to the point in time when we want it to be.”
That’s key because there’s not a lot of time on the clock. The regular season starts in November and the Magic asks season ticket holders to renew for the next season in January or February. If they aren’t using their tickets early in the season, the team quickly wants to help change that course. If someone’s tickets go unused for two games, for instance, the team will send an email asking if he or she needs assistance from a service rep, or the Magic will send a reminder about reselling tickets on the secondary market.
Perez declines to share specific business results about overall renewal rates but says, “We’ve done some analysis and experiments to measure the impact of our data-driven strategies and have seen significant lift in the impact on renewal rates.”
Using Customer Data to Enhance the Fan Experience
Meanwhile, the Magic is exploring other, related ways to use analytics. The team offers visitors a chance to win courtside seats when they swipe their card or scan their ticket while purchasing at a concession stand or retail shop.
That means the team can link transactions to tickets – or in the case of season ticket holders, it may be able to link transactions to accounts. (This is challenging because the purchaser may be the account holder, someone he or she brought to the game, or someone who came to the game without the holder; the Magic is working on models to refine the analysis.)
Merging ticketing data and account-level purchases presents the Magic with many potential applications to enhance the customer experience of its season ticket base and ultimately build loyalty. Perez shares four examples of what he calls “surprise and delight” opportunities:
• If an account regularly purchases ice cream during halftime, a service rep can surprise those in the plan holder’s seats with the cold treat.
• If the account always buys certain types of retail items, the Magic can send an email about new arrivals in that category.
• If an account regularly buys youth jerseys for a particular player, the organization can send an invite to an event that player will be attending.
• If an account spends a lot on retail or concessions, the team can send the plan holder a coupon or credit.
The team is thinking through these kinds of overtures as it evolves its capabilities. “Now we’re trying to determine when we attribute purchases to accounts and the next step is how do we use it in terms of our process flows for email campaigns and also how do we make it available in the most consumable way to our service teams,” Perez says. “What we don’t want to do is within our CRM system, just start dumping every transaction that we attribute to an account so that when somebody goes in it starts to become something they can’t make actionable.”