Anthony Perez is the Vice President of Business Strategy with the Orlando Magic. He and his team are credited with building a predictive analytics ticketing model that can help the Orlando Magic tailor their fan experience to increase customer satisfaction. In this interview, Anthony shares how he uses data to gain understanding of his customers and to establish a loyalty connection.
hrh media: Would you mind telling us a little bit about your background and how you became interested in data analytics?
I started in finance so I think it was a natural transition into analytics. When I first started with the Orlando Magic I was really focused on the financials of the new arena. From there I went into investment banking for a little while, but ultimately came back to the Magic and my focus, again, started out on the financial metrics side of things. But my position ultimately became the much broader role around analytics that it is today.
hrh media: The Orlando Magic is both one of the most technologically advanced arenas in North America so it’s no wonder that you have a wealth of data at your fingertips. Would you share a key business driver behind the Magic’s decision to use analytics to establish a loyalty connection with fans?
Sure, for us it just really made sense going into the Amway Center with the technology that we have, all the data that we’d be collecting, all of the capabilities that we had in terms of leveraging the technology and the building, we really wanted to make sure that we were doing the best job that we could to drive the customer experience. So, analytics was an opportunity for us to expand the things that we were doing in a much more systematic way to make sure every fan felt like there was a customized experience for them and that we are really marketing to them and what they were looking for. For our season ticket holders in particular, we wanted to make sure that when they came to games and interacted with their service representative or anyone else from the team, that they felt like the experience really spoke to them and their preferences and the like. So that was the main driver behind taking a bigger step into the realm of analytics.
hrh media: How have you and your team been able to turn the data that you’ve collected from the scanned and tracked tickets into a tailored fan experience? Can you provide me with an example?
That’s something that we’re continuing to develop. A great example, in particular for season ticket holders, but really for anyone in the building, we run a program where if you purchase something at a concession stand or at one of our retail stores you can scan your ticket during the checkout process and get entered in for a chance to win courtside seats. What this allows us to do is capture the ticket barcode information and ultimately match that back to the account. So we can see what account purchased the various items for that transaction. We start to see trends around what people are buying and when they’re buying.
The season ticket holders are folks that are coming to the majority of our games, so we can get a feel for when they typically purchase merchandise throughout the season and what their preferences are in terms of concession stands. A great example we like to point to is if a family of season ticket holders is taking frequent trips to Cold Stone at halftime on the terrace level, if we see that they have a preference for Cold Stone, that’s something our service rep can use the next time they want to visit that season ticket holder in their seats. Maybe they’re surprising them with Cold Stone, bringing it to them just as a gesture to say that we really appreciate them being a season ticket holder and everything they do for us. I think that’s the small example but that’s the type of thing that we continue to do.
Right now we’re working through taking all that information that we’re collecting from scanned tickets, putting it into a format that’s actionable for our sales team and service team and ultimately delivering that.
hrh media: Retaining season ticket holders year after year can be pretty tricky for sports franchises. Have you been able to solve this riddle with somewhat of a success rate using data analytics?
I don’t know that anyone will ever solve the riddle completely, but I think for us, we’ve done a good job of being as smart as we can about how we target season ticket holders and how we focus on retention. We’ve done a lot in that area in terms of predictive modeling and trying to understand the biggest drivers behind the renewal decision for a season ticket holder and ultimately boiling that down to things that are actionable and then working with our service team to act upon those things.
A great example is ticket utilization, which I think for everybody is pretty intuitive, but we’ve really focused on that and what we found is that season ticket holders that aren’t really utilizing their tickets to a certain level are ultimately ones that are at risk not to renew. We’ve built an entire campaign throughout the season about touching those season ticket holders that aren’t utilizing their tickets and doing it in a way that we can be preemptive so we’re not finding out that they felt like the value wasn’t there for them because they didn’t use their ticket throughout the season.
Instead we’re trying to jump on that if they’ve missed one or two games, and if we escalate the actions we take as they go further into the season and not utilizing their tickets. I think for us it varies from team to team in terms of how the structure is set up for a service representative and the amount of accounts they deal with. For us, one of our service representatives could have anywhere between 400 to 500 accounts, so it’s really difficult to build a personal relationship with each one of those people, so what can we do to help our service representatives cut through all of their accounts to really get the biggest impact and focus on those people who need or want the additional action—that’s where we try to use predictive analytics to help us do that.
hrh media: Are these actions that you’re taking being noticed and appreciated by the ticket holders?
I think so. For us, with ticket utilization, it’s really making sure that season ticket holders feel like they’re getting the value for their tickets in whatever way that means. It may mean selling their tickets for games they can’t attend or it could just mean giving them away to a client, colleague or friend, but we’re just trying to make sure that we’re proactively driving those actions and making sure that it’s not something that the season ticket holder looks back when it’s up for renewal; and in retrospect says, well if I’d done things differently maybe I would have gotten the value, but I didn’t.
I do think they appreciate it and I think they’ll see that as we continue to go deeper and deeper into how we’re leveraging our data I think they’ll continue to see a better experience game after game.
hrh media: What does the future look like for customer data analysis within your franchise?
I think the future is really exciting for us. We do a lot of really interesting things now and I think we’re a team that’s really trying to push the envelope and be more advanced in terms of how we’re applying analytics. Even from our standpoint I think there are so many things that we can continue to do, there’s a lot of unstructured data that we’re really not leveraging as well as we could, and social media is a whole frontier of opportunity for us in utilizing data. Even just outside of social media when we talk about unstructured data, a lot of communications with our season ticket holders are happening through email correspondence, text messaging and those types of things. So how do we really leverage those kinds of conversations because right now without any type of text mining application, we’re really not able to maximize the insights we can draw. We do a lot of research here and even with surveys we see the open-ended responses and we try to leverage that as best as we can. I think there’s a lot of opportunity there in terms of text mining and just utilizing unstructured data better, whether it’s in our predicted models or just driving key insights for our management.