Data Explained...Data or Data?, #PowerOfSports Pro Sports Assembly Series Ep 4 - Christine Lawrence , WWE & Ryan Dastrup, MLS

(upbeat music) - Welcome to today's episode on the Life in the Front Office podcast with the Power of Sports series with pro sports assembly here with my co-host, Crane Million. Really excited to host our guest today in Christine Lawrence from the WWE on the content analytics side and then Ryan Dastrop from the MLS on the analytics side as well and just excited to talk about both of their career paths, kind of how they got to where they are, but also the world of analytics and how it evolves with decision-making, large organizations, and ultimately how it plays a role and a factor in the day-to-day business. So none the less, welcome. - Hi, great to be here. - Thanks for having us. - So let's just start off real quick with, and Christine, we can start with you. How did you get into the analytics space? Was it something that you knew, say like out of college that you knew you wanted to get into and then your career path evolved or was it something you kind of just stumbled into along the way? - Oh. - This was something I knew I wanted to do well before college. I have told this story to a few people. I have known I wanted to do this since middle school. My dream in middle school was to work for Nielsen and that is a very strange dream for a middle schooler to have. - Well, middle schooler knows about Nielsen. That's what I really want to know. - I was obsessed with the ex files as a middle schooler and the ex files changed days from Friday to Sunday and I started following the ratings in the newspaper. It was just this huge thing for me to know what was happening and I was like, I want to do that with my life. And so I went to college, followed that up with grad school, got my focus in research and audience analysis. First job out of grad school, Nielsen, my dream job, right? What do you do when you start at your dream job? You go find a new dream, right? - Well, where you just stay there for 35 years and run out. - Yeah, one or the other, but I went with my find a new dream which was, you know, get involved in this but also sports. And that was, you know, the path I ended up taking from Nielsen to Viacom, to Spike when they had UFC and impact wrestling and then to WWE where I've been for the last eight years. - Brian? - Yeah, thanks, Christine, for that little Nielsen plug as a former Nielsen employee. I went, I think they do some really great work in this space. So that's awesome. - Am I missing out on this Nielsen boat? I did not get the memo. - It's an amazing place to have learned so much about the TV, you know, measurement industry. It's a real great baseline for everything I've done since. - Absolutely. Like a really big springboard into some of the stuff that you're probably doing today in your job as well. I guess for me, when it comes to analytics, I knew that I wanted to work in the analytics space as well. I probably took a little bit of a different path. In sport, I actually started with the Phoenix Suns where I was selling sponsorships, not much analytics when we were looking at sponsorships back in the day. But again, I felt that the power of data and being able to go back and provide the sponsors with more detail about the actual partnership. And if it's beneficial or not, was always something that really resonated with me. Shortly after my tenure with the Suns, I went to the Arizona Coyotes. There was more on the marketing partnership side where that was definitely a lot more data and analytics focused. We used a lot of measurement from digital and social to the TV content. Whatever data and analytics we could provide to our corporate partners at the end of the day to show them that it was a mutually beneficial partnership was really powerful for us. And then from there is when I really stepped into the analytics space, I started with Repucom. Where ultimately I was working with all of the teams and leagues and really the sales team to help them go after and generate new business. But my focus was really on the back end to help give kind of the best practices and the strategic insights to our sales team to be able to go out and generate more new business. And then my time at Repucom ended when Nielsen acquired Repucom. And then I just transferred over to the Nielsen team. So from there, I kind of the same role I was working with our sales team. But in conjunction with that, I started with Nielsen Scarborough. So all the teams, all the leagues, a lot of the brands and agencies use Nielsen Scarborough for the different consumer insights and data that that platform provided. And I was one of the main users of Nielsen Scarborough. I have to admit, my first time or my first four way into data and analytics was Repucom. We used Repucom at ESPN events for our bold games in our basketball tournaments. And I was like, oh, so this is a whole thing that helps us go get more money, got it, got it. And I was their point of contact. And I would be the first to admit, data and analytics was not even a thing I thought was necessary in sports. You just think you make decisions based off of what your fans want and what sales tickets. But now as I'm learning more about the industry, like data and analytics drive conversations. And so what I want to hear from both of you is you've both been in this space of using data and analytics for a while, but I'm sure it's been not as easy or people haven't embraced it as much in the past as they do now. Talk a little bit about how you've seen that trend take place. Now you're seeing teams have a person on staff, like actually playing using data analytics. So it's not just for the front office anymore, but it's being used all over the place. Yeah, I mean, at WWE, we don't exactly have teams, but we do have our different flagship brands. We've got Ross, Macdown, we've got NXT. And they all have talent on them. So we're actually tracking all sorts of information about our talent, how they're performing on the shows, what parts of shows are doing better based on who appears where, or what kind of matches we have. And we actually share that information with our creative teams. And they can use that to make decisions. Kind of like someone would use information about their players to figure out who should bat first or who should bat clean up. Similar concept, but for a slightly different sport. We're also using information to figure out where the best places to host our events are. Where are our viewers who's going to buy tickets to come? Especially now that we're back in a world of hosting live events with fans in them. We want to get the best bang for our book by planning out a route that hits a city where we're definitely going to get people coming and putting butts in seats. And then of course, we have the viewership data itself where we're figuring out how we're doing compared to our competition. And using that to sell to potential partners to advertise in our shows. It's data touches almost every single thing we do. And it's a lot of work. >> I want to hit on that point real quick because it touches every single department. And I think that's a misconception sometimes of the data and analytics group is that. Well, they're just crunching numbers and then they put a report on the CEO's desk like no. You're trying to, in a way, nicely infiltrate yourself into each and every department understand what's going on. Which honestly makes you one of the most dangerous people in the organization because you have a pulse on everything that's going on. What some of the problems are, where some of the solutions are. Obviously, you're working with those teams and conjunction of what they know and all that. But Ryan, just talk a little bit about how you get people to buy into the data and analytics side because numbers are scary sometimes. And not everybody wants to know the numbers either. And then data isn't always good. You got to be careful of what you share, right? There's a lot of, I'm probably giving away some of your secrets or whatnot here, Ryan. But just expand upon that. >> Yeah, I mean, I think when we look at data, it does. It really touches in every department across sports. How do you get people to buy in? I think we're still trying to figure that out. It's definitely been a little bit more of a challenge from both. People, I think, who work for a team or a league versus kind of some of these outside agencies. However, it is important once you have the data to make sure that the data is clean, that you have people on staff who actually understand how to interpret the data. And how they, if they can use that to make more strategic decisions. I will say in my roles, both in the sports business and then also in academia, data analytics is where it's at. But we have to make sure that the people who have access to the data, actually know how to tell that story with the numbers that they're looking at. I think there is a little gap between the industry professionals and the people who are actually kind of looking at it. We're not quite sure sometimes how to tell that story. I think we're getting smarter. We're having the right people at the table when we're talking about these discussions. But it's been a challenge getting people to, I think, buy in and really understand the data. The storytelling, I agree, is such an important part of getting the message across. If you're telling the story wrong, they get the wrong point. And that couldn't lead to a whole host of issues. And you know, people don't always want to hear, you know, your viewership's down. Or your ticket sales are doing this or that. So being able to present that in a way that they're willing to accept. And they get the point that you're trying to make is really the line you're walking. I think in conjunction with that, I mean, even if it's a bad story per se, look at the, look at the benefits. Like find those ways where you can use the data to help you stand out from the crowd. I mean, that's why we have it. Even if the numbers are bad, viewership numbers are bad. Or maybe the numbers are not the best that you're giving to a corporate partner. Find ways to then use the data to your advantage to make it better. Ryan, I want to, I want to jump in real quick before Crane jumps in. In that you sold sponsorships before you got onto the data, the data side. And someone would be like, well, Ryan was a sales guy. Like, how did he get into data? But at the end of the day, you're still a sales guy. You're just selling data internally and selling decisions or selling the stories internally that you were talking about. So your skill sets are transferable. Whereas someone might look at that and go, like, how did he go from that to that? It's, it's transferable. But I think at the end of the day, you, you really have to use the data to your advantage. And in sport, in general, in life, it's really about creating these mutually beneficial partnerships. I think anyone can go out and sell a sponsorship. If you can sell it, you can, you can get a sponsorship to come or more. But it's actually being able to tell us, tell the numbers, show the numbers and tell that story. And make sure that at the end of the day, you've created this win-win partnership for everybody that's mutually beneficial. And that's why you have the power of the data, the story telling to them, go back to show those corporate partners how beneficial that partnership has been. That's, that's where transfers over. I think the selling of sponsorships, again, anyone can do it. But not everyone can understand how to then incorporate that data and help show that, that ROI to the, to the end user. I feel like I'm seeing so many companies come out now and their focus is data analytics, right? We see projects being done, research being done, using data analytics before it was like, oh, if you're an academic or you really care, then you know that stuff. But now we're seeing it all over the place. What is the next frontier of using data and using that to, and, and, and analysis decisions in sports? Where do you see that going? I'm going to let Ryan go first. I don't want to hear what he has to say about this. I think the, as an industry professional sport, we've always had access to some great tools. Nilsen, gum gum, you gov, link fire, I mean, they're, you name it. There's some great tools that we use in the industry. I think that next level though is, is being able to kind of peel things back a little bit more and do an even bigger, deeper dive. So for example, I'm actually a PhD student at the moment at South Carolina, where a space that we're kind of looking at right now is a thing that we've, we've created called a fan panel, where it's taking the data that like you gov and Nilsen is provided from Scarborough. It's great data. But then we're actually overlaying that with the team's data. So then we have this test and control model, where we're able to actually do a deeper dive on the actual fan consumer characteristics. I think that's where it's at. We need to do a deeper dive and be able to really understand who our fans are. What are their passion points like? How can we actually specific tailor these events towards our fans? I think WWE actually probably does one of the best jobs, being able to actually go out and put on a show for the fans. But I'm pretty sure that they've also used data and analytics to help them get where they are today. And I think it's being able to just do a deeper, deeper, deeper dive on who the end consumers are at the end of the day. Absolutely, the fact that we own so much of our data and are able to take different pieces and put it together to really get that 360 view of a fan is immensely helpful to understand what they're looking for, where they are, what's going on in their lives outside of sport. But what's driving them, and then we can use that to make decisions. And then on the other side of that, just from a purely viewership and consumption kind of standpoint, it's not just TV, there's digital, we've got YouTube, we've got Facebook, we've got Twitter, we've got Snapchat and TikTok, oh my God, TikTok, they don't get me started. And we have all these platforms that we can reach them on and trying to understand where they're going and how they're using those to supplement what we're putting out there on TV and how we can best leverage that. And in a world where streaming data is so different from platform to platform, there's no unified kind of, here's how that looks across all of these platforms and we can't necessarily say this person is also this person and this person and this person on other platforms. So trying to understand what that could look like, and then we've licensed out our content to peacock. So we also have another platform that we get data from, but it's not the same data we used to get when we owned our network ourselves. So working with our partner to share data and insights back and forth is also becoming incredibly important, whereas we just owned it all previously ourselves. We've seen one quick point on the TV component because as Ryan and Crane have mentioned, it kind of all flows around the green stuff, right, the money, the revenue, as Andy would say, you know, the blood that flows through the veins of the sports industry is green, right? Because at the end of the day, that's what it comes down to now. When you talk about like apples and oranges on data and like trying to compare one to the other, how do you then help tell that story of, this is a new, this is a new space, right? Like what is this worth? And I think that's probably a whole nother episode of like valuation and what we won't even touch that. But just give us a little quick insight into like, how do you take, what's the approach into exploring that new space and trying to tell that story across the department? At least with some of the platforms, we do get a sense of the revenue we're making from some of the content we're producing. So we can look at what videos we're putting out there that are making us more money, you know, since that is important. And is there a pattern to what's really hitting with fans that's also something we can leverage to increase that, you know, flow of the green in our direction. But without hurting the biggest space, which is TV, that's where the biggest money is. Despite it, you know, getting older and, you know, viewership potentially declining there, it is still the biggest money maker. So we don't want to hurt that at the expense of making a few extra bucks on TikTok. Yeah, and that's, I think you don't want to alienate your core fan base. And we need to make sure that, yes, at the end of the day, it is about the revenue. In sport, we don't do anything without it being able to monetize it. But we also have to be really strategic and cognizant with, we have our core fan base. Now how can we actually supplement this or supplement, like social media platforms to really make sure that we're fully engaged in engaging our fans across many platforms? I think fan engagement and using the digital and social platforms is really almost one of the next kind of the next steps in the next platforms. It's like, we have the TV now, how can we use other media platforms to really hammer home the messages that we're trying to to make our fans aware of? I also think that's really important for sports that don't run 52 weeks a year is how can you leverage outside of TV in the off season? We don't have the advantage of an off season. So our perspective is a little bit different, but as a fan of, you know, many sports other than the one I work for, I'm often like missing that extra piece during the off season. And I think that digital platforms are, you know, where that's going to be. It's kind of like the seasons, right, like, not, I mean, you have seasons of, you know, summer, fall, winter, spring, right? But then like, you kind of know when to expect X, Y, and Z sport to be playing. And that's when you then tune in as opposed to, I'm all in on soccer, right? And this is like, no matter when they're playing, I'm going to be in on the personalities in this and that, right, like, and there's all sorts of other things that you are then paying attention to. I think you see a little bit of that through some of those docuseries, right? Like the F1 and others that are put out there because that's a little bit more of the behind the scenes, but again, different ways to engage people. Korean, what do you, what do you got to wrap up before we get to rapid fire? Something that Ryan mentioned about, and you kind of touched on his transition into this space as we continue to see data and analytics grow, more people need to work. What are, what are some of the things you could tell somebody who maybe isn't thinking that data analytics is the career for them or they don't even know or understand that that's an opportunity. What is it that you're telling them? Hey, if you like X, Y, Z, you can come in here and do a good job. I think that's a cool thing about sport, no matter what your passion is, you can find a job. If it's law, finance, digital and social operations, you can find a job. That's a cool thing. I think that we have as for working in sport, there's a ton of opportunities. I think for those who are interested, interested in job in sport, it's really finding what your passion points are and then being able to bring value to that organization. So whatever that is, just make sure that you're able to contribute and be like the subject matter expert. At the end of the day, we have to know how to use. We've got great tools to help us, got great tools to help us be these subject matter experts. Then it's just really being able to hone in on those skills and be able to show value to that team or that organization. But for those who want to go into data and analytics specifically, it's a great fill. I mean, but you have to be able to understand data. And again, you can do this on the player side, you can do this on the business side, but you've got to be able to do a lot of coding or Python. But you have to be able to know how to use the data and be able to tell that story. That's what it's about with data and analytics. I will say though, if you're afraid of math, there's still roles in analytics. Okay. That's what I was waiting to hear. I am the first one to admit I failed statistics in high school. Math can give me panic attacks, but I still love what I do and, you know, there is math in the job, but it's not the entirety of the job. There's the storytelling. There's the understanding, the landscape. There's all sorts of other areas of it where you use that data and give insights to it. So it's not just, you know, oh, this plus this and the square root of blah, blah, blah. I'm making myself nervous talking about these concepts right now. But yes, there is math involved. And I never, despite wanting to do this, my, basically my whole life, I didn't think I was going to be in a math field and here I am. So it's possible. And don't be afraid of it. That's the biggest misconception of data and analytics from those who you've either worked with in the past or like, because it's probably different in every organization, right? If you're at Nielsen and it's a data focused company, like misconception may not really be there, but then when you go to a team or a league or something else, right? What Christine, what first comes to mind? Oh, well, this is, this is less of a team and league misconception, but just the general Nielsen misconception, I get a lot of people not understanding that if they're not in the Nielsen panel knowingly, Nielsen doesn't know what you're watching. That's a lot of people think that, oh, I'm going to turn my TV on and it's going to help my favorite show, but you have to actually be in the panel and a lot of people don't understand that. I just want to stop right now because do you know how often I do that, like all, I'm like, okay, Bob Hearts, I'll be Shola, I don't want them to cancel this show. I can't watch right now, but I'm going to turn it on so they don't cancel it. And now I'm like, oh man, even I did that, oh my gosh, I've been doing that for a very long time. Even when my friend Maria's show, like, I don't watch a ton of sports on TV, but anytime Maria was on, I was like, let me turn it on so her show will get rating, but they don't even matter now. Hey, can you, hold on, can you explain that? What do you have to do to be in the panel? Yes. I mean, I could go into a whole, I actually teach in Nielsen 101, but there's a, you know, there's a panel and they recruit people into that panel based on census information. How it represents the US, but you're actually invited to participate. It's, you know, invite, accept, get all set up, they attach things to your TV. And then that is what's measured. There's a lot of, there's so many different Nielsen services that, I mean, there's more than just panels, there's, I mean, there's from your viewing consumption to even shopping, purchasing behaviors. There's all kinds of panels that you can actually be a part of with the, with the Nielsen services. It's usually right now. Crane's like, I got, I got a side hobby that she's about to develop. Ryan, Ryan, what's the, what was, because, because you've been on the team and the league side, like, what's the biggest difference between how teams go about it? And how the league goes about it? I actually think there's a lot of similarities at some level. Each league is set up a little bit different. Some of the individual teams have access to these different platforms. Sometimes they're league wide deals with service providers that then disseminate that information down. But I actually think it's pretty comparable where a lot of the leagues and teams are, it's really about the knowledge share, and it's using those different platforms to be able to use the data to really help with your strategic initiatives in general. But I really think that for the most part, it's, it's pretty similar across each league. Yeah, I think we're seeing that kind of best, you know, the team bow version, right? Like it's being built out across every single league and property to kind of help the teams even at their levels. So, Crane, I can just see, like, you're already mind blown. There's something else going on there. I don't want to distract you from the rapid fire because that's important. But what, what's your, what do you got? No, I'm just over here on my second screen, literally on Nielsen contact us so I can get on this panel and he's found out, but no, I'm ready for rapid. Are you ready? I'm ready. Go first. Okay. Data or data? Oh, God. I think it depends on who I'm talking to. Yeah, totally depends on who I'm talking to. I think I go data most of the time though. Data for me as well, okay, Mac or other. Or I should just say Apple or other. Personal life, Apple, work, other. Apple for me all the way. Oh, all right. What is one thing you wish more data scientists did in this, in pro sports assembly? I mean, sorry, in pro sports. Watch the content that they're analyzing. Yeah, make sure that and make sure that data is clean. We see a lot of issues with what. Data's that's not the most reliable or valid. All right. If you could play any sport, which would it be it's both. Let me go across. Those are two very like they use a lot of numbers in those like long history of using numbers. I think they're teams. There's sports now that are just leaning into it, but those two sports, they've been using numbers for a long time. The stats of baseball are amazing. Yeah. The answer coming when she's mentioned earlier that that first or back cleanup, she I got that one. We see to it. Last question for me, I'm going to do sky dive or scuba dive. I've done sky diving. So I guess scuba diving's next. They're going to go scuba as well. Yes, I'm a scuba diver, so I love it. I don't love heights, so I probably will never go sky diving again. At least you've gone. There's been some others where I, including myself, I'm like, no, never, I'm good. All right. Last question for you. Ryan, we'll start with you. If you could live anywhere in the country, where would you live? I like to sound. That's Charlotte made clean city, queen city, queen city, I don't know, I've got, so now I've I've lived in the same town for my entire life. So it would, I don't know, I love San Diego's weather. But I think I'd probably have to pick outer banks, North Carolina for the beach and the weather there. All right. Staying with North Carolina. Got it. Love it. Well, both of you, thank you so much for your time, insights, perspectives, knowledge. It's been an absolute pleasure to have both of you on with our power of sports series with pro sports assembly, green, great to co-host as always. And I appreciate it. Time. Don't worry about us, Christine. I want you to know, my grandmother has not missed an episode of Ross since 1993. I cannot call her on Monday, like if I call her at 750, she's like, I have 10 minutes and then I'm hanging up. So whatever, whatever you need, I'm hanging up in 10 minutes. So you got some serious, like it's, it's like, I don't know if it's bad to say, but co-falling in my grandma, she moved to this country 50 years ago and like, Ross, her J.I. I'm a rice. So I had to end with that. Thank you all for this conversation. I'm not surprised by how efficient it was when dealing with data scientists. So thank you so much. Thank you.