Data-Driven Success in Sports with Erin Kelly

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Hopefully they stay together, but we'll see. Yeah, we don't want to start any

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rumors. There's no rumors.

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We don't want this with the nation after us. Let's not. I don't want them after us.

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Sandy here. Welcome to How I Met Your Data. I have to apologize.

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This is going to be a long episode.

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And initially I was going to break this up into two.

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After listening to it, I just, it just felt wrong to do that. So leaving it out there.

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So buckle up, take it in chunks, do as you wish. But great conversation with

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Erin Kelly from Craft Analytics Group.

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And Anjali and I just truly enjoyed chatting with her and learning more about

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Craft Analytics Does and how they help the sports teams out there in the leagues

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run their business with data. So listen on in.

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Music.

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So, Erin, it is so great to be talking to you.

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Sandy and I kind of realized early on that we both knew you individually of one another.

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You and I worked at the same consulting firm for a period of time.

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I remember meeting you in Chicago where these DNA leaders were all brought together.

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Not entirely sure what the purpose of that meeting is now. It's been several

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years. I think it was back in 2018.

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I was a new mom, so still trying to figure out my new life and traveling and

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working and having a kid. And I remember you and I met and we chatted actually

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quite a bit about, you know, saving space for our kids and kind of prioritizing.

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Kid time with work and how to manage that. And that really stuck with me.

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And when I joined Sandy in this adventure at Cervelo, realized that we both

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knew you and we're really excited that we had this common connection.

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So thrilled that you're here with us as you've now moved on to Craft Analytics Group.

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Kager, as it's affectionately known, is a technology and services company focused

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on data management, advanced analytics, and strategic consulting in the sports

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and entertainment industry.

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Aaron is the Senior Vice President of Enterprise Solutions and Strategy.

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Aaron, can you tell us what the Senior VP of Enterprise Solutions and Strategy

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is responsible for and what you do day to day? That sounds so impressive, by the way.

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What does that all mean? Well, first of all, when you said, why were we together

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back in Chicago, it was connections.

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And I think that if I really think about it, and before I explain a little bit bit more about myself.

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When I saw you guys were putting this podcast out, I thought,

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wow, you two are people that I put up in the world in terms of data, in terms of connectors.

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And I really just see that this podcast is like an awesome platform to connect

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all of us as listeners to a variety of folks with different perspectives.

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So thank you for being great connectors.

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I'm glad that we connected several times throughout the process here.

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And thank you for that introduction from from a Kager standpoint.

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So yes, I am the Senior Vice President at Kager. I manage all of our client-facing delivery.

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So we go to market in two main kind of service areas.

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One is a SaaS-based data warehouse platform and tools, and the second is strategic consulting.

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So we actually started, we spun out of craft sports and entertainment,

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the Patriots revolution, what have you.

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Close to eight years ago, the CEO, Jessica Gelman, has worked with the crafts

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in general, almost two decades at this point too.

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So we started as a product and a data warehouse platform, data management platform,

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because we saw an opportunity, especially in sports and entertainment,

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to help clubs, leagues, industry players better understand the fans beyond just the ticketing.

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Well, starting with ticketing, but really just beyond the ticketing.

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And as we spun up the software data management platform, saw a need as well

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that you can, great, you can integrate and bring data all together,

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but how do you actually apply data to business problems and business areas?

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And that's where our strategic consulting team.

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Grew up over the last five or six years at this point. So it's been an awesome journey.

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I've been with Kager four and a half years, left Slalom after a six-year stint

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there, running the data and analytics practice to jump over to Kager.

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It's been a great journey. I joined, we were 40 people.

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We were kind of at this interesting inflection point. I also joined less than

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90 days before COVID kind of officially hit and the sports and entertainment

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space shut down altogether.

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And so So it's been a really interesting journey.

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But I think what we've seen a lot is that even coming out of COVID,

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it's been such an accelerant in terms of the importance of data and analytics

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to drive the industry forward, to drive the best fan experiences.

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And it's been a super fun journey and where I feel like we're just getting started.

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I think before we get too far, let's just put it to bed and address the elephant in the room.

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What are your favorite sports teams and players?

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I love this question. And I actually was chatting at dinner with my family the other day.

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So I have an eight-year-old girl who plays a bunch of sports,

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and I have an 11-year-old boy, similar, very sporty.

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They do like other things, but they're pretty big athletes. And so I was sharing

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and asking them who their favorite, and then I asked them who mom would say.

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And so initially, my daughter immediately said, well, me.

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So yes, I do have two budding athletes in my house that are my favorite athletes,

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by far, done. So getting that out there in the world.

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But if we switch outside the Kelly household, my favorite player is Caitlin Clark.

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I think she is just phenomenal.

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I mean, beyond just how good she is on the basketball court,

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just how brave she is as a...

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Young woman coming out into the professional arena.

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She has lots of eyes on her at any point in time.

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Just the ability to operate at her level under pressure.

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Like Billie Jean King, I think, had once said, pressure is a privilege.

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To me, she just embodies what that means. And I think it's phenomenal.

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I love that we're talking about Caitlin Clark at home all the time in terms

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of what she's doing now, how she's entering into the professional arena.

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It just, it opens up so many more conversations around women's sports in general.

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From a favorite team standpoint, you know, this is funny.

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I actually feel like it's a little bit of like the yearbook superlative,

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like who's my favorite team with the best mascot?

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I would say I love like the Timberwolves are one of our clients.

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They have Crunch, the wolf is their mascot.

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Love it. My favorite Boston team is the Celtics. I think that's timely right

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now. They're going towards hopefully NBA finals.

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My favorite U.S. team, so I am definitely a, I love the Olympics coming up.

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I love just that camaraderie that nationwide sports brings.

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And the U.S. women's national team, the women's soccer team is my favorite and

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really rooting for them.

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I think they're at an inflection point and excited to see they've got a new

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coach and other things like that.

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So anyways, I probably sound a little little bit crazy. But I think it's interesting

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because for me as a sports fan, pop culture fan, I don't think I'm unique in

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terms of I don't have just one. I have many.

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And I describe them in very different ways that are both emotional and rational.

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And I think that to me is like underpins.

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Why Kager does what we do on a day-to-day basis for our clients.

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How do you actually put context into all the pieces that I've just rattled in

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terms of who my favorites are?

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I love the Caitlin Clark call-out because I think what's interesting about her

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is she's extremely confident, yet very humble at the same time.

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And that is a balance that we often do not see in players, male or female in sports.

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But I watched her just the other day, there was this little thing I saw where

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she was coming out of a, I think it was halftime, she was being actually coached by another player.

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The other player was giving her feedback as they were walking back to a locker room.

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And she was listening intently and having that conversation.

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I thought that was fantastic because it's good to have young women see that. A hundred percent.

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Absolute confidence she has, but yet still grounded and humble in terms of where

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she can grow from others who have been, you know, who've walked that path.

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Yeah. Love it. The other comment you made about being diverse, right?

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And having multifaceted likes, some emotional, some specific because it's your hometown, etc.

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I wonder if you find that it's really driven by a shift in how we consume information.

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Because when we were younger, the only thing we saw was our hometown team.

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We didn't really get publicized in terms of unless it was like Michael Jordan

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or somebody really, really out there.

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We really didn't know the inner lives and workings of people.

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We weren't following them on Instagram.

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They weren't trending on Twitter randomly. I would assume that you find that

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to really change the way people fan.

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Yeah, I think you're 100% spot on there. It's like, yeah, if you think back like 30 plus years ago,

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you know, regional sports networks, you were tuning in to watch what you had

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on your, I don't know, 10, 12, 50s channels on your TV, let alone just the consumption

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that you were having via newspaper and other things like that.

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And yeah, we see so many trends coming that people are following players,

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that they have multiple favorite teams.

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There's this mindfulness, especially at the professional league level,

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to really understand fandom across the sport in general.

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And so we work with all professional leagues, including the NFL and the NBA.

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And those are two, in my mind, that have really understood this and really tried

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to lean in to understand just the ways of how people are consuming information

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and what does that look like,

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even to the point where the NFL

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has tried various digital platforms and viewership platforms and whatnot,

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just as a means to, one, deliver the content out to where the folks are,

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but learning through that. What actually works? Where are people consuming?

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How can you actually use that information to then drive the next wave of interaction?

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We were overseas a couple weeks ago, one of our first European football clubs,

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soccer clubs, and they were talking a lot around how they are digitally activating

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and engaging via WhatsApp from an application standpoint. point.

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So, you know, obviously, like WhatsApp is on my phone, because if you're traveling

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overseas, that's what you're using.

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But it's less like relevant, I think, into daily ways of life, at least for me in the US.

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But just like thinking about like, how do you become a master or at least a

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at least having like an appreciation for all the different touch points that

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people could be engaging from a digital standpoint?

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And what do you do with that information? It's like a treasure trove of tidbits

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there to really understand kind of the fan at a deeper level. Sure.

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So like as you're kind of thinking about like engaging with the fans at a deeper

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level, are you seeing certain trends that are really shaping how sports teams

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are interacting with their fans?

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Yeah, absolutely. So just to take like a step.

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To the side for a moment. So Kager, we work on the business side of sports.

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So we don't do necessarily on the on-field, on-court performance pieces.

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And really, for us, the fan is at the middle of the nucleus of everything from

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an understanding, what are all the different business levers that you can drive

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with a deeper understanding of the fan?

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And so, you know, we think about those levers and kind of a few big buckets,

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and then there's some ancillary levers. But obviously, ticketing is one of those.

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Media, in terms of the media rights, both at the league level,

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the team level, and industry level.

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Sponsorship in terms of big brands and how they're interacting with the clubs and the leagues.

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Retail and then kind of in-venue and game day. I think ticketing is always going

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to be king in terms of those interaction points.

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But at the same time, there is a ton of data and analytics that are driving

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that on any point in time.

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I think where we see from a CAGR standpoint is that a lot of clubs still need

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to get the foundational data management layer right to then be able to do the

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dynamic pricing, the more sophisticated algorithms or whatnot.

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So obviously, there's always going to be more room for improvement there.

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But I think one of our biggest areas that we see is really around the sponsorship side.

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So there's an increasing desire for sponsors of the big brands to understand

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at a deeper level, who are the fans?

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Who are the eyeballs that are going to see that name on the or the patch on

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the jersey, the name on the stadium, the billboard outside when you're pulling

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into the parking lot or the digital signage when you go to the app or the website or whatnot.

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At the end of the day, the sponsor, it becomes a valuation equation.

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So what's the value proposition of that club to the sponsor and vice versa?

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And I think we're seeing an increased level of sophistication and frankly,

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kind of ripe for innovation in terms of how you can get more granular around

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the true value of a fan and of a brand to the club and vice versa. I have a question.

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You triggered a thought in my head as you were talking because.

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Player sponsorship specifically is a big deal.

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Like, you know, I look at Caitlin Clark, she's getting Uber dollars right now.

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Have players come to you? Have players come to Kager and said, help me?

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Because I think about not just their sponsorship opportunities,

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but also thinking about their memorabilia, right?

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Like I know a lot of players resell their own stuff. off.

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I'm just curious, has Kager dabbled in player analytics?

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No, there are kind of big entities out there.

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So like Wasserman is one of a big entity there. They have an awesome women's sports area.

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So it is more your traditional like agency model and that they're working then

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with the brands on behalf of the player.

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But it is really interesting because I think where you're going is like,

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how do we connect all the diverse data points there?

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You have a player, you have a brand you have the

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actual business side of the club that that player it's

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a it's a missing it's we think about like the three-legged stool or

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the 10-legged stool you're missing a stool there if you're actually not

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connecting the business side of the the entity that that player is engaging

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with with the brand and the person it's like you're only then looking at the

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social media channels and the other channels like that but you're you're you're

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missing that triangulation and you know i think i think there are a lot of technologies

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and like concepts out there that

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are trying to push to like more data sharing and pieces like that.

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Obviously, that's, you know, heavily governed.

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And I don't know if that's all the things that you guys are probably seeing

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as well. I always think of Anjali as like the governance queen.

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So you could probably teach me a little bit in terms of some of the new frameworks

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and how people are thinking about making sure we're keeping,

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you know, everything safe and secure and at the right level.

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Yeah, call me anytime. time.

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Awesome. Done. Connection. Check. So, Erin, you had mentioned the NFL and the

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NBA kind of being a little bit more ahead of the curve in terms of their use of data and analytics.

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But are you seeing some other leagues that may be a little bit more challenged,

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a little bit more difficult or behind the curve?

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And what's really driving them to stay where they are and really not take that next step? Yeah.

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Well, besides the fact that they're not working as much with Kager.

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That's one. So I will put my own pitch on that.

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But no, I mean, I think in all seriousness, they're leaving the opportunities

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on the table to actually capture the fan data beyond just the ticketing and transactions.

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They're thinking more around the short-term operational, how can I get this

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event stood up, but not necessarily thinking about all the different parts and

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pieces from an experience standpoint.

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I think as we look across leagues, there are leagues where your traditional

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season ticket member is now.

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Mid-50s and aging. And that's a scary place for some leagues to be in.

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A full season ticket membership for any of the professionals, that's a heavy price.

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One of the NBA clubs that we work with have done like junior membership.

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And it's been a great opportunity to start to pull kind of younger folks in

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and just get the bite at the apple. And at that point, it's not just about the revenue.

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It's about actually forward looking, like how can you make sure that you're

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filling the right funnel here so that you can deliver a great product on the

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court ice field, but also just sustain that kind of community,

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which is really driving that business forward.

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So I guess like said in a different way, what are the laggards not doing?

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They're not investing as much in data and analytics.

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They're not taking technology serious as a true business driver.

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They're shying away from even thinking AI and some of the productivity efficiency

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that they're, let alone alone, just driving new customer experience and how

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to get more efficient with the technology pieces.

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But I do think it's changing. I think the last thing I'll say on this is we've

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seen over the last couple of years, several, there's been a lot of transactions of teams acquired,

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and it's brought even more kind of business-minded folks and entities into the sports arena.

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So you're more traditional folks who have kind of grown up as owners are either

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being displaced or Or importantly,

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adding new thought leaders into the mix that don't necessarily have the history

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of sports as a business, but are.

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Savvy business people that have come from the financial services,

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the consumer goods, whatever, that actually believes in data and can bring those

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skill sets and experience to the table.

264
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Well, that being said, one of the things that I've noticed, love to get your

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thoughts on, is just this intersection of sports and truly entertainment, right?

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Like, I don't think we can have a conversation about sports without talking

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about SWELSI or whatever other acronym and we started to pull together for them, right?

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So, you know, can you talk a little bit about that Taylor Swift effect on the

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NFL and how, like, is Kager really exploring that or doing anything with that explosion of data?

270
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Yeah, I love it. I saw Taylor when she came through Gillette Stadium last year. That was so phenomenal.

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I cannot actually even explain how great of experience that was.

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So a couple, like, quick facts here for you. So even just looking at the U.S.

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Population, I recently read that 40% of the U.S. population are Taylor Swift fans or casual fans.

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So 10% of the U.S. population are super fans, Swifties.

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29% are casual fans. So 40% of people are tuning in, excited about what's happening.

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I also read that they were more likely 83% to 64%. So that's Swifties to general

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population to watch the Super Bowl.

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So that is like nuts. That is never, it's like such a phenomenon in terms of

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what this person can kind of bring to the table.

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And so, you know, even if you're caring less about Taylor, you're just like

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more people are watching an event and that just pulls everybody to the table.

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You want to be part of the water cooler discussion or the chit chat or the community

283
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discussions or whatnot.

284
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So what does that actually mean and translate to? So-

285
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To some extent, that's still really viewership and digital engagement and other pieces like that.

286
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It's not necessarily translating to ticketing yet in terms of how the demographics are changing.

287
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There's parts and pieces there for sure, and folks that are probably attending

288
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more so because she may be in there.

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But I think there's definitely room for improvement in terms of how to capture

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those Swifties to be actual ticket purchasers.

291
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To answer your question, how are we, Kager, thinking about it.

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I think we're following the data.

293
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We're trying to figure out where and how the demographics are actually changing.

294
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Where are they changing?

295
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And is there like, what's the true impact there? We know it's a pop culture impact for sure.

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And we know that the halo effect to lots of different tangential businesses is there.

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But like, what does that actually mean from a sustainable power of the revenue

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streams to the leagues and teams itself?

299
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You mentioned demographics changing for ticket buying.

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And I can only imagine because the men Men are still going to go to the games.

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They're still going to go to the games. So maybe they're not taking their buddy.

302
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Maybe they're taking their spouse, their significant other instead.

303
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And then the other guy has to go buy his own ticket, right? So that might be the story we see.

304
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I did find it interesting because the NFL posted, they admitted,

305
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that they had to look at the schedule based on where Taylor Swift was going to be.

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And it could be because she had a concert at a venue, et cetera,

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and they wanted to work around that.

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But she's literally impacting what the NFL is doing, which is mind-blowing.

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She's awesome. Hopefully they stay together, but we'll see.

310
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Yeah, no, we don't want to start any rumors. There's no rumors.

311
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We don't want the Swifty Nation after us. Let's not. I don't want them after us.

312
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But just think about, like, you've got the Taylor Swift movement,

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and then you have the Caitlin Clark movement, and you have the WNB,

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and you have on the Coco Gauffs, and the tennis side is, like, growing crazy.

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And then you've got, anyways, it just feels like it's such a moment

316
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that is now the like norm of what this all

317
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should be and how all the diversity can can

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find its way in and and it really is going to be interesting in

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terms of what leagues and teams are going to win in capturing the diversity

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and thinking about the product not just of what used to work five ten whatever

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years ago but like how do you make sure that when you have the dad and the daughter

322
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or the mom and the son and the what what the maybe a little bit different mix

323
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that it's a great experience.

324
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And it may need to look a little bit different. You may need to be delivering

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different amenities to folks when they come in the gates.

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And so I think that's just, again, goes back to like the data,

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the understanding, the fan behaviors, tapping into those emotions,

328
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and then how can you actually map the product and the experience to those pieces as well? Yeah, exactly.

329
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And I mean, it's curious because, you know, when you talk about having that.

330
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Almost personalized experience when you walk through the door of a stadium,

331
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depending on who you are, would love to hear like some of the changes that you've

332
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seen over the last five, 10 years from an experience perspective. Yeah, absolutely.

333
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We're working with one client now in the NFL out in California,

334
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who is like going digital with a lot of their season ticket member amenities.

335
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So basically, like, how do you light up when that person scans coming through the gate,

336
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delivering specific content and or

337
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food and beverage or other things like that like really leaning

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into understanding where people are coming from and how to like

339
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customize and personalize the offers it's actually crazy to think about before

340
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covid i think it was less than half of the nfl clubs had had more exclusively

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mobile ticketing maybe it was like a handful maybe i don't know less than a

342
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dozen clubs and so mobile ticketing now is just ubiquitous like that's just what everybody does.

343
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But think about that. We used to have the paper tickets, which meant you didn't

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know who actually that belonged to.

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Now we know who that mobile ticket belongs to. You know at least some level

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of information and you have some way to communicate with them during the event,

347
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before the event, and after the event, which is a great kind of turn in terms

348
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of what's happening there.

349
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I don't think we can have a conversation about data and analytics in any industry and not touch on AI.

350
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So, you know, from a sports and entertainment perspective, what sort of opportunities

351
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have you been discovering in terms of the usage of AI? Yeah, absolutely.

352
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Sales and marketing has been just like a huge area of growth and acceptance

353
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from an AI use cases and whatnot.

354
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We work with an entity that sells Super Bowl tickets and kind of huge opportunity there.

355
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The weekend before the Super Bowl, they have so many leads that come in,

356
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in terms of people that are filling out online forms and all these other things.

357
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Like how can you actually, like yes you can use some analytical modeling and

358
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machine learning, but you have to take it a whole step forward.

359
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Like how do you actually think about understanding who is that person?

360
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How do I, how can I use any other context out there to then deliver back a

361
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some level of message that's going to drive some level of conversion there,

362
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just to be able to, I guess, reduce the overall burden from a manual interaction standpoint.

363
00:24:05,513 --> 00:24:11,073
On an actual level, we're definitely seeing more from a customer experience standpoint.

364
00:24:11,133 --> 00:24:16,393
So kind of in-venue experience, how folks are getting communicated to kind of

365
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personalized content and other pieces like that based off of transactions and whatnot.

366
00:24:20,953 --> 00:24:24,873
I still think we're scratching the surface in terms of how it's being used from

367
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a true sports and entertainment standpoint.

368
00:24:26,833 --> 00:24:31,113
But I think the sales and marketing is really out in front, both from the efficiencies

369
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use cases, as well as starting to customize some experience.

370
00:24:34,353 --> 00:24:38,193
However, as you guys probably are seeing, and I would love to kind of understand

371
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what you're seeing from the other industries too, if you don't have the foundational

372
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data in place, and you don't have some of the underlying data management,

373
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like it's not going to work as well as you want it to.

374
00:24:47,153 --> 00:24:52,053
And so again, it's that, you know, that balance of how can you use the true

375
00:24:52,053 --> 00:24:57,973
power of AI, but have the right confidence that it's going to work the way that you're intending it to.

376
00:24:58,193 --> 00:25:01,493
We keep saying you can't have AI without data.

377
00:25:01,593 --> 00:25:06,273
So to your point, unless your data house is in order, you have the right data,

378
00:25:06,373 --> 00:25:11,813
you understand its context, you know whether or not you should be able to use it ethically.

379
00:25:12,213 --> 00:25:16,993
You can't really be confident in the outputs of your AI.

380
00:25:16,993 --> 00:25:20,893
Well, it's also, I think what's conflated about the whole situation is that,

381
00:25:20,973 --> 00:25:24,633
particularly with Gen AI, not your traditional, you know, machine learning,

382
00:25:24,773 --> 00:25:28,193
that's a different challenge, right? That's your traditional data set that...

383
00:25:28,804 --> 00:25:32,344
Maybe you have some unstructured. For the most part, it's being done against

384
00:25:32,344 --> 00:25:33,724
structured data assets.

385
00:25:33,864 --> 00:25:37,724
And we all very well know from many, many years of data management what we're

386
00:25:37,724 --> 00:25:40,164
supposed to be doing there. Whether somebody's doing it or not,

387
00:25:40,304 --> 00:25:41,504
that's a different story, right?

388
00:25:41,644 --> 00:25:46,544
But I think with Gen AI, the challenge is a lot of that is on unstructured data.

389
00:25:46,904 --> 00:25:52,104
And the data management governance principles around that are still not clearly defined.

390
00:25:52,544 --> 00:25:56,264
And most organizations don't do it. They just don't.

391
00:25:56,304 --> 00:25:59,044
They just drop it somewhere. And if somebody wants to use it,

392
00:25:59,064 --> 00:26:01,784
they're responsible for ensuring it's accurate and complete, right?

393
00:26:01,904 --> 00:26:06,904
So I think that's part of the challenge here in terms of efficacy and making

394
00:26:06,904 --> 00:26:11,644
it right and ensuring you have great context is that there's got to be a new

395
00:26:11,644 --> 00:26:15,784
capability within your organization to ensure that you're creating that,

396
00:26:15,904 --> 00:26:20,444
whether it's a graph database or some kind of semantic around the non-structured

397
00:26:20,444 --> 00:26:24,284
data that you now have to mine in order to get those kinds of insights.

398
00:26:24,284 --> 00:26:27,764
And going back to your point about having all these leads one week before the

399
00:26:27,764 --> 00:26:32,004
Super Bowl, I think about a marketing team trying to figure out,

400
00:26:32,024 --> 00:26:33,224
how am I going to convert those leads?

401
00:26:33,404 --> 00:26:36,744
Now, today, if you only have a week, there's only so much you can do from a

402
00:26:36,744 --> 00:26:39,784
targeting message back and doing that analysis.

403
00:26:39,884 --> 00:26:42,764
You're probably only bucketing three or four messages, right?

404
00:26:42,924 --> 00:26:47,044
You can't get to that micro target where you can really turn the knob a bit.

405
00:26:47,044 --> 00:26:50,924
And that's ripe right there, right?

406
00:26:50,944 --> 00:26:54,904
Because now you can really micro-target that message because you have something

407
00:26:54,904 --> 00:26:56,844
like Gen AI behind you plus the ML.

408
00:26:57,323 --> 00:27:00,283
That's priority giving you all the different categories of, of,

409
00:27:00,303 --> 00:27:04,643
and clusters of, of people and figuring out what the right messaging is at mass.

410
00:27:04,843 --> 00:27:08,163
That is an intriguing prospect for sure. It is. Absolutely. Love it.

411
00:27:08,203 --> 00:27:09,123
Let's go figure that out.

412
00:27:09,463 --> 00:27:11,943
But yes. Give me a call.

413
00:27:14,483 --> 00:27:19,363
Two for two now. Two for two. And dropped her own self-promotion as well.

414
00:27:19,423 --> 00:27:20,823
So I think we're all covered.

415
00:27:21,083 --> 00:27:25,823
Yeah, we're all good. Exactly. You know, one of our clients,

416
00:27:25,983 --> 00:27:28,003
public clients we work with is the Philadelphia Eagles.

417
00:27:28,183 --> 00:27:30,543
I love them. I know that's like right in your backyard.

418
00:27:31,143 --> 00:27:35,523
They're just great, great people. Their CFO, we were down there last season,

419
00:27:35,763 --> 00:27:40,763
almost honestly had me in tears talking about how much they think of their fans

420
00:27:40,763 --> 00:27:46,763
as family and how to replicate that familial feel when you have 65 plus thousand in the stadium.

421
00:27:46,843 --> 00:27:51,283
And he was sharing that there was a season ticket member, a dad and a son,

422
00:27:51,403 --> 00:27:53,043
and they had been in the stadium.

423
00:27:53,143 --> 00:27:57,063
They may have had tickets for several decades. I don't, it could have been 30 plus years.

424
00:27:57,603 --> 00:28:00,443
And unfortunately over the off season, the dad passed away.

425
00:28:00,563 --> 00:28:05,303
And so the first game, the son comes, you know, the customer service rep knew that that had happened.

426
00:28:05,363 --> 00:28:11,183
The son comes with a friend and the leadership of the Eagles met them into this,

427
00:28:11,223 --> 00:28:16,043
in the stands, condolences, and then also also gave them his dad's seat.

428
00:28:16,103 --> 00:28:20,343
Basically, it was like, we have removed your dad's seat from where he has been sitting.

429
00:28:20,483 --> 00:28:23,323
Isn't that awesome? I'm teary already. Yeah, that's beautiful.

430
00:28:23,963 --> 00:28:27,943
And so again, like when we were chatting through that, it's like,

431
00:28:27,983 --> 00:28:30,843
how do you create that emotional connection?

432
00:28:31,503 --> 00:28:35,203
Data technology has to be able to help that in a genuine way.

433
00:28:35,323 --> 00:28:39,883
And they're not going to be pulling every seat out of Lincoln Financial. However, like,

434
00:28:40,453 --> 00:28:44,573
That's why people show up. That's why that family is coming back for their 31st

435
00:28:44,573 --> 00:28:46,653
year and their 32nd year and what have you.

436
00:28:46,733 --> 00:28:51,173
And, you know, that might be their vacation fund for the year is going to nine,

437
00:28:51,253 --> 00:28:55,333
10 Eagles home games. And it gives everybody around them the ability to sit

438
00:28:55,333 --> 00:29:00,053
there and they now feel connected by seeing that action, by seeing that moment.

439
00:29:00,213 --> 00:29:03,473
It's, wow, the Eagles really do care about their fan base. They really do care

440
00:29:03,473 --> 00:29:04,613
about us as individuals.

441
00:29:05,013 --> 00:29:09,113
It doesn't have to be me necessarily, but seeing them do that for somebody else

442
00:29:09,113 --> 00:29:12,553
actually makes me feel more connected to the team as well, right?

443
00:29:12,553 --> 00:29:17,113
Yeah, 100%. And so, well, I do think then our theme of the podcast is connection.

444
00:29:17,313 --> 00:29:20,953
So we'll continue and where data connects, right? One of the things that we

445
00:29:20,953 --> 00:29:24,293
were talking about is, okay, well, we have to make sure then that the customer

446
00:29:24,293 --> 00:29:26,653
service reps are creating the right data.

447
00:29:26,733 --> 00:29:30,513
That was an extreme example. But when you actually have that face-to-face contact,

448
00:29:30,713 --> 00:29:35,073
how are you creating some of that context so that potentially you could be utilizing

449
00:29:35,073 --> 00:29:38,213
that within more analytics or whatnot to understand,

450
00:29:38,473 --> 00:29:41,973
to your point, kind of at that micro level, what is actually the offer or the

451
00:29:41,973 --> 00:29:44,773
product or the messaging that you want to be delivering at any point?

452
00:29:44,773 --> 00:29:48,113
You mentioned Kager has two pieces. It's a SaaS data warehouse.

453
00:29:48,453 --> 00:29:53,433
There's also the consulting and advisory services, I'm assuming, together and separate.

454
00:29:53,693 --> 00:29:58,573
How deep is it in terms of the inner workings of a league or a team?

455
00:29:58,873 --> 00:30:02,053
How broad is that perspective, the entire part of the company,

456
00:30:02,153 --> 00:30:06,393
except for what happens on field? I would say with the industry leaders of each

457
00:30:06,393 --> 00:30:09,973
of the leagues, it's very pervasive. It's a hub and spoke.

458
00:30:10,193 --> 00:30:16,153
It's like you've got that underlying data set and you have your 30 plus different

459
00:30:16,153 --> 00:30:16,973
business applications,

460
00:30:17,173 --> 00:30:20,533
your apps, your whatever actually connected in so that at any point in time,

461
00:30:20,553 --> 00:30:25,073
you can be delivering out to whatever business unit is needed and is there.

462
00:30:25,213 --> 00:30:26,533
And those are the teams that are winning.

463
00:30:26,733 --> 00:30:29,273
Those are the teams that are taking advantage of the opportunity.

464
00:30:29,273 --> 00:30:32,653
Like, we always talk to our clients around like team performance matters.

465
00:30:32,753 --> 00:30:37,433
Yes, 110%. You almost want to be building all these capabilities when the team

466
00:30:37,433 --> 00:30:41,333
is doing really well, because you're one prepping yourself for what happens

467
00:30:41,333 --> 00:30:43,693
if not, and then you're taking advantage of all the upside.

468
00:30:43,893 --> 00:30:47,733
If they're not doing well, hey, let's actually get the house in order. And frankly,

469
00:30:47,893 --> 00:30:52,113
that's what we saw during COVID is that we saw a lot more organizations using

470
00:30:52,113 --> 00:30:54,573
that time, that inflection point to say,

471
00:30:54,673 --> 00:30:59,053
we got to get the data and analyst infrastructure in place because we know it's

472
00:30:59,053 --> 00:31:02,913
going to be even more critical to make those business decisions coming out of

473
00:31:02,913 --> 00:31:05,393
this. We don't know what the fan is going to look like. They're going to look different.

474
00:31:05,553 --> 00:31:08,393
They're going to be consuming from the bubbles and the wobbles and whatever

475
00:31:08,393 --> 00:31:10,073
else was happening five years ago.

476
00:31:10,193 --> 00:31:13,393
And how do you actually then translate that to what's going to end up happening

477
00:31:13,393 --> 00:31:17,213
in your venue? Yeah, I'm still thinking about the Eagles, the Eagles piece.

478
00:31:17,273 --> 00:31:22,213
I will say living in the Philadelphia area as a transplant initially.

479
00:31:22,653 --> 00:31:28,453
I did not understand the fever pitch of any of the Philadelphia teams,

480
00:31:28,513 --> 00:31:29,853
but especially the Eagles.

481
00:31:30,053 --> 00:31:35,213
But you hear stories like that. You see how the sports players,

482
00:31:35,373 --> 00:31:39,493
when they're doing their, their charity events, it's truly done from the heart.

483
00:31:40,120 --> 00:31:46,060
At the events, they're engaging with their fans and really bringing them into the fold.

484
00:31:46,360 --> 00:31:50,520
But then what happens when that player leaves the team?

485
00:31:50,980 --> 00:31:56,560
I mean, there has to be so much data about how they're engaging with their fandom

486
00:31:56,560 --> 00:31:59,800
and how their fans are receiving them.

487
00:32:00,240 --> 00:32:04,060
So then what happens when that player leaves the team and goes to another?

488
00:32:04,340 --> 00:32:08,920
How is that data truly transmitted and and capitalized on by the next.

489
00:32:09,140 --> 00:32:12,520
Well, so a couple of things, because I think that actually goes some of that,

490
00:32:12,560 --> 00:32:16,060
what you're talking about actually goes back to Sandy's point a little bit earlier,

491
00:32:16,260 --> 00:32:21,480
which was talking about the, the agent and the player and the disconnect between the business.

492
00:32:21,600 --> 00:32:23,960
And so there is still a little bit of that disconnect there.

493
00:32:24,040 --> 00:32:29,260
However, we did a project with the NFL a couple of years ago called the non-home fan project.

494
00:32:29,340 --> 00:32:32,660
And so part of this was actually trying to understand on any given Sunday,

495
00:32:32,780 --> 00:32:34,220
where were fans coming from?

496
00:32:34,260 --> 00:32:37,820
How many fans were home fans, how many fans were casual fans,

497
00:32:37,960 --> 00:32:41,080
how many fans were traveling fans, destination fans, what have you.

498
00:32:41,220 --> 00:32:46,860
We found that double digit percentages of on any given Sunday were non-home fans.

499
00:32:47,180 --> 00:32:51,440
And we found that certain teams, and like anecdotally, we knew this,

500
00:32:51,480 --> 00:32:56,160
but the power of that destination traveling fan and being able to do all the

501
00:32:56,160 --> 00:32:57,560
connections there to know that,

502
00:32:57,620 --> 00:33:03,260
okay, the Eagles have just traded XYZ player to this, we're expecting these things to happen.

503
00:33:03,460 --> 00:33:05,620
However, However, when you actually start to look at the data,

504
00:33:05,740 --> 00:33:09,040
you start to see the big opportunities that exist there in terms of,

505
00:33:09,100 --> 00:33:10,800
well, what other offerings should be there?

506
00:33:10,960 --> 00:33:14,380
What's the experience going to look like? Many times in professional leagues,

507
00:33:14,580 --> 00:33:17,600
owners don't want visiting fans in the stadium.

508
00:33:17,820 --> 00:33:23,000
They do a lot of geolocation work to keep them out. They're going to get there anyways.

509
00:33:23,280 --> 00:33:26,960
People can sell on the secondary. What happened? There's a there's a use case

510
00:33:26,960 --> 00:33:30,560
here that if you actually lean in a little bit into the right pockets there

511
00:33:30,560 --> 00:33:33,860
of the destination or the traveling fans, it's a huge opportunity.

512
00:33:34,000 --> 00:33:35,580
We did a pretty robust analysis.

513
00:33:35,780 --> 00:33:39,320
And then we went with one of the clubs and gave them some customer lists of

514
00:33:39,320 --> 00:33:41,020
folks that they wouldn't normally reach out to.

515
00:33:41,140 --> 00:33:45,560
And they they did two email campaigns, multimillion dollars in terms of opportunity

516
00:33:45,560 --> 00:33:48,620
there back from a conversion standpoint, be people buying tickets,

517
00:33:48,840 --> 00:33:53,960
these these casual fans, these traveling fans that might not have made the connection.

518
00:33:53,960 --> 00:33:56,100
Or if they made the connection, it would have been last minute.

519
00:33:56,260 --> 00:33:59,380
And oh, by the way, now that they've actually identified these people,

520
00:33:59,500 --> 00:34:02,740
they can now provide additional experiences or opportunities.

521
00:34:02,980 --> 00:34:04,140
Here's a pre-event hospitality.

522
00:34:04,680 --> 00:34:08,340
Here's another way to stay engaged. Hey, when this team comes next year,

523
00:34:08,380 --> 00:34:09,360
do you want to know about it?

524
00:34:09,440 --> 00:34:14,300
Again, it's creating more and more information there and creating that connection,

525
00:34:14,640 --> 00:34:18,220
that interconnectivity across the whole community and landscape.

526
00:34:18,480 --> 00:34:23,780
I mean, I can't even imagine if, and there's somebody close to home here, Mr. Tom Brady.

527
00:34:24,916 --> 00:34:31,116
He goes to Tampa and then he says he's going to retire or everybody thinks he's about to retire again.

528
00:34:31,236 --> 00:34:37,016
The number of New England fans who got on a plane and they made sure that they

529
00:34:37,016 --> 00:34:42,156
showed up in Tampa to watch their favorite player that they grew up watching

530
00:34:42,156 --> 00:34:44,936
finishes last season. It's a very simple thing.

531
00:34:45,156 --> 00:34:49,596
And honestly, I grew up as a Miami Dolphins fan. I'm still a big Miami Dolphins

532
00:34:49,596 --> 00:34:51,456
fan, even though I'm in the middle of New England.

533
00:34:52,796 --> 00:34:57,216
Patriots land. and I get stuff for it every day, but I can't even,

534
00:34:57,276 --> 00:35:01,336
growing up, if Dan Marino had gone somewhere else, I know I would have been

535
00:35:01,336 --> 00:35:05,196
the first one to tell my parents, like, whatever it takes, we need to go see

536
00:35:05,196 --> 00:35:07,736
him wherever he is because I love them so much.

537
00:35:07,856 --> 00:35:12,356
The Brady factor has probably triggered a lot of things over at Tampa Bay,

538
00:35:12,416 --> 00:35:13,996
for sure. Yeah, 100%. That's actually interesting.

539
00:35:14,256 --> 00:35:18,536
So what Tampa Bay did for their season ticket members the last year Brady played

540
00:35:18,536 --> 00:35:20,256
there was it was a two-year commit.

541
00:35:20,336 --> 00:35:24,976
So it was a commit to his last year plus they had no problem selling them.

542
00:35:25,096 --> 00:35:28,316
The mix may have looked a little bit different, but yeah, to your point,

543
00:35:28,456 --> 00:35:30,576
yeah, you're going to make those moves.

544
00:35:30,776 --> 00:35:35,276
It is really interesting to start to like quantify and put the data behind kind

545
00:35:35,276 --> 00:35:40,396
of some of these opportunities because it starts to just break more legacy thinking

546
00:35:40,396 --> 00:35:43,656
about how to run from an operation standpoint.

547
00:35:43,736 --> 00:35:47,216
It doesn't mean that any team league and organization is going to take all of

548
00:35:47,216 --> 00:35:49,516
those recommendations, but it's just like The data doesn't lie.

549
00:35:49,696 --> 00:35:52,936
It's there. This is how we think from an interpretation standpoint.

550
00:35:53,016 --> 00:35:55,836
Let's test it. We tested it. The value is there. Right.

551
00:35:55,976 --> 00:35:58,836
And I can only imagine it getting more complex over time, too,

552
00:35:58,976 --> 00:36:03,736
just as we said, like social media or even all the other ways we're consuming information.

553
00:36:04,096 --> 00:36:08,116
Whether it's new apps that are coming to the fold, that's going to impact how

554
00:36:08,116 --> 00:36:13,796
people find information, care about other players, care about teams, care about leagues.

555
00:36:13,976 --> 00:36:16,716
I'm a huge Premier League fan because my wife...

556
00:36:17,075 --> 00:36:19,895
Watch his or used to watch now i'm the one waking up

557
00:36:19,895 --> 00:36:22,935
on the morning i don't know how that happened but i'm the

558
00:36:22,935 --> 00:36:25,795
one watching this by myself she got me hooked and

559
00:36:25,795 --> 00:36:28,655
then she decided to sleep it but does she follow any

560
00:36:28,655 --> 00:36:34,115
specific team yeah we're tottenhotspur fans oh yeah just like the miami dolphins

561
00:36:34,115 --> 00:36:40,115
never the yeah never the never the queen of the ball but it's it's intriguing

562
00:36:40,115 --> 00:36:44,635
because it the world has opened up in many many ways well and and and like even

563
00:36:44,635 --> 00:36:47,715
to that point so the world World Cup on the men's side,

564
00:36:47,775 --> 00:36:51,335
FIFA 2026 is coming to the U.S., well, to North America.

565
00:36:51,375 --> 00:36:55,295
Gosh, that is like, it's going to be such an amazing kind of fan activation,

566
00:36:55,575 --> 00:36:57,255
fan understanding opportunities.

567
00:36:58,115 --> 00:37:01,895
Well, because I think there's games in both of our cities, right?

568
00:37:02,055 --> 00:37:05,935
Like, I mean, we have the Meadowlands, and I think that there's some in Gillette,

569
00:37:05,975 --> 00:37:09,275
right? Yeah, Gillette's got seven, which is going to be bananas.

570
00:37:09,535 --> 00:37:14,475
And there's a whole entity in Boston and that's set up to just run the fan fests

571
00:37:14,475 --> 00:37:16,395
and think through what that looks like.

572
00:37:16,535 --> 00:37:20,255
And so, you know, you look at like the messy effect with Major League Soccer,

573
00:37:20,335 --> 00:37:24,295
like it's just the growth and understanding of who's there and how to activate it.

574
00:37:24,375 --> 00:37:27,515
But it's going to be kind of a phenomenal opportunity, one, to bring the world

575
00:37:27,515 --> 00:37:31,315
together, but also just from a like a true like data behavior,

576
00:37:31,575 --> 00:37:37,015
data capture, just learning from a global standpoint and how we at Kager can

577
00:37:37,015 --> 00:37:39,035
take take that to all of our clients.

578
00:37:39,115 --> 00:37:44,475
Yeah. I mean, I was shocked this year to see so many professional cricket matches played here as well.

579
00:37:44,695 --> 00:37:49,975
So yeah, it's surprising, right? There was a huge match, India versus Pakistan

580
00:37:49,975 --> 00:37:53,595
in Nassau Coliseum in Long Island.

581
00:37:53,955 --> 00:37:58,315
So we thought, hey, it'd be great to get some tickets. That'd be fun to go see that match.

582
00:37:58,595 --> 00:38:02,335
Tickets were unattainable. They were in the the thousands of dollars,

583
00:38:02,515 --> 00:38:07,155
like not in the secondary market, like from the get go.

584
00:38:07,315 --> 00:38:11,055
And I'm going, that makes sense though, right? We have a massive community.

585
00:38:11,455 --> 00:38:13,395
Right? In New Jersey and New York.

586
00:38:13,575 --> 00:38:17,455
That absolutely makes sense. Like you have to go where the fans are, right?

587
00:38:17,515 --> 00:38:22,875
If you have a set of fans that miss being able to watch a cricket match, take it to them, right?

588
00:38:23,015 --> 00:38:26,615
I've seen the Premier League teams coming out here and just like the NFL is

589
00:38:26,615 --> 00:38:28,535
starting to go to Europe. They're going to London.

590
00:38:29,055 --> 00:38:34,155
You've got a lot of... And Brazil, they're in Brazil next season, which is nuts.

591
00:38:34,275 --> 00:38:37,855
So that's, yeah. That's going to be insanity. I can't wait to watch that game.

592
00:38:38,755 --> 00:38:43,555
Phillies are going to London this year too. Oh, from a baseball perspective? They may be.

593
00:38:44,355 --> 00:38:47,835
Globalization is awesome. I think from a data professional standpoint,

594
00:38:48,055 --> 00:38:50,655
it's now changing the game. What does all this mean?

595
00:38:50,855 --> 00:38:54,235
How do we think about it from a fan data capture standpoint?

596
00:38:54,235 --> 00:38:57,675
Standpoint, what can be used, what cannot be used, what's the right channels.

597
00:38:57,815 --> 00:39:03,055
It's just, it's like a multivariable equation on how to put it all together, but it's so fun.

598
00:39:03,095 --> 00:39:06,635
And, but it all starts with the underlying infrastructure, get your infrastructure

599
00:39:06,635 --> 00:39:08,355
right. So I have a, I have a question.

600
00:39:09,108 --> 00:39:14,028
Because I have a couple of colleagues who are obsessed with sports analytics.

601
00:39:14,748 --> 00:39:22,288
I'm curious for those fans out there who are interested in data for teams,

602
00:39:22,388 --> 00:39:23,528
what advice would you give them?

603
00:39:23,588 --> 00:39:27,708
Because I do find that probably people don't realize there's two sets of analytics.

604
00:39:27,708 --> 00:39:32,608
There's the business of, you know, having a sports team or running a league.

605
00:39:32,768 --> 00:39:36,908
And then there's the business of the performance on the field,

606
00:39:37,148 --> 00:39:40,288
right? Which is very specific and very different.

607
00:39:40,408 --> 00:39:43,488
And there's not a lot of sharing going on there is my assumption,

608
00:39:43,628 --> 00:39:45,188
right, in terms of what the teams are doing.

609
00:39:45,288 --> 00:39:48,848
So which one's easier to get into? And what advice would you give them?

610
00:39:49,868 --> 00:39:52,508
It's a really great point it's a

611
00:39:52,508 --> 00:39:55,248
really great question to me i think the

612
00:39:55,248 --> 00:39:59,668
business side is just it it is a little bit easier from a from a gateway standpoint

613
00:39:59,668 --> 00:40:05,208
because there's i've seen some of the player specific stats and other pieces

614
00:40:05,208 --> 00:40:09,748
like that it's very very complicated it's just i've heard like basketball stats

615
00:40:09,748 --> 00:40:13,768
talked about and not only do you need to like understand

616
00:40:13,948 --> 00:40:17,248
data and large like troves of data to be able to analyze it.

617
00:40:17,288 --> 00:40:20,828
But you have to understand the game at a very, very granular level,

618
00:40:20,888 --> 00:40:25,628
and like how the interpretation of different metrics and how to think about creation of metrics.

619
00:40:25,848 --> 00:40:28,828
And, and if you're a student of the game, you understand the data,

620
00:40:28,868 --> 00:40:31,628
you want to play with the data, you've invested a lot of time,

621
00:40:31,648 --> 00:40:36,308
energy and effort to do that offline, and you have great data science technology

622
00:40:36,308 --> 00:40:38,588
skills, then you're pretty marketable there.

623
00:40:38,728 --> 00:40:44,248
And so like my boss and her co-founder of Daryl Morey, who's the general manager

624
00:40:44,248 --> 00:40:47,968
of the 76ers co-founded the Sloan Sports Analytics Conference.

625
00:40:48,128 --> 00:40:49,668
So that happens here in Boston.

626
00:40:50,519 --> 00:40:56,059
I think this year was their 15th year. There's a whole half of the conference

627
00:40:56,059 --> 00:41:00,359
that's focused on player analytics and the innovations of those pieces.

628
00:41:00,639 --> 00:41:03,759
I would highly recommend checking some of those things out.

629
00:41:03,839 --> 00:41:08,299
Some of it is on YouTube and other pieces like that. But they've had concepts

630
00:41:08,299 --> 00:41:14,419
be pitched there that are now regular technologies in the space.

631
00:41:14,659 --> 00:41:18,239
It's really cool. But there's not a whole lot that's like that in terms of that

632
00:41:18,239 --> 00:41:22,199
community that can come together and really both understand all the X's and

633
00:41:22,199 --> 00:41:25,139
O's of the data and then be able to interpret that.

634
00:41:25,279 --> 00:41:29,059
Yeah, that's a really good point. It reminds me very much of somebody trying

635
00:41:29,059 --> 00:41:30,759
to get into bioinformatics.

636
00:41:30,859 --> 00:41:34,779
You have to really know biology and you have to really know data science,

637
00:41:34,839 --> 00:41:37,399
right? Those are a few things that have to be together.

638
00:41:37,639 --> 00:41:39,179
And it's funny because you're talking about sport.

639
00:41:39,719 --> 00:41:42,499
You're talking about sport, right? We're all like, oh, it's just a game.

640
00:41:42,719 --> 00:41:47,699
But the reality is to be able to analyze the doing of the game itself,

641
00:41:47,699 --> 00:41:52,499
The mechanism of the game and what's happening on court on the field,

642
00:41:52,619 --> 00:41:58,779
it requires a level of deep understanding of the game itself almost to very much like a science.

643
00:41:58,779 --> 00:42:01,519
It's yeah you're yeah that's so

644
00:42:01,519 --> 00:42:04,599
well put it's like that precision of are you shooting

645
00:42:04,599 --> 00:42:07,899
from exactly at the free throw line or or slightly over

646
00:42:07,899 --> 00:42:10,659
the free throw line or at the time when you

647
00:42:10,659 --> 00:42:15,419
shot was there a defender within a foot of you three feet of you that there's

648
00:42:15,419 --> 00:42:19,339
just a lot of different ways that all of that can come together and at the end

649
00:42:19,339 --> 00:42:24,619
of the day like it's still going to be an input to an excuse me a decision it's

650
00:42:24,619 --> 00:42:30,139
still an input to whether that player is worth going after or resigning or what have you,

651
00:42:30,159 --> 00:42:33,779
or whether that strategy to that opponent,

652
00:42:33,939 --> 00:42:37,439
when that specific star player plays.

653
00:42:37,839 --> 00:42:42,399
You know, it's all the different scenarios there, but it's pretty cool. Awesome.

654
00:42:42,519 --> 00:42:45,039
Well, Erin, thank you so much.

655
00:42:45,239 --> 00:42:49,999
This conversation has been phenomenal and so interesting. I think we've all

656
00:42:49,999 --> 00:42:51,259
learned something new today.

657
00:42:51,459 --> 00:42:54,459
We're, you know, we're a little bit over what we had planned,

658
00:42:54,599 --> 00:42:56,859
but I think it was well worth our time.

659
00:42:56,979 --> 00:43:01,019
So really appreciate you hanging on and sharing all of these great insights

660
00:43:01,019 --> 00:43:04,679
and nuggets of information and chatting sports and analytics with us.

661
00:43:04,779 --> 00:43:06,159
Yeah, no, this was awesome.

662
00:43:06,299 --> 00:43:10,359
I love talking about it. I love hearing the question. I love hearing the questions

663
00:43:10,359 --> 00:43:13,659
and your, your perspectives too, in terms of what you're, I could,

664
00:43:13,679 --> 00:43:17,519
I could hear and feel the things that you're thinking about on a day-to-day basis.

665
00:43:17,559 --> 00:43:20,199
And you're hearing from your clients in terms of the questions that you're asking.

666
00:43:20,239 --> 00:43:25,319
And I don't know it's it's a it's a pretty pretty fun area so thank you for having me.

667
00:43:26,160 --> 00:43:39,062
Music.