MVP Interactive hosted Tinus Le Roux with CrowdIQ and Zack Klima with WaitTime during our August 2023 Roundtable: Driving Revenue with Fan & Crowd Analytics.
In today’s hyper-connected world, fan engagement and analytics have become indispensable pillars for driving revenue across various industries, particularly in the sports and entertainment sectors. This panel brought together industry leaders from MVP Interactive, CrowdIQ, and WaitTime to explore how cutting-edge technologies and data-driven insights are transforming the landscape of fan engagement and revenue generation.
Takeaways
- Discover the power of interactive technologies in captivating and engaging fans.
- Uncover how crowd intelligence insights can be translated into actionable strategies for revenue growth.
- Gain a comprehensive understanding of fan behavior within venues through real-time analytics and actionable insights.
Transcript
We have a good amount of folks, um, and I’ll let others in as they join. Uh, but first of all, welcome everyone. Um, my
name’s Natasha French and I’m with M V P Interactive. We’re a creative technology company, and we host these, uh, webinars monthly. Um, because the, the whole goal is really to educate each other, bring partners, clients, just, you know, thought leaders in the space that talk about, you know, different topics that we deal with every day and help, you know, that some of our clients ask about.
So today we’re gonna be talking about driving revenue with Fanning Crowd Analytics. Um, and to get started, uh, I’m going to have each panelist from James Tennis and Zach introduce themselves. Um, and then we’ll jump into some questions. James, get Started? Yeah, absolutely. Hi everyone. I’m James Gilio, uh, C e O of M V P Interactive. I want to thank everyone again for joining us for our monthly webinar. As Natasha had mentioned, we are, um, very excited to keep our programming going and, and adding, uh, new value to the industry of, uh, sports, sports sponsorship and activation.
And so, uh, today we’re, we’re gonna take a different, you know, uh, path in terms of what brands are doing, or, uh, how crowd analytics is extremely important to drive revenue and really enhance, uh, both the fan experience as well as what it’s doing to the industry. And what’s unique about this panel is that the three of us, although, uh, encompass fan activation and use the crowd analytics, uh, we use it in a very uniquely and different way from each.
So, uh, I’m excited to, uh, educate the group here and, and participate and, uh, talk about how we, we leverage the technology for each of us. Tina? (···1.1s) Yeah. Um, thanks James. My name is Tina LaRue. Um, I am (···0.7s) c e o of Crowd iq. We use high resolution photography, specifically in the bowl to analyze crowds to computer vision. So we deliver everything from demographics to when people on their seats to what they’re wearing, to what they’re paying attention to.
(···0.7s) And, um, we deliver most of this data to teams so they can help, um, improve their game day experience. Um, ticket sales, uh, merge sales, and, and a few other things as well. (···2.3s) Awesome. Yeah. My name is Zach Klima. I’m the founder and c e o of wait time. (···0.5s) And we have the o really the only patented, what we call crowd management solution in the world. So if you think about monitoring cues and lines, uh, all in real time with our patented computer vision ai, but then also it’s about engaging with the guest on, uh, on digital displays and on that team’s mobile application.
Where can I find the shortest line? How can I navigate myself around crowds effectively and in real time? (···0.5s) So anyway, a little bit about myself and, uh, back to you. (···0.8s) Awesome. Well, thank you all for joining us. Um, so to get started, before we dive into questions, um, I thought each of you could broadly talk about crowd analytics and how each of your companies approach ’em.
So James, if you wanna get started. (···1.2s) Sure. So uniquely compared to our other guests here, we really use our concourse activation and analytics, uh, a little bit more unique, where rather than we sort of give this global, uh, crowd analytics the way Zach and and Tina does, we do more of a bird’s eye view where most of our concourse activations or fan experience or are, are at, uh, eye level or within a limited space.
But what’s unique is, uh, as you know, with experiential technology, we provide a lot of the front end, uh, fun and exciting technology to interface with. But, you know, uh, we know the value in what we may call as passive interactions, right? And so you have the equal participants of the, the technology and the event that they’re participating in, but then you have the crowd effect or, or people that loiter around the participation or an activation.
And so the way that we’ve leveraged the technology is anonymous analytics, where we’re using, um, a version of facial detection, um, technology that is able to track demographics, as Tina said, mentioned, as well as sentiment and, and provide a impression measurable, uh, similar to a way a media company would with a billboard or a television commercial. So we capture all of that data and are able to provide our clients a digestible download, a report of not only what the active participation looked like, but how many eyes and what the dwell times and what the attention times were those that were all participating.
Whether it was just a, an eye level view or, um, sort of a crowd view in that we can really break down, uh, the overall, uh, messaging in terms of how captivating, uh, the activations have been. And so that’s been a tremendous value and competitive advantage for us when other sort of XR companies are looking to, um, you know, get into the concourse activation space or the live event space that, uh, you know, having this, this set of data really sets us apart because, uh, both brands, teams, agencies, can take a look at that report and see a real performance that, uh, you know, unlike in the past experiential is, is is hard to measure from a an R o I standpoint traditionally, right?
So we provide as much data and insight to our activations that really justify the budget ranges that we’re, um, able to produce our tech technology variances on.
(···3.1s) Great. Thanks James. And his, do you wanna jump in on that and kind of walk us through how you guys approach crowd analytics? Yeah, I think, um, in differentiating between the three of us to understand what we do, um, our little niche is, um, it’s all about resolution for us. So we started Crowd IQ grew from fan cam, um, we create gigapixel photography, um, uh, of events. And so these allow you to zoom in and find yourself in the crowd.
(···0.7s) And so our strength has an expertise also always been around high resolution. And so we’ve just used that and applied computer vision and a few other pieces of AI magic, um, to be able to extract, um, data. And so, um, everything flows from that. So because we’ve got a picture of the whole crowd in the whole bowl, irrespective of where they sat, um, our focus is naturally also on the whole crowd. So, uh, it’s also my personal interest understanding the crowd as an organism, um, and so much less about, about the individual, more about, um, how many female fans are in this crowd, um, uh, when do they arrive, what are they wearing, um, what are they paying attention to?
How often do they attend? Um, and how those, um, differences in crown composition between one night and, and another night to the first part of the season, the last part of the season between one team and another one, um, can predict, um, uh, revenue, um, and the strength of that crowd.
So for us, it’s a, um, it’s, it’s also a bird’s eye view, but more of the bowl, um, and, um, analytics, um, trying to understand, um, this crowd as a organized, uh, uh, organism, and then apply predictive analytics to that and saying, we think if you, if you, if you tighten this valve and release this one, then we think the crowd is gonna react in this way, in this manner. (···1.5s) Great.
Oh, go ahead Zach. (···1.0s) No, all good. Um, I was just gonna say, uh, if it’s okay with you, Natasha, I just wanna share my screen for a second ’cause I’m, uh, for sure, probably easier just to show, uh, what we do. (···1.0s) So (···0.7s) at wait time, you know, the reason why I started this company, um, similar to tennis and James, you know, there’s a massive problem out there in the industry. So I’m a Detroit person. I’m in a Detroit Red Wings hockey game about 10 years ago at the old Joe Lewis Arena. (···0.5s) And I’m sitting in my seat game, goes in overtime, I leave my seat to go stand in a long line, which of course we all have experienced.
(···0.6s) I stand in this long line, uh, you know, the game, the game winning goal happens as I get my beer (···0.6s) and the horn goes off, everybody runs out. So I thought to myself, I said, a, I wanna know how long lines are, no one is addressing this situation that is completely related to revenue. And then b, I want organizations to handle crowds much better than they already are, which is kind of the wild, wild west at that point in time.
(···0.7s) So what we’ve done over the past better half of a decade, is we’ve developed what we call our crowd management solution driven by our algorithms. (···0.5s) So in all different types of, you know, arenas and stadiums and, uh, you know, different large venues, there’s different types of queues. (···0.6s) So what we do, and I just wanna show everyone this just ’cause it’s the, the power of seeing it is, is really strong. So we’ve developed four algorithms when it comes to wait time, the first one being queing, where we actually mount cameras, we can use the, the strength of wait time is we can use any camera in the world.
We mount it directly overhead. And our ai, uh, is monitoring the crowd growth (···0.6s) by pixel differences. So we don’t, we, we are completely anonymous with our solution. So we don’t track, you know, Bluetooth cell phones, wifi, any object recognition. It’s literally the pixel differences between people, which allow us to get such a high degree of accuracy.
(···0.6s) So ranging from the queuing algorithm to the stanchion algorithm, which is where you have more of a controlled format within your concourse. And I’m sure James, you’ve dealt with this all the time, where, um, you know, you see different concourses, you see so many different things that from an organization standpoint that can be changed, um, based off of, you know, different revenue centers. And each revenue center has its different setup. So when you have more of an organized setup at your revenue center, we leverage this algorithm called stanchion, which is for a structured queue (···0.6s) to then when you have crowd growth, uh, in inside of your arena or stadium, when the camera view is off to the side, this monitors how a crowd is growing, living, breathing.
Um, you know, conversely to tennis, we are not in the bowl, we are completely around in the concourses. So monitoring from a heat mapping perspective, um, you know, this one here. And then lastly is our entry exit algorithm monitoring real time occupancies.
So as people enter into a building before the crowd even starts to grow, uh, how many people are entering, exiting all in real time, 24 times per second. (···1.1s) So just a little bit of a background on, on the wait time solution, just to kind of show you what AI actually looks like, what computer vision actually looks like, as opposed to just, uh, um, you know, it’s, oh, it’s in the air, it’s, it’s, it is working, right? But to actually show it, I think it’s very important here so that we, the way that we look at crowd management and, you know, you know, through computer vision, um, is really through the, the anonymized system that we’ve developed, you know, tracking people in different revenue centers and exits and ingress and points of sponsorship activation from a measurement perspective, all in real time.
(···1.0s) So anyway, a little bit of a, a background on wait time. (···0.7s) No, thank you. Visuals always helpful, so, oh, go for it. It’s Been really cool, Jack.
I mean, I, I know how difficult that is. That’s awesome. Very Difficult. Well, I know, well thanks for, I know we’re coming from, you know, all the same goals. We work with a lot of the same (···0.7s) organizations teams. Um, so I’ll start with a couple of questions. Um, you know, I know personalization and targeted marketing are very, are always emphasized, but can each of you elaborate on how data insights enable more effective personalized strategies that drive engagement and revenue with your different tools you just talked about?
About? Sure. Um, well, I guess, you know, for, from our perspective, um, you know, we’ve been able to (···1.2s) find solutions, create solutions for our partners to have more permanent installation or activations that, that run over a series of time. (···0.6s) And so, um, being able to provide our clients a, a key insight into participation numbers and data for all the digital touchpoints that we’re able to sort of embed in a particular area has been really helpful.
Um, you know, because not only can they determine what the fans’ interests are, they can also use that insight to sell sponsorship, right? And so when we do a range of interactive experiences, whether it’s posed with the pro or, you know, maybe a simulated sporting activity, uh, using high rate sensors or gesture technology, we can really give our clients some, some valuable insight to what the fans are interested, what’s high in participation.
And then also, you know, the age grouping. Because I think there’s a, uh, a stereotype, if you will, or a judgment initially when we talk to new clients that, oh, this stuff is just for kids, right? But then when we can show the data that parents, um, and across genders that are participating this in, in the technologies extremely valuable, so we can hone in the experiences, um, you know, based on that demographic and the interest of the client, Uh, it’s probably my turn, right?
Um, sorry, I’m just thinking about what James is saying. Um, uh, uh, I’ll get to my, my part first. Uh, or or second. I just wanna ask James, um, I mean, you and I have been known each other for a long time, um, and, (···0.8s) and it’s just also what you guys are doing. I’m just wondering if, (···1.6s) I mean, (···0.6s) understanding the, (···1.1s) the, the efficiency, um, of the OR or the engagement level, do you get any pushback?
Are there some clients that don’t really want to know? Um, I, I’ll, I’ll, I wanna put you, I don’t wanna put you too much on the spot, but what I found Sure the industry is that, you know, that’s an old saying of we’re spending a dollar and we know half of it’s working, we just dunno which half and sometimes Yeah. Solutions and, and you get clients saying, (···1.0s) rather we know it’s working, we just don’t want to (···0.5s) know exactly. Uh, do you see Anything, uh, you know, it’s, it’s a fair question, you know, and I think when it comes to (···0.6s) a certain budget level and justification of the investment data is extremely important.
And, and so we do have some clients that look for monthly reporting, uh, season reporting and such. And, and I think, you know, when they’re looking to continue to monetize the experiences, that’s where we tend to see more interest. But then we’ve had brands, for example, that just basically just want to own the experience and, and that’s it. They wanted to provide fans, uh, a really fun engagement that’s unique to their brand, to the event, to the, to the game.
And, and they don’t ask us for a bit of data. So it’s a fair question. Um, but I think, you know, we’re starting to see, because, um, our progress has gone into more permanent installations where, you know, year over year, they wanna make sure that content is fresh, you know, where fans are, are going, what’s interesting, and, you know, do we need to evolve these experiences over time? But (···0.6s) I would say probably a 90% valuable piece, and then 10% of our clients, you know, it doesn’t matter at all.
(···1.8s) Got you. Thanks. No, and I, I, I won’t be so unfair of just asking you the question, answering it myself. Um, we have a similar experience, although we work, typically we work more with teams, and then within teams we find that you often work with the folks in business intelligence or data analytics, (···0.6s) and there is this interesting discussion with them. And the sponsored folks, or the sponsored folks may not always want the data. Um, what I found interesting is that if you do start looking at the data, um, the live event space outperforms any other challenge, uh, uh, any other, um, channel that’s out there.
And so a lot of my discussions is helping the data analytics teams go to the sponsor folks saying, do you know what this sponsor, you know what the alternative is? It’s to buy, um, uh, web banners. And so you’re bringing 50,000, 20,000 people together on this on a, on a weekend, and they’re all excited and putting the brand in front of them, you, you’re the best show in town. Here’s the data to prove it. So that, that’s a bit of my ex my experience in terms of the question, Natasha.
Um, I’ll give you an example. Um, and, and also, um, I’ll also share my screen here so that people can understand what it’s, (···1.0s) what it’s about. Um, so we, um, lemme (···0.9s) just find the under example here. So we, it looks like we essentially every second, and then I’m, (···13.0s) I’m speaking somewhere, somewhere.
(···4.7s) Is that better? Better? (···0.9s) No, no, it’s not. It’s (···5.3s) I’ll, I’ll, I’ll it, I show this, (···0.7s) I’ll stop screen screen grabbing, uh, screen sharing. So let’s see if I, I stopped screen sharing. That’s good. No, I think actually (···1.0s) try now.
(···0.9s) Okay. Uh, I’ll screen share again. Um, better. (···1.2s) Yes, that works Better. Cool. So attention tracking, this is, uh, um, a product where we capture the, the, the crowd every second throughout the game. (···0.6s) And as you can see, um, computer vision is, is relatively simple to explain in that, uh, you can train it (···0.6s) to deduce what, what humans deduce. And so as, as we can see that this person is looking at the screen, this person’s looking at the screen, these are looking, watching the game, (···0.5s) we, we have an algorithm that does, does the same.
And so the, the output data looks something like this. Um, and throughout the game, we can see when people look at the screen, when they’re look looking at their phones, there’s a, is a big screen moment. Um, and then if you overlay what was on the screen at that time, you can see which pieces of content work and which don’t. And so, back to your question, Natasha, um, one of our N F L clients had a longtime partner that had this, um, annual, they had a specific activation that they loved, (···0.8s) and no one was paying attention to it, and the team knew it.
And every year they went back to the, the, the client and said, um, let, let’s, let’s, let’s renew this, the creative. Um, and the client said, no, this is, we, everyone loves this. And then we gave them the date and they could go back to them and say, look, everyone looks away, please change it. And they helped the team change it, and they added something. And this is not just to the benefit of the sponsor, um, for them creating a campaign that gives them better r o i, but also to the, to the team, because now it’s content that the fans enjoy.
And so now the following seasons, we could track and show them that there’s an uptick in attention there. So that’s one of the examples of how you can use technology to really measure fan engagement and have that feedback loop back to the sponsors. (···0.8s) Can you share what that experience may have been? Um, it was, um, I dunno if I’m gonna get trouble.
Um, No, no worries. If you can (···1.0s) Wanna know. It was a, it was a, a a a, a big brand and a big N F L team, and it was well handled and it’s working better now, so. (···0.7s) Perfect. (···0.9s) Well, Zach, thanks Zach. If you wanna kind of touch on, um, that, and then we’ll jump into some other questions. (···1.2s) Yeah, absolutely. So (···0.5s) the way that we view this, you know, from a, from a concourse standpoint, I’m gonna share my screen one more time (···0.7s) from a concourse standpoint, is wait time is generating a lot of data, (···0.8s) and we generate from wait times to serve times to how many people are in line to how many people have abandoned the line.
We give this information, uh, end of end of day reports to all of our clients. So what, as you see here, this is a, an example of a Miami heat, one of our key N B A clients, one of, uh, one of their data points for one of the, uh, revenue centers.
So this is from the start of the game to the end of the game. And this data is just crowd movement and, and, you know, line behavior. This has nothing to do with point of sale transactions, this has nothing to do with, you know, anything else. So what we’ve done is we’ve designed our system and our data to be intermingled with other data. (···0.6s) So as an example to this question is (···0.7s) you, if you have I P T V screens in certain areas of your concourse, like a, a Cisco vision, for example, (···0.7s) and they, you sell advertising on it, (···0.5s) you might wanna sell advertising and, and (···0.7s) play the highest ticket advertising when you know that over certain threshold of people are in that area, which is all enabled by wait time software.
So (···0.6s) really, you know, once you know where people are inside of your venue, it, it really starts to, uh, change and you start to adapt your revenue strategies based off of it. And you don’t really have anything outside of point of sale transaction when it comes to the movement of people.
So that’s what the other half of the equation that wait time adds. So that’s how we think about this question, is we, we give the anonymity aspect of, okay, we know where people are and we’re constantly aggregating that information. So you’re able to, you know, organizations are able to take that information in and adjust their strategies when it comes to advertising, sponsorship, et cetera, knowing that you’re gonna get the most hit because you know where people are inside of that lo inside of the different locations in your, in your venue.
So that’s how we, we think about that from a more, less personalized, but more from an accuracy standpoint of where people are in real time. (···0.7s) Gotcha. And before we, I’m sorry, go James. Sorry, I just, I just had a quick question. Have you seen, you (···0.6s) know, I think we’ve all been in business for generally about the same time, right? You know, spanning, uh, close to a decade here. And, you know, one of the things that I always recall is, you know, the fan experience when we all started our businesses was, was a much different focus, right?
It was very, you know, remedial in the sense of what venues and what stadiums and, and ownerships were looking at to, you know, create a better experience. So we were at this really nascent inflection point of (···0.9s) where fan experience is now (···0.7s) over the years, how have you seen your clientele leverage this technology from maybe was it, um, blob detection or crowd detection at the ING gates, and now is it more focused on concessions and creating a, a better ingress egress, kind of walk us through that journey.
I’d, I’d be interested to learn that. (···0.9s) Yeah, so that’s a, that that’s really the, the trajectory of the company. You know, we started off by saying, you know, as, as I mentioned, the reason why it was started in the first place is I wanna know how long lines are as just simple as that, because that in itself is a massive dissatisfier for any live event experience. Um, and any, you know, anyone in in the world would agree with that.
So what once started off as a guest experience play, fan experience play, and we were driving that, you know, obviously James and Tin is the same as you, uh, for a long time, and that it took a very long time for people to start to actually realize what a true operational experience means to their guests. You know, this isn’t some fufu, uh, app integration that of course we all know, uh, about those along the way in our, in our, in our tenure. But you, you see a lot of these different things about, okay, separate it from what’s a nice to have and what’s a need to have.
So as time went on, and obviously the pandemic struck, we really changed the narrative from, okay, we’re not gonna lead off with guest experience, although it is a massive part of our system, is we’re gonna lead off with, this is very operational. So there is that transition from fan experience to operations. So the way that we have, um, kind of changed the way we think about things is we change it from not just a guest experience, but from an all-inclusive crowd management platform.
And a byproduct of that is a better guest experience. So we almost reverse the way we talk about it and the way we think about it, which, you know, helps us move faster with clients that they wanna adopt bleeding edge technology, they wanna adopt ai, but they just don’t necessarily, either they don’t understand it or they’re scared by it, et cetera. We help to really bridge that gap to make people feel comfortable in the terms that they understand of operational intelligence, you know, um, you know, monitoring egress to, um, you know, not just, you know, how long is a line.
So it’s, it’s a, I could go on for hours about it, but it’s really a shift in the narrative, uh, flipping the model from strictly guest experience to guest experience being a byproduct of an operational platform. (···0.6s) Now, I can jump in there as well, because I think it speaks to you. I think Dennis asked a question about, um, how we persuading stadiums to adopt this bleeding te edge technology. And so Zach, you spoke to that.
I think that’s, that’s, uh, the three of us share that challenge and the frustration in that it’s, um, Dennis, it’s hard work and it requires a, a bit of, a bit of grit and, and longevity. Um, I think two things on it. The one is (···1.0s) to realize for myself that (···0.8s) although we’ve been at it for a long time, we’re still early. (···1.6s) That, I mean, it doesn’t feel natural to me, but it’s the reality, um, it takes a long time for, for, for these industries to, to adopt technology that entrepreneurs, um, assume to be obvious.
Um, so, so that’s the first thing. And the second thing is just as, as Zach said, said, it, it’s a, um, you find whatever, um, it’s there is to be scratched on, on, on the, the, the, the team level of, in our case, um, sometimes a a team (···0.6s) wants to know the demographics, um, more so than the merchandise analysis and use that to get in the door and get the installation going. And over time there’s an upsell.
And what we found is, um, since we started Cloud iq, we’ve had zero churn because, um, the, uh, as soon as you’re in the door, the team starts understanding the value of data centric decisions. Um, uh, it, it, it, the value of the product speaks to itself. But there is a, um, there is a challenge there, there, um, it’s, it, it requires, um, um, a lot of thinking, a lot of talking, um, and also a lot of weight waiting, um, pun, pun intended.
(···2.4s) Yeah. But I, I think, you know, the exciting point, um, in, in the tenure of, of where we are in, in sports and entertainment (···0.7s) is, you know, Zach, you had a good point where things that were once viewed as a nice to have, have transitioned into a must have. And, uh, again, when, when it comes to operational type of technology like yours, or even experiential technology, leveraging, you know, cutting edge technology as a consumer engagement piece has really shifted.
And, and so budget allocations are now, um, really being strategic in where assets are going to invest, um, because it is, we all are in the experience business, right? And so, uh, being able to, to monetize and value consumer, uh, relationships, uh, through these technologies is, is really becoming more of a must have. And, and so that’s exciting for, for our, for all of our futures, and I hope the industry continues to press those limits. And, um, you know, it’s not just simply, you know, what’s happening on the court, the field, or you know, on stage, right?
There’s a lot that really goes into this and, uh, many stakeholders that have, um, their own (···0.6s) agenda, right? And so all of this technology can accomplish, you know, satisfying or satiating that there are those particular agenda points. Uh, James, just, just to react on that because, um, I think, (···0.7s) I think you’re a hundred percent right. There’s definitely been a massive shift. Look, my job is to be frustrated because that, that’s the entrepreneur’s job in saying you, you see the wall as it (···0.6s) as it should be and, and, and how it is, and that, that gap creates frustration.
So, so don’t mind me in terms of that. Uh, it, it means like, and our priest, um, uh, pre-show prep, um, sometimes we get to, to smell the flowers, so it’s smell the flowers. There’s definitely movement there. But, um, I also think that within the industry, there’s often this very K p I focused thing, um, where you say, how can I measure this? And it needs to, um, lead to revenue immediately in this manner, um, which is fair, but if it’s, (···1.6s) if understanding the cues in your venue (···1.3s) is not worth money to you, (···0.5s) then I’m questioning the way your, your business intelligence.
Um, if, if having a general understanding of demographics in your venue (···0.8s) is not worth money to you, then I, I, I dunno how you, you’re running that stadium. So there’s also that part that, that i, I want to keep on pushing and saying, look, we are all in the crowd business.
A lot of people in in sports industry think they’re in sports business. We’re not, we are not busy, um, uh, making plays and buying players. We are busy in the business of crowds and in the business of crowds, there is still, it’s still early days in understanding how those clouds operate, how we can create a better experience for them, and data is there to be used. So yes, smell the flowers is a lot of movement, but there’s a, there’s a, there’s a whole new world waiting for us in the, in the, in the next couple of years.
So, and before we, uh, really quick, I just wanna get a couple, um, things James, real quickly with. Are each of you also capturing emotional sentiment? I know someone, um, wanted to get a sense of that. (···4.5s) Yes. So (···1.5s) I’m sure No, It’s like, I guess we’re not, We’re respectful to towards each other. Yeah. (···0.6s) But I, I, I think, again, this is just another unique way that, um, leveraging similar technology, but (···0.6s) the output and the, and sort of the agenda behind how we in integrate it is so different between the three of us.
And so, uh, being able to, um, capture sentiment, uh, from a traditional digital signage or interactive content perspective, you know, we would be able, we’ve leveraged in the past that, you know, if somebody were to walk up to our display, we could do targeted content based on the sentiment or even the demographic information in real time, right? And so, um, you know, this was something done in a retail perspective where, uh, really selling this as a service that, you know, you could be able to, you know, go by a display (···0.5s) as in walking past a display and as two females in a group and have targeted, uh, content relating to, to that, uh, demographic and so on and so forth.
So, um, I think, you know, from a sentiment standpoint, that’s how we would leverage it. And Tina, and I’m sure you know, your analytics is, is more tracking. If I were to make the assumption I, what happened in the fourth quarter, what were people feeling at at this moment in time?
So I’d be interested to hear case studies on that. We, we, we do track it. It’s, it’s because we have the resolution, um, we can do it. Um, there are, we haven’t seen many use cases for it, um, because back to my previous comment, it’s, it’s early. Um, we find that some teams just, um, it’s just amazing to know the age of the crowd. So, so we’re busy helping them use that data.
But the nice thing about visual, um, uh, data is that we can go back, so we can now go analyze, um, pre covid the emotions of pre covid crowds and post covid crowds and merge that with demographics. And there’s a lot of interesting stuff there. If, if, um, yeah, the really interesting, um, opportunities there. (···0.9s) How about yourselves, Zach? (···1.4s) Yeah, so we don’t, we don’t capture sentiment, you know, it’s, it’s really about, you know, from an emotional standpoint, uh, we do not capture any of that.
We are completely anonymous. Uh, but what we do do is we ca we capture the sentiment of the venue as an ecosystem as a whole, right? So sentiment doesn’t have to be just, um, you know, strictly emotional as, as the question came in from Rick, uh, Hey Rick, how you doing? Uh, the, uh, you know, it’s really about the sentiment of the crowd with their behavior as a whole. So that’s the way that we think about it. We capture the sentiment of crowd flow inside of your venue with a very precision level of accuracy, but we don’t capture anything that’s personalized ourselves.
That’s why, you know, wait time working with tennis, working with James is so complimentary because we offer such different skill sets to one healthy ecosystem moving forward. Yeah, yeah. So on, on the anonymity piece, you know, we, we leverage it the same way. And, and tennis, I I have a question regarding your product, because a, you know, I think you’ve done a lot of, um, international events and, and crowds and, you know, G D R P and then privacy is, you know, by region, by country, by by nation, is, um, you know, ever evolving.
Have you, because there’s such a personalized, at least your fan cam product where fans are able to kind of zoom in, find their photos, take that memento, how do you handle the sort of privacy, um, whether it’s an objection from a client or the, uh, sort of concerns around that in, you know, throughout the globe? (···0.6s) Yeah, we’ve, we’ve captured (···0.9s) I think 20 million fans (···0.6s) by now, if, if not more.
Um, and I think we’ve had three or four objections, um, in, in, in that, that time. Um, and, and part of it’s because we’ve, we’ve, (···1.0s) we’ve designed the company to be non-intrusive. I don’t want anyone spying it on me when I’m at a, at, at, at a game. And so we have two products, (···1.0s) A fan cam one is published pub, public facing one, where we publish these images where fans can zoom in and find themselves that’s very separate to the Crowd iq.
Um, so there people can tag themselves, um, if they want and share it. Um, the crowd IQ side, we take the so same images and put them through computer vision. Um, but there’s a technical, and I wanna bore your guess with the technical, um, part of this, but if you look at a stranger’s face, (···1.3s) even if I give you a thousand pictures, pictures in four D, you cannot identify a person based on their face. (···0.6s) And recognition is a recognition.
So if you know that person and see the face, again, you can match them. But if you build a dumb system looking at pictures of, of people, you can’t just say, oh, that looks like a Tom. And I think Hiss emailed is tom@hotmail.com. Uh, it’s, it’s not possible for humans to do that. And it’s not possible for computers to do that. So we’ve built Crowd IQ to be a, a, a dumb and isolated system where it’s, you essentially think of it as an intern counting the number of male and female fans, because that’s what it, what it does. It’s not taking that and matching that against an existing database of, of, of personalities.
So that’s allowed us to, um, because as you can imagine, James, um, every client, and we work with 30% of the N F L and, um, we are installed in 20 venues in the states. Every client wants to know that, and every legal department wants to know that. And we’ve been able to, to explain it and answer to them. Um, if someone does come through and say, I don’t wanna be captured, we’ll blur their image out. Um, I think there’s a case, uh, to one of the baseball teams where, um, a season ticket holder that didn’t want that, so we’ll just put that in there.
But we’re (···0.6s) very, um, we’ve done a lot of hard work to make sure that, um, this is not something that’s trying to identify people, it’s trying to count them to count the number of fans wearing Packers jerseys at a Vikings game. Um, and it’s essentially a, um, an intern that doesn’t, doesn’t, um, doesn’t get, get tired and doesn’t make mistakes. Sure. (···0.7s) Cool. So for each of you, I think it would be helpful too, I don’t know if there’s examples and maybe we start with, um, Zach, uh, could you maybe describe a example or a client you’ve worked with where data insights led to operational changes, um, to help improve fan satisfaction or drive revenue?
Yeah, definitely. So, um, part of wait time product is, is we have a, an operational dashboard where it’s a web-based dashboard on a tablet or an iPad or whatever.
And operations team, they’re able to click on any and all locations that wait time is monitoring. They’re able to see the video pop up, and they’re able to see the AI over that crowd. (···0.5s) Now they’re able to set threshold alerts. So for example, if it hits a certain number of people in line, if it hits a certain density or occupancy or throughput, automatically send me an alert to our operation. So we can be very proactive regarding the flow of people, um, as opposed to being reactive, which is what they’re doing now or what they have been doing.
So there’s a, there’s an example. Years and years ago, uh, the head of food and beverage at Levy was receiving a red alert for a location at an arena that we started off with, um, called the Palace of Auburn Hills, (···0.6s) and this is years ago. And they were receiving this red alert about, okay, this is very, this is an aberration. Why is this always hitting red? We don’t even have that long of a line, which is very interesting. So what he did is he went to the back of kitchen (···0.8s) and the, at this, uh, certain, uh, concession, they would serve, you know, hamburgers and things like that.
(···0.6s) What they found out, which is why it was causing such high attrition and the red alert was going off from an operational standpoint, (···0.6s) is they were putting lettuce and tomatoes on each hamburger, (···0.5s) which was actually supposed to be a special order. (···0.6s) So it is just simple and as stupid as lettuce and tomatoes, they were adding, it was adding 13 point something seconds per order. Uh, and it was, they’ve been doing this for about eight months of inventory.
So if you think about, you know, one piece of lettuce, one tomato, but six months worth of that, um, that, that’s a, that’s a, that’s a big ripple effect, right? So even so from wait time software, they were able to identify, uh, incorrect food preparation. So very simple things like that that you would never even think of, um, that, you know, that’s one example. I’ll give one more example. Part of wait time system is showing where the wait times are on, uh, I P T V screens.
So on Cisco Vision and other I P T V providers, where can I find the shortest line? Do I go right, do I go left? And the green, yellow, red spectrum bar is the traffic system for people. (···0.8s) What they did is they actually trained their portable beer and concession hawkers that walk around, uh, and sell mo from a mobile standpoint. They actually train them to look at the signs (···0.6s) to go to where the long lines are, stand in the back of those lines and flip attrition into sales.
(···0.8s) They said there’s a 89% chance that once a line gets cer ba uh, gets passed a certain length, people are gonna start falling out. You times that attrition number by that specific per cap of that revenue center. That’s potential loss revenue. So what they did is they just magically appeared at the end of these long lines, and they were able to sell to the people that were probably not 89% likely fall out of line. (···0.9s) So it’s really cool. It’s kinda like a, like a, like a police radio scanner, uh, about where crowds are in the venue from a targeted standpoint as opposed to them aimlessly wandering around and kind of, you know, (···0.8s) not having a, like a, a true North star.
So anyway, those are two examples. I love that, and especially the first one because back to my previous point about the importance of just having that in your fans, imagine you pitching Yeah, you, you need to get away time to, to know if someone’s putting too much tomato on your hamburgers. I mean, it’s something that you, you, you can’t foresee until you have the data.
And because they made a principle decision to understand it, they’ve saved that money. It’s, that’s, that’s an awesome one. Um, And tennis, I think with three, um, you talked a little bit about how you turned a negative sentiment into a positive sentiment with that first example. Do you have any examples of how some of the data driven insights you guys have gathered translated into actually revenue enhancing strategy for our client? (···0.9s) Quite a few. Um, one (···0.6s) recent one is, um, an N F L client, um, who, um, saw quite a lot of opposition team fans, um, uh, attending games.
So they’re in a market where that happens. (···0.7s) And, um, their problem was that, um, they had email addresses of transferred tickets, but they didn’t know if those people were home team fans or away team fans. So they’re sitting on a 160,000 email addresses that they don’t want to use because (···0.6s) if they send, um, buy a season ticket fan to, uh, buy a season ticket, um, and it’s opposition fan, then they’re unsubscribe and if they, um, sell a single their, their ticket, then they unsubscribe.
And so they came to us, is there any way you can help us to segment this? And we said, sure, just give us the seat numbers and we’ll do merchandise analysis, which we did. And so we took the, those transfer tickets and said, (···0.6s) these are all (···1.0s) home team fans and, and these are all away team fans in the different buckets.
And they could then, um, uh, tag those emails and, and use them appropriately. Um, so it’s a, it’s a, it’s a, it’s a relatively simple thing. Once again, if you have all the data, it’s one of those things that I did not foresee coming, but it’s a, it’s a massive revenue opportunity for the team because it helps them to, um, add 160,000 email addresses to their database that, that, that, that wouldn’t have otherwise been there. (···3.4s) You’re muted. Uh, sorry, James.
Uh, a lot of the experiences, you know, that you guys are offering are during the game engaging fans, (···0.6s) but how, can you, maybe if you can elaborate an example on how do you engage with fans (···0.7s) post event as well to continue those interactions to help impact revenue streams for your, the clients you’re working with? (···0.8s) Yeah. Well, similar to Zack and and tennis, we, we have a backend platform called M V P Live, which is our data distribution data center, right?
So we’re able to sort of report that information into the teams’ c R m, where they can then, uh, begin to, uh, retarget those consumers, um, based on the messaging of the may, maybe the experience that they participated with, right? Where, uh, as I mentioned, we have a few different interactive technologies, whether it’s, uh, an augmented reality photo or a virtual reality experience or, you know, something more tactical in a, um, you know, a participation based experience. But you could really, um, tie into that insight and, and retarget and resell that particular consumer for merchandising and or, um, uh, season tickets or just other, uh, promotional activity or promotions that you’re going through.
So that’s generally how we leverage it. Uh, on our analytics piece, we really haven’t (···0.6s) used that subset of data as a targeted host activation approach. Um, but I do think it’s really helped allow our clients to evaluate, again, breaking down some of the stereotypes of, you know, this is not just a generational (···0.7s) activity for a child, but even, you know, with, with generally in sports, you think it’s a male-centric interest and, and it’s not.
And so being able to provide data that says, Hey, we have equal, if not more female users and participants in this experience, um, you know, really goes to show how, how global the interest of, of, um, your team, your fan base is. And so, you know, that’s kind of the insight that we use.
So less, less on the analytics piece, but more on the sort of interaction point. Um, but you know, again, speaking at a retail experience, you know, we’re able to capture data in participation. So I bring back a, an experience at the N B A retail store in New York City, um, when you talk about a global president, N B A does a fantastic job of really getting fans from across the globe interested in basketball. And so, you know, we had a photo engagement at the retail center, you know, the retail section of their, um, their store.
And, you know, month after month we (···0.7s) identified what the top, (···0.7s) you know, the favorite team was. And so I think what the store was able to do is take that data and start promoting. At the time it was the Golden State Warriors, they were really trending. And so really driving messaging out to those users to, you know, sell more merchandise and sell more product for the team. (···1.4s) No, I love it. I’m gonna jump to a question of the audience from Heather. Um, so feel free to each, anyone answer this. How have changes in the game changed in engagement with fans and crowds us?
Well, I, I think for us in particular, selfishly, (···0.6s) it’s dramatically increased our opportunity and, you know, expanded our addressable market because again, 10 years ago, um, you know, maybe Gates opened an hour before, uh, a game started, where now it’s more of a full day event where it’s 2, 3, 4 hours free game. And so when you talk about sponsored activation and, and you know, the ability to capture more users, um, that the culture of change, uh, of the culture of, uh, game day activations has have changed for us.
And so it expanded our, uh, opportunities just merely by the interest and draw of people getting there early and staying late, (···2.7s) but nothing on the on field. Things have really changed our end, even in baseball with the, the sped up times, uh, that, that really hasn’t impacted us.
(···1.5s) Zach Kenne any change, especially in the baseball side of things, Um, yeah, I mean, for us, (···0.5s) everything, everything affects crowds, right? So, um, you know, if there’s a, (···5.9s) Oh, we lost you. Zach, (···6.0s) Zach, (···0.5s) Zach, we, we lost your audio buddy.
Uh, he is just going for it. Sorry. Go, (···0.7s) Go. (···0.9s) We’ll just let him run. (···0.8s) Say it, man. (···1.0s) Preach, brother. Preach. (···3.2s) Have a (···1.0s) crowd. Exactly. Back. (···0.5s) Okay, I’m back. (···2.9s) I can lipread and it was brilliant, man. I, I just think I’m Gonna say every, everything changes, uh, whether it be on the field to, on the concourse, you would, you would be very surprised with how that changes the data that wait time is producing.
So it could be, uh, a fight on the field. We all experienced a fight, uh, in, in baseball a couple weeks ago that got national attention. You, you, you’d be very surprised to see how that data changes from something that happens over here to what happens on the concourse, you know, people have bend the line immediately is it is, uh, to, you know, go to different areas based off of what’s happening.
So everything affects, uh, you know, the, the flow on the concourse, um, that wait time data is aggregating. Um, and just another question for you, Zach, from Mike in, um, the audience. How are he’s interested in how you’re scaling your business? (···1.6s) Yeah, good question. So (···1.0s) really our biggest partner out there is Intel Corporation. (···0.6s) So over the years and tennis, you know, and James, we’ve seen this, there’s been quite a change of the way people think about things.
So (···0.8s) we saw we, you know, we’re, we’re preaching the future of the future of the future, right? But now the future is happening. And so organizations that own the world of technology, i e Intel Corporation, are now, uh, putting us in worldwide distribution. So they’re, they saw the, the writing on the wall with wait time five plus years ago and said, okay, Zach, we’re gonna get you down to the microprocessor chip level, (···0.5s) and so optimize your solution to run an intel hardware.
And then we will, uh, and obviously optimize your software along the way. You know, we will put you in worldwide distribution. So everywhere that that intel goes, wait, time goes with them. So as opposed to hiring internal salespeople, ’cause we all know how that goes. That’s, it’s a long road (···0.7s) integrate into the global sales motion of companies that own the world of technology.
So when it comes to the way that we view partnerships, uh, from, from a growth perspective, that’s the only way that we’re gonna grow and scale. So, Mike, to your question, I would say, you know, hands down, Intel Corporation is our biggest partner. (···0.7s) Obviously it helps with credibility, but also the (···1.0s) steroids that they have injected into our software and our hardware has made us very scalable. (···2.6s) No, thank you. Um, and as we, we, we have a few more minutes here, um, so if any last minute questions, I know, uh, tennis, I just threw one towards you, so maybe take time to chew on that.
Or, um, in the meantime, what other metrics, um, can you track or are you tracking, um, you (···0.6s) know, through your (···0.7s) different experiences or even (···0.6s) metrics that you’ve been asked to track, um, that you’re seeing changing the game in terms of gathering crowd analytics and data?
Uh, I (···3.2s) don’t know if it’s, um, I (···2.2s) don’t think the biggest problem is is, is metrics more integration of, of, of, of data sets, um, and then extracting intelligence from it. So, uh, a few years back, people were talking about we could possibly do this, and as, as Zach uh, said there, the, the future is here. The technology is not the, the bottleneck, um, the, the, the bottleneck is (···1.2s) organizational structure.
So having organizations that are, are willing and, and understand the value of data-driven decisions. Um, I was just down at the lions in Detroit, um, Jack and your, your neck of the, um, the world, and they’ve got eight, I think 11 (···0.6s) data, data folks, data scientists and analysts on, on their team now. I mean, that’s unheard of five years ago. Mm-hmm. And I think they’re, they’re, they’re leading the charge. They’re, um, uh, young, smart people working with data all day.
And so (···0.6s) that is (···0.7s) what we need to see. It’s less about metrics, less about what cameras you’re putting in. It’s less about, um, it’s about (···0.8s) pulling all of that data into (···0.8s) place and then having the, the, um, clarity and intelligence to be able to present it in a way to leadership (···0.9s) that they can make decisions. So, um, sorry, I’m, I’m, I’m not dodging your question, but it’s, it’s No, that, Don’t think the metrics important on that point though.
Um, I think, um, in our respective worlds, um, the, the, we all use AI in terms of computer vision, but the, the advances in, um, uh, in, in that, um, large language models chat, G B T and, and, and, and, and the like, is gonna accelerate that, um, uh, solution, um, that it’s gonna take that bottleneck away because what these things (···0.7s) allow you to do, because it’s language, ’cause code and data is also language.
(···0.5s) It allows non-data people to start asking questions of Saks data and my data and, um, and, and James’s data. And so I think that’s the next thing that’s gonna create a, a big acceleration. Um, if a smart C-level person sitting there and they’ve got a log in saying, which activation ation saw, um, was most successful, (···0.5s) and that language model can then go pull, um, James’s data, um, it can look at the, the cues around that area.
It can, um, compare the, the, the, the data, the demographic data we are delivering and saying this activation and that corner outperform the rest by 30%, and you can then ask it why, and it’ll, it’ll, that’s there now if, if someone wants to put it together. And so, um, I’m going down a bit of a rabbit hole, but, um, it’s less about metrics about these, it’s about tying these things together in a way that the whole organization can get access to, to this data.
And it’s not just stuck in some database somewhere where it’s cool to talk about it on a panel, but it’s being used and people are, are asking intelligent and unexpected questions of the data. Like, are we putting too many tomatoes on our hamburgers or not? (···2.9s) Dave, I don’t know if you wanted to add anything there. Uh, You know, I, I can, I can’t, uh, follow that me perfect call back there. You know, I’ve got, I’m never gonna be to order a hamburger.
(···2.1s) I don’t wanna hold the line’s. (···1.1s) Right. (···1.7s) Well, with a few more minutes left, I think one thing that I always find question asked that sometime where does, if someone in this audience today wanted to get started, right? A partner here on a team, a brand (···0.5s) and is like, I need to track this. There’s a lot, like, where does one start or what advice can you leave, um, folks today on how to think about your different products and integrate them and maybe something they’re doing (···3.5s) Well?
I, I think for us, um, you know, given the very small piece of the, you know, holistic, uh, pie here, uh, I, I think, you know, when we go through our discovery phase, we, we really try to think a little bit more broad in terms of what we’re doing from a technology or a interactive standpoint. And really tying that into, uh, you know, the pillars of the particular brand and the overall goal of (···1.0s) what they’re looking to accomplish.
And, and kind of go from there, you know, again, from the analytics piece, um, to Tina’s first, you know, or early question is, you know, we drill down on that, how important is that, right? And is there budgetary concerns in terms of taking that menu item, you know, off, right? And, and so, you know, just really drilling down to, (···0.7s) you know, the key pillars and what’s important to, um, to the client, what they’re really looking to accomplish.
And then going from there, Zach or tennis, you wanna share My word count is, is high up. I’m, I’m, I’m staying quiet from this one. (···1.5s) Zach can go, (···1.5s) Or Zach, if someone Yeah. How, where does one start when they’re thinking about using something like wait time? (···1.6s) Yeah, I mean, it’s all about, (···0.7s) you know, we’re not here to create a problem.
We’re, we’re here to solve a problem. And you know, really it’s about the mindset of how you think about crowd flow. Um, you know, back when we first got started, we would, we would come across these different old school thinkers where they would say, oh, we know our venue inside and out. We know we have lines. We don’t need anything, want anyone to tell us that we have long lines. And I’m sitting there and I’m like, what don’t you see here? This is not about knowing that you have long lines, you know, and, and it’s about the next 10 steps down the road, right?
Um, you have to think about it chest, not checkers. Um, you know, there’s the reason why that there’s leaders out there in the sports industry and there’s a reason why, you know, per caps at certain teams are so low, right? And so for us, it’s all about changing the way that you think about to a technology and b, artificial intelligence and computer vision. And c is, you know, we have, uh, you know, again, if you want to get more in detail with wait time, I put my email there in the chat, reach out to us. We do use case workshops all the time.
Um, when it comes to identifying how we can start with a solution. So, you know, from the 49 ERs to Melbourne Cricket Ground in Australia, to the Sydney Cricket Ground, to Denver Broncos to Manchester City, we have kind of a worldly view on, you know, rules of thumb. So I would just say reach out to us. I mean, we’re super accessible. I think we’re pretty fun and funny to hang out with. So, uh, we can solve some problems along the way. (···1.4s) Cosign. (···1.6s) I love it.
Well, I know all (···1.8s) in the chat. Um, so if you wanna get in touch with them, uh, please reach out directly. This will also be available, um, online next week, so I’ll share that with each of you. But, uh, wanna thank three panelists for joining us today, all the attendees for, uh, taking the time outta your day to hear (···0.6s) all things crowd analytics. And hopefully you’ll be able to join us next month, um, at our next webinar. But (···0.6s) Yeah, thanks to all the attendees, I would never be able to sit through an hour of listening to other people like this.
(···1.0s) Yeah. Thank you. Yeah, great respect, great respect for all of you. (···0.7s) Awesome. Yeah. Thank you so much everyone. See you. Thank you too. (···2.4s)
