The Founder Journey from eBay and Uber to Movo with Jason Radisson

Speaker 1: 0:00

Hey folks, this is a special startup edition of the Winning Zone. You asked for it. You want to hear from startups the things that they've been challenged by, the ways that they've overcome them, the things that might have been surprising in their startup journey. Here are all of the tools and tips and tactics and techniques from friends of mine and folks that I've reached out to to help bring you insights, to help you have the greatest opportunity for success in your startup.

Speaker 2: 0:27

You're entering the Winning Zone.

Speaker 1: 0:31

Jason, welcome to the show. Hey, thanks for having me on. Glad you're here. I hear it's still actually warm in Minneapolis. Huh, you know yeah moderately.

Speaker 2: 0:42

so we just had a heat wave.

Speaker 1: 0:45

The legs boil or is all well you?

Speaker 2: 0:47

know it was over 100 degrees, which you know Was it really. Yeah, wouldn't have happened 20 years ago, for sure.

Speaker 1: 0:54

Isn't that something? So is that your next startup? You're going to move towards climate tech? Yeah, you know.

Speaker 2: 1:01

I'll move to Alaska and start a climate tech company.

Speaker 1: 1:04

There you go. I love it. Someone listening. Just give us some credit for it, right? Yeah, exactly, tell us a bit about what you are building at Movo.

Speaker 2: 1:16

Well, movo is a platform for the frontline workforce and for the companies that run large frontline workforces.

Speaker 2: 1:25

You know, places like hospitals and logistics companies, big retailers, clean tech companies, telecoms companies with field engineers all of these kinds of companies and professions essentially skilled labor that you know may involve to your technical degree or higher qualifications like a masters of nursing or those kinds of degrees.

Speaker 2: 1:54

We are basically empowering both sides of that world. We have a lot of real time tools and functionality for our clients, for the employers, they essentially get a white labeled version of a gig economy platform where there's all kinds of automation around scheduling and onboarding and all of these kind of critical things for maximizing making sure that the right person is in the right job at the right time anywhere in your company. And then for the employee, they have just this really seamless, modern experience. And one of our clients said recently you know, we're all kind of gearing up for a world in two or three years where everybody that's coming into these entry level semi-skilled and then up into this climbing the skilled ladder, is going to have worked for Rappi or a new burr or a door dash or something like that. They'll expect to be on a modern workforce management platform that's real time in your pocket. All shift along. It's just going to be the expectation of the new generation that's coming up in these jobs.

Speaker 1: 3:01

So that's the world we're building into. So help me understand, just for folks who might be listening and we get the idea of a frontline worker, but maybe not how employed inside of an organization, some of those gig economy applications would be relevant. What is the problem that's actually being solved, or what is the challenge that's being? About this folks.

Speaker 2: 3:19

So we have a number of just very modern use cases is how we think about these problems and the solution. So one example would be the automatic substitution of a nurse. You know, which may seem like a pretty mundane thing unless you know you have 60,000 nurses and you know you're managing across a really big metro or really big region and you've got, you know, obviously, exposure to diseases and people get sick in healthcare, just to be obvious, and so that's a really strong use case. Labor forecasting is a really big one. So you know, what do we think we're going to need on the demand side? And then our platform automatically slots, fills the slots that your labor forecast says that you're going to need.

Speaker 2: 4:08

So this kind of real time optimization of who's working where inside your company is a really important use case. Related to that, in a little bit longer time horizon, is our internal talent marketplace. So making sure that there's upward mobility, that there's reskilling, cross-skilling in the different types of roles that you have inside your company. If we stay on the healthcare example, making sure that your nurses who haven't worked in the ER and aren't internally qualified to work in ER but are RNs and perfectly qualified otherwise, that they get the internal exposure and are able to work and become resources that can be pulled into the ER if you need More resources there. So there's kind of internal cross-skilling and mobility that can mean also moving markets. You know We've got way more demand.

Speaker 2: 5:00

One stayed over Would you consider taking a job? One stayed over and making that happen internally. So internal talent marketplace Is another really strong use case that we support Mobile time clock and all the kind of anti-time fraud things that go with mobile timekeeping. So we're usually able to replace traditional time clocks, fingerprinting, you know, fingerprint readers, all of this kind of more Heavy hardware focused tech with a mobile timekeeping system with a bunch of just modern anti-time fraud. And that gets into some really cool use cases because you know Not only can you have a more robust timekeeping system With geofencing and QR codes and all this kind of modern stuff around a work site, around a hospital, around a warehouse, but you can also deploy it in really interesting field use cases like making sure that that field technician who's on a particular work order gets that job done and it's got an audit trail. It's geo stamped.

Speaker 2: 5:59

It's time stamped their evidence, photos and videos that are all related to that. You actually know that that person was there and completed the work, that the part that was ordered, that was actually there and used all these kind of things that otherwise lead If kind of fraud doors open and a lot of companies time fraud and materials fraud doors open If you don't have a modern system like that If you go look at a lot of the way field work gets done in our country and in particular in middle-income countries, like there's not a lot of tech there.

Speaker 2: 6:27

People have some cobbled together systems, a bunch of Excel, a bunch of text messages To have it all in one unified platform to be able to delegate jobs in massive scale Automatically and then track everything in real-time dashboards. It's like it's really just a very modern approach and that's really that's the experience that we're building for our clients and delivering for our clients.

Speaker 1: 6:48

When you speak of experience, is this something? Is this a field that you were involved in prior to starting the company? Did you have deep expertise in this kind of field, field services management and frontline worker management? Where did the? Where the?

Speaker 2: 7:00

idea. Yeah, yeah, oh, all the way back, yeah, so in different, different places. You want to go there? No, I started, I was. I was a grad school dropout and I started at McKinsey at the Sort of end of doc, in the beginning of the dot-com crash.

Speaker 1: 7:17

I knew there had to be some analytics associates.

Speaker 2: 7:20

Totally totally and I did two things I helped. I helped big telecoms companies Rationalize and a lot of post merger kind of situations and then just a lot of field workforce Alignment and efficiency improvements. And I did that For the most part in Latin America with some really big clients there. I also did a bunch of algorithms work. There was a group of us in the McKinsey Mobile commerce special initiative way back when we were working in early use cases for mobile commerce and and and you know what was going to be calming. We had iMode. We didn't have I mode, the, the Japanese version of an in mobile commerce platform. Right, that was there in the early aughts. We didn't have the iPhone yet and so we're doing a lot of work with the telecoms clients on trying to get some good use cases stood up In mobile commerce and there were a lot of just really cool out the algorithms that we were developing.

Speaker 2: 8:22

There was a group of guys there was. There was me sort of you know recovering a grad student Working on a bunch of that stuff, together with a bunch of guys from from that had been through the Neils Boar University in Copenhagen and done a bunch of analytics work there. Yeah, so we were kind of the algorithms guys In telecoms in McKinsey back in back in those days. And then I had a number of roles in big US rollups Also for about 10 years worked on rollups and in a couple of big US consumer industries and telecoms and in hospitality and I think you know what was really cool about that situation in my role was generally, you know, we were in a big fortune 100, fortune 200 company and we were buying another company and then Classically kind of what happens in that situation is you've got, you know, tens of millions of consumers on either side and they have their accounts and there's different things like the company will have some really important algorithms and and and campaigns and treatments and things that they're doing, you know, automatically with that customer base.

Speaker 2: 9:31

And and my team's job was we would always take the databases and kind of a clean room environment, merge the databases, re-score everything, decide from all the algorithms that we're inheriting in this merger like which algorithms were the ones that were really important in which programs we needed to sustain, and then we would kind of refresh and rewire all of that for whenever kind of the go live date was. So it's a kind of special, special role. It had a lot of different titles you know you'd be head of like integration, post merger integration, those kinds of things, yeah, and it usually went. Some of my titles were like I would own the database marketing or the database portfolio, the algorithms portfolio or statistical modeling and some other teams like that. But generally speaking, like I think one of the key things that I learned during that phase is like there are just algorithms that work and everybody's seen this kind of in popular literature, but you don't really think about it a lot. Like you don't really think like what's the winning algorithm for getting somebody onto a credit card or what's the winning algorithm for getting somebody to travel to a destination property Right, and in sort of the companies that were generally ahead as we saw a lot of rollups, especially in the odds and in a bunch of US industries those companies were usually also the algorithmic winners.

Speaker 2: 10:59

They were the ones that just were the sharpest on those particular programs, had been running on with the most experimentation over time and it got into a point where they were just doing the hill climbing thing, like their systems were just getting smarter and smarter and smarter and more and more profitable and some of the examples like I worked for Gary Loveman at Harris Entertainment.

Speaker 2: 11:19

We rolled up the casino industry.

Speaker 2: 11:21

We bought World Series of Poker, we bought Binion's Horseshoe and the Horseshoe Properties, we bought Caesar's Entertainment, which has gone through a lot of transitions and particularly has been a bunch of books written about the work that we did there and what happened afterwards too, particularly with the LBO that came after that.

Speaker 2: 11:42

So you look at, like those industries my old boss, gary Loveman, used to say it really was. He said, like it's not, the other guys don't have valid strategies. It's just we have this one's actually very cost effective and we just have honed it and honed it and that sort of I think is the skill set that really has served me well. And you kind of walk around with the winning algorithms and a bunch of industries in your head and the cool thing is you can apply those out of context. And I say my approach as an entrepreneur is largely I apply things out of context. I look at the gig economy and I go and you kind of forget kind of the labor market pieces of it and maybe the politics and the government relations pieces of it. That's a whole nother podcast or two, right.

Speaker 1: 12:37

That's right.

Speaker 2: 12:37

But if you look at the technology and go, well, they're doing real time hiring, real time deployment.

Speaker 2: 12:46

Wouldn't XPL logistics benefit from that?

Speaker 2: 12:50

Or any number of really large employers of blue collar folks where the skills are relatively defined, they're pretty easy to model and you can remove a ton, a ton of friction.

Speaker 2: 13:05

So that's been done in multiple ways. And just if you look at the sort of history of these days we call it AI but kind of the history of machine learning and the big algorithms, in our country in kind of a modern rich country context, there's probably 100, 150 that really shape our world and there are a bunch of us out there who are kind of walking around knowing how to build them and knowing how to deploy them and it's really really valuable skills. So to bring that all back around to Movo, that is very much kind of the approach that we've taken here and one that is really proving a ton of value. Our customers are talking about a 10 or 20% improvement in overall productivity of their workforce and generally, if you employ 50,000 people, 100,000 people like it's a big number and it's a big improvement on top of a very big number. So that's really the game that we're playing right now.

Speaker 1: 14:11

You know what's interesting to me? I even think of, like Jeff Bezos, coming from an investment banking background and being able to apply a lot of the insights and just algorithmated that he had to Amazon, from the standpoint of starting with books and then having a clear idea of being able to funnel thousands of other products that are set on a regular basis, on a daily basis, given the cadence of the Amazon truck at my house. There are other folks, I'm sure, who are sitting in consulting roles and who have this type of interaction with customers and are applying these types of algorithms and are doing this kind of analysis inside of their organizations. The challenge, though, is how do you take what your knowledge is with respect to an algorithm and then create a product out of this that, on a frontline basis, has a user experience that allows for someone to engage, which creates enough data for the algorithm to be able to do its job on an admin and analytics side, provides enough information without it, provides and decipher, is enough information for executive leadership to be able to make decisions and engage with the product as well? So how did you take this stuff in Jason's head and then get into product format? Curious to know what was MVP? How did you? You know that point of productizing this knowledge? Hey, folks, go to Foundersalesacceleratorcom If you are at that stage of trying to figure out.

Speaker 1: 15:37

Go to market. It's critical. It's a 90 day program, but let me tell you something it starts off super fast. In the first 30 days you're gonna lock down data sourcing, you're gonna lock down your market in personas, you're gonna lock down the messages that you're gonna use on every single channel and you're gonna get those things out of the gate so you can start developing top of funnel to mansion.

Speaker 1: 15:57

30 days out from that, we're gonna be looking at the proof, looking at the activity, looking at the data, looking at the iterations, looking at the conversions on the work that you've done the first 30 days and then, by month Two, we're already looking at scale. This is where we're looking at the volume. We're looking at the resources you wanna throw at these opportunities and ensuring that we are tweaking your pipeline to optimize for the highest level of conversion and qualification. This is the GTM accelerator that actually works, as opposed to beating your head against the wall trying to figure out how you're going to get to market and how you're gonna generate revenue. So go over to foundersalesacceleratorcom. Click on book a strategy call. I will be on the other end of that call and I'll talk you through the process.

Speaker 2: 16:39

Well, I'll, maybe I'll start with a high level answer because it might be generally useful for your audience. The high level answer I think there are like three approaches, right, one of them and I've kind of done all three in my career. So one approach is you literally build an algorithm shop. You build a headless algorithm shop. I ran one for a number of years helping kind of larger e-commerce companies compete against Amazon and basically putting, if you look at the homepage and usually like that's where a lot of the magic happens right in e-commerce, you know there are, and some people have done this really well If you look at Selly, at rich relevance or the company was originally rich, relevance, right, and I think a lot of that early work was Jeff Holden, together with Bezos, and if you followed kind of Jeff Holden's career, he was at Groupon working with our board member, rich Williams, and then the former CMO of Amazon, he was over at Uber.

Speaker 2: 17:45

You know Jeff's been around and it's a lot of kind of these same things right, building a lot of these systems and capabilities for the companies that he works for. So one approach is headless you build an algorithms portfolio and an algorithm's team and you ingest your clients data and then you return answers back for their optimization of their properties.

Speaker 1: 18:10

That was one shot While training a model.

Speaker 2: 18:12

While training a model, Right, yeah, and you know it's a bag of strategies, right. Like rich relevance at one time was. I think their pitch was something like you know, Amazon has like a thousand strategies for the homepage that are competing for your eyeball. For your eyeballs, you know, we have 600 that we've done so far, you know, and that kind of thing. So that's a great way to do it. I spent three years at eBay with a very large Accenture team.

Speaker 1: 18:40

Did you really.

Speaker 2: 18:41

Doing the same thing, kind of. Our work orders were a list of algorithms we would develop each quarter and how we would traffic them and the sort of the traffic spectrum that we would need and then what we would hope to gain from that. So we were basically, you know, doing work for hire on an algorithm basis.

Speaker 1: 19:01

Okay, which is another?

Speaker 2: 19:03

way of doing the headless thing. I think the other way that you do it is you take a job with the company and you are the product lead for a big piece of real estate in that company. You know, if you're at Jeff Holden's level, then you have the overall responsibility and you hire a bunch of product managers Else. You kind of take a product management job and you know you're the person responsible for trust and safety and so you go and develop the 30 algorithms that you need for trust and safety. Or you know, I've had some experience working with the Dede senior team and, like Dede, has more than a thousand anti-fraud algorithms for China alone, which is kind of mind blowing, is that right Wow?

Speaker 2: 19:47

Yeah, yeah, yeah. So I think that's another approach is your product lead. And a third approach is you go out and start a company and build an algorithmic product that you know can go and fill a need, an unmet need out there in the world. And I think, if you look at so, obviously that's the mobile approach and if you look at what we're doing, I think there are some really like clear I would say the model for our approach is not it's not even the gig economy approach. Like we are not about. We're not about that other stuff of the gig economy. We're not about, like you know, allowing people to cut corners and get like flexibility on their employment market.

Speaker 2: 20:32

We're working for W2 employers by and large and their staffing companies. Like we also work with, you know independent contractors and outsourced resources. They're part of our workforce automation solution world just like anything else. But we're like. We're like this virtual reality layer on top of your running massive operations.

Speaker 2: 20:58

So, so everything's there in real time. Everybody's interacting. That's it impacts culture. It impacts a lot of a lot of soft topics, not just the hard topic of, like I've reduced my overtime exposure because people are allocated more efficiently, right so, and I would say the model for that really is the massive multiplayer online game Cause you look at it and it's. It's got everything right. It's got, it's got UX, a lot of UX. It's got maps and an overlay on a virtual world and it's got yeah even some of the models have employment models.

Speaker 2: 21:39

right, you can go be part of a crew that's building part of a city in a virtual world and this other stuff. It's got quests, it's got experience points. These are all the things we need to help level up employees. So we've got a little bit of gamification, a little bit of the social interaction and these different features.

Speaker 1: 21:57

When did you have that realization. When did you have that massive multi-user gaming realization?

Speaker 2: 22:03

I think when I took, you know, my first role at Uber, I think I had realization yeah, cause it like what's so very different and I think, from a client experience. So we, we work with you know our ICPs are, are operators.

Speaker 2: 22:17

You know they're the, the regional president or, you know, the president of US store operations and those kinds of role profiles, and they're also the CHROs and the people who lead HR tech in big companies. And I think what's what's more or most different about the experience is like we tell them it's more. It's more like being in the cockpit of, of a really large platform or like a trading floor or something or what used to be, you know what is now a virtual trading floor Back in the day, like you are watching on a bunch of screens.

Speaker 2: 22:52

Everything happened in real time, because in the onboarding process we always get these questions. But but you know what's my weekly dashboard gonna be? It's like it's not a weekly Right. You're gonna be looking at the map. You waited too long, that's right. That's right. You're gonna be looking at the map 15 minutes into your morning shifts, going oh crap, we have six different issues, let's go solve them, right. And, and that's the once you, once you go real time, you just can't go back. So so that's the. That's a little bit of the change management, just in terms of the client experience.

Speaker 1: 23:24

So this sounds like and I know you've said before that you think one counterintuitive position you have is that an MVP should be more robust than people think, right, that that you should actually go ahead and build out, not just scale back and and dump everything else into the roadmap. It sounds as though that was by virtue of necessity with Movo. I mean, you couldn't have really gone to market with a half-baked product. You have too many masters you're trying to serve. Is that the case?

Speaker 2: 23:50

Yeah, it's a big part of it. Yeah, I am it's. I just think if you're in a generic way, if you're doing something transformative on multiple levels or with multiple stakeholders, you just end up with a bigger, a bigger MVP or I like the Bergleman term, the MWG. Yeah, I think it's more of over at the minimum and in game. Like like what does it take to actually land a real-time platform in workforce management and something as stodgy and outdated as workforce management, Like what is real-time, like that's, you know, we're like taking the experience 20 years ahead of where it is right now for a lot of clients.

Speaker 1: 24:28

Well, that's what's fascinating, too, from a from a go-to-market strategy perspective. You know it's like you think of this. You think of the types of folks that you're working with, the folks that you've listed as number one being a little bit risk averse, number two being a little bit slower moving, right, you know, they're just still kind of talking. Most are just talking about digital transformation in the board, right?

Speaker 2: 24:47

Right right.

Speaker 1: 24:48

They're talking about gamification of the entire.

Speaker 2: 24:51

That's right, that's right.

Speaker 1: 24:52

So how do you, how are you having that conversation? What was what were kind of like your early wins and how to do it?

Speaker 2: 24:58

Yeah, you do with pilots and I'm gonna you know like we're just, we're just sharing the bag of tricks here.

Speaker 1: 25:04

But yeah, one of the other ones. One of the other ones, that's right, that's right.

Speaker 2: 25:10

These are good. These are good confessions.

Speaker 1: 25:11

These are good confessions, you know worst case?

Speaker 2: 25:14

we're worst case. Some people are successful with these.

Speaker 1: 25:16

Right, that's not a bad outcome.

Speaker 2: 25:18

That's not a bad outcome. So so my my take on it is you know, I let me say one thing before I go there, which is which is I firmly believe that that early stage teams there is only product market fit.

Speaker 1: 25:34

I don't care what else and.

Speaker 2: 25:35

I see a lot of pitch decks that you know, here are esteemed you know six angel investors and here are esteemed six co founders and whatever. And I, you know I love people, but like literally, that doesn't matter to me.

Speaker 2: 25:50

What matters to me is like you either have to have product market fit early, really strong signs of product market fit, or you have to be a product market fit machine and and like an experimentation machine. And I've I've had I've had one boss in my career who was really really trained me on that. I had a boss in in in one stage. I worked for him for four or five years and literally we on the management team of this company we had kind of like we had a playbook and this was not a small, this is a mid cap company. We had a playbook where you had six weeks and 10 engineers to make something happen and that meant product deployed in market with traction and and it was, and it was no risk. The only risk was like if you came with no ideas to meetings but like, but like that you know it was.

Speaker 2: 26:43

It was just a play, just go you know, yeah, like, and it just, it just drilled you, it like any other drill. You know, like you were just in a point where you're like okay, you know it was hard for the first quarter or two, but once you got into it you're like yeah, yeah, it becomes muscle memory right Like muscle memory, like muscle yeah yeah, exactly, exactly Great idea.

Speaker 2: 27:03

When are we trafficking this? And we had a ton ton of traffic, so the getting the traffic wasn't a problem. You didn't have to go. You know, engineer a complex GTM. You were just kind of dumping into the existing deploying. Yeah, but still, but still, so, anyway. So so my first point those would be my first points which is, like you know, you've, you've got, you've got to have that product market fit machinery going. That's that's really, that's really what it, what it takes to get there.

Speaker 1: 27:34

If it starts at the top, how do you, how does that permeate through the dev team? You know what I mean. How do you get to that point where there's there's a, an iterative process, rather than sticking the landing you?

Speaker 2: 27:47

know what I mean.

Speaker 2: 27:48

Yeah, yeah. So I think it's, you know, I think it's. You have to have a culture where experimentation and risk taking is just fine. I, you know, I am the first one in our company to confess that I all my ideas are not great. Most of them are kind of terrible, and you know, a few of them eventually hit, and you know you just have to have that really healthy attitude towards, towards experimentation. I think it's just, it's just key. And you know, and, and you know the bandwidth and it gets really challenging, like because I think you're in early stage, you know we're at, we're at 35, 40 employees, right, and and in early stage you're like, you're in this, like this plasma of rules aren't defined, like it's just, you know it's I don't know how to use the cosmology metaphor, right, like nothing's the physics aren't really defined, the roles aren't really defined, and that's all fine, because if it were, like, what would you be doing with a matrix organization at that point, right?

Speaker 2: 28:56

So? So part of that is like just encouraging everybody to be like they're, you know, and completely dialed in to looking for experiments and things that would really move the needle, and then to be executing on those and just like you know, no, you know, with no baggage, like you know, oh, yeah, we're going to try that, okay, go. And then we go and try it. And it didn't work, okay, next topic. And just keep, keep, moving on.

Speaker 2: 29:23

The second the second part of of your client or what I was going to answer.

Speaker 2: 29:28

There's a real the question you were just asked, the answer, the short answer for like, how do you get this done in, you know, fortune 100, fortune 200 companies is you go to middle income countries that are completely wired for sound and you go and run with clients that really want to get stuffed on and and oftentimes you know there's just and it's not everywhere. You know, if it's a family owned company that is very conservative in their culture, like that's not, you know that's not something that your salespeople should know. Those aren't doors your salespeople should be knocking on at that stage. But if you've got a company that is just leader in their industry in that country and they are just kicking butt and taking names, those are the doors you want to go Knock on and you want to go work and partner with those or the clients in your beta phase, like that is that is the answer, because those are the clients that are going to be, you know, like early adopters early adopters.

Speaker 1: 30:29

you know, we, exactly, exactly, we've got.

Speaker 2: 30:32

you know the, the board members are the management team and oftentimes, if you're talking Eastern Europe or southeastern Asia or or Latin and some of the markets, you know the, the, the early adopters are going to be, it's going to be the CTO, together with the CHRO, and they're both like just absolute rock stars looking to get a lot of stuff done.

Speaker 1: 30:53

That makes a lot of sense. Those are your clients, those are your clients for enterprise. Go find like mines. I love that. Those are your clients, yeah.

Speaker 2: 31:00

Those are your clients, those are your early clients for enterprise. Go put a couple hundred thousand you know users on your platform with them and then come and knock on some Fortune 100 company doors and show them what their world in five years looks like. That's right. So hey, that's a great segue.

Speaker 1: 31:17

The final question I've got for you what is the world of frontline workers look like, the frontline leadership and management look like? For, if Movo has its panacea, if you paint this perfect picture, yeah, yeah, what is the impact you've made and what is what is shifted and how does that look?

Speaker 2: 31:36

Yeah, definitely. I mean great question. Our long-term impact should be a material improvement in productivity on the frontline and if you look at kind of you know, BLS Bureau of Labor.

Speaker 1: 31:54

Sorry, can we? Yeah, it's not.

Speaker 2: 31:55

BLS. But if you look at BLS statistics, if you kind of look at what the Labor Department is putting out, it's, you know the definition is somewhere around 30 million people in the US. So you know what's a 10 or 20% improvement in productivity and living standards of 30 million people? Like that's the prize, that's really what we're gunning for. That's in the US and you know, and we're in more and more middle-income countries every week. So you know the impact there is just phenomenal. I don't kind of want to give away our GTM strategy, but but you know, look at those hard-charging middle-income countries that are looking to really kind of step up on the global stage in a number of industries, and that's where you'll find us. And if we can have a 10 or 20% improvement in their workforces and their frontline workforces, that's the kind of impact that we want to have.

Speaker 1: 32:53

I love it. Jason Radisson, I wish you continued success with Movo. You actually have given away a ton of nuggets. If not, your free to go to market strategy. I think you've made a huge impact here and there are folks who will benefit from the conversation. Thanks so much for joining me on the winning zone.

Speaker 2: 33:07

Hey, thanks for having me on. It's been a real pleasure.