Josh Jones - Founder & Chairman at QuantHub

(Joel Beasley at 00:00:00) Hello, my friends. Today we are talking to Josh, the founder and chairman of QuantHub, and we discuss how he and his father built a medical center in the jungle of Costa Rica, the importance of having a data literate frontline workforce, and how QuantHub is helping companies upskill their workforce in data science. All of this right here, right now on the Modern CTO Podcast. Here we go. This is the Modern CTO Podcast.

(Joel Beasley at 00:00:38) And our first conversation, I really love what you were doing with QuantHub, and so I definitely want to hear more about that. But I want to start out with your origin story.

(Josh Jones at 00:00:47) Yeah, absolutely. So let me sit on the psychologist couch for a second here. Let's go back to the, we'll call it the nineties. So I'm from Tuscaloosa, Alabama originally, and early on went on a missions trip in my church. I was about 13 years old. Went to Guatemala, absolutely loved it. Convinced my parents to actually let me go back. And they sort of threw me a curveball and they said, well, we'll help you get there, but we're going to drive you down.

(Josh Jones at 00:01:15) So we bought a Jeep Cherokee and drove through Mexico down to Guatemala, spent the whole summer wandering around, and I was really hooked after that. So the next year, the summer I turned 15, I moved to Guatemala to go to language school and studied Spanish probably eight hours a day. And then, you know, internet cafes were really just coming online. Everybody was turning coffee shops into internet cafes where you could rent computers by the hour. And for someone in eight hours of Spanish language a day, I was looking for somebody that spoke English to hang out with. And so, of course, that was sort of the hub in Central and South America where you could meet folks from other countries.

(Josh Jones at 00:01:53) And so I spent a lot of time there and noticed that they were really using essentially legal pads to manage their business, clock people in, clock people out. And so I had been sort of hacking in high school, playing around with BASIC, which sort of led into Visual Basic, and I offered to write them some software to manage their internet cafe. And honestly, I really just did it on a whim. I was sort of trading for free internet. And then one day, the owner came in laughing saying, hey, the internet cafe down the street tried to steal your software. And I thought, well, that, you know, was pretty stupid because I hard coded it for this cafe. There's no way that's going to go anywhere. But it made me realize, you know, maybe this has some market viability. And so, long story short, I dropped out of high school, moved to Latin America, went back and forth quite a bit, and rewrote the software to really take it to market.

(Josh Jones at 00:02:41) So I ended up traveling up and down from Mexico to Panama, about eight countries there that I would sell the software in each of those countries and sort of go back and forth between doing medical and dental relief work with different groups from churches to Rotary to Lions Clubs. At that point, I was translating. I was also starting to lead some teams, getting some medicine donated internationally, and really was focusing both on the nonprofit side as well as the business side. And so I would kind of spend one week in one business and one week in the other, and did that for about 10 years.

(Joel Beasley at 00:03:12) Were your parents pretty supportive of this? What age were you when you were doing this internet cafe software?

(Josh Jones at 00:03:18) So I wrote the first version, two three and a half inch floppy disks. I think I was 15. And yeah, I dropped out of high school. I ended up, the University of Alabama let me enter early through doing well in testing and some other stuff. So ended up spending about seven years at Alabama, just a couple of classes at a time, sort of checking the box to get through an undergraduate management degree.

(Josh Jones at 00:03:43) And the computer software business led into internet consulting, ended up selling that business in 2000, ended up starting a digital marketing web design firm, ran that for about seven years, sold that in 2006, 2007 era. So they were, you know, they were slightly nervous at first. I didn't die after a couple of trips, and came pretty fluent in Spanish, had a lot of contacts, ended up just getting really comfortable going back and forth. And so, actually, at one point, I'd—so it's sort of a rabbit trail. I won't take us too far down, but someone had donated 250 acres of land in the northern Costa Rican rainforest to us to start some medical facilities.

(Josh Jones at 00:04:22) No power, no running water. My nearest internet connection was an hour horseback ride away. I would connect to a government satellite phone and use a sort of over the air dial modem if you remember those things that made all the screeching sounds. So I was out in the middle of nowhere. Called my father one summer. I think I was 17 and asked him if he would—he was a carpenter—so I asked him if he would come down and help me build a medical facility. So he took two months off work, dropped what he was doing, and two weeks later, we were out in the jungle building a medical facility, chopping down trees with a chainsaw, made a sort of makeshift mill, and sort of everything was built on site.

(Josh Jones at 00:04:59) So, yeah, they were pretty supportive, I guess you should say.

(Joel Beasley at 00:05:03) That is so cool. I've got to be like, I've never heard this type of story before. I love it.

(Josh Jones at 00:05:09) Yeah. It's, it was an important, I would say an important part of my development in the sense that I really, I've always sort of seen sort of two paths. One is sort of the development relief path, helping people, serving others, and the other is really the business side, the entrepreneurial side. It's all about solving problems. And from the internet cafe business to the digital marketing firm and to later StrategyWise and what we're doing now with QuantHub, it was really about just understanding business problems and challenges and looking for solutions.

(Josh Jones at 00:05:41) And whether you're troubleshooting a renewable solar system in the middle of nowhere in the jungle or whether you're writing code in Python, you're essentially finding problems and creating solutions.

(Joel Beasley at 00:05:53) So what is QuantHub's current project? What does that do?

(Josh Jones at 00:05:57) Yeah. So to sort of fast forward towards the latter part of my career. So 2007, sold the web design business. My wife's Japanese. We spent about three years living in Japan.

(Josh Jones at 00:06:06) We met at the University of Alabama, and we spent three years in Japan, started and sold another business there, came back to the US, did my MBA at Emory. And so this was, I did the weekend executive MBA, so I could actually drive over while working. And I was working in a dental organization, one of the larger dental practices in the Southeast, clinics all over the state. And hopefully, you know, this is your audience. The CTOs will appreciate sort of this era in terms of the change and really development of the data science industry, if you will.

(Josh Jones at 00:06:38) But I realized that data science in some form has been around, you know, we'll call it thousands of years since people using stars to predict where to go. And then you've got folks that have war stories from the sixties and seventies, punch cards and so forth. But where I would say there's really sort of a turning point in the industry was in 2010, 2011, 2012. A couple of key things were happening. One, our internet speeds were really starting to hit a critical mass in terms of what you could send over pipes. You've also got the creation of data is increasing at an exponential pace.

(Josh Jones at 00:07:09) And so at one point, you know, we were saying more data was created in the last two years than the rest of the history of the world. Modern expectations are that's going to quadruple in the next year or two. So just the explosion of data is there. You've got that coming from sensors, but more importantly from smartphones. So you've got those internet speeds. And then a real critical turning point was the democratization of servers. And so servers went from millions of dollars, hiring six figure people to manage that, to, hey, any Joe Schmo can go rent an AWS server by the minute and run really big models on those servers. And so you see the convergence of those in that 2010, 11, 12 era is really where I would say data science was really starting to bubble up into something really compelling.

(Josh Jones at 00:07:53) So I was learning how to do regression models and basic prediction models in our MBA program. And then in the dental clinics, I discovered, hey, we've got all this practice management software. I can run a debug tool, write SQL queries, and extract 150,000 rows of patient data, and start looking for problems to solve and things like that. So it was sort of this business case real world with all of this new data that I was getting on how to build predictive models and stats and that sort of thing.

(Josh Jones at 00:08:22) And so I was working in a nonprofit clinic, and we found that over 25% of our appointments were not kept because these were Medicaid appointments. People were not paying out of pocket for them. Major problem. So you overbook your appointment slot, then people get mad when they have to wait in line, and they don't show up next time, and it becomes a spiraling problem. So what I did was I said, well, let's extract all this patient data and see if we can build predictive models to predict who's going to show up for their appointment and who won't show up.

(Josh Jones at 00:08:50) Got a lot of signal, basically, that, hey, we could predict this. And a lot of things you wouldn't have thought about, like, the closer they live to a clinic, the more likely they were to miss an appointment. And so our best hypothesis was if you live 30 minutes away, you're sort of planning your day around that appointment. If it's down the street, then you're not really putting as much priority on it.

(Josh Jones at 00:09:09) Anyway, lots of things like that. But I discovered early in that process, we can really start using data in new ways that businesses, I don't really think, have used before. So we started StrategyWise in 2013, really, I would say, as a consulting firm with sort of this data charge, but very quickly into it, one year, two years in, every company we're working with is saying, what you're doing is really interesting, but let me tell you what's keeping me up at night. It's we have all this data. What do we do with it? So some of our early clients were Toshiba, Samsung, and we did a lot of work for Southern Company, Alabama Power. And it really morphed into, can you help me leverage my data to achieve some sort of competitive advantage? And so what we saw is, I'll say over the last 10 years, there are a couple of sort of major, we'll call them trends for lack of better word. You've got Hadoop, and everybody wants big data.

(Josh Jones at 00:10:05) And what we saw is everybody wanted Hadoop whether they actually needed Hadoop or not. It's just the CTO down the street needs Hadoop, so I must need it as well, or we've got budget for it. And so and then for a while, you get dashboards. Everybody's got to have a dashboard. And now our current phase, AI and machine learning. Right? Everybody's like, give me machine learning. Here's my budget. Go give me some machine learning. And obviously, there's more sophisticated approaches than that to it, but there was very much, and there has been over the last couple of years, this sense that here's this new techno—oh, yeah.

(Josh Jones at 00:10:36) I forgot blockchain. We'll throw that in there as well. So there's this new technology. Surely, it's going to be a big deal, so we should be investing in it. And so what StrategyWise did—so StrategyWise was a data science and AI consulting firm for about seven years.

(Josh Jones at 00:10:52) And what we did, really, we found our niche was in advising larger corporates, so Fortune 500, Fortune 1,000 companies on how to build data and AI strategy. And the reason that I believe that what we did resonated with our clients was we would say, don't let the technology tail wag the dog. Let us find what your business case is, and let us help you build a data science strategy around that particular, whatever those business cases are. So a couple of quick examples. One of our clients is one of the largest fast food companies in the country, and we worked with them on a number of different projects.

(Josh Jones at 00:11:29) But one of the things that was really important to them was caring for their guests, for their customers, creating a meaningful experience, positive customer experience. And the more you know about someone, the more you know someone's story, the more you can create a positive experience for them. When you think about the data creation that's occurring through mobile apps and rewards and things like that, if you just have a credit card, you have a credit card hash, you can't really identify the individual or if they're paying with cash. But, again, this is really any industry. If you can get them to use a rewards program, you can start to begin to monitor those patterns, routines, preferences, and behaviors, and create a more positive experience.

(Josh Jones at 00:12:05) Classic business strategy, it's around differentiation, creating that positive guest experience. On the other side, whether it's utilities or anywhere else, there are all these different ways that you can build competitive advantage through leveraging data. And so a lot of what we would do is say, ignore the technology for a minute. How are you competing in the marketplace? Is this a differentiation strategy?

(Josh Jones at 00:12:27) Is it a low cost strategy, a niche dominant strategy? We would start with the strategy and then say, well, let me tell you about the different technologies that are available out there or the ones that you might want to invest in and really help lead them to the best possible series of projects that they should undertake along the way. So to answer your question about QuantHub, so one of the, I would say biggest challenges we found in this process is we made a series of hires that we questioned after the fact. And I thought, you know, this guy's got a PhD from an Ivy League school. This person came from a top four consulting firm, and they're not working out. What's the deal?

(Josh Jones at 00:13:07) And, you know, Mark Twain once said a strong opinion adds 10 points to your IQ. And, you know, I've thought about that a lot because if you talk to someone who's just really opinionated on something, sometimes you want to give them more credit than they necessarily deserve. And I think one of the things that we were struggling with is that resumes, experience was not always correlated with their skill sets as a data scientist. And so, ultimately, what we did is we said we've got to build better vetting tools. And then particularly over the last 10 years, data science is this new job. You get all this stuff. Everybody's putting all of these keywords in their resumes. So we said, let's build a really good vetting and training platform so that as we're going to grow and scale our organization, we can vet these data scientists and know that we're getting good hires, not just because of their pedigree, if you will. So we mapped the taxonomy of data science into about 75 different skill sets. And if you think about all of the different areas of going down sort of a tree of predictive modeling on one end, you've got data engineering, data hygiene.

(Josh Jones at 00:14:12) You've got writing SQL queries. You've got statistical accuracy and all of that. So we mapped this whole taxonomy of data science out, and we built an adaptive test that said, essentially, the candidate will self-assess or, first of all, you describe your position. What are the positions? What are the skills that I need the candidate for this position to possess?

(Josh Jones at 00:14:33) And then our test builds a, our system builds a test around that. It gets easier or harder as the candidate goes through the test. So it's an adaptive response based test, and it builds a confidence interval around their level of expertise. And then the next step is we will give them a business case. So we give them a problem, a dataset.

(Josh Jones at 00:14:55) They download a solution, and then wherever they'd see me, they download a dataset, upload a solution, we can see how they're responding to real-world problems. And then that third level is a code editor where they can actually edit code in the website, in the platform, and we can see what their coding skills are. So that really gave us our first version of QuantHub was really around that vetting and assessment. We used it internally for ourselves, sent thousands of candidates through it. Before long, Stratus clients were asking about it.

(Josh Jones at 00:15:26) They were like, hey, can we use your tool? So that's when we raised our initial seed capital. I hired a CEO that was CTO of a software company, put him in charge of the company. We spun it off as a separate product.

(Josh Jones at 00:15:38) And really, to get to market the first round, we worked with one of the top, we'll call them top three consulting firms in the world as a beta customer. And so over the next year, you know, we'll give you good pricing on this if you'll meet with us on a weekly basis and help us build the perfect product for you. So that went well. They've since rolled that out globally. We've gotten really good response from that.

(Josh Jones at 00:16:00) And so sort of version two, and I'll sort of wrap up this monologue here. But sort of version two of this was realizing that data literacy is becoming incredibly important to organizations. So I talked earlier about all these different predictive models and things like that. You can't build a model to predict, let's call it machine failure or predictive maintenance or predict customer churn if you don't have good data. You can have the world's best data scientists, but if you have garbage data coming in, you can't build a good model.

(Josh Jones at 00:16:30) And that starts really with your frontline employees. Organizations more and more and more need data literate employees to be able to build competitive advantage as we move into an economy of the future. So if you think about all the places machine learning and data science are really coming, taking over, you need a data skilled workforce to be able to get there. And so our real focus right now is on data literacy. We're working with a number of large organizations rolling out this data literacy program that essentially for frontline, for everyone in the organization, not just data scientists, we deliver data literacy training in the flow of work, and it's usually five, ten minute daily bite-sized increments of training that are based on that taxonomy that I talked about, about understanding all the different areas of the importance of data in your organization.

(Joel Beasley at 00:17:21) Yeah. That didn't register with me the first time we talked. And so I think that I get it now. I did not pick up on, like, I know you said it. I'm listening, like, in my head back to you definitely said data literacy training.

(Joel Beasley at 00:17:36) But the way you just described it, it clicked for me. So tell me, let me check myself real quick. So let's say I'm a technology company. I've got 200 employees and maybe 30 of those are like support people. Right?

(Joel Beasley at 00:17:51) They're answering support tickets on it. But then I also have, you know, data scientists at my company. I have software engineers and everything like that. So this data literacy training would go out to like the support people so they could start picking up and understanding these terms that are being thrown around the organization?

(Josh Jones at 00:18:06) Yeah. Absolutely. I'll give you a couple of practical examples. So I mentioned earlier the predicting customer appointment keeping behavior. The reason that failed the first time I tried it is I couldn't get any signal in the noise, and I thought there's gotta be something here. What's going on?

(Josh Jones at 00:18:20) So I said, well, let me put the computer down for a minute. Let me walk up to their front desk. And I'm just gonna watch how front desk office, you know, just the folks working the phones, the dental hygienist, how are they using our practice management software when they track a failed appointment? And what I discovered was that some of the staff, whenever someone would cancel or fail an appointment, they would mark it as a failed appointment.

(Josh Jones at 00:18:44) And that would go into the system. I'd see them on the back end. Some people were simply dragging that appointment over to the side, over to a Saturday. They'd call them, reschedule, and drag it back in the calendar, and so it wasn't marking that as a failed appointment. So what was happening is I was getting bad data that looked like good data.

(Josh Jones at 00:19:01) And if those frontline employees knew, hey, there's actually someone using your data to build a model that drives the future of the company or some major component, they probably would have changed their behaviors. Not a lot, but just enough to understand, hey, this data that's coming in right here is very valuable to us. You know, think about salespeople putting information into a CRM tool like Salesforce.

(Josh Jones at 00:19:24) Understanding that this isn't just so that my boss can kinda keep an eye on me, but the information that I put in here may predict a churn model in the future or help us drive some sort of important product feature. Another example would be I had a utility come to me and say, we wanna build a predictive maintenance model that will tell us when to change gaskets, and we'll call it an oil pump. Right? And so okay, great.

(Josh Jones at 00:19:49) We can probably do that. How often does it fail? You gotta have a lot of instances of this failing to better predict future failures. Well, ultimately, what it comes down to is they're not actually putting the receipts for the repair parts when they replace it. They're not putting those in their ERP system.

(Josh Jones at 00:20:03) Instead, they're in some filing cabinet somewhere at best. And so we don't have any records of that historical event occurring. So the project ended there. We could not build a predictive model. If they understood the value of data and were capturing that in a meaningful way, we could then do that.

(Josh Jones at 00:20:18) But what's more important is you want those people that are taking those receipts and throwing them away, you want them to be aware of the value of data and saying, hey, this is, I know we always do it this way, but we should stop, raising their hand, stop the assembly line. Hey, we need to be capturing this data because what if we did X, Y, Z? And some of the smartest data scientists that I've met in the field are not in the IT department, but they're wearing hard hats when they're in the factories or they're on the front line.

(Josh Jones at 00:20:46) And if you sit there and you ask them, you know, what are your pain points, they give you great ideas around, you know, what we really ought to be, I know your project's over here, but let me just show you this over here. And that sort of thing is really data literacy, is helping them understand data is an asset to our company. Data can help drive our competitive advantage in the future. And then, of course, it also goes to the boardroom, just like building good charts and graphs, just creating good, you know, being able to storytell with data. Those are also parts of data literacy.

(Joel Beasley at 00:21:15) How are you delivering this training?

(Josh Jones at 00:21:17) So our goal there is to deliver it in the flow of work. What we found is that just giving somebody 12 chapters of a book or a series of videos where they just attempt to go do this nights and weekends really doesn't work. So our goal is just to deliver this in as streamlined of a fashion as possible. So we have a website. You can use that.

(Josh Jones at 00:21:38) But also, we deliver it through, we've got a release next week or two where it'll be coming out through Teams. So you can actually interact with our bot in Microsoft Teams, and we'll add Slack on as well. And so someone comes in, they take about a fifteen minute test. That maps out their skill sets, what they're strong and weak in. On the data science side, if they wanna progress to the next level, we chart what are the skills that you need to have.

(Josh Jones at 00:22:00) And so we build a content recommender engine behind the scenes for each individual employee. And then what they will do is they log in, and they essentially are given either a video or an article or something like that to read on a regular basis. We do lots of quizzing in between just to identify, are they actually learning from these materials? That helps us refine the recommendations for the individual as well as the system as a whole. It helps us really understand what materials work well and what don't.

(Josh Jones at 00:22:29) So really, our goal there is as much as possible to deliver that content in the flow of work and then customize it for the organization. So, for example, Southern Company is a client, and so we're going to be customizing a lot of the content around utility-related data literacy. So that might, you know, one of our clients is a Blue Cross Blue Shield. So what does this look like in the insurance industry? So creating relevance around the content is real important.

(Josh Jones at 00:22:54) And then also, we were talking this morning about our product pipeline. In the coming months, we'll be revealing an in-browser spreadsheet tool so you can actually play around with some of the things that you're learning. So the goal is really to get it back into folks' hands so they can apply what they're learning right there on the spot.

(Joel Beasley at 00:23:10) Have you ever thought about, you know, like Glassdoor, like big job site? Yeah. It would be kinda cool. I don't know if they do assessments or anything like that, but it would be kinda cool if you could integrate. Obviously, the data science part is really great. But it's super niche.

(Joel Beasley at 00:23:28) Right? Like, it's very a very specific job type or job category. But then, the data literacy, that you could have like a generalized data literacy training and people could take like an assessment in that. And then that would make them be like, it would be kinda cool if you could have like a QuantHub certification or something. And if I'm applying for jobs on Glassdoor, I could take one of these skill tests and then run around with it, and it shows that I'm a data literate person.

(Josh Jones at 00:23:57) No, I think it's a great idea. And really, our whole system, we've built some APIs for a couple different partners. But just the ability to connect into applicant tracking systems, we've looked at, you know, the applied and freelance platforms. I think there's a, we're still a young company. We've got a lot of opportunity ahead of us, but I think those integrations are certainly a great idea.

(Joel Beasley at 00:24:18) Yeah. With so many different paths, that's one of the struggles with the startup. Right? There's so many possibilities on how to make it work, and really, it's about narrowing it down to the one or two that'll generate cash flow immediately so you can grow. What's the one or two things today that you've narrowed it down to that's generating cash flow?

(Josh Jones at 00:24:40) Yeah. So the vetting is certainly one of those. So for any data science team that is hiring, our product, we really have scaled it down to a pretty affordable version for smaller companies all the way up to enterprise-wide. Like I said, you know, testing tens of thousands of people a year with single sign-on and all that sort of stuff. So we have both of those.

(Josh Jones at 00:24:59) Those are generating well. And then on the data literacy side, we have, that essentially is a scaling price model for organizations. So we signed up a nonprofit yesterday for 10 licenses. We also signed up a larger corporation a few months ago for 500 licenses, and they've come back and said, now we're thinking a thousand. So both of those sides of the, so those are sort of the core products, but what's really interesting to me is benchmarking.

(Josh Jones at 00:25:24) So if I'm testing my candidates, how are my candidates testing, say, compared to the rest of the world? Are we even recruiting well? Or, you know, on the data literacy side, how are my employees progressing through this process? Can we actually come back? And so I think there's some opportunities, and this is more back to your comment about, you know, entrepreneurial, try this, try this, try this.

(Josh Jones at 00:25:46) Just the ability to report out and say, you know, what are companies hiring for these days? Like, what are companies testing for? And then take those skill sets. We got a lot of universities that we have sort of budding partnerships with. The professors wanna know, you know, what are your corporate partners?

(Josh Jones at 00:26:01) What are they testing for? So that we can put that into our curriculum. Hey, can we go ahead and send our students to that? And so there's a lot of opportunities there. And I found, one of the things that was really good strategy-wise about using QuantHub is I could send a large pool of candidates through the system, and I could sort by the highest scoring candidates and only interview the top five or 10 candidates for culture. And I knew if these people are virtually acing this data science exam, they've got the technical chops. Now we can move on to some of the cultural things. Whereas a lot of times, you would get someone who would have all the right stuff on their resume, but when they come in, they just they don't really know their content. And it may take you a couple of interviews, you know, a couple of cycles.

(Josh Jones at 00:26:47) You're pulling a senior data scientist off of a job. Now you're pulling two hours of his time to come vet somebody. So that was really valuable as well.

(Joel Beasley at 00:26:55) What's been the winning go-to-market strategy for getting these customers?

(Josh Jones at 00:27:01) Yeah. It's a little different. I would say on the data literacy side, one of the challenges is if you think about the world, some organizations totally get it. They understand if we're gonna be competitive into the future, we have to transform. Data literacy is important.

(Josh Jones at 00:27:17) So some of our clients that we've signed up, I would say, with relative ease, and that's almost never the case with a corporate client. But with relative ease, if you will, is those that have some sort of organization-wide initiative that said, we have to upskill our workforce. And if you look, you know, PwC did a study that showed about 77% of the workforce want to completely retrain and improve their future employability. This is, I mean, it's a really big. And also if you look at, like, I think it was McKinsey did a study comparing high-performing organizations to underperforming organizations, and it was in the 60%, 63, 64% of executives had data literacy, you know, high data skills versus 43% of those at underperforming companies. So so a huge gap in terms of the data literacy.

(Josh Jones at 00:28:08) So back to your original question, companies that know we've got to get our employees upskilled and data literate are relatively easy to sell. The ones that are hard to sell are the ones that don't really, that wolf is not what's biting them now. Like, they have some other corporate prerogative or initiative or they haven't identified data literacy as a core challenge. And so the challenge there is essentially you have to educate the market. And as a small startup, it's expensive and, you know, time consuming to try and educate the market.

(Joel Beasley at 00:28:38) I wanna switch gears a little bit because I'm curious about you as a person. So it sounded like when you were, you know, younger, you started, you were doing this like missionary type of work. Now, a lot of the conversation has been about, like, around QuantHub. Is this just what you're doing, like, during your professional hours? Are you still contributing forward, like, as a person, like non-QuantHub stuff?

(Josh Jones at 00:29:05) Yes. So strategy-wise, I think I mentioned earlier, we sold it to a company called eSource out of Boulder. They're backed by private equity, and they serve the utility space. And so I'm just now completing sort of a tour of duty, if you will, with them of helping transition and that go really smoothly. Great company, but they're really focused on, you know, a specific area.

(Josh Jones at 00:29:28) And I'm more of an entrepreneur, which is, you know, pretty obvious, I would imagine. So sold Stratus. Strategy-wise, I'm chair of the board of QuantHub. I don't actually have a sort of day-to-day job, if you will. I'm more of a coach, spin the flywheel, helping on sales, helping on connection, innovation. I've, you know, written some of the early code, test questions, all these sorts of things all the way from in the end, so I understand it.

(Josh Jones at 00:29:53) So I'm spending a decent amount of my time there. I have a handful of other investments as an angel investor, and so I was going to get more involved in those. And Alabama Capital Network approached me a few months ago about taking over as their CEO. And they're an economic development organization that essentially connects investors with entrepreneurs in the state of Alabama, and they want to raise a venture capital fund. And so to me, it was interesting because it was a play of both investing and helping coach and upskill and really advance these entrepreneurial ventures, which is a lot of what I've been doing anyway, and then also the economic development play of building a startup community in Birmingham and the rest of Alabama.

(Josh Jones at 00:30:34) So that will be where I hang my hat. And so I'm going to continue again involved very heavily in QuantHub and some of these other areas. In terms of other involvement, I'm very involved in our Birmingham Rotary Club. We have the largest Rotary Club in the world, over 600 members. So I've rotated on and off the board there and a handful of other local nonprofits.

(Josh Jones at 00:30:56) And so really getting involved from that area. And I've got three girls, so I've promised my wife I would not take on any more board roles in the near future. So that's kind of, yeah, my day in a nutshell.

(Joel Beasley at 00:31:11) Now I've heard people talk about Rotary Clubs 100 times. I've never asked. Tell me, what's a Rotary Club?

(Josh Jones at 00:31:17) So Rotary International has over a million members around the world. It was founded in the early, I'm going to call it 1900s. And it's a community organization that is focused on doing good in the community. Our slogan is "Service Above Self." It is not a faith-based organization in the sense that it's a values-based organization.

(Josh Jones at 00:31:38) Each Rotary Club is going to be a little bit different. Ours is, you'll have a pretty broad spectrum of the community. Ours is a bit unique in that most of the members are CEOs of larger corporations. We've had senators, congressmen, and CEOs of the larger Birmingham area organizations in our club. So it's truly a bird of a different feather.

(Josh Jones at 00:32:01) You'll see Rotary Clubs that are 20 or 30 people that do pancake breakfast once a month. They're usually raising funds for some particular cause. In our case, so we meet once a week. We have a different speaker come in. One of the great things about our club is the level of speakers is pretty nice.

(Josh Jones at 00:32:18) We've had the CEO of Target, of Coca-Cola come speak. We've had different folks like Marco Rubio, Miss America. It's a really good speakers gallery, if you will, once a week. We also have a project in Sri Lanka around doing mammograms, and we're globally, Rotary is focused on ending polio. So we've got a big partnership with the Bill and Melinda Gates Foundation working to, we're almost there, eradicating polio.

(Josh Jones at 00:32:43) So meet together, talks, do civic activities. We built a Rotary Trail, turned a railroad track into a nice trail in our city. And so we maintain that, and then a lot of just one-off projects throughout the year.

(Joel Beasley at 00:32:56) That's pretty cool. We have rails-to-trails projects. Yeah. They basically took all the railroads and paved over them. And a lot of bikers and runners use it, and it's pretty fantastic.

(Josh Jones at 00:33:11) Yeah. I should know the details on this better, but I think there are some easements related to those railroad tracks in different states where the state gets to keep it if it's used for transportation, but they lose it if it goes to some other purpose. And so technically, moving from a railroad track to a running track still checks that box of transportation. And so there's some interesting loopholes and support and things like that communities can get by actually turning the railroad track into a walking track of some sort.

(Joel Beasley at 00:33:43) Yeah. I learned that when they were laying the fiber for the Internet, they were using the railroad tracks and paying licensing fees to be able to use those existing tracks and dig up and bury Internet. So that's how they were still making money even after railroads stopped being super popular for transportation. They're making money off the Internet licensing fees.

(Josh Jones at 00:34:06) Well, and for cities looking to retrofit or add quality of life, railroad tracks are really sort of a hidden gem because they really are perfect size, location. They sort of traverse the city in a lot of cases. So it's been, if you look at Birmingham, the economic impact of the Rotary Trail has been pretty substantial because the four blocks that it covers was heavily blighted, and since then you've got luxury condos going in, you've got all sorts of ice cream shop, coffee shop type stuff. So it's a really neat economic development play at the same time.

(Joel Beasley at 00:34:37) I mean, don't say ice cream too loud. My kids hear that, they're going to go crazy. Yeah. See, you have these three girls, right? And they see you giving back, being a part of society, being in the Rotary Club, you know, running companies.

(Joel Beasley at 00:34:51) How is this impacted their development? Are they talking about it with you? Are they asking how they can get involved?

(Josh Jones at 00:34:58) Yeah. So my girls are 14, 12, and 8. And so they're split out: high school, middle school, and elementary school. So this last year, I guess you have to say this last year notwithstanding. One of the things that was important to us is really to expose them to this and get them involved.

(Josh Jones at 00:35:16) And so we actually were planning to go to Costa Rica this spring break to take them back to the medical facility that I set up years and years ago, almost 20 years now, and really expose them to that. And that was unfortunate that we didn't get to do that. But my goal is to start taking them. So I've gone with our Rotary Club to Sri Lanka and really just in some of the local stuff, the community stuff of, you know, cleaning, when we have the Rotary Trail cleaner days and stuff like that. So trying to get them involved in that, and I remain hopeful that we're planting DNA and seeds that will stay with them.

(Joel Beasley at 00:35:54) You mentioned earlier that you're an angel investor. You've made some investments. What type of companies?

(Josh Jones at 00:36:00) So one of them is Preferred Medical Systems out of Memphis, Tennessee. And Preferred Medical is a distributor vendor of ultrasound equipment. So primarily in women's health care market. We do some point-of-care medicine, sports medicine, but mostly if you're going to the OB to get your baby imaged, that sort of stuff, but really more and less of that and more on the health care side. So when we started, they were actually a client of mine at Strategywise and had the opportunity to invest later.

(Josh Jones at 00:36:32) When we started, they had a five-state territory for Samsung. So Samsung, GE was sort of the king of ultrasound market. Samsung bought a Korean company in 2008, 10-ish, in that neighborhood, and brought all of their imaging quality to the market. And so they rebuilt this company into the Samsung ultrasound business. Really been doing a fantastic job making a really great machine.

(Josh Jones at 00:36:55) When they were our client, we helped them grow from five states to 10 states. Now they're in 28 states. So if you buy a Samsung ultrasound in over half of the U.S., you're going to be buying it from Preferred Medical if it's a new device. And so they've just got a really good business model. They've got a really good CEO, great sales team, and just see a lot of opportunity there.

(Josh Jones at 00:37:17) COVID was a little bit of a hard year in terms of hospitals not buying new equipment, stuff like that. But ever since then, the last two quarters, this Q1 is the best quarter in the history of the company. So we're really, things are going well there. So I've helped them in a number of areas: marketing, pricing, warranties for used equipment, because a lot of it goes back to that data. I mean, how do you warranty a used piece of equipment?

(Josh Jones at 00:37:41) Well, if we're servicing that equipment, we're capturing that data. I can tell you how often that's going to break in the future. So if we sell another one and we want to predict the failure rate, we know what the parts cost, what it costs to get service out there, and so we can actually price that. You know, that doesn't sound like it is data science, but it is. It's also intermingled in the sense of how you use data.

(Josh Jones at 00:38:02) And I'll give you one more quick example. In marketing, we started using dynamic telephone numbers very early on. So you can go on your website and use a company like CallRail, it's a great company, where they'll give you a basket of phone numbers. And everyone who visits your website gets a different toll-free number at the top of the website. And so if they pick up the phone and call, it goes to your switchboard.

(Josh Jones at 00:38:23) But we now can track that back to your Google search history, what pages you went to on the website. So we can create this end-to-end data trail in our CRM system of where did we source a client from. You use those same toll-free numbers in your postcard, you know, whatever kind of direct mail you do, all that sort of stuff.

(Joel Beasley at 00:38:39) That's pretty cool. That's what CallRail does?

(Josh Jones at 00:38:42) Yeah. Yeah. And I did not get paid for that endorsement.

(Joel Beasley at 00:38:46) That one of the companies you invested in or no? It's just a great company.

(Josh Jones at 00:38:50) They had Strategywise on their website the first couple of years because we were using them so much. They used us as one of their, like, very first case studies. So if they had approached me, certainly would have been interested.

(Joel Beasley at 00:39:04) You're very entrepreneurial. You understand how to grow these businesses, not only grow them, but sell them and exit and raise capital for them, the entire gamut. Right? I want to know what makes a great sales team.

(Josh Jones at 00:39:15) Oh, man. That has been, I would say, one of my greater challenges is building great sales teams. So honestly, I would much rather hear your answer than mine on this. But I'm sure folks have heard you opine to some degree. You know, I think you have to divide it in your B2C and your B2B space.

(Josh Jones at 00:39:36) I think those are going to be two different animals, if you will. And then you look at the, data science has been historically complex if you're selling solutions versus selling finite products. And so for, you know, I would say this, it really goes back to building relationships and who has relationships. And I think one of the areas where I've failed is hiring a salesperson that does not have connections in the industry. I think whatever industry you're selling to, if you can hire someone who really has been there, who has the bona fides, who used to work at Company X that they're selling to now, and before that, they worked at Company Y, and you look at their LinkedIn contacts and they've got people in all the different businesses you're selling in, I think that's probably one of the biggest factors. I think building a really good CRM tool that you use and you live by and you really hold people to metrics is really good.

(Josh Jones at 00:40:30) And then, of course, having a really, maybe another area for potential failure is not having a good relationship between sales and marketing. So if you've got sort of CYA going on in both cases. So ideally, a good marketing team is generating those leads and sending those to the sales team. And then the sales team is saying, "Hey, these are good leads. These are not good leads." And, you know, dysfunctional organizations, I've seen it where the two don't work really well hand in hand. And so I think you've got to have a good synchronization there.

(Joel Beasley at 00:41:00) Yeah. I'm no expert. I've done it once well. I've done it well once. Yep.

(Joel Beasley at 00:41:05) I tried, it took me 14 people to find the one person who was like me as an engineer, but in sales. He was that good. And—

(Josh Jones at 00:41:15) When you say 14, you mean you hired and fired 14 people? Or—

(Joel Beasley at 00:41:20) No. No. I hired, onboarded, lived with, and, like, not lived with, but like, lived with, had them on the team, and fired 14 people before I found the person who taught me how to do it.

(Josh Jones at 00:41:34) What did you, what was it? Did you figure out what the difference was?

(Joel Beasley at 00:41:38) Oh, yeah. Yeah. So there's a couple things that'll get you. So there's a million ways to do it. But I'll tell you how it happened, how it rolled out for me.

(Joel Beasley at 00:41:48) So the first big problem was understanding where the pressure was in the market. You said that earlier too. Right? What is the bait that's going to catch the person that's going to buy what you're selling or is interested in the problem you're solving and things like that. So figuring that out required a lot of testing.

(Joel Beasley at 00:42:07) So outbound, B2B, email, trying different email content, talking to people, just kind of honing it in. So that's something that, I'd say the founder kind of has to do, right? Kind of figure that out. Make those first, have those first 50 meetings or whatever to figure out what the trend is and where you're going to be. Then once you can get meetings predictably, right, so the first mistake I was making was thinking that a salesperson would be able to figure out how to get me predictable meetings.

(Joel Beasley at 00:42:35) Right? Very rarely can you take a salesperson, from my experience, it didn't work 14 times in a row. Can you take a salesperson and say, "Go get me meetings so we can sell this product. I'm, you know, I know this is useful because I've talked to these few people."

(Joel Beasley at 00:42:52) You have to actually have the infrastructure in place where the system's actually sending the emails out and the meetings are getting booked and you know how to book. So once you've got a flow, like a stream of incoming meetings, then you can go get the salesperson. And that's the key with that salesperson is the first thing I learned is some people call themselves salespeople and they're not salespeople. Just like your data scientist problem. Right?

(Joel Beasley at 00:43:18) They're like account managers or something and maybe they get a commission for an upsell or across. But you have to get that salesperson who eats, sleeps, and breathes. They love sales. They'll tell you they love sales. They're great people to talk to.

(Joel Beasley at 00:43:33) You like them instantly. Right? And then that they care and that they're hungry, that they're hungry. They want freedom. They want financial freedom.

(Joel Beasley at 00:43:42) They want to make a bunch of money. They're excited because that, you know, excitement will definitely transfer to your customers. So we found that person, and then we gave the leads to that person. So they're not really doing, we want to get that person just on the phone all the time or on a Zoom call all the time with the customer. And then, that person's typically, it's hard to find a really organized person that's very charismatic. It's like typically, I find people who are either very organized and less charismatic or very charismatic and less organized. So you have to find the balance. They have to be charismatic enough for you to like, to make a sale.

(Joel Beasley at 00:44:23) But they also have to be organized enough to take notes and send proposals. Right? So they have to take notes because they have to ask these certain questions every time so we can sort of feel out what's going on in the customer world. And then they have to be able to be organized to follow up and send proposals. Obviously, we use software that helps with that, make that easier. And so that's where I've spent a lot of my time is relieving pressure from these people who are making the sales through advancements in technology and improvements in our technology systems.

(Joel Beasley at 00:44:48) So now I've got the leads coming in, the good people who are organized enough to do the job and really great to be around talking to the customers. And then, of course, then you have delivery and organization. We use this tool called ClickUp for delivery. So a customer comes in and they get a certain number of tasks that happens. So that would be the way that we do it, finding those people.

(Josh Jones at 00:45:15) Do you use any testing, whether it's a Kolbe or StrengthsFinder or Myers-Briggs, DISC, any sort of those personality psychographic? Have you played around with any of those?

(Joel Beasley at 00:45:27) I've seen them. I think they're really cool, especially as we get to this next stage. I really liked how they could—I saw a couple of them could analyze the team and what the culture is like and then help you find people that fit into that. So I've seen a couple different flavors of it. But right now, what we've done is we spend some time with them beforehand and then we fire fast.

(Joel Beasley at 00:45:50) So, you know, it's really hard to tell pre-hiring. You can get a good idea, but what it comes down to is can they perform. And in sales, it's very clear, very easy to understand. I mean, if anything, the first salespeople I hired, I let them stay on way too long. And that's when the guys over at Florida Funders counseled me a little bit as an entrepreneur.

(Joel Beasley at 00:46:14) As you see your bank account balance draining, all of a sudden you get really—you really care about getting the right people in.

(Josh Jones at 00:46:23) So not to put you on the spot here, but this is probably valuable for folks listening. When you say "fire fast" versus "way too long," can you put some more order of magnitude on that? Are you talking weeks, months?

(Joel Beasley at 00:46:36) Yeah.

(Josh Jones at 00:46:37) What would you say is way too long versus fast?

(Joel Beasley at 00:46:41) So for sales, it's clear because we have a known model. So we know it takes three months to ramp a salesperson to quota. So it's tracked. We know it. They're either doing it or they're not.

(Joel Beasley at 00:46:52) We know how many people they have to contact today. We know how many meetings they have to have a month. We've got about, I think, five or six salespeople. And since the second salesperson, those numbers have held steady. So we just keep plugging in more salespeople.

(Joel Beasley at 00:47:07) We know that if they reach out to 150 people a day, that they get about 70 meetings a month and they close about $30,000 to $40,000 in business. But it takes three months for them to ramp to that. They usually make their first sale sometime in their second month. Make one or two sales maybe in their third month. And by the fourth month, they should be hitting their quota.

(Josh Jones at 00:47:34) I love it.

(Joel Beasley at 00:47:35) Yep. That's all I've learned. That is it. Sorry. Good.

(Joel Beasley at 00:47:40) Sorry. We got way off topic probably.

(Josh Jones at 00:47:42) I was interviewing you there.

(Joel Beasley at 00:47:44) Yeah. What else did we want to cover as we start to wrap up here? We want to—definitely—oh, you said earlier I don't want to let this go. We were talking about the data literacy. You said there was a website. What's the website? How can people learn more about the data literacy program?

(Josh Jones at 00:47:59) Yeah. Thank you. Yeah. QuantHub.com is the website. We've got a great blog, lots of articles, lots of stats. So if folks are wanting to win over others in their company, we cover a lot of material there. I'm easy to get to. [email protected]. If folks want to reach out to me, we're happy to talk about everybody's situation. We're in a really—I would say we're in a good state as a company in the sense that we've been battle tested by some really large enterprises. And so we've had to go through those battles to prove that we're scalable. We can deliver at enterprise level, but we're also still a rapidly growing company. So we're very tightly listening to our customer feedback. So folks that sign up really get that white glove experience, if you will, of talking not to three layers of abstraction before it gets to the dev team. They're actually talking, in many cases, to the dev team. So really, QuantHub.com covers most of that material, and we appreciate your asking.

(Joel Beasley at 00:48:55) Yeah. Is there any QuantHub certifications yet?

(Josh Jones at 00:48:58) You know, a lot of people have encouraged us to do that, and we've talked about it. I don't think it would be a very hard lift to do that. I think when you look at all of the things that we could do with the resources that we have right now, it's just sort of fallen on the list, behind some other features that we're pretty excited about. And a lot of what we're doing is sort of an iceberg in the sense that we really want to get the education and the training right. We really want people to learn well, to be engaged, not just to study something, but to truly internalize it.

(Josh Jones at 00:49:32) And so a lot of what we're doing, the learning theory behind that is kind of—it's not really visible at the surface, but below that, again, with the adaptive testing process, with the constant tracking of what are they learning, what are they not, and then pulling out these different dashboards to show an employer where their team is going. Some of those features are—we're really—I would say we're investing very heavily in. But I'd love to circle back someday to the certification for sure.

(Joel Beasley at 00:50:01) Yeah. I think there's a better business model in what you just described. I mean, the certification sounds nice. It sounds cool. But if I were taking a product to market—I mean, I know there's a lot of CTOs out there and they have thousands of people at their organization and they have no concept of where they sit versus the benchmarking thing you mentioned is something everybody wants to know. Right? Where are my 50 or 500 data scientists in relation to the rest of the market?

(Josh Jones at 00:50:31) Yeah. Yeah. And I'll say some of the things that we're aspiring for that are really important to us is going back to the data. So one of the great quotes we've got on our website was actually from someone at HP. Michael Pollack was talking about just how our assessments correlate to how a candidate performs in the interview and how they actually perform on the job. So more quotes like that we can get the better where we can show their score here correlates to how long they're going to be in the organization, how successful they're going to be.

(Josh Jones at 00:51:06) We believe we can get those. Now our data vetting product is a little bit—it's, you know, we've had a couple more cycles with it, so we've got more long-term data. The data literacy is a relatively new product. Again, really formally launching about three or four months ago, so it's super early. One of our clients went—they tested with 40 people, had really strong results, and then they upgraded to a 500 user license.

(Josh Jones at 00:51:30) So they saw it internally. But what we want to do is capture that across our customer base and show companies that are going through this data literacy program—we want to tie that back to employee performance and employee success. Unfortunately, that's going to take us another year or two, if you will, of just capturing that data to show where folks go. We believe it's in the data. We believe it's there.

(Josh Jones at 00:51:49) But as true data scientists, we're capturing all that data, watching it, and then using that to iterate on our product itself and to make sure that we're constantly offering the best possible training we can.

(Joel Beasley at 00:52:02) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you would like to hear discussed on the podcast, either add me on LinkedIn or send me an email, [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.