(upbeat music) - Hi, I'm Eric Dots. - And I'm John Wessel. - Welcome to "The Data Stack Show." - "The Data Stack Show" is a podcast where we talk about the technical, business, and human challenges involved in data work. - Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. (upbeat music) - Welcome back to the show. We're here with Cameron Jago. Cameron, we're so excited to chat with you. - Awesome, I'm glad to be here. - All right, well, tell us a little bit about who you are and what ProcureView does. - Sure. So I'm the original founder and currency of ProcureView and it's been my passion project for basically 13 years. And what we're focused on is improving, basically improving profitability for businesses by leveraging their strategic sourcing. You know, one of the axioms that we go off of is most companies are about 10% operating income, which means for every $1, we help them save on the purchasing side. It's worth $10 of sales. And it's right to the bottom line. So that's our focus and sort of our claim to fame is just our really in-depth and really deep granular analytics that we provide. - Very cool. All I can't wait to hear more about how you do it. - Yeah, so Cameron, this area is very near and dear to my heart. I actually ran a procurement team for a short amount of time. So I'm really excited to dive into that. I love data as well. So I'm excited to dive into that. What are some topics you wanna cover? - That's a good question, I didn't expect that one. (laughing) - Right out of left field. - Yeah, that is governing up guard. - I mean, I can sort of chat about this. You know, my hobbies until kind of the past couple of years have been around this stuff. You know, this was what I was doing for my day job and then I'd go home and night. And I'd cut up algorithms and projects at home and then sell them on the side. And you know, once it became the full time job, I was like, okay, I gotta find something else to do. - I gotta find another hobby, yeah. (laughing) - Yeah, so I don't know. I guess what are your guys' minds? - Yeah, sounds good. All right, well, let's dig in. - Okay, Cameron, I wanna tell, I wanna start actually, this is rare, but I wanna start by telling you a brief story that relates to something we talked about just before we hit record on the show. - Okay. - So there's this somewhat new donut shop in town and they make these really exotic donuts, okay? And I can't remember what the ingredients were, but I mean it was, it's along the line of pistachio, frosting with chunks in it. Or there was one where I was like, ooh, is this like fennel in this donut? And so anyways, my wife and I are eating them. They're super tasty, but of course, as you're enjoying a really tasty donut, your average person starts thinking about margins and how many of these they have to sell and how much they can rise. Then how many staff are there and how many-- - I'm just thinking it's like, okay, these are like real pistachio chunks here, like how many of these do they have to sell to like, you know, those are not cheap, you know? Are they shelled already? Like the reason I bring that up is, you actually solved that exact problem for a real donut shop. So can you tell us a little bit about that story? And then I want to transition to the lessons you took from there into a large multi-billion dollar company. - Sure, yeah. So yeah, back when I was in college, I at the time was driving cars for the day, driving experience and then anyway, the CEO of RPD, he decided he wanted to start a bakery. I guess really his wife wanted to start it out of the details. - Someone wants a bakery. - Right, for whatever reason, they asked me if I would basically run it day to day for them. And yeah, that's a 21 year old. You're like, yeah, why not, right? - Were you still able to drive race cars though? 'Cause like, so the Richard Peder driving experience, so like, because if someone was like, you know what, your day job right now is driving people around and race cars like going way over the legal speed limit for all the average people. - Now, can you figure out how to make a bakery work? Seems like a little change of speeds in the front. - Yeah, no, that's fair. Yeah, I still drive cars for a while. And I think part of it was I traveled a ton with them. So, you know, I'd be in class during the week and on and then at the shop at night, and then we would fly out to wherever, you know, Iowa, Kansas, Texas, what I mean. On a Thursday, you'd work 18 hour day Friday, setting everything up, prepping everything. And then you'd spend, you know, about six hours in the car, both days. - And then you fly home. - Which is not easy in a race car. I mean, I don't have a ton of experience, but like, that's pretty brutal. - Yeah, not a relaxing drive. - It's about, you know, the cars are about 140 degrees inside. - Yeah, so you just lose all weight. You know, it kept me in shape at the time. I thought I had to ask Abilism, I think it was just that. But anyway, they were, to be honest, I'd ask them if I could basically stop traveling as much. - Yeah. - And to be honest, I think that might have been part of it. In other part, their brother-in-law, who also drove at Patty, him and I, DJ'd weddings on the side, had a DJing couple. - We were a busy apartment girl. Wow. - I've always had at least like two or three things going on. I'm better or worse. And so anyway, they asked me that. And, you know, I went into it with, to be honest, a lot of naivety. You know, I've worked restaurants here and there, but obviously never run one, much less a bakery. I don't know how to bake. I don't know, I didn't know any of the things. And anyway, we created a pretty, I thought the problem would be sales. And so the rain precautions, all these things. But sales wasn't the problem. Yeah, there was where we were in Harrisburg, North Carolina, about the race record time. There wasn't a whole lot of similar options. Other work ins or things like that. So that happened really quick and really organically. And then we, you know, sales kind of plateaued and had more of that organic slow growth. - Yep. - The problem was, is we were, we were still losing a crap ton of money. You know, we're talking $15, $20,000 a month losses. - Yeah, I mean, for a small bakery, that's-- - Yeah, that's a problem. - That means that we're not going to be baking for much longer. - No, and that was a, you know, that was a problem they kind of put on my shoulders. Which is, I'm seeing right, you know. - I mean, since you were running it, were there any conversations where it was like a sit down of like, hey, listen, we're losing 15 to 20 grand? Like, tell us about that. - Yeah, yeah, there were. So I mean, I came to them really first on it. I have to believe they were aware of it to some extent 'cause, yeah, money is leaving accounts. - Yeah, right, yeah, right. - But I don't think they were of the extent or the regularity, and I used to look at them and I was like, look, for, you know, is basically for every dollar we were making in revenue, it was costing us, it was like $1.40 on or so, right? And now I tell them, it's just, this is sustainable. And I thought it was going to be, like I saw my role at the time as, I'm just essentially from a house manager. Yeah, I'm keeping schedules gone, I'm keeping the operations gone, and they're running on the other side, but then flipped it on me and I said, it was, what are you gonna do to fix this? (all laughing) - All right. - Yeah, to be honest, I remember going home that night and just kind of racking my brain, like how do you go about this? And then at the time, I took a search, to just route through college. I graduated undergrad with a two, was it like 208 credit hours of the 120 I needed? (all laughing) - Overachiever. - I would call that, not the best GPL way through, but I had a three years mechanical engineering. I had three quarters of weight a systems engineering degree/operations research. Most of the math degree and most of the physics degree by what would be my penultimate year. And I was like, you know, I've got to hold this information and all these projects we've done, like this has to be applicable. And, you know, the first thing I looked at was just, okay, most companies are about, I used while learning and operations research and manufacturing, which isn't gonna necessarily be true for the bakery and didn't as whole true, but I started off the assumption of, you know, most manufacturing companies or product companies, you can assume roughly half of their revenue is going into their direct costs. - Yep. - To just make and sell them products, right? - Yep. - So I was like, okay, if we're taking us $1.40, we're selling for $0.95 per donut. We had other things, but those were our big sellers. I was like, all right, it's got to be in those product costs. This is what I first went to. So, you know, as we talked earlier before the recording started, I was like, all right, how much does it take to make a donut? And I'm sure I'd annoyed the heck out of our bakers 'cause I followed them around with the stopwatch. - Yeah. - Clipboard. - Yeah. - Taking us back to how I'm like, don't rush. Like do this for 10 about here. - Normal speed. - Right, normal speed. I need good data here. And anyway, we did that over about a week period and that collated the data and averaged everything out and so forth. And when you include, you know, your raw materials, your labor time, your machine costs, which is pretty low in the donut, but because they have to proof for 16 hours, you're running a really high humidity, high heat box for all night that eats live electricity. So, we do that. And we found a fried, but otherwise playing down it was 12 and a half cents to us. - Roughly. - Now I go, okay. - Cool. Like we're good, 12 and a half cents, 95 cents. Like, I was like, okay, well, let's just, how much is it when it's decorated? Because what we were doing at the time was, you know, we'd take whatever we had left the donuts at about 10 a.m. in the morning. And we'd have the hourly staff just go and decorate it. - Yep. - So that you could get the inventory 'cause the next day you can't sell 'em 'cause they're dried out. - Yeah, you might as like, hey, they're already made might as well put them out there, right? And that way, when people come in, we have full cases. It looks nice. - Looks nice, yeah. - Yep. And the first one I did though was our chocolate donut 'cause I had a hunch that it was probably gonna be more expensive than we thought because one of the things we'd done instead of using a pre-made chocolate frosting or sauce, we were hand-making one. And anyway, when he added the chocolate to it, that donut on its own was right around 75 cents now. (laughing) - Just to clarify then, the price was the same between the two donuts, okay, yeah. - Yeah, so all donuts we sold from 95 cents. You know, you see that similar model like Krispy Kreme's and Duncan's, you know, all donuts outside of some specialty are all the same price, right? - Yeah. - At Glaze donut, I think came out to, those weren't too bad. I think they were mid-40s. I don't remember exactly 'cause the chocolate was one. I'm like, all right, we're not, we brought out chocolate donuts, we're not making decorating any plain donuts to be chocolate unless one, right? - Yeah, right. - Because we were getting to the end of the day and we were throwing away a couple hundred of these. You know, any, it's easy to do the math on, they're about almost a dollar piece. - Yeah. (laughs) - Burn a couple hundred bucks a day and, you know, it adds up, right? - I mean, it's just, it's something that's a little bit counterintuitive, I think, where it's like, we have these donuts. Like, let's decorate them, they'll look nice in the case. Like, people like chocolate donuts. Like, we don't wanna throw them away, but there's actually a point here where it's like, actually, we should throw these plain donuts away versus decorate them to be chocolate donuts. And that would not intuitively work. - Yeah, yeah. - Most people's mind. - No, and it was, to be honest, it's probably one of the first times in my life that I'd used analytics in a way that did really invalidate an intuitive assumption. Growing up racing, you know, we used analytics all the time. And I got really comfortable with, you know, reading like trace lines and like, okay, this one I'm losing time and making comparisons and all these things, right? But those were always pretty close to intuitive. It was just figuring out how much to do something or where you're at. - Yeah, right, right. - And in this case, it wasn't. And I mean, I remember taking it to them like, hey, I'm, 'cause I didn't wanna make the decision without their input. - Sure, sure. - They tended to come into the shop in the afternoons. - Right, and so their view of like having an empty case, they're like, well, you're not running this place well. - Right. - And like, they looked on camps and all these things, right? And so I brought them and they, you know, it took a little bit to get them convinced, but yeah, we basically just went through the model. Yeah, like, look, these are the things they're going into. What do we take out here? What are the biggest change? And, you know, one of the problems that is implied here too is setting correct par. How many don'ts do we make per day? 'Cause it takes 18 hours. So we'd have to start the day before and on days where we're closed, we'd have to come in actually off days to make them, right? But I thought like, look, even if, you know, forecasting methods at the time and even now forecasting methods, a good forecasting method, you know, mean absolute percent error plus minus 25, 30% is good. I was like, our shifting cost is so much that we can't just rely on a correct point. We need to be more strategic about our whole production process. And so that was our first change. And it had a obviously a major set change in the business was not decorated. And then we followed on with that with us. Like, okay, now let me go work on the part. And yeah, at the time, not being, let's say, maybe the Bryce Googler. I didn't even think about the fact that I could probably find packages for forecasting models. So I sat down on a Cinno notebook and I wrote out a Taylor series method for converging. Wow, that's hard core. - Yeah, that is hard core. - It's one of those things like looking back and I'm like, yeah, I'm idiot. - You've been so easy, but I was like, you know, well, if I set it up this way, I know I can get it. I can know I can solve this analytically. And I don't need to write much code or whatever. So we started that and we did it based on hourly sales. And that was another sort of a, not as intuitive things because we've been approaching everything as daily sales. - Yep. - But when we went and looked at it hourly, we found like on weekdays, it was what, it was like over half ourselves were in an hour and a half window of like 6.30 to nine o'clock. And there'd be a light lull until probably mid morning, 10.30, 11, and we'd get some more sales in and then essentially it dies. And there'd be this just slow trickle till we closed at six p.m. - Woof. - And they wanted us to stay open at six because where we were, where we were located, we get a bunch of car traffic coming to us from Charlotte. So, okay, we'll get people in the way home to get dessert to what have you. So doing the hourly forecast, sales forecasting, before we got it accurate enough to go down to the product level and fix par, first time I was like, hey, we need to shift to our hours. One, we get like no sales. We don't get enough sales on Monday to cover our labor overhead, much less the product, right? - Yep. - And at that time, I was like, hey, if we close Monday, we're gonna just lose those sales. But you can't make money when you're losing it. - Yeah. - We did that, we closed on Mondays. And then we also shifted our closed time on most weekdays to, well, it ended up being two o'clock. Camera everyone went right to two o'clock, but we cut that down. And, you know, the other thing, where we now have better hourly sale rates, we can see, all right, we need one person, like front of house person to come in earlier in the morning, and then we can bring in a second. And said bring them in at the same time like we were, we can stagger them, and now we were not paying double labor at times when we don't need it. And that, you know, that opener can leave earlier, and the closure obviously closes. And doing that change made up, and it was something like little over half of our losses. - Yeah, I promote it. - So I'm curious about this. One of my burning question here that I've always wondered is, do you think, so I would imagine a lot of companies to determine, do some kind of probably less sophisticated version of that when they first start? Like, all right, we'll be open these days, like we don't want to be open, you know, and some of it could just be personal convenience or whatever, who knows what the reasons are. But I often, I have the theory that most people don't reevaluate that. Because like, say you, that's what you started with, and like you're five years in, and like there's probably like a spot where like, hey, like if we opened later here or earlier here or whatever, did you end up reevaluating some of these things? - Yeah, so we did it once a quarter, and going forward, one of the things that we thought we'd see would be a lot of seasonality. And we had some, but not enough at the time with the length of the data that we had to really tease it out. So we checked it poorly and it did lead to some shifts. So one of the things we started in was actually, we started opening later on Saturdays and Sundays at first, and then we ended up shifting Saturday back down to early opening. Just blow in my mind that people are showing up at the locked door at 6 AM on a Saturday. - Yeah. - But-- - Review the security camera footage, right? (laughing) - Well, you're already in here, you're already working. Yeah, if you're opening-- - Oh, that's right, you're there, yeah. - Yeah, you're there, people like peeking through. - Yeah, that's hilarious. - Well, reevaluated there, but to your point, I think part of it, and this was the hardest thing to get through to the owners, was there's a sphere. If I'm not gonna be open, essentially, all the time, what am I going to lose instead? - Yeah, what am I going to mess? - Yeah, yeah, yeah, totally. - Yeah, yeah, yeah. And it really just came around to asking them, and obviously at this point, can't remember exactly that. But the gist of it was, are we optimizing for number of cells? Top one, are we optimizing for profitability? - Yep. - So this has been, number one, one thing I love about it, there's a couple things here, Cameron, that are amazing. So one, like these people, having you solve this problem, using like really advanced data technique for a donut shop and you know, outside of Charlotte, North Carolina, is like, wow, they absolutely struck gold. But now, okay, let's fast forward. So donut shop, you did all this incredible work to help them optimize. It's still running, by the way. - Now they ended up closing about two and a half years after what? - Okay, that's not surprising, it's told us. However, but they closed with full cases, that's what we know, full case of chocolate donuts. - Probably did. - Okay, so you would think that all of those problems that you solved would be, you know, sort of, you know, fully solve problems at a really large company. Later in your career, you went to work for Newell Rubbermaid, which is an enormous, I mean, I don't know how many brands like every, you know, I think people are familiar with Rubbermaid and the plastic products, but they own, I mean. - Yeah, it's sharp. - Yeah, I mean, the max, when I was there after we merged at Jordan, it made us a 16 billion dollar company. We had 200 something brands. - Yeah. - And what did you do there? What did you do at Newell Rubbermaid? - So I actually got the position at Newell, because of the bakery, which is amazing. I was at UNCC's campus, where I was getting my undergrad and my now wife, then fiance was getting her master, finishing her master's there in architecture and she went to the job fair. So I was like, "Hey, you know, I'll go. "I've got a little bit of time, I'm good with this." And I just started talking to one of the Newell recruiters and honestly, I can't remember how to start her, how we got on the topic, but then she was like, "I have to place a call to someone." And she was calling a VP in their strategic sourcing for providing plastic components. Got connected and they ended up making me an internship role. And then seven, eight months later, I had to pitch a full-time job to the cheap purchasing officer, cheap procurement officer. - Wow, out of company that big. That's a serious, that's a heavy title. - Yeah. - Yeah, I'm so much, I'm so-- - I'm sure why I have to do that part. - Maybe turn bitch to the back. - If he can do this, he can definitely do the job. - Right, yeah, three months. - I mean, I started out, it was basically, "Well, what value have you brought us in these seven months?" And in that time period, what I had brought over to him was that clean chain that should cost them long. And just doing it on a bunch of our different projects that essentially we're looking into for what they call value added value engineering, where you cut costs out products or you redesign them, things like that. And I was happy to say, "Hey, you guys have paid me $15 an hour in the last seven months. I've been worth that year, I was worth like one and a quarter million to them." - That's called leverage and salary negotiation. - Yeah. - Exactly. - So I think you should make me a role. - And pay me more than 15 an hour. - And pay me at least 15-85. - Yeah. - Yeah. - But yeah, I got the role there and then that next year I was one of the lead analysts in our classic components, Harry, which is we spent right around a half a billion dollars in plastic components at that time. - Wow. - And like on the supply side. - Yeah. - Like plastic resins and yeah, yeah, it's like that. - Oh, yeah. So actual plastic resins, we're about a billion in just the raw resins. The plastic components are like-- - Oh, these are close. Okay, so these are-- - Yeah, so we'd be buying from a supplier like just this backing for the remote. This is one of the components. - Got it. - Wow. Things like that. And we had a good year. In our group, Newell themselves had a kind of rough year, so it helped leverage wise as well. We beat our goal by like three X and a lot of it was just tied to making it fact-based. What do these products actually cost? What are our actual ordering patterns? What has been the history? You know, and how do we come to a, in a lot of ways the decks I would end up making for negotiations, I'd get our vendors to say yes, agree to everything the whole way. And then we'd pop out the completed model then like, "Cool, so you agree with the whole thing?" - Yeah, sure. - Well, eight percent. I'm glad we had this conversation, right? - Yeah. - All right. - That's fine. - Yeah, and the kind of, well, side thing to that, at the time we had, we had McKinsey and his consultants. And, you know, what they were doing was just taking forever, making spent cubes and then just talking. In my opinion, they were just talking. Yeah, I was like, "Well, we think you should do this." And if you say you're gonna combine spins and like, "Yeah, but what are the hard figures on this? "What are we gonna deliver out of it?" And I can be a bit of a competitive person. So at night I was redoing, I was doing all the, their projects in parallel to basically in Swallow Ego and just, yeah, then. - You're a glutton for punishments, I love it. - Yeah, especially looking back and like, "Why would I do that?" But kind of, I don't know where I, one of the partner, junior partners there, came friendly with me. Well, we struck up a good conversation and good work on relationship. And yeah, probably about six months into that, yeah, I announced that they were gonna start a, McKinsey had talked the C-suite into starting a center of excellence for analytics. And that they were gonna put me as the senior manager for it, which is quite elite from a, from an, you know, bottom tier of the full analyst. It was like a, it's like seven, seven rungs higher or something like that. - Right, right, right. - We started the department and then HR knocked down a bit, but it's good. I was not ready to run an entire department by means, but really when it came down to especially once again, the plastic components and started working on, we did projects on finished good tools to help negotiate everything from a, you know, screwdrivers in that to 60 foot tall saw blades to use the manufacturing facilities. - Holy. - Ah. - 400 piece printers that would clinchy every piece and all the products that go into it. A whole bunch of things. And really what I found with it is they're, had all the exact same problems of the bakery yet. - Mm-hmm. - And I kept thinking, I kept waiting for this like, oh, how a moment, like, oh, this is the magic, right? - Right. - It was, you know, and instead it was the, I mean, it was, it was the same thing. It was bad data. It was, there's too much for people to go through manually. So they'd be like, oh, let's just look at the top couple. And that'll work it all out. You know, we'll use the parade or work it all out. - Which actually really does seem crazy when you talk about buying half a billion dollars of like finished plastic parts. And it's like, let's just look at the top couple. And it's like, that is larger than most companies in the world, like your cost for this. - Yeah, yeah. - Oh, yeah. And I mean, so like we, so one of the negotiation decks that we had from Kinsey, they clinged sheeted a single pin cap. And they're like, we see this much gap on what you're selling to them and this one. And we could go and ask for the whole. And I did it across the whole thing. And when we did it across from all the skis over time and prior for using the smart builds I alluded to earlier, once you waited for, you know, waited those gaps per, you know, annual spent, it was 50% higher than what McKinsey thought it was. It's just they chose a bad single one to go on. But because of how they do it, they weren't able to expand it out. And that's nothing against them at all. There's super smart people there, everything else, right? But they have a playbook and they've all the playbook. You know, I had, but it blew my mind 'cause we'd be in convert literally, we'd be in these negotiations across the table. And like, well, you know, it may be the suppliers out in China and they're like, you know, labor in Guangdong has gone up, you know, 10% every year since 2009. Like, yeah. And they're like, yeah, but resins down this amount. And these negotiations would be just these picking back and forth and really similar to if you're the horse trading at a flea market, essentially. - Yeah, sure, yeah. - I remember sitting at these tables being like, I put more research into when I buy a car. Like, I bought my first new car that same year and I spent four and a half months collating pricing data across the country on it. And so I've narrowed it down to two dealers. I thought I could leverage. And in that day, I drove 10 grand off the cost of it. But I came out of the stack of papers and I had them lead me through. I was like, show me where I'm wrong. You know, I do it nicely. You know, we build really shit. But like, if I'm doing that for this stupid I bought a car for $23,500. Why are we doing this for half a billion? You know, I mean, putting it for spare, total spend at Newell was, what I left was $10 billion. And that's how almost all of it was running. And that really coalesced to me, okay, I need to take these tools and these methodologies and iterate on them, improve on them, but really abstract them out. Make it into a, basically just a go-to-market strategy for the stuff. You know, same way that nowadays, if you're gonna buy a house, you'll look at this estimate. - Yeah. - So that, I mean, a lot of ways, that's what procure view is for our clients. - Yeah, 'cause you productized. So you're doing this at Newell Rubbermaid. So you're taking, I mean, this is a crazy story. From the race car to the donut shop, you take the lessons from the donut shop into Newell Rubbermaid, and then you productize that. And so now you offer that as a software platform that you can run or that your clients run. - Yeah. Yeah, we've got 200 users now. I, you know, we're companies and companies, like customers, we're like. - No, not in users. So we take a, one of the things that we do differently than a lot of other players in the SaaS space of this is we have a hybrid approach. So we pay or could basically consulting, almost like an outsource analytics team with the software. - Okay. - Partly because I'm not a, I'm not a UI designer and a common refrain is it can be overwhelming. And there's a lot in there. It's very detailed. It's very granular. - Upside being, we've now had our outputs, our data audited by the top four accounting firms for a major equity event at one of our clients. There's a one half billion dollar equity event and they found us to be the, what was called the single source of truth. They made all the decisions and put all based on what our data was, right? Which is pretty funny 'cause you have accounting systems and everything else that are tied to your actual bank accounts. That should be. - Yeah. - Right. - No, that wasn't accurate enough. - It's like, that is awesome. But also it's, oh, that's a little, oh, same. I didn't know that was happening. I got told after the fact. - Yeah, yeah, yeah. - Did you notice that? I'm like, I wish I'd know that. Like, with fries, you know, like that. Well, John, you had some questions. Like, what's happening under the hood? I mean, you worked a ton with natural language process. You've got a couple of questions about like, what's happening under the hood? What's the magic, I mean, or not magic, you know, going on with the data? - Yeah, we talked a little bit before the show. You've got two sides to this, right? So we've been talking about the procurement side. There's also a pricing side. They're very much tied together because obviously you procure for X and you sell for Y if you're in any sort of, you know, business where you're selling goods. So I guess starting with the procurement side, I think it'd be really interesting to walk through some, like for our listeners, data people, like walk through, walk us through some of the tech, maybe even walk us through some iterations. Like we started with this tech. We moved to this tech. I think that'd be really interesting to talk about. - Yeah, sure. So I'd say there's probably, there's two separate tech stats that I kind of developed in parallel and then brought them together for this. So the first is going to be related to the, you know, the pricing and your source standard analytics off of transactional data, you know, what we built a number of proprietary algorithms out, the two major ones are what we call a purchase index, which is a algorithm that bootstraps a market index for all of your transactions, all your purchases. So you can see real time, we update monthly, but go and go real time. Basically where is your pricing moving? Just like you've watched the Dow Jones go up and go down. And everything tied to that is going to be on more of your source standard, like standard economic and econometric sort of stack. And I say stack and I'll actually mean that in our truth, like infrastructure stack, I mean more just like concepts, right? And then the other side of it is our, for lack of a better term, what we'll call right now our data cleaning side, which is probably the more interesting side when it comes down, it doesn't sound like it, but when it comes down to it for most people, 'cause that's where we leverage a number of internally and develop AIs to speed that up. So we have a foundational embedding network that vectorizes all the products we get in. - Like from an ERP, so like, okay, so you create, so you like would connect to an ERP and then you create a bunch of embeddings in a vector database. - Yeah, so we actually don't generally connect directly to the ERP because of security reasons. I'm just against it, that's the number one vector for ransomware attacks into any fashion companies. - So that makes total sense. - Yeah. - As a, it's about a $1 million a year company. I don't want to, I don't want to risk that. In fact, what about... - So we're talking SFTP is what I'm hearing. - SFTP flat files, and we talk about like, I can write an API into it. - Sure. - Please don't make me. - Yeah, yeah, yeah. - Yeah. So we take that, yeah, we embed all the products into a common space, and then for every client, we stand up, like usually at least five, sometimes more, very rarely less, but usually about five classification and record linkage APIs. Hardly, I'm a big fan of the distillation method. So, you know, train out five model or let's say five models. You use them as teachers, along with like a temperature constraint on their outputs for a fourth, that's smaller, things like that. Love those kind of models because they're cheap to run once you train them. - Yeah, yeah, yeah. - Really expressive, and then quite a bit on the record linkage side, because again, when you're at these large companies, it becomes really easy to get redundant items, or to lose history of items. So, by linking together, you know, green t-shirt medium with medium green t-shirt or t-shirt green or what have you, we can now see are there differences in their costs? Or did they change, you know, any, you know, choose your own adventure kind of thing. So, but we leverage those for creating what got an item master, and that's where we can put, you know, cleaned group linkages, cleaned descriptions, especially if they drop them all off, or we have one client that buys a lot from Uline, and all their Uline products, all they do is put the item code and not the actual description. So, get all that filled in so that we know what the things are. And then, that we run just combat classifiers on it for categorizations, usually three to five levels, D of a hierarchy, and our target for that is within the first month, we should be at least 94% accurate on unseen data. And our best, right, current client is, we're at 98.62%. And that's actually in six different languages coming in. - Oh, wow. So like, okay, wow. - International, yeah. - Which actually is such a good use for that type of technology, my God. - Oh, yeah. I can't spend the time doing that. I mean, like, we're a small group, basically for every company we go into, we're gonna do the equivalent of about 30 to 50 people's worth of work. - Wow. - But, generally speaking, they don't have those people yet. The companies tend to get the ones who are looking at making this investment, and they're like, hey, before you do that, yes. And now, try to tell them to hire people 'cause I'm like, well, I wanted to be sassed long term. I want to train you how to fish, and then push the boat out, and you go. - Yeah, yeah, for sure. - So, those are the two main stacks where we probably won a lot of business initialies on the data cleaning side, especially in what's called the indirect spend. So, it's the stuff that you put on, like your purchase cards, your credit cards, flights, travel, all those things. Everyone's like, no one, you can't do anything with it 'cause there's no item codes. We don't have to put them together. Well, Best Western Hotel or Lando is probably the same as Orlando, Best Western. - Yeah, yeah, sure. - And if it says three nights, and those are four nights, maybe we just say, what's the average correct, right? - Yeah. - We have a good met account. It's a widget. We tend to get our foot in there, and then we drive it out with the product pricing. You know, doing those market builds. One of the things we pride ourselves on is our market builds where we're clean sheeting every single transaction and product category. And some of these categories might have 2,000, 3,000 skis. We're on average on any given month within two and a half percent of a bid if they went out and bid it right then, or what you could get. Now, I can tell you that's someone. And if someone said to me, no one believed in Tossot. So when we're working with our clients, we'd tell them, hey, how do they test it? Put it out, you know? What if things are wrong and things are wrong and you know, first iterations? Those suppliers are gonna give you more information. When you tell, you know, if you tell them, hey, I think a can of Coca-Cola is 90% cost the serve. It's not, right? It's like 1% of it. That guy's gonna tell you, you know, you're an idiot. It's mainly water. Sweet. - Yeah. (laughs) - Load that into the algorithm. - Yeah. - Exactly. We update it and we actually have a live tool where you can do it not tied to every single item, but as a category level, you can do it live with them, which is some good interest on the end users. But like I said, our big thing, you know, for me, having worked in the quote-unquote AI space, 'cause I've been working on the convolutional neural networks and then deep learning since 2014. The current thing around it, I don't think it's all hype. I think there's chunks of it that are hype, but the thing I tried to tell all of our clients, everyone we meet with is we see it as a tool. It's a point of your stick, yeah? And if it doesn't deliver significant value quickly on it, then it's not the right tool, or it's not ready for you, you know, what have you. And yeah, I tell them the proof's on the button for it. So, anyway, did that answer that question? - Yeah. - Yeah, that was awesome. - Yeah, that was awesome. - John, did you have a question about the pricing side on like selling, 'cause that was something you dealt with at the time, which is sort of the inverse, right? So like, 'cause you obviously help people create significant leverage in what they're, the prices that they're paying to their suppliers, but then that is a margin question for my business, but I have to turn around and go sell it, and you guys had a bunch of inventory and bought a bunch of stuff. - Yeah, so it's really interesting, and I think there's a lot of businesses that still operate this way. They will take cost. It will often not even be fully landed costs, it'll be like cost of goods. There'll be some like unknown gap that maybe they like categorizes overhead, like, all right, cost is 30 bucks, had $5 for overhead, 35 bucks. - Well, look at the top couple. - And we're good, yeah. And we're gonna mark it up 37%, you know? Like, it's surprising how, you know, how many businesses it's run that way, right? Like they have like, oh yeah, that seems about right. And then they check some competitors pricing like, okay, let's tweak it a little bit. Like, that's it, period, that's it. So-- - Yeah, yeah. - There's a lot of sophistication you guys are doing with on the procurement side that I think also applies on the pricing side, and there's a big, for my perception, a big gap in the market, especially, like, when we did research on this, there was one firm that was just a little bit too big. We're talking like, you know, quarter million dollars just to engage with them. Like, it didn't make sense for us. - We did, it's a profit. - No, it was a different one. - Okay. - But anyways, so there, I think there's this gap in the market, and it seems like a lot of this logic would, you know, translate into pricing. So let's talk about that a little bit. - Yeah, I'm one, you're right. I mean, sourcing, I wouldn't say procurement itself, but sourcing is just the other side of the coin of sales. - Yeah. - Yeah, the salesman's not the one pulling it, the product off the shelf in a box and shipping it. Same way that your sourcing lead isn't gonna be the one placing the actual PO. They're gonna be negotiating, what is it for how much, and how long is this price good for, right? - Yeah. - It's this business side. And those costs, or those inputs that are identical. And, you know, really what we do when we're doing those market builds and clean sheeting, we're making that pricing model for those suppliers. And we build in the margin, but we try to build the margin in a way that is fair and sustainable. I'm a very big on that. I will buy tooth and L with my client's son. I'm not gonna make you models, push someone to the bone. I've gotten to call it Friday night. Well, Friday night in China. Friday morning, for me, when it happened, that they were basically pushed this once supplier too far and they were putting our half a million dollar of injection molding tools out onto the loading dock. They're like, "And it's gonna rain all weekend." So I go figure it out. (laughing) - Oh my god. - Oh, I was like, cool. I, and I was, at that time, I was essentially just an internal consultant. I'm not, I own this supplier in LA. - Sweet. Now I have to take this up to this director who, you know, but that really struck with me because one coming from a small business background, I remember how it felt to get just bullied. - Yeah, for sure. - And five cents not worth it, it's just, it's not. And so we build those models to be fair on both sides of it, but what that really means, we've just made a model that is, that is a, basically an abstracted version of what takes to produce their items. And when we've done, like so when we've done audits on it, we aim for within two and a half percent, mean absolutely percent error on it. And then on some products, especially ones that are high-value or closer to like a commodity style, things like stretch film or corrugate boxes, we tend to line within half a percent plus minus of all their actuals. So to your point for pricing, if we, when we tie it and we're doing this in beta now, the only step change we're making between doing the procurement side and sell side is now with sell side, we're concluding in the procurement costs as well. To try to watch, you know, what that margin is. And now you're not gonna, again, necessarily try to leverage suppliers on your costs and be like, hey, which customers are buying for the right price for most? Who do we wanna give discount to? 'Cause what we're gonna grow with in their strategic partner. And, you know, some of the things that we found is for one product group, the lowest price by vast margin was to their, one of their smallest customers. And it's like- - That's not uncommon. - You're making more margin when you sell to Walmart, but not to, you know, whoever, and I'm not telling you, and I'm like, hey, go screw over this little guy, but like, one, when Walmart finds that out, you're gonna be really screwed because you can't, you're not making any money over there. You're like, oh, we're giving you the product for cost. - Well, a lot of people don't know this, so I think it's Walmart specifically, I'm sure there's others, like you sign agreements where it's like, you know, we get the absolute lowest cost, you know, and if they find out, like, it's a problem. - Yeah, you can, I can't say a lot outside. Yes, yeah, it is a major problem with that. But it's one of the things that comes down to, can you parse the data? Can you make sense of it? And then once you're parsing it, we're talking, you know, companies with, yeah, millions of billions of lines of transactions and sourcing teams are small. Yeah, new for, even when we're 16 billion, our entire strategic sourcing, and including our execution engineers, everything else on the ground, and China, India, so forth, was about 200 people. - Oh my gosh, yeah, that's surprising. - Right, so you just, you don't have the time to go through it, so everyone's just using these heuristics. And that's where, you know, that's where you're losing on it. So, yeah, one of the things that we've been crawling about from our marketing side is, and this wasn't something new would happen going into it. It's been a, say, happy output. Our worst customer's return on our, you know, on our cost is about 22 and a half times every year. - Wow. - Living gain on, and it's not, again, it's not because they're bad, they're jobs or anything like that's because he just can't make sense of it all. And what we're going to get at is finding those needles in the haystack where you can pop it up and ask them why. - Yep, yeah, that's great. Well, Cameron, this time is flown by, unfortunately we're at the buzzer, but I have two questions for you, okay? Well, actually one, just to reiterate for the listeners, where can they find out more about procure view? What's your website, just in case anyone's? www.fury.com. I will say, do you use the wwws? We migrate our website last year, and now it doesn't like it without 'em? - Yeah, and that's me just for those keeping score. Okay, two questions. One, do you ever get out to drive cars really fast anywhere anymore to get that adrenaline rush? - I've got, I've got two sports cars at home. - Okay. - GR86 and a Yamaha R1 powered low to seven. - Okay. - But I actually, I had a bad ski accident in 2014. - Oh man, okay. - I killed off all my death reception and I've lost the ability to make sentences and a whole bunch of things. And yeah, I can't get a race license anymore, so I was summarily let go by Ferrari North America, Porsche North America, VW. (laughing) It was a rough, it happened in January by March and it kind of filtered out and I was having some of my two race bikes, so both the race bikes. - Wow. - And I was just getting like these emails and I'm like, "Thank you for everything." But like, see ya. (laughing) - Yeah, that's tough. - Wow. So now I just do it virtually. - Yeah, okay. - Well, okay, and then last question is, when you go into a donut shop, are you able to order a donut without thinking about the cogs? - No, I hate men. - No. - I've had, I'm not a very social person in settings. I like to, like I hate getting my haircut 'cause I hate when they talk to me. (laughing) I just say I am, but like I've been at line at Donut Shops and someone's complaining that there's a great donut shop in Land, Georgia by the name of Spline Donuts. - Okay. - Yeah, giving them a little shout out. They opened 09, they're amazing, but they're about $3 a donut. And a couple years ago, we were down Atlanta and we're in line for them again, like so we used to go in why I went to college at Georgia Tech and anyway, a person was complaining about it. Now I couldn't hold my time. (laughing) With this person. - We were actually, it's 18 hours. - I felt like they were tacky. Like, wow, I love that donut shop. Like we've known the owner for a long time. Blah, blah, blah. I'm like, like you got to be an idiot. Like you see that's a broiled marshmallow on top? You know how long that takes up. Like, and then I'm realizing like, oh my God, I'm gonna have fun. Like in a meeting. - It doesn't take any eye for you to know that this is a very expensive donut to produce. - Right, okay. I wrote out an entire academic paper on this, right? (laughing) - Right now. - Oh man, that is so great. Well Cameron, this has been such an awesome show. So great to learn about your company and just congrats on an amazing journey and we will view all the success in the world with Pre-CareView. - Ah, I appreciate it. Thank you guys for having us. Love to let's catch more of the show. - The Data Sack Show is brought to you by Rutter Sack, the warehouse native customer data platform. Rutter Sack has purpose built to help data teams turn customer data into competitive advantage. Learn more at Rutter Sack.com. (upbeat music) (upbeat music) (upbeat music)
