Choosing the Right Compensation Data

[00:00:00] Another Totally Rewarding Chats. And I am once again back with Mr. Ryman, who is probably questioning life choices as we're partway into season three now, right? I question life choices all the time. And this is not one that I question, Sean. Give yourself a little bit of credit. Wow. I thought that was going to be the most consulting-ish answer you got where you completely deflected into a larger thing.

[00:00:27] Larger thing. I'm a reflective human. I wonder every week, should I really be a soccer coach? I wonder all the time, you know, should I only wear the same t-shirt pretty much every day? But I have not yet questioned my choice to stay on. The t-shirt thing is interesting. I have all black, gray, or blue, and there's like two red shirts in there. And my kids, anytime I wear either of the red ones, they're like, what's up? Totally. Laundry?

[00:00:56] My kids assume, so, you know, I never leave the house. Like, it's basically my prison. And my kids, when they see me wear a collar, they're like, where are you going? Like, they assume I'm traveling or something is happening. So, anyway. It's a coach's meeting. Tell them it's a coach's meeting as you get going. Well, I am stoked we have Stuart Reeves here. You've been remarkably silent so far, Stuart. That's well done. Look at you go. I was just listening to the clothing. I was like, well, I wore collars, you guys didn't. So, I guess.

[00:01:23] Well, Paul and I both, I think, even when I'm at WorldatWork, I tell people, the time I wore a collared shirt to stay in kind of the lemon theme or the margarita theme, people crapped on me all day. So, if I wore a collared shirt here, people would really wonder. Although it is about, I think by the time this airs, I will do my annual now tuxedo run in London at one of the events. There you go. There you go.

[00:01:49] It's the wide range. I present during the day in a t-shirt and jeans, and then I move over to an alternate ego and go with the, you know, the tuxedo. Sure. So, I'm stoked you're here. And as usual, so we don't screw it up. Give us your background and bio and what you've got going on now.

[00:02:13] Okay. Sounds good. Thank you. And it's a pleasure to be here. A little bit about myself. I have been doing HR-related things for 31 years now. I hate saying that. Absolutely hate saying that. We go with over 30. Own it. Over 30. 30 plus years. Yeah.

[00:02:30] Yeah. Started in staffing, headed into corporate talent acquisition. And then, you know, what got me into the compensation side and really learning and loving this was I was working in American Express. I kept having to go to these compensation people and get approval before I could extend an offer. And finally, I was like, who are you and why do I have to do this? And just absolutely fell in love with it and just started doing everything I could.

[00:03:01] Have been in my own consulting, compensation, HR consulting business for the last five years. TSG Consulting. TSG Compensation Consulting, I guess, technically. And just eight days ago, launched a new product, a new compensation intelligent platform called Compify. And just honored to be on your episode with you and have some chats with you guys. But thank you for the invitation.

[00:03:27] So before we go, and this way I can box in the selling, you know, so you don't go. So tell us why, why Compify, what it is exactly. I'm going to give you one minute. I might even put a timer on. Okay. One minute, what it is and why it's different than other. I was going to say crap, but stuff caught up in the market. Compify is in a nutshell. It brings together all of the internal company dashboards so that in one place,

[00:03:56] you can go see what your compensation for each employee, each region, each area, each department is at any given time. And then it links together with a backend data source so that you can pull real-time benchmarks and do comparative values within. The trick is, is that we also add on advisors, personal advisors, to help you understand what it is you're seeing should you not need that. Yeah. And just since I can't stop, right, because I'm a comp tech guy, right?

[00:04:26] Just to frame a little bit for the audience, too. Within our Novo Insights framework, you know, it blends across our categories because you've got use cases in market pricing. You've got use cases in analytics. Focus more on the mid-ish market, I think. Yes. In our definition, sort of small enterprise and below being a key differentiator. So, you know, it doesn't mean you can't. I'm not going to say you can't serve, but it's more your model is built to suit sort of that thousand employee and smaller organization. Yes, it is.

[00:04:55] We definitely target the mid-market groups, the ones who basically have grown out of spreadsheets, but they're not quite at their big boy software companies yet. Okay. Got it. Yeah, and I knew there's one of those, like, being married to a lawyer. I knew the answer to the question before I asked it, which is one of the reasons I wanted to have you on here. So before we dive into how, you know, how we kind of marry consulting and data together,

[00:05:24] like how you do that practically, we have to get to the speed round of questions. Okay. So are you a tea or coffee person? Coffee, 100%. Coffee, 100%. And then the follow-up, are you a coffee snob since you gave the 100%? I don't know if I'd call it a snob, but I only grind my own beans when I'm making the coffee. So far, let me be clear. Anyone who says I'm not sure I'm a snob butt is followed by coffee snobbery explanation. Yeah.

[00:05:54] Although, hang on. I'm going to push back on you a little bit, Sean. So, like, you know, I need to grind the beans as, like, stage one. Like, okay, that just means you like things that taste good, right? Then there's the I have a preferred brewing method or a preferred type of coffee, right? Like, I only drink Latin American coffees or it must be pour over. Or that's another level of snobbery. And then when you mix all of them together, all right, then you're me. So we can have, like, a 1 to 10 snobbery scale pretty soon? Yeah, for sure.

[00:06:23] And, like, I want to grind my beans as a 4. Like, it's not that snobby, right? Because then I could throw anything at you and be happy with it probably. You know, whereas I'm not happy just with grinding my beans. So I'm going to throw this out there for TR27. We need to come up with some type of on-the-fly quiz on where you are on the snobbery scale. It'd be interesting to see. It'd be interesting to see because, you know, it depends. It depends how you look at it. Where do you think you are?

[00:06:52] Take the quiz and see how it matches up. I mean, look, no offense to conference providers, but if you're drinking the coffee there, you're not a 10. Right? Like, if you can tolerate conference coffee, you are not a coffee snob in the truest sense. Like, you are south of a 5. In the semi-snobbery thing, I travel with many times. I will pre-grind because you can't find them. My beans bring them along with my pour over.

[00:07:22] That's legit. And that's what I – so when you see me walking around the hall with the tall, you know, container, that's kind of what's in there sometimes. That's still snobbery because then you plan for it too. Like, you absolutely chose to, like, bring with the pour over. But you got to remember, on that snobbery slash not, on the other end of that spectrum, Paul, is how bad is the coffee inside one of those? Always. So I guess, yeah, I mean, you're still not a 10. It's not saying I'm a snob. I just don't want awful. Okay.

[00:07:52] But that puts you in the middle. Like, you're at 6, right? Okay. Anyway, that was not a speed round at all. We just went super deep on that one. So do you have a hobby outside of, you know, building products? You know, I have two, really. Golf. I'm in Arizona, so it's always golf. It's got to be golf. But one thing I just get obsessed with, and this time of year definitely, I do light shows for holidays on my house.

[00:08:22] And so I wire it and build everything to make the house dance. It's going to be the slowest speaker on it. So was that show, The Great Christmas Light Fight thing? Yeah. Yeah. I kind of do those kind of things on my house. But wait, on the scale of they're a 9 or 10, where are you? 6 or 7. Yeah, that means you want to be a 9 or 10. I'm really working towards a 9 or 10.

[00:08:50] Like, I know people that are on this spectrum, and they are like, I just can't afford it, or I don't have the time. Like, there's a natural constraint to get to that point, because, wow. Thank you, Stuart. But the Christmas lights before Thanksgiving thing is a big one for me. Like, I'm like, if they're on before Thanksgiving, like, we got to talk. Okay. I don't turn them on before Thanksgiving, but it is set up before Thanksgiving. Okay. You got to put the work in. It is set up. Okay.

[00:09:17] We're okay with that in Chicago, too, because if you don't set them up before Thanksgiving, you might be setting them up in, you know, frigid weather. But they may not be plugged in prior to— I don't think Stuart has the frigid weather issue. Yeah, you don't have that problem. We definitely don't have that. We definitely don't have that. Really important one, and glad to have you on, Stuart. You have some time away, and you get to choose. Would you like to have a posh weekend away or go camping in a trailer, like, say, somewhere in Colorado?

[00:09:49] Oh, you remembered. He remembers. That's very specific. Yes. I love to hook up the trailer. I absolutely love it. I mean, I do. I know you and I have talked about this when you were starting your tour. Paul's like, does Stuart actually know any shit at all about comp, or did you just know he's got a trailer? He was brought on specifically to be a ringer on this question. Yeah, that was very specific.

[00:10:15] Now, my wife 100% is posh resorts, but I love the trailer. I love seeing new areas, and I just—now, I'm not saying I don't stay at posh RV parks, but I do love it. This is going to open up. I knew it. I knew as soon as you said it where Paul was going. You got to understand, Paul's camping is like mid-tier Marriott, but not even the bottom. And so when you're like posh RV park, he's like, wait, what?

[00:10:44] Yeah, there's definitely like a two-dimensional matrix here around like poshness and sort of natural embedded services and comfort. And like Sean is in the bottom left of I will sleep on the ground on a rock and dig holes when I need to do those things. You know, I'm in the other corner, which is if you can pamper me, I'm good with that. And, you know, a posh RV park is a different corner I don't think I even knew existed.

[00:11:13] I think I'm going to have to get—this is the slowest speed run. I'm going to have to go find who's in charge of comp and HR at KOA and have them on. Be a great guest. And wait until they tell you they can't wait to get away from the KOA. So the next two should be easier, hopefully. Do you believe outside of compensation they should use the word comp or ratio or not?

[00:11:39] You know, I would say not because people inside a compensation usually don't sometimes don't even know what that is. Okay. I mean, you know, you're like, oh, well, here, let me help you with the comp or ratio. And they're like, well, yeah, if you can tell me what it is and why I need it. That's, you know, so I would say yes and no. That's a tough one. And crunchy or creamy peanut butter? Creamy. That's about as fast as I can say it. Creamy. Crunchy is just—it's just weird.

[00:12:08] It's just an incomplete job. That's how I get it. Yeah, it's like someone got lazy. You went to Christ's peanuts and left. Yeah. Like, left the work undone. They got lazy and said, hey, let's see if we can sell it. You know, it worked. So the real reason I had you on besides I knew I could finally get another one in the camp spot was you've built this product. You've had the consulting business for a long period of time using data.

[00:12:34] And then not only did you see a niche, but obviously you're thinking about tying the two together. And so for me, it was about how do you see pulling in the right data when you're consulting for each individual organization? And how do you think about making those choices around data? Because you've obviously spent some sleepless nights thinking about it because then you had to wind yourself up and build something. Sure. So it bothered you enough. So what were you trying to solve?

[00:13:04] But most importantly, what are some critical items when you're trying to figure out what data to use? Well, you know, when I was looking at it from the consulting side, but before CompaFi even was thought of, it was the biggest thing I was trying to solve was how can I get them data that will remain current? When you're giving someone data at a client, you know, whoever that may be, it's accurate right now.

[00:13:33] It's not accurate tomorrow. It's not accurate next week. It's not accurate when AI takes off. It's not accurate at any time after that. It's just a snapshot. So it was helping clients to be able to make those decisions while at the same time really, really helping them understand what those decisions are and the ramifications of it. So that was really why I chose different things from the consulting to the software.

[00:13:59] But how do I determine what data I'm going to use is really I look at it by the industry break. You know, how is it going to affect them? Which provider has the biggest cut and the most accurate cut? And how am I going to be able to relate that to the company directly? So I use multiple sources. When I first started, I used one. I had one partner. Now, you know, the TSG side of the house uses multiple.

[00:14:28] So we look at it from the sense of how can we give our clients the most complete package? The most complete answer, I guess I should say. Yeah, there's a and there's probably no right answer to the follow up question. When you mentioned sort of the most accurate data, how do you know that? Like, how do you think about and judge the quality or accuracy of that data? Well, that's a good one. That's a good one. You know, it's not just I don't look at it from the sense and I tell my I tell my hiring managers and leaders and HR leaders and everything.

[00:14:59] We're not benchmarking a title. We're benchmarking the whole scope of a position. And so by doing that, we can narrow it down to being as accurate as we possibly can. Is anyone or anybody always 100% accurate? Really, I don't see that that can happen in any industry, any profession. It's never that aspect.

[00:15:24] You're working with the data that you have to get to the point that meets what the client is looking to learn about a position. You know, and that's where it kind of gets fun is from consulting and anybody who's in that business has at least ran into this once is your HR leaders going to be. We want you to scope this position and here's what we think it should pay.

[00:15:50] It's like, well, you know what you think it should pay isn't exactly what it may pay. Sure. Yeah. Yeah. So, I mean, I always think I always say is it's funny. I coming from the outside in, although now, you know, pushing. You're in the inside now. Like the one the one thing I tell people, you can almost be guaranteed that no one's making is the number that you come up with. Like it is a guy, you know, it's a guide as you go.

[00:16:18] So, how when you're selecting sources, how do you do the matching of job? Like have you found that to be more difficult from a consulting aspect to both of you? Because I think for me, I've usually done it, you know, my side hustle and how I've done this stuff is do it from the inside somewhere and do a couple side projects. You guys have done it a lot. There's always pushback on matching the job and doing the whatever and then getting the data to go with that. But how have you kind of tackled that issue?

[00:16:48] You know, like to get the jobs match first. Do you kind of back into it via the numbers? Like when you're looking at the data and I'm really particular, like how you do it with a tool because you take the tool is now responsible for the art as I look at it. Like, so how are you tackling it from those two pieces? How have you traditionally done it? And then how do you think tech is going to be able to do this? Because, I mean, humans are going to be trying to game the system right after you build it. Sure. Sure.

[00:17:16] I would say from my aspect is, is I work it from the job description or the job profile forward to the peg. Instead of backwards saying, here's how much they're paying this person. Let's build it back so that my job description matches what they want to pay. And the reason that I do it that way, and I'm sure many do either direction, is because I'm trying to help them price the job.

[00:17:42] Not the job meets the price. And I find that I find that I find it that way because it's more accurate. It just helps them to be able to hit what they should be paying. It shows the market. It's fair to the candidate. It's fair to the candidate. And ultimately, it's fair to the company because you're trying to retain talent. You're trying to attract talent.

[00:18:07] And just making up numbers based upon, oh, well, this is what we think they should do because this is what we want to pay. You know, well, it's may not be what it should be. And sometimes you have to help them auto level that position. Yeah, the tricky. So it's always, always has to be about sort of the value of the work. Right. What does it take to get somebody with the right skills, experiences and capabilities to get an outcome? Period. Now, the hard part, particularly when I think about your focal area, kind of down market, right?

[00:18:36] Sub 1000 employees. There's a small ton of unique jobs in organizations like that. Right. You know, we talk a lot about the transitions companies go through from one to 10 employees, 10 to 100 employees, 100 to 1,000. Right. And, you know, at 10 employees, you know. John is finance and for other things at 100 employees. John might still be finance doing a lot of things and he might have a helper.

[00:19:06] You know, at 1,000, now you've got some specialization. Right. So that's the tricky part, Sean. Like from an advisory perspective is some of these jobs are built around humans at smaller scale. And as a result, there is no great benchmark for the person that does this, this, this, and this. Right? And working with clients to say, like, don't try to force them into one bucket. Yeah. Like, yeah, I get it. Like, you need to understand. But they are the finance person, the HR person. They have a law degree.

[00:19:35] So they work with your lawyer. You know, and they run that operational process. Because they can. Right? How do you value that? Like, that's tricky. And it's not usually the benchmark for the director of FP&A. You know, because that's, you know, a clean benchmark. Right. Right. And I have found. Oh, sorry. Go ahead. I mean, it's roles, not souls. Right? Like, I mean, that's the, you know, that's the, that's the piece.

[00:20:01] I think what also comes out of it though, sometimes is, and this is the art and kind of working with clients. The title inflation gets real. Right? Right. Like, no, no, no. Paul's really a VP. He's really a VP. I'm like, well, you got two choices. You can call him a director and we can benchmark or you can pay him like a VP. Those are your two choices. You know, neither of them are your favorite. But this is, this is kind of the thing. And so I do think it's, that's the art.

[00:20:28] And I guess I'd ask Stuart, like, how, how do you, how do you see tools tackling that thing? Because you've got this matching that's hard, particularly in the small and mid market where, not in a bad way, they don't know better. Like, this is my company. I'm matching a role. Paul's a VP. There's only 40 of us here and we have 35 VPs, but he's still a VP. How do you see that working through? You know, I look at it and this is usually where the person to person comes in and then the technology.

[00:20:58] And that's because I don't think the person is ever going to be out of compensation. I just, I really feel strongly about that. And, and the reason being is, is that you need to find out exactly what someone does. And then the computer can help you realize, okay, what percentage of this do they do? What percentage of this do they do? So they may be a VP, but that, that VP role is where the technology can come in and help you put a weighted average based upon what they do.

[00:21:26] Um, you know, and then you can look at, okay, well, the 80% of their job is finance and 20% sales. Well, that's not a VP of sales, you know? So it's, it's looking at it and the, and technology helps you do that. And it really helps you to be able to define what those aspects are. But the human had to tell it to what to do. So I'll spin that out. Wasn't on my list, but like, it's where my brain went. So sucks to be used to her.

[00:21:53] Um, how, how, um, how do you see, like, are you a believer in the blended job role? And so having the hybrid role, the hybrid pricing, the blended job pricing, what's your perspective on that? Because Paul won't be surprised. I have one. Um, but what's, what's your perspective? You know, six months ago, I would have said, nah, I'm not really a big supporter. And I don't know, maybe this is something that's happening.

[00:22:20] I have seen so many of them over the last six months, you know, just so many because people are, you know, they're not downsizing, but they're right sizing. I guess it would be price it. I'm more into like, how do you price it? Like think about the method a little more mechanical. How do you believe in the, I'm going to match the two or three jobs and wait and rank them and, and go through that hybrid pricing? Yes. Yes, I do. I truly do. And I think that's because it gives you a more accurate, uh, more accurate pricing on

[00:22:50] what it is that the person actually does. Um, I feel that it helps the company also to understand what the person actually does. Um, because sometimes the CEO doesn't realize that, and I'll just pick on Paul, Paul, the VP does these five jobs. Well, if I put that together into a weighted average or whatever that you, you may call that, it helps them to understand what it is and why we've come up with that number. So I'll push back and kind of a semi mean way. Please do.

[00:23:19] You, you've just asked Paul to leave the company. Um, and so the reason I firmly believe in that is if Paul is doing three jobs, um, I believe his market rate is worth the highest one he is capable of doing. Um, because otherwise if I price him lower, Paul's going to go find another job that pays him more, um, for that skill. He's capable of all three. And so why, why are you going to let him go to market and be, why are you going to penalize

[00:23:46] him for doing other crap in your company and being multifaceted? Now, let me argue for that or against that, Sean, just to be argumentative because I enjoy it. Right. If somebody is all three of those things, odds are they're not actually doing all three of those in a full-time role. So now I'm on record. I actually agree with the math, the way you're describing it. Like if I am doing primarily one set of skills, the most valuable one is where I would go if I had that choice.

[00:24:10] However, you know, for my own company, I am sort of the CEO, the CFO and the head of operation. No, I'm not the head of operations. That's definitely not me. I'm the CEO and the CFO. I couldn't do any one of those well by themselves. Right. Like, so I, that's the only challenge I would say is I'm not necessarily worth the most viable thing because I'm probably not doing that the way a fully skilled, just that expert is. It depends. You get to hear me say it first.

[00:24:37] Um, I think at the very high end, you're probably true because what you've got is a small midsize company where I'm, you know, COO slash CPO. Sure. You're not, you know, but if I'm, you know, doing some multifaceted stuff in a company of 150 to 500 to a thousand, like I probably have to be good enough at those roles to go do it on their own or I would suck at my job is kind of my thing. And so, you know, like I'm very, probably the point I'd like to highlight is I'm very

[00:25:07] much that you should not have to like, I think people get in this black or white, they're doing multiple things, hybrid the job. And so for me, it's like, think about that. And the, and sure, the internal view is like, well, I only want to pay Paul for what he's really worth to me and the value he's providing for these things. And like, I think people forget a lot of times are like, there's somebody across the street going like, I certainly hope you pay Paul less.

[00:25:31] Um, we would like Paul, um, like I think that external market piece and understanding what each one of those market prices as, because sometimes it's trivial too. I also think comp people being comp people, they'd love to get it right. Like there's not a lot of slack asses in comp who are like good enough. It's just not their, the mojo generally. Right. And so they'll do a lot of work to be like, well, that price, that job isn't priced at 190 K it's priced at 184, 250.

[00:25:58] And I'm like, bro, you just spent all that time matching three jobs. You know, it's about one 85, right? Sure. Semi speed round out of your, uh, second speed round. Look at Paul. Sean's just changing stuff. I am. Um, just give real quick, your pros and cons for each data source type. Um, and so it's a semi speed round. So traditional surveys, pros and cons.

[00:26:28] Traditional surveys, I think is stale quick. I think that they, they, they, well, okay. Stale might not be the best word. So I'll try and stick with the semi speed is, uh, they get, they get slowed down quick in the sense that roles are changing and, and aspects of companies are changing and companies that are midsize today or enterprise tomorrow. And so I think it's learning the different cuts.

[00:26:55] And I think that the traditional surveys are slow to update what their refresh is. You got any pros? They're wide. They're widely accepted and people recognize them. Yeah. And if I, if I could add a pro, the quality of the methodology is often stronger still than some of these alternatives.

[00:27:19] So like knowing that someone smart has thought about, is this job that when that data goes in, rather than it being algorithmic, rather than being just by title, which is what we ended up doing with postings. Like just different data sources struggle with quality sometimes because there's no taxonomy. There's no rubric that sort of describes the job. So that's the other pro I would say for surveys is there's been the most IP development on how to categorize jobs. There's some others that are there.

[00:27:47] Don't get me wrong, but like almost every survey has got some sort of taxonomy that helps you do a quality job of matching. Sure. I'd argue the pro I, the pro, you know, one of each, the pro that I'd add is peer cuts, um, compared to aggregated data. So the ability to kind of really, if you're in a niche space, um, you can really dive deep into a smaller subset of people or roles. Um, I do still worry. I know you said that I think the methodology, the company themselves is good, Paul, but I

[00:28:14] still think in large companies, the person filling out the survey is the lowest level person nobody likes. Um, and so like their ability to go level jobs across multiple locations and do that, right? So the garbage in thing and how much can the provider clean up? Cause they do spend an inordinate amount of time trying to clean it up to make sure that, you know, the entry level cost accountant doesn't make $385,000. You probably got that wrong.

[00:28:40] Like they catch a lot of that, um, aggregated survey data. So you're, you know, ERI salaries, pay scales, whatever it is like pros and cons. I just don't think they're accurate. I don't, I don't. Well, okay. I shouldn't say, okay, let me take that back. Let me take, let me rewind that. You can edit that. Not accurate out is I don't, they are not at the level that I would call consistent. I wouldn't, I would say they're not quite consistent.

[00:29:10] And that's the sense that how much do you really make or how much do you think you make? And that's what I'm going to report. And so, you know, I would say that they're not consistent as a negative on the positive side. It's data that is real pure market type data. And I think that that flows together. And when I say pure market, I mean, not pure peer, um, is that it helps people to understand

[00:29:39] what they actually are thinking they're making. Oh, so you're talking just so I'm clear, you're thinking about candidate submitted data and aggregate data. Yes. Secret submitted. Yeah, yeah. Yeah, yeah. Yeah. And I didn't have that on my speed run. That's an interesting one. I think, um, there's a couple of things that go with it. I think are like, to your point, I don't know that people always know what they make. They generally know what they make in their W2. Like that, that part, how they get to that number can be a million different ways.

[00:30:08] And of course the W2 is post tax. It's, there's a bunch of stuff. So that I actually think in that space, the really cool pro and not everyone's dug in yet. There's things out there like, how far would you drive for this salary when you hire data? That is super cool. So they, they just have to be ballpark, right on how much they make. And you can start to be like, you know, how far from Elmhurst, Illinois is someone willing to drive for, you know, X amount of dollars.

[00:30:36] Because when you do the interview and someone says, I really want to come work for you, Paul, I really want to work for you. And then, you know, and Paul's like, you're willing to drive 90 minutes. They're like, oh yeah. Paul can look at the day and be like, that's what you say today. But like statistically, that's not how it works for our area and whatever. So, and you know, what they value and things like that. I think there's some cool insights that are gained. I actually think the regular pay to your point, people don't actually know.

[00:31:04] If you ask people what's in their W2, a lot off that without them going looking, I would bet most people don't. They wouldn't be able to identify it. And they certainly couldn't tell you what the total compensation is. You know, what's the cost of everything else that they don't see in behind the scenes from the company. Yeah, that's true. Yeah, they have no idea. And it's, you know, their benefit that they don't know. I will say though, and this is sort of flipping that problem on its head.

[00:31:33] The one pro to sort of that self-supplied data is it's as clear of a view of what somebody's likely to think they should be paid, right? Because it doesn't mean it's right, right? But like no employee is going out and buying Radford data, right? Like they are going to LinkedIn. They're going to Glassdoor, you know, that they're going to levels, you know, name, name the places that are sort of self-provided data. That's what they, their perception is shaped by that.

[00:32:01] And I actually found that really helpful to know what their perception likely is. Doesn't make it right. We had to fight that perception all the time. But, you know, if I knew we had an unhappy director in finance, the first thing I did is look and said, well, what do they probably think they're worth? You know, just because it shapes that view of like, well, yeah, but you're forgetting about our stock program. You're forgetting about this, right? Like, so I think it's helpful. It doesn't make it accurate, but perception is reality sometimes. Yeah.

[00:32:30] Levels is really pushing, they are pushing the envelope on the quality of that data because it is, what they are building is pretty kick-ass. I'm not going to lie. Yeah. The topographic, shout out to Zaheer, that like is like, if you haven't seen his topographic map of like. It's pretty cool. That's Comp Nerd Festival right there. Yeah. You find yourself in there. Super cool. Looking around. And so I think he's proving out in a spat that is very niche with the data and whatever,

[00:32:57] but that's like from a proving ground and having a beta product out. Theirs is very cool to not beta, but like to being broad market acceptable. Yep. He's showing like, that's a good, you know, to your point, Stuart, there's a lot of potential there while we get there, you know, so keeping an open mind. The last one I'll add then on the speed round is what I would generally call aggregated data. So the salaries, pay scales, et cetera, where they're aggregating multiples of surveys,

[00:33:26] your pros and cons there and how you use them. Well, the pros for this one, I'll start with pros. Last two, I've started with negatives, but we'll try and be positive for the last one is the pros are that they have really, really went in. And I think, and maybe this is the, like Paul said, perceptions, reality is really that they've mastered the algorithm, so to speak. And that's that people trust them.

[00:33:55] They've got the, they've got the, you know, the name and not only do people trust them, companies are willing to provide their data for them. And in that sense, that means that they trust them as well. So they're providing data that consultants are turning around and giving to them. So I'd say that that's a positive because they've really set the market and they've helped to expand an amazing profession as well as practice.

[00:34:22] The negative to them is the exact same thing as their positive. Sometimes, you know, you could get one of these organizations, pick one at random. They have a bad year, whatever it may be. And, and I'm talking the salary survey companies, you know, and it's just, they're a little bit off. They've got the name and people trust them. And even though the data may not be a hundred percent accurate, so their strength could be their weakness, their weakness could be their strength.

[00:34:54] Yeah. For me, I think it's, there's a lot of data there. I'll usually push back and say, you know, people who are like, well, you know, the five and 10 companies thing, like they're aggregating a lot of data. So there's a lot of stuff there. And so I think you can buoy, buoy off the numbers better. So I think it is kind of the future ish thing, but a ways away. The negative is though, anytime someone tells you, you think about it personally, well, what's in there? Think about food. What's in it? I'm not going to tell you.

[00:35:22] Nothing good in your brain follows that. And so you can't do peer cuts, but also they're like, where did you get it? And the other thing that's kind of there with some of them, but kind of not is how much algorithm was there from the end size to like not, you know? So by building this off of 10 incumbents or a thousand incumbents somewhere. And that's hard because of the secondary and tertiary algorithms that you're calling upon. This role is like this role. And so I'm benchmarking.

[00:35:50] And so that piece of lack of control and, and honestly, visibility to explain to Paul, like, Paul, we're going to do this. Well, how many companies did you look at? And I'm not going to tell you, you know, you know, well, what industries also not going to tell you, Paul, you know, well, it's kind of this industry. And so Paul's first gut reaction is like, oh, so you made up a number to make it work for me thing. And so I think there's some defensibility that they're still working on to get to.

[00:36:19] I think, I think that's changing super fast right now, though. You know, a couple of providers have come out with new predictive set. We call it predictive data, but predictive data sets. And it's leaning into the fact that people aren't as afraid of the word algorithm anymore, right? Because that means something and like, oh, I can see the value in machine seeing patterns that humans don't get as fast, right? And as a result, all right, like I could ask a human, how much do I pay this thing in Elmhurst at this time?

[00:36:49] And it might take them a while to get there, whereas the algorithm can get there quicker, right? So I think there's a little bit more acceptance for predictions as being valid sources of data for one. And I think the second is, and I actually beat this drum really, really loud. Everybody realizing that even the biggest comp data sets really aren't that big. Right. And you're splitting hairs between, okay, well, this one's built off of a million incumbents. This one's built off of two million incumbents.

[00:37:17] Forgetting that there's billions of people in the world working, right? So it's like every data set is small, like in the grand scheme of things. You know, so it's like we're splitting hairs around, is it built on a good data set or not? Like they're all samples. Let's just realize that. And you have to understand the sample. But I think there's been more acceptance of, okay, well, maybe math is okay because ultimately humans are making predictions anyway. And why not let the machines do that?

[00:37:44] So I do think that there's a change afoot right now because of like a technological wave where, you know, it doesn't need to be AI to build an algorithm. But like people are getting that, okay, maybe it's okay to trust the machine here. Well, to your point, the acceptability of typing something into Claude Chat or whatever and get an answer out and assume it's true. You know, we've gone past a little bit the, you know, if it's on the interweb, it must be true, you know, means.

[00:38:11] And so, you know, the theory of, and I think, Paul, a lot of it to your point of their believing it more is the term large language model, I'll nerd myself out, you know, back when was creating algorithms in the late 2000s, people were like, yeah, that's the dumbest thing ever. You can't do that. You have to use real data. Now, almost to your point, Paul, they're on the opposite page. Well, what type of large language models are you using to check your work and doing it? And if you're not, then it's kind of like, well, why aren't you?

[00:38:39] And so I think it is changing super rapid. But again, that's why I think, you know, kind of going, wrapping things all up in a weird way is like, that's why I think the human aspect to your parts, Stuart, what you said earlier. I like to think never, but I think at least in the short term, the bots are at bay and Skynet. Skynet won't completely take over until, you know, a little ways out because there is this art. And the last thing I'll add when people are doing this is everything you guys mentioned

[00:39:10] around every company being individually unique. Like, large language models by default return you an answer that kind of drives towards the median. And every company wants to be better and has unique, you know, they all have unique humans in a unique place with the unique managers and unique owners. Like, you start thinking about the variables involved. It's really hard to use it as your final answer, I think, still, the large language model. Sure. Sure. Yeah. Yeah.

[00:39:38] And just real quick, and you guys have probably seen this as, you know, going back to the large language models and the AIs and the scariest thing in consulting is when a HR leader comes, hands you a spreadsheet and says, this is what Claude says we should pay, you know? And you're like, well, how'd you get this? Well, we uploaded the spreadsheet and this is what they said. Can you confirm or deny that this is what it should be? And you're like, oh, this is going to be fun. So, yeah, I get what you're saying there.

[00:40:08] But I think we're around for a while. I think we're around for a while. So, the thing we end with, I appreciate all that because it's a unique perspective as we get out, you know, get out there and see all the different data types and kind of pull them in and then try to tie the consulting into it in an app. But with that all in mind, if you could automagically solve one thing in all of HR with your 31 years of experience, what would you solve?

[00:40:39] You know, I would solve in HR that HR understands what compensation does. CHROs don't always understand what we're doing. So, it would be the problem to solve is that understanding what it is compensation professionals are looking at and they're understanding why we're looking at it. And, you know, when we give you this, this is what it is. And, you know, it's not that we just made up the numbers. And it's really just understanding that.

[00:41:09] It's not just we didn't pull these numbers out of the air. We're actually here's there's there's a reason behind the madness. That's awesome. Yeah, I do think I mean, it is interesting to me, the lack of understanding throughout the C-suite in general, let alone whatever, it's literally half to more of your expense line. And they will know everybody's name, children, family member for people who run the supply chain to save like a tenth of a point.

[00:41:36] And then you've got this giant expense nut out there that they don't always understand how you get. And so that's that's always mind blowing to me when it happens. And this has been great. I appreciate you taking time out while you're getting this going. Well, thank you. We will tag you, of course, so people can go find out what you're doing online and check stuff out. I really appreciate you coming on and sharing some time and reminding Paul that camping is a great way to spend some time.

[00:42:06] In the theoretical world where you're unencumbered and you don't have a spouse who tells you what to do anyway. So the interesting part was, oh, I forgot to tell you, Stuart, at one point I am. I think we used we had a guest on Shana and told us she went boondocking for a couple of weeks, to which I get a text from Paul, by the way. We talked about it. What is dry camping or boondocking? To which I texted him back, knowing good and well what was coming. Why would people do that?

[00:42:35] Like it was literally. Yeah, it's miserable. It's absolutely. No, no, no. Oh, no, no, no. Okay. Maybe that's the difference between posh and camping. Yeah. Boondocking is. Yeah. No, no. I can't live off a generator and making sure everything stays in a tank. No, no, no. No, no. Come on. Oh, wow. You're one of those. Okay.

[00:43:01] And that'll be the sound bite that Sam grabs and sends me. You're one of those. You're one of those. He's going to change his email signature to one of those. Looking forward to seeing what you, how things go for you. Hey, thank you guys. I appreciate the time as well. Yeah. Thanks, Stuart. Okay. Okay. Take a break.