Cary Sparrow - Is Your Pay Data Accurate, Or Already Too Late?

What if your compensation data is accurate, but already too late?

In this episode of the People/AI Strategy Forum, Sam Reeve speaks with Cary Sparrow, Founder & CEO of WageScape, about why traditional salary surveys are struggling to keep pace with today’s labor market and how real-time labor intelligence is reshaping compensation strategy.

Most organizations still make pay decisions using historical survey data that may already be months old. But in a volatile labor market, conditions can shift multiple times before that information ever reaches leadership teams.

Cary explains why compensation leaders are increasingly turning toward forward-looking labor market signals, localized pay intelligence, and transparent hiring data to make faster and more informed decisions.

If your organization is navigating hiring pressure, retention concerns, pay compression, or rapidly changing workforce dynamics, this conversation offers a valuable perspective on where compensation strategy is heading next.

In this episode, we discuss:

• Why traditional compensation surveys are often too slow for today’s market
 • How real-time labor market data changes pay decision-making
 • Why localized compensation intelligence matters more than national averages
 • The growing importance of transparency in workforce data
 • How WageScape tracks hiring and advertised pay across labor markets
 • Why hourly and highly specialized roles are especially sensitive to market movement
 • The limitations of relying solely on AI tools for compensation benchmarking
 • How AI and labor market intelligence may reshape compensation systems in the future

Key takeaway:

Compensation strategy is no longer just about benchmarking against the past.

It is about understanding where the labor market is moving next.

Organizations that rely solely on delayed survey data risk making decisions that are already behind the market.

Guest:
 Cary Sparrow
 Founder & CEO of WageScape
 https://wagescape.com/

Learn more about CompTeam:
 https://compteam.net/

Watch full People/AI Strategy Forum episodes on YouTube:
 https://www.youtube.com/@PeopleStrategyForumPoweredByCompTeam

#PeopleStrategy #CompensationStrategy #FutureOfWork #AIinHR #LaborMarket #TotalRewards

If you enjoyed this episode, follow the People/AI Strategy Forum on your preferred podcast platform and join the conversation! 

About the People/AI Strategy Forum
The People/AI Strategy Forum explores how leaders navigate the intersection of people strategy, leadership, and artificial intelligence. Hosted by Sam Reeve, Founder & CEO of CompTeam, the Forum features conversations with executives, practitioners, and experts shaping the future of work.

Learn more about CompTeam and the People/AI Strategy Forum at compteam.net.

Transcript

0:01

What if your market data isn't wrong because it's old, but because it's telling you what's already happened in the labor market that's already moved three times since. In a, market that's volatile, accurate can be still dangerously late. Welcome to the People Strategy Forum. I'm Sam Reeve, your host and CEO of CompTeam, where we help organizations design people- centered systems that attract and keep top performers in their place where you need them. Today, we're tackling one of the toughest questions on every leader's desk right now. How do we set fair, incredible pay when the market is moving faster than our surveys and pay budgets can handle? Our guest is Carrie Sparrow, founder and CEO of WageScape, a labor market intelligence company that's built around forward-looking pay data and a team that can really, truly help you pull this all together. So let's welcome Carrie.

Cary 1:02

first Carrie, I mean, just for our listeners out there, maybe we should, tell them a little bit about yourself. I mean, how'd you get into this business? I mean, I know you were in the, the military. Let's start there. Yeah, sure. So right now, uh, I run a company called Wagescape. Uh, I founded it about 10 years ago, but before that, uh, you know, my training was in computer engineering the Navy was good enough to pay for my college and, uh, so they gave me a job as soon as I graduated and I went and became a submarine officer running nuclear powered submarines, uh, until I was about 30. And so I served on a few and, uh, got to do some really interesting things which had nothing to do with computers. It was, uh, you know, running nuclear power plants and operating submarines in different, interesting parts of the world, And, and knowing how to do that, When I got out of that, uh, some of the stuff I had done caught the eye, of a management consulting firm, and I went to work for them. I started off as a junior associate and found out I had kind of a knack for that work and, uh, you know, grew in responsibilities and, experiences over time and, uh, ended up, uh, ultimately being a, uh, managing partner for one of the global businesses- then I got a call one day, uh, from a recruiter looking for an executive at a big company in Minneapolis, which is where I live. they, they were looking for somebody that basically did what I did on the consulting side and asked me if I knew anyone that might be interested. And I said, "I think I do.

Sam 2:31

And

Cary 2:32

a few months after that, I went to work for the company- uh, was there for about eight years. And then, um, along the way, I just had a natural kind of inclination to see new business opportunities, you know, where is there, uh, where are their needs that could be addressed that nobody seems to be, paying attention to. And saw the same thing happening with, uh, of all things, uh, information about the labor market, which all kinds of organizations depend on and it frankly is really bad. And I thought that there was a better way to, to provide transparency in the labor market, uh, in ways that allowed companies to be more efficient, but also allowed a lot more innovation, a lot more kind of new solutions- new ways to manage careers, new technologies, some not even imaginable. And so that's what the impetus for Wagecape, was Yeah. So, so you, you just, you found that there was a, a niche in there that labor market information was needed. did, did you have, uh, a compensation experience before that or was it just kind of- a- Oh

Sam 3:38

yeah. Yeah. Yeah. So,

Cary 3:40

you know, when I was in consulting, one of the things that my firm specialized in was helping companies figure out their talent strategy and then setting up the capabilities around it. one of the things I was particularly good at was running really big projects with lots of teams. And so a lot of times those teams include teams looking at compensation, so I got very directly involved in, in that. And then when I went to work on the corporate side, uh, a lot of the foundational capabilities had to do with how we set up things like compensation and so forth. And I was, I was the executive sponsor for those. So I was very, very familiar- Yeah. ...with the work of compensation professionals and, uh, the way the compensation frameworks actually needed to exist and operate effectively in companies. comp teams jump through so many hoops- in order to just answer what is a basic question, which is how much should we pay for a certain job? I mean, if you think about it, it's a very simple question- and it is so hard to answer with any kind of reliability. And I just thought that that was way too much work and it was dependent on, way too many, uh, things that were too complicated, uh, and that there, if there was a simpler way to do it, a more readily accessible way, something that, uh, was more uh, something that was easier to understand, that there would be lots of benefits- Not just for comp teams, but for, leaders in all types of businesses and all types of communities. I mean, if you think about it, the labor market touches every single one of us. Touches every individual, every family, every community, every organization, and the information is really just, it's out of date, it's hard to understand, it's not localized, it's just wrong in a lot of cases. Uh, so making even a tiny dent on that, I felt would be a pretty worthwhile pursuit. and so there's, then so then you, you developed WageScape and there's, but then, uh, was this an answer to, to some of the market data that was available at the time? Yeah, where we started was we needed to, you know, we, we wanted to find a way to get a lot of information about what's going on uh, with hiring. Okay. Yeah. So recognize that the labor market's actually much bigger than the hiring. and- Yeah. You know, who's available and what are they looking for and all those different things. But we started with, hiring and, uh, in order to, uh, really build out a solid data platform where we could merge data from multiple organizations in a consistent way- mm-hmm.. You know, people call that normalizing- Yeah. you know now and normalizing architectures like that, process a large amount of data. We looked at job ads and originally I was thinking that we would get data from like applicant tracking systems- Yeah. and basis payroll systems and things like that. And turned out that was really infeasible, not technically infeasible, but it was infeasible because of the way that those contracts were written. Those information wasn't allowed shared at the time. And so we used job ad information and the feedback we got was, actually the job ad information fills a really important intelligence role- for folks that they had not had access to before because it was, there was no expectation of a lack of transparency. It's not like pay data where you're expected to lag the data, you're expected to not share where it comes from. Um, the whole kind of, uh, safe harbor, uh, approach in the US is a little bit, the, the the boundaries around that have become a little less clear the last, couple years- but nonetheless, there's a very clear expectation from regulators that you're not gonna share that kind of information. Dealing with job ads, there's no expectation for that. Job ads start in a public domain, it's something companies are voluntarily sharing with the entire world. Uh, and so we just collect it on a massive scale, We collect data from 800,000, uh, career sites worldwide, over 200

Sam 7:31

countries-

Cary 7:31

combine it all together. We're tracking data from, in the US, for example, data on about 95% of all job openings. And, and we capture it every single day. And so, we've got a massive scaled operation that allows you to see kind of everything that's going on from a hiring and pay standpoint. Now, it's not the same as like salary surveys which are measuring incumbent pay. It's measuring what companies expect to pay for the positions that they're hiring for right now. But when you put that side by side with incumbent pay, you're able to see, not only, where, what is the market currently paying, but where are their pressure points in terms of whether it's, being pushed upward or where is it, staying flat? Or Maybe even receiving a little bit. So you can make much more informed

Sam 8:16

pay decisions based on that.

Cary 8:18

And you can get really localized on it too because jobs you can get down to ...sometimes you can get down to the individual address level. We track things at a zip code level- uh, which allows you to get very, very local. whereas surveys, typically, if you want to be at a metro level, you're talking about very custom surveys- in most cases, uh, state levels more common, but pay, we show time and again that pay really varies at the city level. And so that's the, that's the location level that most companies that are competing strongly really care about. Yeah. And where do you find, what, what, what types of jobs are, are, are your customers that come to you, what types of jobs are they really interested in tracking pay that are really sensitive to those micro movements So the most pay sensitive jobs are hourly, you know, blue collar blue collar jobs. Yeah. And a movement in, you know, 50 cents or even a quarter, uh, an hour, uh, will really have a difference. Yeah. Also, skills can be more fungible for those types of jobs. So if you've got somebody that's that's working, you know, maintenance in a hotel, being able to go work, uh, for a job that uses similar skills, but isn't necessarily entirely different industry is completely feasible. So your competition for talent is very different. It doesn't follow traditional lines. It's much more based on location- Yeah. and, and skills than it is on kind of the, the job title and the industry Yeah. So let, let's talk about job titles. So so a lot of the, the posting information you know, they, they, may use a, a variety of different titles. How do you make sense of all that information to understand, you know, to normalize it? Right, exactly. And we've, we've, invested a lot in terms of, uh, normalizing job titles and job have our own job architecture that's got about 25,000 jobs in it that we look at the job content, we look at the job title, we look at the industry, and we compare that with, uh, it's a corollary to benchmark job- uh, requirements. Uh, in order to discern what's the closest match

Sam 10:21

there.

Cary 10:21

Uh, but beyond that, every company has their own architecture and, uh, all the survey providers have their own architectures. And So we don't compete with survey providers, we're really a compliment to them. And so we've invested a lot to be able to map our architecture to other architectures, uh, whether it's for individual clients or if it's for surveys that they use, uh, so that, um, it's much easier to, apply what we're providing into the processes and the tools that companies are

Sam 10:54

using. That's great. So you can match to other architectures and, mm-hmm. ......and bring that directly into your product. And so what is the, what, what does the output look like? How many, How are, uh, those companies using your information? Is it, uh, system or so we're a data company, So we provide data. we don't provide systems

Cary 11:11

the way that it looks depends on what the clients need. So We have some clients that have very powerful talent analytics and, and rewards analytics teams that are used to working with high volumes of data. They have processes to groom that data and integrate it together with other data sources. And in those cases, those guys just want a direct feed of everything that we're getting, and that's millions of new jobs every month, uh, that they ingest. Most other companies want the data already prepackaged a bit. So they want us to do the work to say, for all the jobs that they care about, what's the current advertised pay distribution, um, for a defined period of time, say three months or 12 months, and then what's the period over period change? Uh, that's a very typical form. and that doesn't have to be really sexy. I mean, a lot of times it's just literally a data, a data spreadsheet, uh, that's provided on a monthly basis. Uh, we can put a, we can put a browser, based, um, dashboard on top of that to make it easier to kind of, explore and, and to show other stakeholders. But for the most part, folks just want the data. Um, so really it ranges. We do A lot of companies have, um, very specific jobs that are uniquely defined for their business environment where they want exact matches. So they don't want to use our architecture. They don't want to use architectures of like the Radfords and the lowest Watson and the Mercers of the world. Um, they want something that exactly matches their job, in which case, we will do the exact match And then we'll provide them the data in whatever delivery format they need, but they want, they, they definitely want the kind of custom, uh, custom matching and the custom that Yeah. Yeah. I think there's, there's there's, a, a, fine line. Well, the more exact you get, it seems like the, it's kind of like the microscope effect. I mean, as you look into things, you lose the periphery, right? So sometimes that can lead to, to, uh, the wrong information, right? deliver what they, what they're looking for and of course it's their responsibility to use that data. Well, and sometimes it's both, right? I mean, sometimes people say we can make do with the jobs that aren't the ones that we have the most unique requirements for. But where we get the most value is with those really unique jobs. And You're exactly right. The more you hone in, The less you see kind of what's going on outside of that, but you can do both actually. You can show, both the really precisely matched and the less precisely So, so just like surveys do and they'll be able to provide a variety of different cuts, you can do that as well with your

Sam 13:46

information.

Cary 13:47

I mean, a lot of times too, people want to be able to see what's underneath it. Um, they don't want it to be a black box. After they gain comfort with the data, they just want the data to show up when they need it to show up. But initially, especially, they'll want to see who's doing the hiring, what's the descriptions for the ads that are being included, so they gain confidence that the matches are right. And that's something that right now for survey providers, you're trusting the survey provider because they can't show you that level of we show you that level of transparency the traditional survey providers are typically 90 days in arrears as far as their information, sometimes a, year. You're providing a more real time product and so forth. So those who need more data from us typically get it on a weekly basis. Those who want kind of prepackaged data, their own processes are fine if we provide it on a monthly basis. So that's about what we do. Um, nobody in like compensation or talent acquisition is asking for things on a daily basis. We get asked that from, uh, folks in like financial services that use our data as an indicator of business outlook. Uh, and they want things much more immediate. But a lot of times the company's own decision processes around pay, uh, are, are, are not sensitive to whether it's, a day or a week or even a month in a lot of cases. I would say that more companies that are using our data are becoming much more proactive about how they use it to set expectations for what's in the market. So if you're a comp professional, you're constantly reacting to what hiring managers are saying is going on in the market, because they're fighting to recruit people- and they are fighting to keep people and when they lose someone, uh, especially when the individual says it's a pay issue, they make sure to talk about what the pay is and how that's, they're not equipped to be able to match Uh, so you're constantly under fire, right? And so being able to take a transparent view of what's in the market and then shape truth is, this is what the market conditions are and turn that into an ongoing process, ongoing, like feeding information to your constituents on a, on a monthly basis, uh, about market trends in every location that you're hiring in, for example. Is something we're seeing more folks do. That's great. So you can really take a custom approach. Right. I wanna revisit back to what you, you said the, the customers you serve. I mean, you, you mentioned that there's financial as well as Can you give us a, a variety of, I mean, who are the different types of businesses or types of industries or segments that you serve? So from an enterprise standpoint, we work across all industries. I mean, we have, our data is so expansive that it supports all, The only one that we don't have regular, uh, interaction with, uh, where they're buying our data is, I would say government People who serve government, um, buy our data, but the government itself right now is, is not. And, and they have different ways of managing comp- Well, think so too. And, and I'm talking about at a national level. When you get down to the state and local level, then we absolutely have interest there- as well, because they're much more, front lines with kind of the market realities. so, um, but otherwise, it's all industries and then it's also, we deal with nonprofits, we deal with academic institutions, we got a whole list of academic, uh, institutions that use our, data, economic institutions, consulting firms- uh, in addition to enterprises. Okay. So let's talk about scope. So are you serving mostly in the United States or, or do you have global information as well? Yeah. So we, uh, our data platform covers 240 countries in major territories, right? So, for example, Antarctica's not a country, but- But believe it or not, we track jobs that are being hired, for Antarctica. And so um, so it's global. It's truly, uh, global in nature. Uh, most of our clients since we started in the US, uh, still are in the US, but we've expanded to, global companies that, that get data from country, for countries around, the world. And then we're seeing, I would say it's changed over the last couple of years. I mean, it used to be that, uh, there was a minority interest coming out of Western Europe, much more interest there. Uh, the Middle East, uh, very interested in, in our data, um, Central America, also very interested. Asia to different degrees. There's a lot of idiosyncrasies in terms of pay in, in Asia though in many of the countries. so, but yeah, it's a global platform and it's able to serve, global and international

Sam 18:21

companies.

Cary 18:22

Folks are looking at it for pay sensitive populations. if if you've got, if you're in an industry with, with types of jobs where there's just not a lot of volatility- There's not a lot of volatility in hiring, there's not a lot of volatility in pay, then frankly, the real time information is not used from a pay Um, but if you have any degree of localization or volatility, that's where the, the more real time data comes into play. And real time is just one of the features. The localization is actually just as important. The transparency is just as important. Uh, being able to get in and see what's actually happening in a specific market, uh, who's hiring, uh, who the employers are, what they're hiring for, and, and so forth, uh, are really important features of, of what we provide. So pay sensitive, uh, and highly volatile, a lot of times that's hourly, it's definitely hourly employees. I would say two thirds of our clients use our data, uh, specifically for their hourly populations. Uh, because you can really get in and, and look at very, not just, not just, within a city, but down to, a few blocks- uh, of, uh, of where you need to. And then also specialized skills. So where skills are evolving quickly or you need to be able to attract very specialized skills, that's, those folks use our data as well. Mm-hmm. AI is all the, the rage right now as far and will probably continue to be. Now the, the thought is, uh, what do you respond to those, those customers who are saying, "Well, I'll just query ChatGPT." So what I say when they say I got X from ChatGPT is say, okay, go ask the same question to Gemini. Or go ask the same question to, uh, Claude and, and see how confident you are in the answers when you look at all three of those. Uh, because they're all going to be completely And when you look at where the data comes from, a lot of times you'll find it's from online online job boards, but it'll only be one job board, and when you look at it, the timeframe will be old. Uh, or it'll cover a type of job that's not really what you're asking about, but the AIs go, they present with high authority. And so if you take what's at face value there, that might be good enough for a job seeker to go in, but seeker's going to get shut down very quickly with somebody who actually has, you know, has much better data And the data that's coming out of those AI platforms right now is not nearly good enough for a compensation team. It's barely good enough for a recruiting team, I would say. WageScape has a couple of product lines. So you have a new one that just was, was being launched. Can you tell us about that? Sure. We call it Wage Track and it's designed to supplement salary surveys in a very easy way. What it is, is a survey of advertised pay rates- uh, about 25,000 jobs. Uh, it's, uh, core configuration is for the US, but it can be any And what it does is provide a monthly update for every one of those jobs for what the pay distributions are for what's being advertised in every metro market, in this case in the US. Uh, it does it on three month and 12 month intervals and it looks at the period over period change. It's super easy to use because, uh, because it is a survey, it's downloadable in a survey format, so you can integrate it directly into your compensation management tools and all your benchmarking processes, just like you would another survey source. Everyone who uses it knows that it is not survey information. It's advertised information- but it's another dataset to help triangulate on, on what's going on, uh, in the market. So it's also priced, uh, a little bit less than what you pay for a seat. uh, a salary survey seat. So we were very deliberate to make it an easy to use supplement, uh, to surveys because that's what, we tested this in the market a lot and that's what people said they, they wanted. They said that the data is really compelling- but they need it to be easy to use. They need, they can't create new processes, they don't want new applications, they want something they can use within their existing processes, their existing tools, and that's why we, and for existing price points, Three quarters of the people that buy, Wage Track are substituting out one of their other surveys. Uh, and typically a seat on for that. Uh, so they've got constrained budgets and they, need something that's very competitive from Yeah. Well, I'm glad you're taking a simplistic approach because I think that's one of the, the, the things that are driving people crazy, especially right now with, uh, AI developing this and tons of different software and it just seems to be an, uh, information overload for people to try to learn new Right. thanks for that. simple is not simplistic, right? Yeah. So we do, we've got a version of WageTrack that's for the restaurant and hospitality industry- because they have some unique requirements. We're, um, in development for the manufacturing industry and we'll have others, uh, along the way. We also can use that format for the custom job mapping that I talked about where if you want something that, you're getting monthly updates on, on advertised pay and being able to compare that with like a national market or a local market, um, we do that too. So it's pretty easy for us to, to do those things. Nice. So the next thing is, is if we if we think about where the, uh, technology is taking us, uh, how information like this can be used in the do you, do you have any insights as far as how, uh, uh, the, the future compensation tracking, compensation overall or market data might be used in the next few years? Well, I think that there's a lot of processes in place right now- that work off of, they, they're adjusted based off of annual budget cycles. And So I don't see companies internal processes dramatically changing over the years. I see a lot of pressure from the tools that would be used to support those. Um, I see AI playing a really big role in that and I see, uh, AI agents coming to the market, uh, that go well beyond any of the, any of today's providers. Uh, it's getting a lot easier to create highly intelligent tools for much lower price points. It's already affecting the tech, the tech industry, and you play it out a few, a few iterations, and you're gonna have a whole suite of, of uh, of application providers that just don't even So, uh, when you think about, uh, advice here, you get to an executive, uh, making a decision on whether to to go with WageScape or to use this type of

Sam 24:47

information,

Cary 24:48

Well, I would say that, uh, you're, you're wasting a huge amount of time trying to figure out what's actually going on in the having a source of truth that shows you exactly what's happening in a way that's fully transparent and credible, uh, and can be used for, um, not just understanding what has happened, but being able to look ahead and being able to hone in on the things that you really have to pay attention to will put your business in a much better, uh, better situation. And that's what we try and do with Thanks, for the conversation today. Now, one last question for you. is there something that WageScape is looking forward to the, the, the rest of the year here in We've got a number of things coming out that relate to AI, uh, a number of, um, uh, feature data sets that are directly supporting, uh, helping AI models learn, uh, using using our data- which is, uh, which will help address the point you raised earlier- of what AI models can, answer. Uh, and we've got, We just finished one of our best quarters ever and Q2 is, is shaping up to be even better from a revenue standpoint. So I would say that, uh, we're gonna be taking full advantage of, of that kind of traction There's nothing specific that I, uh, I can announce at the moment, but standby there will be. All right. Thanks, for your time today. thank you for having me. for all those listening in to the People Strategy Forum, we'll bring you, uh, more information like this and we'll keep, we'll track, uh, Carrie and his activities at WageScape going forward and so thank you and we'll see you next All right. Take care.