Jeremy Feinstein and Kevin Plunkett – WorldatWork 2022 Series: The Past, Present, and Future of Compensation Surveys

Connect with Jeremy:

Connect with Kevin:

Connect with Dwight:

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• dwight.brown@salary.com

Connect with David:

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• david.turetsky@salary.com

 

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• WorldatWork Conferences
• Compensation Consulting Services
• Compensation Data
• HR Data Labs Season 4 Episode 7
• HR Data Labs Season 4 Episode 5

• “The AI-modeled data and the crowdsource data are very useful for an individual just to understand what their value is when they're negotiating a compensation package with an employer is very valuable . . . I think there's always gonna be a role for salary surveys and the more they can automate it, the better.”
• “We're gonna have to go beyond just compensation and look at the total rewards package. Just drawing intelligence from all the different compensation data and deriving more analytics from it and providing data for decisions, more than just the raw data itself.”

Announcer: 0:02

Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record for their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, that count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

 

David Turetsky: 0:46

Hello, and welcome to the HR Data Labs podcast. I'm your host, David Turetsky. We're still here at the WorldatWork conference. We're introducing you to interesting, fascinating people inside the world of compensation and compensation analytics. Right now. We're going to be talking to Jeremy Feinstein from Empsight. Hey, Jeremy.

 

Jeremy Feinstein: 1:06

How you doing, David?

 

David Turetsky: 1:07

I'm great. How are you?

 

Jeremy Feinstein: 1:08

Great.

 

David Turetsky: 1:09

And we have with us our co host with the most host Kevin Puckett

 

Kevin Plunkett: 1:14

Flying in buddy, your co-pilot for the day.

 

David Turetsky: 1:19

Man of many voices. And so Jeremy, why don't you give us a little bit background of who Empsight is

 

Jeremy Feinstein: 1:25

Thank you. So Empsight provides specialized compensation data to Fortune 1000 and large multinational companies. So we provide a lot of compensation data on functions such as legal compliance, risk management, IT, supply chain and other functions. And we have a survey suite called the works that more than 200 companies participate in each year.

 

David Turetsky: 1:49

That sounds awesome. It's the works.

 

Jeremy Feinstein: 1:51

It's the work.

 

David Turetsky: 1:52

That's awesome.

 

Kevin Plunkett: 1:53

The whole Magallon

 

David Turetsky: 1:54

Does it work?

 

Jeremy Feinstein: 1:55

It works.

 

David Turetsky: 1:56

Excellent. Sorry, it was a softball. I was starting up there for you. So Jeremy, we typically ask what's one thing that no one knows about you?

 

Jeremy Feinstein: 2:04

I'm from Minneapolis.

 

David Turetsky: 2:06

I'm sorry.

 

Jeremy Feinstein: 2:08

I'm from Minneapolis, Minnesota.

 

David Turetsky: 2:09

No, I know. I said I'm sorry. I have friends that are from Minneapolis. And I tease them a lot with the Is it snowing today?

 

Jeremy Feinstein: 2:20

I don't know what it is now. I live in New York now. I don't keep track of it. Well, it's probably snowing today. Yeah, it's actually the summers are very nice there.

 

David Turetsky: 2:28

Okay. So what's our topic for today?

 

Kevin Plunkett: 2:39

Our topic today is What is the future of surveys? And let's look at, you know, kind of your, your perspective on kind of where they have been, where they're kind of today, and where you think they're going to be in the future? You don't have to answer them all right now.

 

David Turetsky: 2:59

But let's start with the past. Let's give you a perspective on where they've been.

 

Kevin Plunkett: 3:03

And actually, quite frankly, Jeremy, give a little history on Empsight and how and how you guys, you know, came into existence? And what was the what was the impetus for starting the survey to begin with?

 

Jeremy Feinstein: 3:15

Well, we actually heard through a client meeting, that there was a need for an in house legal survey of fortune 500 companies, because they had lost the data source. So we actually jumped on the opportunity and sent out 500 FedExes. At the time, there was no email wasn't as prevalent. And we sent 500 FedExes. So the 500 fortune 500 companies and about 200 of them joined came on board as our start.

 

Kevin Plunkett: 3:47

What year what year was that?

 

Jeremy Feinstein: 3:48

It was in 2003.

 

Kevin Plunkett: 3:50

Okay, wow.

 

Jeremy Feinstein: 3:53

So and we ever since then, we've expanded into other functions related to legal, and we expanded out into all the corporate functions and even into supply chain.

 

Kevin Plunkett: 4:03

And in those earlier days, what were kind of some of the challenges with with you all, either with data collection, or data quality, or what have you, what were what were some of the challenges you guys are having as you're building that survey?

 

Jeremy Feinstein: 4:15

Well, I mean, there are challenges, but they're actually benefits, like at that time, like they're less compensation was outsourced. So like the quality of the data, like, was quite good from some companies, honestly, because they worked directly with the business line, like with the legal department to find out everybody's legal specialty and, and the data, you know, the data was, was good. I mean, we had to go through a more manual cleaning process because we didn't have a data scientist working with us with an algorithm to identify the outliers, which we now have. Also, the safe harbor compliance was what Safe Harbor is Department of Justice guidelines that are used by large companies especially to ensure that is the day that they're not colluding with each other. So it was more difficult to comply with it without an algorithm that we now use.

 

David Turetsky: 5:11

And so you've evolved, you've got beyond the 200. And tell us what that period was like between 2003. And basically 2022?

 

Jeremy Feinstein: 5:23

Well, that means that there was a lot of economic cycles, there's been ever since 2000, if there was much higher inflation at that time, and the mayor budgets were higher, but then in 2008, we had the crash. And then ever since then, there the mirror budgets have been about 3% flat until this year, and now there's a big spike in demand for compensation data, again, because you can't just age of the data 3% anymore.

 

David Turetsky: 5:56

Right? Right. But there's also been evolutions in, for example, the internet. And there have been lots of call or need for new and different types of the jobs that you collect, right? And so you've had to evolve.

 

Jeremy Feinstein: 6:10

That's a great question. So when we launched our IT survey, the jobs were so different than they are today, there wasn't there weren't cybersecurity, there wasn't AI, there wasn't data science. So those jobs have evolved. There's a lot of different specialties of software engineering, like DevOps. And yeah, now, though, especially for full stack, back end developer, though, that used to be called a web developer and webmaster. I mean, it's really, especially in the IT realm, the jobs have changed. I mean, in some other functions, they haven't changed like in, you know, in legal, but actually even in legal that's changed to there's legal aberrations.

 

Kevin Plunkett: 6:52

Well, there's a lot there's a lot more in compliance. Now, there's a lot more compliance is really,

 

Jeremy Feinstein: 6:57

oh, yeah. And because of the Sarbanes Oxley, actually, right around Firefox, two there, the Enron scandal and the Sarbanes Oxley and then compliance became a big thing.

 

Kevin Plunkett: 7:08

Yep. Yep.

 

David Turetsky: 7:09

And so one of the wonderful things if you think back, compensation surveys have been done for quite a while. But this talks to a lot of the evolution that it's kind of had to live through to get to where we have been around the 2020s. And so there's more demand in the world for data. Data is much more ubiquitous. So how do you see or how have they evolved to be able to deal with some of the demands for more data more quick and accurate data sources?

 

Jeremy Feinstein: 7:39

Right, Lego. So as we've seen, especially when there is an increase in demand for talent, like companies are going to other sources than just traditional compensation survey, they're looking at crowdsource data. They're looking at recruiting, recruiting data that they hear from recruiters, they're looking at job postings, all those sources. From a compliance perspective, the large companies need to have transparency around where the data is coming from. So there's still, I believe, a very strong demand for traditional compensation surveys, supplemented by other data just to see this is a course correct. But I think that traditional compensation data is still very important.

 

David Turetsky: 8:26

And we'd like to say the, that they need to triangulate, they need to not just use one source, but to be able to make sure that you're making an appropriate decision based on as many sources as you possibly can get, without either getting too expensive or going crazy with submission processes and matching and all that other stuff.

 

Jeremy Feinstein: 8:46

Oh, absolutely. Like, actually, you bring up a good point that I guess around two, anyway, early 2000s have started to emerge from the market pricing platforms, which make it a lot easier to analyze, and, and participate in compensation surveys, such as Salary.com.

 

David Turetsky: 9:04

That's right, right. CompAnalysts.

 

Kevin Plunkett: 9:07

Thank you for the plug.

 

David Turetsky: 9:10

This is a thought leadership only podcast by the way.

 

Kevin Plunkett: 9:12

You'll find your 20 bucks are in your pocket.

 

Announcer: 9:17

Like what you hear so far, make sure you never miss a show by clicking subscribe. This podcast is made possible by salary.com. Now back to the show.

 

Kevin Plunkett: 9:28

But the And so today, right. You know, one of the challenges we've heard obviously is continued to be and or has been in the past and continues to be you know, the the friction in around survey submission, right and getting data, pulling data out of the organization, getting it mapped, getting it matched, putting it into the survey that still continues to be an issue even with you know, technology that has helped bridge that gap a little bit it still continues to be a problem. How often are you seeing that or work? How is that manifesting itself with your clients?

 

Jeremy Feinstein: 10:05

I think getting the same data that companies have matched to and used over the years is relatively easy. But to make changes to the matches, and to continuously update the analysis, as it were, the challenge is like the companies, it's, you can automate a lot of things, but you can't automate decision making of what the right match is. I don't think AI is quite there yet.

 

David Turetsky: 10:33

But you can innovate by showing patterns that don't make sense inconsistencies, or things like job families where you know, a 123 for an accountant, leads you to potentially have differences in what the reported levels are for those different positions, right. And so you could provide the intelligence built into the technology to be able to say, Well, isn't there a difference between an accounting one accountant one, an accountant two, right? Why did you match them to the same job?

 

Jeremy Feinstein: 11:03

Oh, right. That's a big problem. Like that's something we look at with our data cleaning process. And there's multiple jobs match to a single job in our survey. So yeah, that's true.

 

David Turetsky: 11:15

And so you're automating, and providing technology to help the end user, to Kevin's point make that process?

 

Jeremy Feinstein: 11:22

Yeah, we have, we have a data science program written in Python that, that analyzes those kinds of things. And it prepares a report for a client that shows the outliers such as those.

 

David Turetsky: 11:34

Trying to make their life easier.

 

Jeremy Feinstein: 11:35

It also looks for it looks for part time, full time and multiple job grades match to the same job code, and many, many things like that.

 

David Turetsky: 11:45

Right.

 

Kevin Plunkett: 11:46

So what kind of challenges are you seeing Oh, you know, just in general, with your, with your customers today in around data or, but

 

Jeremy Feinstein: 11:53

There needs to be an incentive to make matching easy, like, you know, some clients are very generous and match. Because they want to know, for the entire organization, whether or not they use every match, they want to provide as much data as they can. And then there's, you know, there's some companies who want to supply the bare minimum, because that's what they match to. And that's fine. But we we need to encourage every organization to match as much data as possible so that we can provide the most robust results for all clients.

 

David Turetsky: 12:30

Great.

 

Kevin Plunkett: 12:31

And what kind of incentives or what kind of things are you doing to to help and motivate customers to do that?

 

Jeremy Feinstein: 12:38

Well, we provide one on one matching sessions, on zoom with every client and we really take one, we really make an effort to provide a one to one experience to get the matching right, because then over the years, we'll get good data from that

 

David Turetsky: 12:53

So you're actually becoming the calibration.

 

Jeremy Feinstein: 12:58

We're becoming?

 

David Turetsky: 12:58

The calibration, you're helping them calibrate against the other participants and making sure that everything matches up.

 

Jeremy Feinstein: 13:04

That's correct.

 

David Turetsky: 13:15

What would you say, are the biggest innovations you see coming in the survey world?

 

Jeremy Feinstein: 13:21

Well, as I mentioned, there's the other sources of data that we need to use, such as you said, to triangulate, there's also the issue of total rewards. And you know, it's not just the base salary, and even short term incentives that's going to motivate employees to stay with the organization or to attract them to the organization you need to have you need to provide you need to be creative and provide the total rewards. A lot of a lot of employees want job flexibility, right now. They're working from home, they don't have the childcare they used to, maybe they don't need to be paid overtime if they had the option to work different hours.

 

David Turetsky: 14:00

Right. So building flexibility, and but how do you capture that if you're a surveyer?

 

Jeremy Feinstein: 14:04

Yeah. So that's so and also Yeah, so there's, we can capture that in a policies and practices survey, which we offer every year. Also, there's a lot of off cycle increases right now. And SWAT bonuses, hiring bonuses.

 

David Turetsky: 14:19

Sign on bonuses, Yep. Yep.

 

Jeremy Feinstein: 14:21

And like a lot of that data wasn't traditionally captured and compensation surveys will, it will be increasingly.

 

David Turetsky: 14:28

Yeah, one of the things that I've been, no offense, but I've been disappointed in surveys is not being able to value the benefits package that goes beyond comp, you know, everything beyond long term incentive, and be able to try and find a way a common way, not in a practices document, not in a practices survey, but being able to provide a common way of being able to express it. Like we're talking about total total compensation.

 

Jeremy Feinstein: 14:53

I mean, I know there's some benefits consulting firms that do valuations like we're describing

 

David Turetsky: 14:57

Yeah, but those are too complex I'm talking about just like total direct comp or total cash is easy

 

Kevin Plunkett: 15:03

With some benchmarks.

 

David Turetsky: 15:04

Yes, right.

 

Kevin Plunkett: 15:05

There's some benchmarks around car allowances or concierge services or

 

Jeremy Feinstein: 15:11

We actually collect car allowances in our survey.

 

Kevin Plunkett: 15:13

Well, there you go.

 

David Turetsky: 15:13

Yeah, but but even if you can put valuation on medical or dental put something, yeah. So when you're talking about what's total cat, what's total cost, we don't do total cost in, you know, surveys, we do total direct compensation. And then benefits are just usually taken as a load, it's usually done as a percentage. Right? Right. And that's disappointing because the organization when they're hiring, they're not hiring a total direct cat cost. They're, they're, they're hiring it total direct cash plus benefits. And so it's not really apples and apples.

 

Jeremy Feinstein: 15:48

Right no, there needs to be, not only does this need to be a good valuation, but there needs to be a good communication of like, is this being offered, right? And, and also different employees value different benefits differently for their own personal situation. Like, if you're single, you don't really need maternity leave at that point. Or if you're an, you know, a frontline worker, you may not care about 401k, because they're not going to be there more than 8 months.

 

David Turetsky: 16:16

Right. But there's a way and there should be and then what I'm espousing is, there should be a methodology of being able to say, and here's what the benefits benefit is, and put a cost or put some valuation associated with it. So that when I'm comparing Company A to Company B, as a an employer, as well as as in doing analysis, that we can say, Hey, listen, everything else held equal. We're on target. We're at right, and we really can't do that right now. Because we don't know what benefits does to that package.

 

Jeremy Feinstein: 16:51

I mean, yeah, I fully agree with you that that's something that needs, you know, if we could report total total rewards. You know, and that would bring in the value of, of some options that people may not have even considered like, like a lot of government jobs have amazing benefits with pensions. A lot of people work in government for that reason. And they, you know, it needs to be better communicated.

 

David Turetsky: 17:18

Right. But compare technology jobs and government versus technology jobs in the in IT and Silicon Valley. People like, Oh, you gotta go there. Well, what about job security? What about being able to take home a pension and all the great benefits you get from being part of the government?

 

Jeremy Feinstein: 17:35

Right, right. I mean, a lot of people make some are making some quick switches now, during the Great resignation, or some people are calling it the great reorganization or re shuffling.

 

David Turetsky: 17:46

There's a lot of greats.

 

Jeremy Feinstein: 17:47

And then a lot of people are, you know, joining a company and realizing it's not what it was cut out to be and And thus, we could also say the great grass is greener, no, not then they're going back to their old organization. great.

 

Kevin Plunkett: 17:59

The great boomerang effect.

 

David Turetsky: 18:00

That's exactly the Great, the grass was greener. I just couldn't see it.

 

Jeremy Feinstein: 18:06

Right. Yeah.

 

Kevin Plunkett: 18:07

What about some of these new datasets? You'd be talked about that a little bit? Right. There's now some more crowdsource data. There's now scraped data being put together through AI. There are? Do you see do you think that trend continues? And does that trend? Assuming that that's, your answer is yes, that that would threaten the traditional survey sources as we know it today? or enhance it?

 

Jeremy Feinstein: 18:31

Oh, no, I don't, not at all because those

 

David Turetsky: 18:33

because it lacks validity. Okay, so let's start with the crowdsourcing. crowdsourced data

 

Jeremy Feinstein: 18:39

It doesn't have validity. Secondly, you is provided by individual employees who don't represent their organization on an organization basis. And they're know, with a crowd sourced data, like the long term reporting is not allowed to disclose on a company listing, which company they're from. So from a transparency perspective, a lot usually pretty terrible. And also, you know, it's difficult. of fortune 1000s. And large multinationals can't use that data for official benchmarking, There's a lot of part time employees, like employees don't employees understand base, they don't always understand everything else. Right. So that's, it puts it in. I mean, I know you have a lot of AI that can try and accommodate some of those errors, but it's not real. It's not unedited real data. Right. As far as the AI. Yeah. So I think the AI model data and crowd sourced data is very useful for an individual just to understand what their value is when they're negotiating a compensation package with an employer is very valuable. And it's also it gets valuable just sanity check some data to triangulate it as you're saying, right. But I think there's always going to be a role for salary surveys, and the more they can automate it, the better.

 

David Turetsky: 20:03

Absolutely. We're trying to make the lives of our clients easier. And at the same time, give them better data.

 

Jeremy Feinstein: 20:11

Correct.

 

Kevin Plunkett: 20:12

And in some cases, that's also more choice. Right. But But intelligent choices, right? We want them to want the data to be transparent, right? We want to know that you can you can compare an apple to an actual apple. Right. Right.

 

David Turetsky: 20:26

Right. And that there's validity in the data, right?

 

Jeremy Feinstein: 20:29

I mean, there's been a lot, you know, over the past few years, there's been a lot of press about disinformation. And, you know, when you have validated datasets, you know, people are seeking validated data sets that, you know, anything that's self reported, or model that and you don't know where it comes from? It's, it's a lot harder to validate it.

 

Kevin Plunkett: 20:52

All right, Jeremy, I'm pulling out your crystal ball, putting it on the table. What, you know, five years from now, what is this business look like? And what are some of the innovations, you think that that will be become, you know, status quo?

 

Jeremy Feinstein: 21:07

I mean, I think that, as we were talking about before, we're gonna have to go beyond just the compensation and look at the total rewards package, and just drawing intelligence from all the different compensation data and making and deriving more analytics from it and and providing data for decisions more than just the raw data itself.

 

David Turetsky: 21:33

I like that.

 

Kevin Plunkett: 21:33

I do. Yeah, I like that a lot.

 

David Turetsky: 21:44

Jeremy, thank you very much for being on the show.

 

Jeremy Feinstein: 21:46

Thank you, that was very interesting. Thank you for inviting me to join you.

 

David Turetsky: 21:49

You're welcome.

 

Kevin Plunkett: 21:50

Absolutely.

 

David Turetsky: 21:50

Kevin, thank you.

 

Kevin Plunkett: 21:52

Always a pleasure.

 

David Turetsky: 21:53

And thank you very much for listening. Take care and stay safe.

 

Announcer: 21:57

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