How can credit union boards build more effective executive recruitment and retention packages? Further, how can executives advocate for themselves when discussing pay and benefits or considering an organizational move? 

On “C.U. on the Show,” Doug is joined by guests Sharon Messmore, products and services coordinator at CUES, and Dave Piper, vice president of operations, and Sophia Rizzi, project manager, from Industry Insights. CUES is an organization that provides professional development and the latest information on the credit union movement to keep leaders informed. Industry Insights is a research organization that turns complex datasets into user-friendly, actionable insights. The group discussed how the recently launched CUES Executive Compensation Survey could help empower credit union board members and executives with data to reach more effective and fair salary agreements.

Use Cases of the Executive Compensation Survey

Released every year in January, the survey requests credit union salary and benefits data encompassing 23 executive positions, from CEO and CFO to diversity and inclusion officer. The survey allows credit unions to benchmark their organization against others based on factors such as region, membership size, and more. The study is unique in how credit unions can use the gathered data. Unlike similar surveys that provide static reports, the Executive Compensation Survey allows board members and executives to filter and customize reports based on criteria such as their location, asset size, and education. 

While many credit unions engage third-party firms to help determine fair and competitive salary packages, the survey empowers them to access relevant data to identify a median baseline. As a result, credit unions can develop executive compensation packages at or above the median to attract and retain top talent.

The survey is also helpful for credit union executives who want to discuss a higher salary or different compensation packages or make a move to a credit union in another region or asset size. Sharon shares that the data reflects a high correlation between pay and asset size. The next most influential factors are education and years of experience. With this information, credit union executives may decide to get a higher degree or more certifications that may heavily affect their pay.

The survey has great potential to help credit unions and executives advocate for fairer compensation packages, which is only possible with the participation of credit unions. Sharon shares that 242 credit unions completed the survey in 2022. Since more involvement is even better, the goal for 2023 is 450 credit unions. The team from Industry Insights explains that this goes a long way in providing more accurate results and ranges year-over-year.

Learn more about how the survey can help streamline salary conversations and the tools, access, and insights credit unions and executives can gain from increased survey participation, plus:

  • Hear about the new features the survey will have this year, including an interactive calculator and infographic
  • Learn which benefits credit unions are adding to create more robust executive offers
  • Why the team says to approach the data with a bit of caution

Stream the episode.

Sharon Messmore, CUES, Dave Piper, Sophia Rizzi, and Industry Insights are not affiliated with or endorsed by ACT Advisors, LLC.

Audio Transcription (pulled from the podcast)

Doug English  (0:00)

My guest on today’s podcast is Sharon Messmore from CUES. CUES is a credit union membership organization whose vision is to empower the credit union leaders of today and tomorrow to realize their potential and transform their organizations. Along with Sharon, I also talk to Sofia Rizzi and Dave Piper from Industry Insights. We’re talking about the CUES Executive Compensation Survey. They provide insight on what differentiates CUES and particularly interesting, the new executive compensation calculator they’ve brought out. 

Welcome, everyone. I’m delighted to have you join me on the show. And what we’re here to talk about is the CUES Executive Compensation Survey. So can you guys tell me a little bit about the survey, about its history and process and how our credit unions have participated in the survey?

Sofia Rizzi  (01:02)

Industry Insights has been partnering with CUES since 2013 to conduct the executive compensation survey. We do it annually, launching it in January of every year. With that survey, we also have the employee salary survey, which is for admin positions, accounting, marketing, Member Services, and other positions that aren’t C-suite. So that is also with the executive compensation survey. The executive survey covers 23 positions—CEO, marketing, executive, HR, executive, CFO, those types of positions. New this year, we had enough data to report on the diversity and inclusion officer, which was added to the survey in 2021. We didn’t have sufficient data that first year but now that it’s becoming more popular in a lot of companies, we are getting more data every year on that. And then this year, we had 242 credit unions participate.

Doug English  (02:02)

So 242 credit unions participated. And is that enough to get valid T stats— if that’s the right language?

Sofia Rizzi   (02:12)

Yeah, for sure. We usually look for about 30 records to create a dataset that is complete and usable. And so that definitely exceeds that rule. And then 242, that was the amount of CEOs we have, since that’s the most popular position. So a lot of people use the study specifically for the CEO data.

Doug English  (02:32)

That’s interesting. So does the CUES study differentiate itself in methodology or breadth? Obviously, there’s several industry studies. How does the CUES study differentiate itself? Or does it?

Sharon Messmore  (02:50)

I think one of the big things that differentiates us is the way we allow our reporting to be used. Survey data itself is going to be pretty similar group to group, especially if you’re getting information from the same credit unions. But what often is done then is it’s put in one report, and that report is provided as a static PDF or something like that, for the credit union to look at and make their decisions based off of three or four reports they pulled together. What CUES allows is we house everything online and we allow you to pull as many reports as you want to be very customized and filterable. So if you want to look at the CEO position, you don’t have to look at just a nationwide CEO position; you can look at the CEO position for the western region in the asset ranges between one and three billion. And then you can really drill down into something that’s a bit more useful when you’re really thinking about that. And there are some new tools, we’ve also added to the online reporting portal that Dave or Sophia could talk to you about that will be available this year.

Doug English  (04:04)

Sharon, do you want to tell us? 

Dave Piper (04:09)

I can take that. Like Sharon said, there are a couple items we’re adding in 2023. We’re planning to add an executive compensation calculator, and the great thing we really like about the calculator is you never will see insufficient data. So we take all the metrics we get and then we will quantify that within terms. We haven’t actually decided yet what we’re going to use; we kind of have to see the data to see how it rolls out. But based on your education level, your years of service, where you are regionally, the asset size of the credit union, we take all those metrics and then we roll that into a calculator they can use. They can make their selections and they will get a number back and they will get a range back. And it’s really useful when you really want to kind of target, hey, where about should I be for a CFO sitting in St. Louis, Missouri, who’s at a credit union that’s at 1.5 billion? You can kind of put all those metrics in there and then you get a number back that says, this is my typical range for that CFO, and then here would be kind of the benchmark number. And so we really liked that. Because when you drill down into the data, sometimes, depending how much data we have, we get insufficient funds back or insufficient data to give you a number. And so that gets kind of frustrating to a user, then they’re like, okay, well, I’ll take away this metric, or I’ll take away that metric so then I can actually get something I can use. And so while the online tools are awesome, and we think they’re great, for number crunchers, you can really dive into the data, occasionally, you get that insufficient data metric back, and that can be kind of frustrating. But the calculator will always give you a number. You know, we use a regression-based analysis to ensure you always get a number back when you put in a certain set of threshold. 

Doug English  (06:39)

Can we talk a bit about how to use the data? A lot of credit unions hire a third party to try to have an impartial, broad approach to getting good salary data to work with our HR salary committee. And as the experts how would you suggest that credit unions go about figuring out what fair comp is strictly from a data standpoint? What’s the best way to do it?

Dave Piper  (07:10)

What we do provide is the data, right? And so as you look at that, I would think most credit unions want to at least be at the median. They want to be at that benchmark and preferably to hire the best talent, to retain the best talent, they’re going to want to be a little bit above that. And so when you look at that data, we find the medians very useful. And how do the medians change year-over-year? We try to get the same credit unions, same positions, same people and compare them year-over-over. We don’t know that Joe Smith was at a certain position, but we can look at their data and say, okay, their president or CEO was there and had 25 years of service. And now that’s changed to 26 years of service. And we can make the assumption this is the same sample. Okay, when we look at the demographics that come with it, we don’t know who the individual is, but we can kind of get those same samples. And that is a really good benchmark when we compare year-over-year stats to really know well, because we are getting a different figure sample every year. So while we got to 242 this year, maybe we got to 246 the year before, but 70 of them were different credit unions, right? And so now our samples kind of changed and were tweaked a little bit. So when you compare year-over-year stats, they’re not quite as clean when we know we have that same sample. And we can really look at those benchmarks, how they moved, how they changed. So now as you’re looking at those medians, you can feel pretty good. Hey, we were above the median last year. When we look at that same sample, they went up 4.5% and we’re doing 5.5. We’re staying above that median, we feel good about those kinds of things. So I think when we suggest looking at the data, you have averages but we really like those medians. Because as long as your sample is big enough, it’s going to provide a more accurate representation of where you’re at. Because averages can kind of skew your data; you get a couple of people that really like to pay a lot or like to pay a little or whatever it might be, it can continue to skew your data a little more, especially as the sample gets smaller.

Doug English  (09:33)

So is it useful to the industry consultants? Now, I have not worked with them so I’m not sure how exactly they go about pulling additional data. But the assumption would be they take a CUES survey and some of the other surveys, and they pull them all together and create an aggregate of aggregates. I wonder, there’s got to be some duplication, right, because your datasets are probably inclusive of each other. Is there any additional value to that you foresee?

Dave Piper  (10:09)

I think there’s always value in getting additional data points. I would think that assuming those different sample sets are collected in a similar manner and provide similar data they would see a lot of similarity. If your samples are relatively large, I would feel pretty comfortable saying they should see some similarities. I think, as Sharon pointed out, one of the beauties of the online tool is really being able to drill down a little bit more. Because I have a daughter who works in the Northwest up in Seattle. And I know they pay a lot better up there than they do here in the Midwest, just based on my own experience, and the job she’s in is a field I’m very familiar with. And so I would expect the same as we look at the credit union industry, that it all is very regionalized, especially in terms of compensation.

Doug English  (11:18)

Do you have a hard time with any particular point and asset size to get enough participants to have good data? Do you get all the way from the 100 million to the multibillion, good data across the spectrum?

Sharon Messmore  (11:33)

We have pretty decent data. I would say definitely the smaller credit unions are ones we would love to get more from. Ideally, CUES will get participation from every credit union in the country, right? That would be the goal. I’m sure that’s what Industry Insight also wants to do is crunch the numbers of every single credit union but the ones that are smaller, less than 500 million, really are the places where it’s harder to find the data. Once you get them up it’s that much easier, and especially in that one to five billion range, you got all the data you could ever need.

Doug English  (12:13)

All the data you can want. Interesting. So I’ve seen surveys, and what I’m used to seeing is some ranges, exactly like David had mentioned. And it being highly correlated with asset size. And then obviously, regionally correlated, but the additional benefits area is one I’ve always wished for, to try to be able to help the credit union executives dial that part in a bit. And what I mean by that is the executive benefits you’re typically seeing in the credit union space 457(b) makes a little bit of a difference, right? It’s a supplemental plan that helps. 457(f) can be a very substantial difference. Collateral assignment can be a full doubling of the present value of comp. How’s it going with the data around some of those additional executive benefits? I’m used to seeing kind of like a percentage of folks that have the benefit, but it never really goes further than that. And it’s probably because it’s hard to get the data. Any comments on that?

Sofia Rizzi   (13:35)

I can speak a little bit on the trends we’ve seen over the last five years. As far as the 457 beating the 401(k), both have steadily been increasing in terms of credit unions that are offering it. Not huge, but every year we have seen increases. But 97% of CEOs are eligible for 401(k). And in this year’s study, or last year’s study, 49% were eligible for the 457(b).

Dave Piper  (14:08)

And then a smaller percentage for the 457(f).

Doug English  (14:13)

Yeah, the collateral assignment and 457(f) seem to me like they usually come in around 35%.

Dave Piper  (14:19)

Right on; 34% for CEO.

Doug English  (14:22)

I told you I read your survey; I wasn’t kidding. You know what I do is financial planning for credit union executives, right? So I see these plans in reality, and you don’t often see someone have a 457(f) and a collateral assignment. You see one or the other; I’ll often see the collateral assignment replaced the old 457(f) that used to be there. Does your data go to the level where it can tell if someone is likely to have both? Or just one?

Sofia Rizzi (15:02)

Yeah, it is a check-all-that-apply question on the survey. I don’t have a stat on those who selected both of those. 

Dave Piper  (15:13)

In our reporting, though, I’m not sure we would provide that differentiator, right? You’re referring to what percentage have both. 

Doug English  (15:32)

I think it’s interesting, but not a game-changer. But this next one is. As you may know, in most employees’ cases, they can save enough money by contributing to a 401(k); you contribute to a 401(k), you do 10% of salary, you do it for lots and lots of years. And you build up a substantial value. But when you get into the executive suite, and get into pay levels that are higher, you get capped out; you get the same contribution limits as everybody else—contribution limits don’t go any higher. So that’s why you have executive compensation strategies to try to help those folks replace a reasonable percentage of pay. And for most Americans’ retirements, you’re looking to have 60 to 80% replacement of what you made before when you retire to have a comfortable lifestyle. So the executive suite can’t do that without these additional plans. But my question is what I’m used to seeing executive benefits specialists work with the board. They’re targeting a replacement level for these executives usually in that 60% range—60% of final pay. And I’ve never seen a survey attempt to do anything with that. And maybe it’s not possible. But I think it’d be really interesting and useful data if we could give the industry some information about how much replacement compensation a particular credit union is targeting. Any discussion or thoughts around those ideas?

Dave Piper  (17:17)

I think the statement’s a very valid one. I do think the data collection would be a challenge and how we frame that. It’s making me think for sure. Is there something we could do in that space? I like the idea. 

Doug English  (17:40)

Yeah, I think we could explore that concept; I think there’s a way you could back into it. The 457(b) and 401(k) is a balance, right? It’s easy. Do you have a 401(k)? Yes. And do you actually gather balances in the survey? 

Dave Piper  (18:00)

Yeah, we don’t today. I think the challenge is a lot of the time, it would appear—not making a definitive statement here—but it would appear that oftentimes the HR manager or HR VP, whoever is filling this out, would not have access to that level of detail for sure. You know, what’s my CEO’s 401(k) balance or whatever it might be? So I think that would be the challenge in the data collection. 

Doug English  (18:37)

I think maybe it is problematic, maybe it is not possible. But I know it would be very useful if we as an industry figure out a way to get that transparency for folks. Because, again, in my work, I’ve calculated that the present value of the executive benefits is greater than the salary and bonus sometimes. It depends on the structure, of course, but sometimes it’s more than the part that you can get good data on. So it’s a big one for us to try to figure out how to capture at some point in the future. Sophia, were you going to say something on that?

Sofia Rizzi   (19:19)

No, I think the data we collect definitely shows what you were saying. The employer contribution has been 5% for the last five years; it hasn’t increased or decreased. So it’s been very steady. And we have seen some of those other perks increase; I think the two I had were personal financial planning and paid education benefits both increasing over the last five years. We’ve read some reports before this. And just that type of benefit trend data. 

Doug English  (19:50)

I’m so excited to hear you say personal financial planning. What a great subject. 

What percentage of folks, I think that’s around 30% as well, right? 13? An area of opportunity for sure. 

Dave Piper  (20:00)

And I think the challenge is, if you will, on the survey is do you have this as something you’re eligible for? And we really don’t take that next step as to at what level is the contribution of the credit union on your behalf or whatever it might be? You know, like club membership, right? Correct me if I’m wrong, Sophia—Sophia knows the data way better than I do—but I don’t think we say what are you contributing to their club membership? Like actual dollars. Right?

Sofia Rizzi   (20:56)

It’s all about eligibility.

Doug English  (20:59)

Yeah, well, plus, you got to get them to fill out the survey. So if you go too deep, and ask too many questions, we’re all going to say, oh, I don’t think so. Yeah.

Sharon Messmore  (21:07)

From the side of the participation I’m sitting here hearing all these questions like, oh, this is really good information. What can we take out that people don’t use? So we can add in these new things so we’re not adding more things for people to then have to add? Participation is a struggle sometimes because it does take time to fill out all this information—less time than maybe it used to since we’ve made improvements over the past couple of years—but it certainly takes time and effort.

Doug English  (21:38)

Would it be fair to say there is no means of credit unions being asked by multiple providers to do a survey? There is no means of cut and paste in this area; there isn’t an agreed upon format or a credit union could be quicker about getting that across, correct?

Sharon Messmore  (22:01)

That’d be nice.

Dave Piper  (22:05)

There isn’t an agreed upon format. That’s correct.

Doug English  (22:09)

I hear opportunity, but okay. All right. Now, unless you had other comments on that question, another thing I want to think about is, pretend you are a young executive as you may very well be, and you’re attempting to kind of set some career goals. Maybe you’re running a $300 million credit union right now. And you want to target your move upward? The obvious data point is just pure asset size and comp being super highly correlated. Are there any others, like the second highest correlated area? Or are there other factors?

Sofia Rizzi   (23:03)

I can talk about that a little bit. Dave kind of hinted at it when we were talking about our compensation calculator that’s going to be regression based. We actually have a section in the reports, the statistical modeling section, that is kind of what’s behind how the compensation calculator works. We focus on asset size, which is the most heavily correlated, but education level and years of experience are the second two most correlated with compensation. And so I would say, especially for a person early in their career, maybe using those perks you have through your position to go back to school and get a higher degree and MBA or something like that would be a heavy influence on your compensation.

Doug English  (23:55)

Asset size, years of experience—that one’s going to take a while—and education. So two of these things you can control with growing your credit union, with working at larger credit unions with mergers, and then education. So those are highly in your control. One of the things I hear often from CEOs is that these are servant leaders, right? They’re not in the credit union movement to enrich themselves. Yet as an industry, if we don’t make sure they’re paid at the median level for their area of the country, for their size credit union, they’re going to be headhunted aggressively by other industries. So we need to make sure they are, but the hard part is these servant leaders don’t like to go to their HR salary committee and talk about this stuff and talk about my salary that is double or triple what any of the board members ever made in their entire career. And it’s a hard thing for many executives. So have you foreseen a way with this new calculator that might be any sort of simplification of that issue or ways to get the board members directly involved with the survey and using it so the CEO doesn’t have to come to the board and ask for them to make sure the comp is appropriate?

Sharon Messmore  (25:31)

I think that’s where the infographic that I won’t let Dave talk about comes in. I think that’ll have some helpful things for CEOs to be able to bring to their board to let the board know at a quick glance, here’s some information that might be useful while you set my pay.

Doug English  (25:54)

Yeah, it would be interesting if the board could directly have the data from the third party, from CUES, and not have the executive have to bring it to them. 

Sharon Messmore  (26:05)

We do often give access to board members. Many board members run reports in our surveys and are pulling that information so they can be aware of what they should be setting compensation for.

Doug English  (26:19)

So a CUES member credit union already has that option for a board member to go and pull this data. And that would probably be a very appropriate use of the data. But the big change this year is that instead of looking at tables of data, which makes folks like Sophia and me very happy, they might just go into the calculator and put the data in and come out with a range of numbers, and then they can bring that back to the committee for our conversation, right? Nice work, guys. That’s really smart. Is that the first in the industry? Is this the first time someone has a calculator to simplify the process for them? I hope?

Dave Piper  (27:01)

I’m not sure. Sharon, do you know? I mean, we’ve done calculators in other industries. 

Doug English  (27:08)

I’m certainly used to seeing it in other things, but not in credit union executive comp. I think it’s pretty neat.

Sofia Rizzi   (27:15)

I’m super excited. 

Dave Piper (27:19)

Yeah, we from Industry Insights are actually very excited because we do believe in this simplicity. And it’s exactly what you described; instead of going and digging through the numbers and trying to find, hey, this is the number I need, literally within 30 seconds you go straight to the calculator, you make your selections, and you get a number. And it’s just so simple and will give them some kind of basis on which to draw upon that a board member or as you say, a CEO who doesn’t feel comfortable can say, hey board member, can you just go on the calculation? Just run the calculator for me. 

Doug English  (28:00)

So is there a situation you can imagine when it would be an error to do that, when they really need to have a broader poll on the data?

Sofia Rizzi   (28:12)

Oh, well, one of the things we run into when we do these calculators for other industries is somebody may come in later on in their career, they’ve had 10 years of experience already but they took some time off maybe to have a family or whatever. But they have a higher degree that could throw things off because they could be just re-entering the workforce later on or taking this part-time position. So there are those discrepancies, I guess, that could occur.

Doug English  (28:43)

So if you had that kind of a situation, what should you do?

Dave Piper  (28:49)

I think you still need to use every piece of data with a little bit of caution, right? You know, you may have some special circumstances, then you need to kind of factor that in.

Sofia Rizzi   (29:05)

And we also can say how not to use the data points that are in the study. And the number one thing is these are not absolute standards, these are recommendations. The goal of the study is to provide free information to credit union executives to better make decisions. It’s not saying this is the standard, this is what needs to be paid. So that’s the one thing we try to remind everybody when they’re utilizing any type of data, whether it’s the static PDF report or this new tool that will be coming out this year.

Doug English  (29:42)

Yeah, so you have to take the data and then make it yours, customize it to your strategic objectives and the standards within your credit union. But the idea of the calculator could eliminate or maybe reduce some of the need for the third party to be interpreting the data from numerous sites and then bringing that to the board, which then has the discussion with the executive. Nothing against folks in that field but transparency, and simplicity are of value to all of us. Did you have any further thoughts for our listeners on the CUES executive comp survey, that is, again, the request to get that completed. And we need to get your participation up to try to beat the 242 credit union number from last year. Sharon, we’re going for a certain number of credit unions. 

Sharon Messmore  (30:49)

Yeah, I mean, we could set the reasonable goal of let’s say, 300 new—that’s another 60 from last year—or you could set a bit more audacious goal and say if we could get 450, that’d be pretty great.

Doug English (31:04)

Right? Yes, that would be a huge change.

Sharon Messmore  (31:06)

That would certainly help everyone who’s accessing the reports throughout the rest of the year. And we keep reports year after year. So when you go in, you can still see the 2022 reporting and use that to compare against 2023 and continue to do all of that. It’s just a really great tool. And the more people participate, the better the tool becomes.

Doug English  (31:29)

Very good. Well, Dave or Sophia, any further thoughts for our listeners on anything to do with a survey—best uses, the things not to do, and just how to make the most of the data?

Dave Piper  (31:42)

Yeah, nothing more to add, for me. I’d reiterate the participation. The more data we get, the better. I really like the idea of regionalized data. And so that’s where participation really can come into play, and really, really help. And because that’s where I think the rubber really hits the road, sometimes with regionalization. When we get beyond asset size, and some of the things Sophia mentioned, that regionalization really can come into play with some of the compensation numbers.

Doug English  (32:18)

So the calculator is not going to have regionalization in it? 

Dave Piper  (32:22)

Yeah, we’ll have to see when we need it, as we put that data into our model, to know we have a good correlation with the data in terms of what it will do. And we can give you back a good, significant number. That is the hope.

Sharon Messmore  (31:59)

We want the calculator to be reliable. So if that means not having regional data because we don’t have it, then it won’t have it. But now, hopefully, there’ll be enough people who participate so we can do it.

Doug English  (32:10)

Yeah, so maybe 300 would do it and we get the original data. If we get 300. 

Sofia Rizzi   (32:40)

Well, I think our smallest sample was from the northeast part of the United States. 

Doug English  (32:51)

My homeland. Northeasterners are all rushing about, that’s what we did up there with no time for completing surveys. And thus we don’t have good data to make sure we’re being paid fairly. So Northeastern credit unions, please fill out your CUES survey so we can get regional data for the whole credit union industry so everyone can be fairly compensated. So thanks very much for joining me today. I appreciate your insights. And I look forward to seeing the survey when it’s released. Thank you.

Keep listening on the following platforms:



Google Podcasts:


Pocket Cast:



Share this: