Raviga Roundtable on Driving Engagement and Productivity on Distributed Teams

Q4 of last year, we started getting a bunch of requests for compensation data. Hey, our portfolio companies are looking for compensation data. Who do you recommend? I had seen this VCECS survey for years back when I was in private equity and it got passed around lists. Maybe it shouldn’t have been, but I had seen it a couple of times. And so we reached out to Pave to see, can you provide this to private equity firms? It turns out, yes, they have a whole program for that. Got introduced to Willem.

He can tell you a little bit more about Pave, but we’re excited to pull some insights. They have over 7, 000 companies that provide data into this dataset. It was completed, it came out early this year. And I’m particularly excited to look at some of the geographic cuts to see some of the trends by geography. I think we can look at both engineering talent and sales talent. So I’m stoked for that part of the conversation. Willem, do you want to share a little bit more about Pave and your private equity program?

Totally. Hi, everybody. Thank you so much for joining us on your Friday morning, Friday afternoon, wherever you are. Very excited to be here. Excited to have a lively little chat to either kick off your day or hopefully give you a break from maybe a otherwise tedious day, perhaps.

We’re from Pave, myself, Katie Aldred, Frances Mitchell.

They are going to be the stars of the show today. I’m going to talk and then I’m going to shut up very soon.

I lead our Venture Partner Program.

And through that, Pave is partnering with 350 investment firms of all shapes, sizes, geos, focuses, whatnot, largely around this big old compensation dataset that we’ve been building now for four or so years. Through this, we benchmark comp everywhere from salary to variable comp to equity, everything from entry level to exact talent. And we have about 8, 000 companies who are using that dataset and sharing their own data into that dataset. And that spits out about 850k worth of employee data points. The data is real time.

That’s what we pulled all of the insights we’re about to show you from. And most of the companies find us because of the partner program that I help run and have helped build where either private equity or venture capital firms come and partner with Pave. We give them access to the data that we’re about to show you some of the insights from.

So any of you who partner with me today know that you can log into Pave at any time and pull whatever you want.

But then you also can get your portfolio company set up with the data for free. And when they get set up, their data joins the network and it continues to grow and so on and so forth. I’ll add one last thing, which is we acquired Option Impact, who had VCECS, which is this big executive survey that many are probably familiar with.

And Pave today is very tech company based.

The companies that make up the data we’re going to look at are basically all tech companies. But today, our tool is very, very full of venture capital companies. And while we partner with a lot of private equity firms and a good amount of their companies have joined Pave, we are very close to turning on something like a private equity versus VC filter for the data.

And the one thing that’s stopping us is just getting a few more private equity companies to join the data set. So if you like what you see today, shoot me an email. Love to chat with you. This is definitely not a sales pitch, but I do want to give you our data and I would love for your companies to check out our data as well. So my email is my name here.

That’s all I’ll say. Thank you so much.

With that, I think, yeah, Katie Frances, you want to take it away?

Yeah, I will share my screen here, like everyone can see. And yeah, to start off, I think we have an agenda and an intro. So Will already gave his intro. So I will start with mine. And then Katie, I’m Frances. I’m on the data team at Pave. So I work on building those data products that go into our market data platform and also love diving into the data beyond what’s in our app to surface some insights and trends and try and use data to help answer the most pressing questions in the world of competition. So I’m excited to dive into some of that today.

And my name is Katie. Really nice to meet you all. I’m pretty new to Pave. I’ve been here for about three months. Prior to Pave, I was at Sequoia Consulting running their benchmarking surveys. And prior to that, I was at Radford on the surveys and consulting team. And at Pave, my role is focused on consulting partnerships.

So similar- ish to Willem’s role, but really focusing on building a great partnership with our compensation consulting partners so that they are leveraging Pave data in their engagements with their clients, which I’m sure many of them may well be some of your portfolio companies. Awesome.

So we’ve got a couple of different sections of analysis today, starting with the VCCS or exact compensation highlights, and then some global analysis, like they mentioned, as well as some equity- focused insights. And we’ll stop for questions throughout, but feel free to add questions in the chat or just shout out some questions, and we’ll be happy to answer or discuss.

Well, I’ve already kind of touched on this piece, but just to give a little bit more of a snapshot of what our data set looks like, over 7, 500 companies, over 850, 000 employee records from those companies. And then we have a highlight on our investment partners. And it’s all real- time data. We have live data feeds from our customers’ HR or equity management systems. So we’re able to get much more real- time than the classic survey data.

And then this section on the right kind of goes through our data compensation by what percent of our data points or employee records come from companies of different sizes. So you can kind of see, while we have a lot of companies in the smaller range between like one in 100 companies or 100 employees, since those larger companies obviously have more employees, we do actually get a lot of our data set from those larger and sometimes public companies as well.

Anything else to add here, Will and Katie?

Let’s get to the good stuff. Let’s jump in. Let’s talk data.

Cool.

So we’re starting off with the executive compensation insights. And to get us started, this is just a basic one. But we’re looking at CEO role and looking at the median base salary over time. So just to kind of level set what this chart is, on the x- axis, we have the date. And on the y- axis, we have the median base salary. And the sample we’re looking at here is around 2, 400 CEOs. And we’re looking specifically at companies between 500 and 500 employees, so kind of that midsize private.

And we’re seeing that we’re seeing a steady increase in base salary over the past year or so. Nothing particularly surprising here, but we were around 265, 270K at the beginning of last year. And now we’re up to a median of around 288K. And then jumping into this next one, this is the same focus. So still looking at that group of CEOs among 50 to 500 employee companies, but now taking a look by metro. So seeing year over year, what were those median base salaries across CEOs? So now we have that median base salary on the x- axis and metro on the y- axis.

Of course, we see San Francisco Bay Area popping up at the top, but we wanted to take a look here to see how does that range between San Francisco and the rest of the country change year over year? And are other cities catching up to San Francisco?

And yeah, a couple of things that we thought were really interesting here. So firstly, based on my experience, I found that CEO pay is often based on looking at similarly sized companies, similar revenue, maybe similar level of invested capital. Geography isn’t usually considered as much, rightly or wrongly. And those CEO roles are often benchmarked on a national basis. But I think what’s interesting in this chart is if you follow that sort of blue bar, which is looking at last year, you can see there’s a good amount of differentiation by geography.

And then flash forward to May 24 this year and look at those hotter markets catching up with San Francisco. So we’re seeing that convergence of pay by geography at the CEO level. I think remote work has a role to play there. But yeah, we’d love to, we have some discussion questions coming up, but I would love to hear if anyone thinks this is surprising or if this is kind of what you’re seeing with the CEOs you’re working with as well. Is this where the CEO is located or where the company is located if they’re not co- located?

The CEO. That’s a good question.

Yeah.

It would make sense that, yeah, the CEO’s role is being more comped nationally. It’s interesting. Do you agree with that?

I mean, this seems pretty low, especially when you talk about companies that are 500 employees plus. So I’m guessing that this is actual data pulled from HRS system, not reported data, right? So this is accurate. But it definitely seems low on the skewed side, especially for medium.

Interesting.

And I repeat, this data is inclusive of only VC or also includes P. And then if it includes both, is there a general distribution between the two?

This doesn’t include any sort of filters beyond just the location like we have here and the company size in terms of employees. But like Willa mentioned before, it’s definitely very heavily skewed towards VC funded and mostly private tech companies.

Oh, interesting.

So there could be some founders in there bringing down the average.

Yeah, the complete equity package certainly plays a role here and is a big reason why getting a private equity breakout is a huge goal of ours in the near future.

Hi, I have a quick question. This is Brittany Patterson. I do have a partnership with Pave already. And I know that in the prior VC ECS reports, we were able to pull founder versus non- founder. Is the 2024 report available already because it’s not in my dashboard?

The 2024 report will come out towards the end of this year, but it will be pulled from the executive data that’s in the app itself. And there is a founder, non- founder filter in there you can utilize.

It will be from Pave data?

Correct. Yeah.

It’s not going to be the survey data anymore?

Exactly.

We run a big push around the time that Option Impact used to get more companies in. But any companies that are in Pave data will also make their way into the VC CS data. It’s effectively one and the same.

Okay. And I have one more quick question. So the trend, the prior slide that had the trends, I actually went through this enormous project to pull a few different years of VC ECS data and populate my own trends over the last few years. And because that wasn’t really available, it was just kind of the Excel document. Is that sort of thing available now so that we’re able to see the trends more visually?

Or did you guys have to pull all this stuff together yourselves? Yeah.

Frances had to bring all this together. But Frances, I’ll pass to you. I think the idea of productizing or at least like having analysis as some sort of feature within the tool is, it’s something we’ve thought about since the inception of Pave. And so certainly remains something we think about and would like to get in. It isn’t there yet, Frances, maybe. I’m not sure if we roadmap this or if it’s still a little ways out, though.

I don’t think it’s quite on the roadmap yet. But something that we hear a lot of appetite for within exec comp, but also within other equity practices, any type of comp trends. People would love to see this kind of market trend over time and something we’d love to invest in in the future.

Awesome. Thank you.

Of course.

Good to see you, Brittany.

OK, next up, shifting focus to the VP role. And this is now across all of our data set. And it’s looking at across the different VP function areas, how that median base salary changes. So kind of the same metric on the X axis, but VP level. So we can see how it ranges from VP of sales and customer success on the lower end to VP of legal and engineering on the high end.

And I think a couple of things to flag here, then. So if you look at the top of the chart, you can see the cash for that VP of sales role is lowest relative to other VPs. To me, that makes sense. Those roles are more commercially driven. A bigger part of the package is going to be their commission. And the other thing that’s interesting to me here is that across all of these VP roles, they’re all within about 15 percent of each other.

Suggesting that pay converges at those higher levels and employees at this level, they are being hired more for their business acumen, their strategic impact on the organization, more so than maybe their coding skills, which is what you look for for some of those lower levels. So in practical terms, what this means is if you are working with companies that have salary ranges, where they are grouping multiple jobs at the same level together into a grade, you could extend that salary range up to the VP level.

And then as long as your ranges are wide enough, they’ll actually be able to be broad enough to capture all of your VP roles. And you don’t necessarily need to benchmark them on an individual basis like you might be doing for your C- suite jobs.

And then this is the same view, but stepped down to director and senior director levels. So, again, we have kind of directors and senior directors across those different functional areas with their median base salaries. Again, we’re seeing customer success and sales near the lower end and software engineering and legal near the higher end. And also wanted to include this to compare to the last slide in terms of that range that Katie was talking about. When we look at the range between the lowest and the highest, I think it’s around 30 percent here.

Yeah. So lower down in messages, the lower down you go into the organization, you start to see more variance in pay based on skills. Some people find it funny that legal often comes out really high, often higher than engineering. That actually happens quite a lot on the individual contributor track and the management track. And a lot of times when you’re doing analysis for G& A, it makes sense to just carve out legal because they are like their own, their own area that’s even higher than engineering. The area that’s even higher than engineering.

Cool. So just wanted to pause there and see any major takeaways from that and just hear how you all are addressing comp when it comes to your VP and C- suite, if you are benchmarking individually, grouping roles together, like what kind of differentiation are you seeing when you handle cash comp for those roles?

I mean, we’re typically looking at similar companies and similar industries and what they serve, I guess. Is this all primarily tech and SaaS focused companies? It looks like it, but trying to understand like what the range of companies that you guys serve.

Yeah, mostly tech, mostly VC backed, mostly private, but we have a few larger and public companies in the data set too.

And so is there a way to like filter by, I guess like vintage of the company, like how long they’ve been around, when they were founded, kind of trying to get a sense for like funding and how mature these organizations are.

Yeah, we don’t actually have one for years since founding or even like round name, but we do have capital raised, valuation, number of employees and revenue, as well as whether you’re public or private and industry to filter by.

Anyone want to chime in with any comments on how you’re all approaching comp and any major differences between your sort of exec roles relative to those roles that sit below? I have a question. How do you see companies handle, a lot of, you know, 50, 100 person companies have an exec team of different levels, some C level, some VP level, some director level, and they’re all technically part of the exec team, heads of their functions.

How do you see companies manage that in a way that’s crisp and consistent when they’re members of your executive team, but they’re at different levels of seniority and experience? It’s a good question.

I think some of that comes down to needing to have good level, good level guides internally that are clear on what the scope of the role is at M5 or M6 versus an E7. And even if your M5s, M6s are sitting on your executive team, they maybe are, they’re also responsible for enacting the strategy, whereas those VP roles, I think it’s good to make sure that people understand they’re more for setting the vision and the direction and maybe the M5s in the meeting are going to be responsible for enacting that strategy, but not necessarily shaping it for that function.

But I think a lot of that clarity can come from having a good, crisp career level guide in- house up to probably that SVP level, if that role exists in a smaller organization. Yeah, I think that’s probably the best way I’d say to handle it. That makes sense.

All right.

Anything else? I’m going to say that my situation is probably a little bit different, but we don’t necessarily aggregate or evaluate our comp at a holistic level, but I know that we do look at it differently from VP to C- suite and it’s role by role and split by services and SaaS and, and, and, and so there are many kinds of the data that we have that actually make it quite difficult.

So we typically find it actually counterproductive to try to aggregate that data, but it’s good to see how you guys are splitting that because I think there are some possibilities because we, we, there is still an appetite for it. Yeah. And it’s one of the, even though the data says what it says when you’re on the ground and you have to do work with comp data, it can be different.

And there’s a whole, there are so many schools of thought on whether you should aggregate data, which is maybe less data work, perhaps, but harder to explain versus benchmarking on an individual basis, which easier to communicate, but the amount of analysis you have to do to make sure that data makes sense is a whole different beast. All right.

Anything else?

Let’s keep cruising.

Great.

So next piece is around location.

So this is kind of focused around global expansion as companies grow and start to hire internationally. But also within the U. S. as we’re in a more remote forward world and where it makes sense to hire talent within the U. S. So starting globally, here, we’re looking specifically at software engineering and we’ll get into sales in a minute, but we looked at a bunch of countries in our dataset and looked across three dimensions for where might identify a good candidate to expand and start hiring software engineering.

So the three dimensions we looked at were talent availability, so just based on the number of employees that we see in that country, in that role, the base salary differential. So that is the location differential that we calculate based on the median salary in that role in that country compared to the equivalent median salary in the United States. And then lastly, the salary increase year over year, just in aggregate. We want to look for ideally places that are affordable to hire talent, but also aren’t.

So here you can see a handful of countries by those three dimensions. Of course, places like India and China, we know have a lot of good software engineering talent at a low price. Unfortunately, we don’t, in our data set, have year- over- year data for those countries. And then there are some in Europe that might not be as well known, like Poland and Portugal, that still have high talent, low differentials, and are relatively flat year- over- year in terms of how that average salary is growing.

I’m surprised not to see Latin America. Oh, yeah, a lot of firms that we’re talking to are really focused on building international dev teams in Latin America for time zone advantages. So I’m surprised that you don’t see more of that in your data set.

Interesting. Yeah, there was so much data to choose from here, so this is just what I stuck on a slide, but I can definitely look into that if you’re interested and follow up with what kind of these metrics look like.

Yeah, I’d be curious. Thank you, Sheryl and Larissa. I’ve actually also heard a lot of companies, slightly different roles, but I’ve heard of a lot of companies hiring creative roles in Mexico. There’s a big art scene and art colleges there, so that’s a big scene, a big area for marketing and creative and lower cost.

Cool.

Sorry, I derailed you. Let’s talk U. S. Yeah, that’s great.

And the next one, yep, within the U. S., so same exact metrics, but now we’re just looking at, oh, it says country, that should say metro within the U. S. So a lot of things here, I’m sure, are really expected. Obviously, the SF Bay Area is high across kind of all metrics, and what was interesting as I was looking into this, there aren’t really any cities that have greens across the board. It’s hard to find a place that has great talent, is cheap, and is staying cheap.

If there’s a place that has great talent and is cheap, I’m sure people know about that and are hiring there and would increase that salary year over year, but there are some places like Salt Lake City that have good talent with low differentials, and places like Orlando and Phoenix, where we see pretty good data across the board.

And so when you say base salary differential, is that versus the average or versus the Bay Area?

Sorry, yes, versus the average in the entire United States.

And yeah, I was just going to add, I think we’ve heard for a while now, Salt Lake City, big hub, and we’re seeing that change, we’re seeing, that’s why we’re seeing that impact year over year.

Research Triangle is also something we’ve heard about, and a lot of these locations tend to align with, for the lower level talent, aligns with colleges, where there’s a good pool of talent you can collect from colleges, but yeah, we’re looking into the data to see if we could find you a magic location with tons of talent, lower costs, but in the US, it can be tricky.

Is there any like next level down on the software engineering differentiation of like front end, UI, UX, like backend, DevOps, like really trying to understand like, where there is differentiation in cost by the type of engineering, and their role?

Yeah, we do have that breakdown in app within software engineering, there’s like front end, backend, even ML, DevOps, kind of all of those breakdowns. So I haven’t included that here, wanted to keep it holistic, but that you definitely have the ability to drill down and see where by city places are more expensive. We don’t have the kind of talent availability or number of metrics besides a sample size, like range in the app. But if there’s any cuts that you’re specifically interested in, I’m happy to pull them and follow up.

Yeah, I mean, it’s more just if that’s available, it’d be cool to see kind of what granularity you go to. And then also, I’m guessing, is that available offshore as well? So internationally, it would just be another cut of the data.

Okay.

Yeah, yeah, I was gonna pop in and say that one of my favorite, I’ve watched the Pave tool itself grow for three years now. And we recently released a Pave differential tool, which basically tells you the story. But you can just as Frances said, customize it with different roles with either being high level job families or more granular titles. And then the coverage is, you know, 60 countries, 90 individual metros, like we’re seeing here, both US and globally.

And it is one of my favorite ways to play around with the data that I’ve ever seen Pave produce. So this is a great snapshot into what sort of the app has for everyone.

So, and last one here is now also within the US, but focusing on sales. Interestingly, I saw a lot of high growth areas here and not a lot that had high talent availability and low base salary differential, which I thought was interesting. Similarly, we see Salt Lake City as high talent, low differential, but very high growth.

Anybody have companies in Salt Lake? That seems like the place to be right now.

Agreed.

And it was funny because we were sort of talking a little bit about how we pay before the call, how you pay software engineers versus sales roles. And so a lot of companies will think about sales roles on a national basis and they’ll pay them at a national level. And that’s because in theory, your sales roles are aligned to a territory and your sales people have equal opportunities, but there are a lot of companies out there that do differentiate.

And obviously a commission is a huge part of the package and to those roles, probably more critical than the base salary.

Does pay give the option or will it to actually get down to that city level that you’re showing here? I think when we looked at it before, it’s only been able to show us the tiers, like tier one, tier two, tier three.

Yeah, we’ve added increased geo or increased like Metro functionality, even for like a base user, which is like for a port co, one who just shares data with us, we give them our data back. That’s sort of the like handshake that Pave has always been about. We have some good Metro coverage even for a base user, but that like premium data access that most folks are now coming to us for, unlocks all of these metros. And for even just the US, I think it’s a total of 30 or so different metros beyond US and then our tier breakouts and whatnot.

And then same can be said for the globe.

Yeah, so I don’t know if anyone is willing to share if they found a magic location, but for you all, what does hiring look like? Are you putting a lot of energy into finding those hubs or are you kind of sticking with status quo? Like how much energy are you putting into hiring outside of your HQ, either globally or within the US? I mean, I think we try and take an approach of does this need to be onshore?

Can it be offshore? What are the locations off shore that matter? Like Kate mentioned, thinking about time zones and do we need something in region or not? So we’ve been doing a lot of that over the last few years and trying to find different locations. Like Poland was actually one that jumped out to me on your prior slide.

We actually moved a number of companies out of Poland given the increases that we were seeing there that were rampant during COVID compared to like, it seems like now things have kind of flattened out on your side, but that was a big change.

We were talking at the beginning of the call around whether there’s different appetite to have distributed software engineers versus other types of employees. And at least in one guy’s example, our software engineers are remote. And I’ve seen a couple of blog posts around the philosophy of requiring some people to come back to work, but not others. And software engineers are often exempt.

I don’t know.

It feels like if anybody’s going to be remote, developers are the highest or one of the highest candidates. I’m curious if others have that same sentiment or disagree.

Well, Kate, you were really doing a good job being the straw that stirs the drink here. For us, if we think about some of our port codes, we’ve had some that have just gone virtual only, and then we’ve had others that are still maintaining a nexus. And so the approach for like sourcing the W2 folks is maybe less role dependent and more about like the approach that that company took relative to are we going to really be a virtual company or not? So that’s what we’re seeing.

Yeah.

Consistency philosophically in the company is valuable to not have two classes of employees.

And then I think there’s the policies that come with moving as well. And even just what you do with that, do you change pay after what period of time?

What’s considered a move?

How do you track moves? It’s some of the stuff that sounds a little bit easier to verbalize, but when you operationalize, it’s much harder.

Katie and Frances, have you seen companies be able to maintain different pay scales if you’re working at headquarters versus not?

Yeah, I’ve seen companies manage it. A lot of it really comes down to governance though. It’s not always the most fun part of managing that part of the comp strategy, but I think a lot of it comes down to governance, having really clear rules for employees about, and part of this comes down to pay transparency as well, and depending where you are on the pay transparency spectrum and how much you wanna share, but being clear with your employees, this is how we handle HQ, this is how we handle remote.

If you move, this is what we do, and addressing that really difficult question of if you move to a lower cost of labor, do you decrease pay or not?

Which is a big question, and we could probably have an entire separate webinar on.

Paves, software functionality beyond the benchmarking data. Pave builds a bunch of tools around compensation, not just this benchmark data set.

That was one of the first things that we built out right around the pay transparency era, kicking in, I’d say, COVID turning into pay transparency was, in our tooling that we provide companies, you can plug in that logic, however you build it to say, this is our HQ, and if someone falls into any of these other buckets that we build, we’re gonna automatically apply a plus 10%, a minus 10%, whatever it is to our HQ pace to help automatically keep that structured.

I’m sure that many tools do the same thing, many companies do the same thing without the use of Pave, but we see it because we’ve built that into the functionality for sure. Great.

This next couple of slides is specifically around machine learning engineering, so taking a bit of a pivot, but this is something we hear a lot of questions about and know is a hot topic, and something we have a lot of data on, so wanted to share.

So this first slide is specifically looking at the latest merit cycle season, since so many companies ran their merit cycles, some even within our Pave tools in Q1, and so we did a big analysis around looking at how many employees got raises and promotions and what those raised values actually were, and so this is a breakdown of median base salary increase or merit raise within the engineering family.

So I know someone was asking earlier about kind of the breakdown within engineering and those more granular roles, so this is kind of a snapshot, I think we even have maybe more granular below this of some of our different job families within engineering. So we’ve got job family on the X- axis and increase on the Y- axis, also broken out by whether or not the employee got a promotion. So we wanted to highlight this for the ML engineers on the left, we see getting the highest raises, whether or not you got a promotion relative to other types of engineers.

Yeah, so I think even within engineering, not all jobs are created equal, and I don’t know how surprising it is to see ML at the top of the pile. A few years ago, it was actually the security engineering job that at least from a data perspective was hot in terms of getting high comp, but now it’s machine learning and even going lower down into the org, you can still see there’s some differentiation there.

And then here’s a similar view comparing machine learning to the rest of, or to the general software engineering, we’re looking at P4s and P5s. And so we can see a general like 7% to 8% premium to those ML engineering roles over the general software engineering role.

Yeah, so that difference, it is within 10%. So it’s not a huge difference. And I know in the press, there’s a lot of crazy stories about AI comp, but at least from the cash side, equity may be a different story. On the cash side, we’re seeing in our data, and we’re actually also hearing from total rewards leaders anecdotally that sure, things are kind of crazy on the ML front, but not necessarily as crazy as those million dollar offers that we’re hearing about.

And some of those stories that make the press are potentially the executive level, individual contributors, the ones who are creating the industry and driving an entire company forward at some of those lower IC levels, at least on the cash side.

There’s not a huge differential. And our team is also working on building an offer data product to not only look at what we have now, which is what current salaries are, but what is happening in the offers world. And we’ve already seen kind of some early insights around what machine learning offers look like. And so we’re excited to kind of have that difference to see what it’s looking like in active offers versus what are actually people being paid now.

Yeah, so anyone wanna share how things are going on the AI or machine learning front is the talent just difficult to find full stop. And then once you have those roles, is it just a real battle to keep them from a conference perspective?

I’m curious, my suspicion is that a lot of companies within portfolios on the call might not have any AI or machine learning specialists yet, or maybe don’t have it in the plan. Yeah, interesting. Or maybe it’s a skill set that is part of your software engineering hire rather than a standalone.

Yeah, we’ve taken a bit of a different approach, as I feel like we always do. But have had our AI or product and tech team at headquarters hire really smart people, MBA interns, and then find them out as resources to our portfolio companies. So that is a very targeted and specific solution, rather bring them on to their payroll.

I’m curious- I’m part of the hire, I don’t know, because they drop.

I mean, there was quite a bit of demand to apply for the role. I think it’ll be interesting to see how that evolves as we get more crisp on who we bring in.

This is a trend I’m interested to explore. So I’ve just heard of the first couple of firms hiring AI operating partners, so specifically ops partners at the firm whose mandate is AI. And I’m curious to see that trend play out and what the role of the AI operating partner is and becomes over the next couple of years. I think equity is up next? Yeah.

Awesome.

So we’ve got this section here on equity, which we know is top of mind for everyone and a lot of our customers. So we’ve been putting a lot of time and effort into building out our equity data, not only like what the offer should be, but the practices and policy around equity programs. So had to include some insights here. So these slides are looking at different policies or practices around grant structure, investing structure. So there’s a lot going on here, so I’ll kind of walk us through.

We’ve got private companies on the left and public companies on the right. And first up, we’re looking at the vesting duration of new hire grants. So this is our company’s most commonly offering four- year grants, three- year, maybe something shorter or maybe something longer. And this is year over year. So in each graph, we’ve got the x- axis with the year and the y- axis of the percent of companies that are using that practice. So on the left side for private companies, not a lot of movement. That’ll be a theme throughout the next couple of slides.

Private companies are pretty much sticking to their four- year grants. But with public companies, we do see a lot of movement, particularly moving from four- year grants to three- year or two- year grants. And so kind of a common theme here is public and late- stage private companies are needing to manage their equity burn, getting a lot of pressure from their board or whoever, investors, to manage that burn. So a common strategy we see a lot is switching to that three- year grant. So you can grant the same amount of annual equity, but by spending less burn.

So no movement really across the private companies, but if they wanted to sort of follow the lead with public companies and start to manage that burn, this is the way to do it.

Yeah, I think it’s interesting to see things staying relatively consistent in the private world and then changing more in the public world. And changing your vesting duration can help companies manage their equity spend. And there could be some companies that do keep that same annual grant value. There will be others that actually use the three- year vest as a reason to decrease the grant value. So it is a way for some companies to manage their equity spend. So it does often result in a smaller grant value for employees, but doesn’t always have to.

It depends on your structure.

There was a lot of talk in the press a couple of years ago around Stripe and Lyft changing their policy here from a four- year to a three- year vest, and a lot of debate about how good or not this was for the employee. But it will be interesting to see if private companies start to follow suit.

Same kind of setup here, but now we’re looking at vesting duration. Again, private companies are pretty solidly in their monthly cadence. And public companies are becoming more quarterly or even yearly and reducing the amount of monthly grants. And then next up, looking at structures, this is whether the grant, new hire grant, is linear or back- weighted or accelerated.

Again, private companies sticking to that four- year linear grant, but public companies were starting to see more of those other strategies, specifically back- weighted grants, which again can be a way to manage or reduce equity burn.

Yeah. And I feel like I’m the voice of the employee. Back- weighted grants, not always as popular with employees because then they are tied to the company for longer to get a bigger portion of their grant. So as with a lot of this, I think it comes down to communication and making sure employees understand what they’re getting and why and all of the nuances that come with equity grants.

And then the last one of this flavor is around cliff and whether that new hire grant has a cliff and if so, how long. Again, seeing that much movement around the private companies, it’s very typical to have that one- year cliff. But at public companies, it’s becoming a little bit more common to not have the cliff in a new hire grant. Next up, shifting gears to participation. So we know shifting kind of your grant structure is one way to manage burn or play around with your equity program.

Participation is another big one we hear and it’s something we’re actually actively building a product around. So as companies think, where can I cut equity? Something like lower levels, something like certain roles or even certain geographies can be good places to reduce participation and thereby reduce burn. So here we’re looking at job level on the X- axis.

The three different colors are for our different departments bubbling up to GNA, sales and R& D. And the Y- axis is what percentage of those employees are receiving equity, new hire equity specifically.

Yeah, and I think a couple of interesting things here are with seniority, the proportion of employees receiving equity increases, but among VC- backed tech companies, which I know, especially from an equity perspective, are often structured differently to PE- backed. Even that entry- level grad role, the vast majority of roles regardless of what you’re doing are receiving equity.

And then it only goes up and you can also see as you get more senior in the organization, it doesn’t, kind of with the cash, it doesn’t really matter what you’re doing as much, it’s more your level that impacts whether or not you receive equity. So I think as well, if we were at sort of 80% at that senior director on the M6, far over to the right, if we add in E7, E8, E9, obviously I think GNA, sales, R& D would converge and we’d be looking at 90, 100% participation.

Yeah, but a little different in the VC- backed world where- This is one I’m curious to see when you can do the PE cuts.

I suspect there’s a lot less participation at lower levels.

Yeah, one voiceover that I get from a lot of our private equity partners is obviously, A, I want to see this for private equity as a filter.

That’s a given, but then also B, where do these stats even pre- private equity filter become helpful in terms of like hunting for talent who might be used to this package, knowing what it is they might be seeing in offers, what does the market look like for some roles they’re looking at and how can a private equity firm compete by maybe taking PAVE’s salary data, not changing it up to a higher percentile and getting more aggressive on that. That’s a common strategy I see. I’m sure this is not new. I’m sure folks on this call are very used to that.

100% the case that VC- backed companies and PE- backed companies are recruiting the same talent. And so it would be really interesting to have more data on two different offer letters, one more cash heavy, one more equity heavy. Like how does that impact the impression that the candidate has?

As a preview, Pave is launching offer data as a benchmark pretty soon. So we should be getting somewhere around there. The next year, we’ll include some offer data in the- Yeah, absolutely right.

This next one is a bit of an obvious one, but just kind of shows the very clear relationship between participation and earn. So as you offer more employees equity, you’re spending more equity. Which kind of speaks to how participation is such a strong lever for equity burn and can be a really strong strategy to manage it.

This is the one slide that I created, by the way. Thank you, everyone.

I was gonna say, it’s too perfect, isn’t it? The data is just too perfect with the percentage of employees. Yeah, so I think it goes without saying, burn rate increases. But yeah, acknowledging it’s a little different in the PE- backed world, but how do your portfolio companies handle equity burn? Or is it a topic or really not as much?

We’ve got about 10 minutes left. I have a couple of pre- submitted questions on other topics that we can hop into. So one of them was around bonuses for non- salespeople. Do you have any insights on what metrics, like company performance or individual performance, are the most likely to impact bonuses for non- commissioned employees?

This is somewhat anecdotal. Right now, we don’t have bonus practices information in the tool. I think as we keep growing and getting more data, practices in general and program design is a big area we want to tap into. So this is somewhat anecdotal, but I believe it’s more financial- related metrics, so maybe cash flow positive, as an example. But yeah, that’s all I’m hearing. So not too detailed, but I believe they’re just related to financial metrics for the company.

CTO might have a product- related goal, so getting x number of products to market or x number of products into a different phase. But yeah, CEO, CFO, cash flow positive would be my best guess.

From a data perspective, we don’t have any analysis at this point on what actually drives that bonus payout. But we do have variable pay for any type of roles, not just sales role as a compensation type in our product. We had another pre- submitted question around inflation and how that’s impacted general pay increases. Have you seen that as inflation goes up, the average annual increase has gone up, or not really?

I think we ran a merit cycle webinar a couple of weeks ago, and I think the median increase was maybe 4%. So I think from a US perspective, we aren’t necessarily seeing inflation impact pay increases. And I know that internally that can just be a really tough conversation with employees explaining that difference in cost of living versus cost of labor. But from a data perspective, at least in the US, I don’t think that we’re really seeing it. Some Latin markets, like Argentina, that’s when you see.

They’re having massive, yeah.

That’s when you start to see that there is an impact and maybe a little bit closer correlation between inflation and merit increases. Frances, I don’t know if you’ve seen anything different in the data. Yeah, nothing to add there.

Cool, we can throw it open to other questions on any comp topic. So going back to some of the regional data, which I found particularly interesting, on my other screen, so do you often see this trend where the more affordable markets ultimately catch up with the other markets? Or is that a new phenomenon? Is the leveling of CEO pay catching up across the board, cities that are affordable now catching up across the board, is that a repeated trend? Or is that a new phenomenon where maybe as work becomes more distributed, pay becomes more even?

From my perspective, I think it’s more likely that pay will even out. A few years ago, tier one was just San Francisco. And now when we look at pay, New York is pretty similar and often considered tier one. Seattle, same thing. Seattle, sometimes it’s more driven by talent and the competition rather than the pay levels. But it’s still often considered tier one these days. I do think that when we look back five or 10 years and you look at the cities that sat in tier one, two, and three, we are seeing them move up.

So I would expect to see, I don’t know if I’m going to be as bold as to say Denver, but part of me wonders, because you’re based in Denver, but part of me wonders if Denver is going to creep up. Chicago, Atlanta, these are tier two. But my guess would be they’re actually coming at the top of tier two. Are we going to see them in tier one next year?

Probably not.

But I think if we’re having this conversation in three years time, our tier one is going to be looking bigger.

Based on the experience on the ground, that wouldn’t surprise me. Another one that was interesting to me, so average salary increase, 4- ish percent, within machine learning, upwards of 10%, within other engineering disciplines also higher. So I guess.

Is it, it’s mostly competition for talent that drives salary increases as opposed to broader economic trends? And where are you seeing, where are you seeing more of that upward pressure? What types of roles? Engineering in general, any others?

Let me think, definitely engineering. And with engineering getting the bigger raises, it means that G& A functions often get lower increases or maybe not as much of a promotional budget. I’m trying to think if there’s anything else. I think it really comes down to engineering and then ML over everything else. I’m trying to think if there’s anything that came out of the Merit Cycle webinar. I don’t know, Frances, if you remember any other roles in there, but I think it was engineering.

Yeah, from the slide we showed earlier around, actually I can go ahead and share again.

The raises were actually pretty consistent across the board.

So while these promo raises are higher around like eight to 10%, and we saw kind of a higher, over 5%.

Oh, you’re right. I was misreading that.

I latched onto the 10%, but that’s with the promotion.

This is a good number to latch on to. But yeah, it was interesting. I was expecting to see more variation across families as I was doing the analysis, but it was really 4% across the board. And some maybe like percent changes within specific hot families, but I was a little bit surprised and interested to see that there wasn’t as much variation between sales and engineering and GNA, for example.

Thank you for calling that one out. That’s an important distinction. Another one, you might not have this offhand, but so this data is five to 500 employees, big swath. Where do you start to see inflection points in salary increases as companies get bigger? Like, are there trends around, hey, 20- person companies, a lot different than 100- person companies? I would suspect, but.

Yeah, we actually have a slide on that that we shared at the Merit Cycle webinar, so could definitely send that to you. If I remember, I think it was a little bit of a U- shape almost that smaller companies were able to give larger raises. Hypothesis there being kind of your, maybe your promotion especially might be a bigger jump. You’re going from maybe an entry to a mid rather than a P2 to a P3. And then kind of medium around mid- sized companies and then also larger among large companies that just have more cash to be able to spend on Merit Cycles.

Oh, so I wasn’t thinking about raises, but that’s interesting. I was thinking more just about general base comp. Like, where do you see step changes in a director at a small company versus a director at a larger company? Or do you?

So in my past life, we used to do analysis on pay group data. And if you’re a 20- person private company, should you include Google in your pay group? You can argue, yes, because you’re competing with that talent.

So you should know what Google is paying.

On the flip side, if you’re a 20- person company, there’s every chance you just don’t have the cash available to pay what the Google employee is getting. So there’s almost two sides. You know, it’s interesting to look at, but it doesn’t mean in reality, that’s what you’re gonna do. So even though that’s the case, I think looking, if you’re a smaller company, looking at similar sized companies up to, so if you’re a 100- person company, I’d say looking up to maybe the 250, 500 mark probably makes sense.

And then 250, 300 is maybe that inflection point where I would start looking at bigger companies because you are, at that point, competing with more public companies or late- stage private for talent, and therefore should probably think about updating your pay practices. But with all of that comes, do I include a bonus or not? Do I reduce my equity participation because I’m getting bigger? So yeah, maybe that 250, 300, I’d say is an inflection point, but every company is different.

And maybe there’s a 20- person company out there that can pay like Google.

Yeah, probably not a ton of them.

Very, very anecdotal, I suppose, but I popped into the Pave tool, ran a software engineer search, all US, and then just tabbed through each of the company stage with the different employee size buckets. And it stayed relatively stable until you reach around the 250- plus employee mark. And that was where we saw the first notable spike in salary. I didn’t plug in equity, but my assumption is that that’s gonna be an inverse relationship there.

And so 500 to 1, 000 and then 1, 000- plus is almost like a 20, 25K bump in this one specific role’s salary that didn’t exist until that point. And we could rerun this for any role very quickly in the tool, and of course, create analysis out of that.

Cool. Well, I get that the answer to a lot of these things is it depends. And so we’ll share contact information for Pave. One of the things I love is that there’s actually a delightful amount that’s available for free if you participate. So we’ll share contact information if anybody’s not a Pave partner and wants to become one.

Well, thank you all so much. This has been a blast. I had a ton of fun working with Frances and Katie putting this together. I hope they had fun as well doing the actual hard stuff. And thank you so much for hosting us today.

This has been wonderful.

Thanks.

We’ll do it again next year.