Thanh D. Nguyen
Thanh has spent the greater part of his decades-long career partnering with founders and investors on compensation and HR strategies. He is a leading expert in his field and has helped thousands of tech companies, including Airbnb, Coinbase, Figma, Lyft, Pinterest, Uber and many others. As founder and CEO of OpenComp, Thanh brings to market a solution that combines his experience with powerful data and technology to help companies get compensation right and pay employees fairly.Follow Follow
Storytelling About OpenComp with Thanh Nguyen
Welcome to the Use Case Podcast, episode 138. This week we have storytelling about OpenComp with Thanh Nguyen. During this episode, Thanh and I talk about how practitioners make the business case or the use case for purchasing OpenComp.
Thanh is an expert in all things compensation and HR strategy. Before founding OpenComp in 2021, Thanh spent most of his career partnering with investors and founders on compensation and HR strategies. He has worked closely with thousands of tech companies, including Airbnb, Coinbase, Figma, Lyft, Pinterest and Uber. His passion to help companies get pay right really comes through during the podcast.
OpenComp empowers high-growth companies with trusted market data, planning, analytics and professional consultation in one complete platform. OpenComp greatly simplifies compensation planning, helping to solve the problems around salary. More than 2000 companies use OpenComp.
A few of the questions we answer today: Should salary, or at least range of salary, be disclosed in job descriptions? How does OpenComp guide customers to find, communicate and be transparent about pay equity? What is pay compression, and how does it impact an organization?
Give the show a listen and please let me know what you think.
Show length: 25 minutes
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Welcome to RecruitingDaily’s Use Case Podcast, a show dedicated to the storytelling that happens or should happen when practitioners purchase technology. Each episode is designed to inspire new ways and ideas to make your business better as we speak with the brightest minds in recruitment and HR tech. That’s what we do. Here’s your host, William Tincup.
Ladies and gentlemen, this is William Tincup, and you are listening to the Use Case Podcast. Today, we have Thanh on from OpenComp. We’re going to be learning all about the business case, the use case for buying OpenComp. So without any further ado, Thanh, would you do us a favor and introduce both yourself and OpenComp and give the correct pronunciation of your first name?
All right. My name is Thanh Nguyen, kind of like the old astronaut drink-
… Thanh and Nguyen, even though it’s spelled N-G-U-Y-E-N. I am Vietnamese and ethnically Vietnamese, and grew up here in the States in Springfield, Missouri.
Fantastic. OpenComp, tell us a little bit about OpenComp.
Yeah. OpenComp is we… What we’re trying to do is really solve the problems around compensation. Virtually, whether you’re a candidate or you’re an employer or a company, you’ve had issues around developing or fundamentally establishing pay or equity, and OpenComp serves to solve those problems. It serves to solve the problems around obscure information, limited and stale data. Really, the problems surrounding compensation have always been very manual and analog, meaning data employee or HR data sits in a lot of different systems and a comp professional, like myself, would have to gather that and bring it all together and create analysis and models and interpret the data after we do all of that. So OpenComp ultimately encompasses all of that and really takes that whole compensation journey and makes it really simple and easy to use [inaudible 00:02:31].
Well, let’s unpack a couple things, pay inequity, huge today, and it has been for a while. So, it’s not like we woke up one day and we’re like, “Oh my God, we have pay inequities. How did it happen?” Let’s start with a couple things that you guide clients and give advice to clients about. What’s your stance on salary being disclosed in job descriptions or ranges? What’s your take on ranges of salary, et cetera?
Yeah. Look, this has evolved over the years, right? I mean, this pay inequity data has always been, and it still should be confidential in nature from an individual perspective, right? But our stance over the years has been… You see a lot of deviation or a lot of deltas in same job, same role, same scope, and same responsibilities, and you’ll see a major difference in pay, right? Whether you’re black, green, or yellow or a non-binary, male or female, companies understand what they’re hiring for and the scope that they’re hiring for, and it’s part of their process to evaluate that. Now, you shouldn’t have such a wide swing between base pay inequity, right? When you do, there’s a problem with the program or the process from which you’ve come on board to take that same role.
So for us, we really deeply believe that by making pay programs more transparent and the pay ranges more transparent, it reduces the influence of bias in that process. Because naturally, I don’t think people want to be biased in that way. Without structure, you are off to your druthers and you’re just handling case-by-case situations. Those case-by-case situations, if you don’t have a really strong guiding north star around this stuff, it tends to deviate. And then when you do a look back across your organization, you start to wonder why you have such a distribution of pay inequity in your organization, even within the same department, even within the same role and the same job level, right? So, as you can imagine, when you do that and you do that at scale and you look back and you see that visual scatterplot of data being all the way, all everywhere and it’s not centralized, then it becomes a problem for the company. It’s a problem for the employees. It’s a problem across the board, quite frankly.
So one of the things that I’ve been reading and following, especially since COVID is the job, you’re paying for skills and outputs, right? I’m doing mostly with knowledge work, but you can apply it to retail if you want to. That’s what you’re paying for. For this dollar, here’s what we get in terms of skills and outputs. And so, ranges gets me to a place intellectually where why would it be different other than the depth of skills. Again, maybe there’s a depth or breadth of skills or location-based pay. So, what’s your take on just like… And really, it’s your guidance for your customers is really what I’m trying to get to is how do you guide clients and customers through this journey of getting into a place, where not only do they have pay equity, but then they can communicate and be transparent about pay equity?
Yeah. Yeah. Look, I think it’s just there’s a whole journey in developing a framework that allows you to be really consistent in your pay practices. Quite frankly, it’s fair and somewhat transparent. Transparent, it doesn’t mean in my opinion like an employee sharing their actual pay with somebody else. That’s not transparency. Transparency is around the overarching compensation journey in the paid programs. So it starts to your point, we’ll get into a little bit of detail here, but it starts with the actual data, right? The data that’s being collected for that very specific job title, and let’s just say simply job level, meaning experienced, moderate or inexperienced in their career, right? So if we simplify it in that way, those are what you’re looking at from a data perspective.
Now, ranges are important because it allows you to cluster a lot of different jobs or job titles and levels in similarly associated market pricing, right? So when you do that, it forces this level of consistency within your organization. Meaning, if you’re paying somebody in a similar type of scope or role that the market deemed very similar in pay or equity, then you know that within your organization, you’re being fairly consistent. Now, not to get too-
No, no, geek out. Geek out, man. Come on. That’s why you’re here.
You could individually look at market-based data for each level and each role, right? But that’s really comprehensive and most companies can’t scale to that. Most startups or early stage organizations will use that basic what we’ll call mark-to-market principle. We’ll just look up what we believe you are and what the data says. Based on that midpoint or that average or that percentile that we want to use, we’ll pay that in an individual perspective. So a lot of companies will choose to go down that path, but that’s not scalable. You start to hire a thousand people or 30 people a month, and you’re starting to do that individual benchmark every time. The reality is if you don’t have the right data and the accuracy on that, you’re basing that person’s comp and equity on that dataset and-
It’s almost like that they should be able to input the skills and the experience or years of experience, and it should tell them here’s what you should pay for that.
That’s right. That’s right. Knowledge on both sides, I think the enterprise or the company knowledge, but there’s also knowledge that quite frankly a lot of candidates or employees just aren’t enabled with is the reality, right? I think whenever you tie skillset or someone’s assessment of their own skillset and the scope and responsibility, that really tightens up their own perception of what they’re doing and how they’re defining it. Now, I think the perfect marriage is, “Hey, I know that my skills are at this level and I’ve held these X, Y, Z roles and I’ve perform or I’ve mastered this skillset.”
The ultimate connect is if the enterprise has a framework, a compensation framework and a job framework that actually aligns to how you’re thinking about your own skillset and they’ve got salary bands and they’ve got the whole infrastructure, meaning comp philosophy structures, they modeled everything out and they know that based on this skillset mastery, they’re paying at this philosophy or this market positioning because they’re a company at this size and they’re a company with this many employees, they’re a company in this geo and they’re a company with this type of financing, then it becomes much more clear. But I think there’s this notion that, “Oh, I’m just going to look up head of product.” Well, there’s a lot of dimensions-
That’s right. That’s right.
… within that head of product that range from being paid $120,000 a year to possibly $350,000 a year, right? I think this is the confusion that always happens with compensation at the most basic and elemental level. And that’s when somebody starts to recruit or somebody is looking for a job, right? This is why compensation and OpenComp has exploded more recently and compensation over the years, because people have started to understand. People want more transparency. Employees want more transparency. Investors want more transparency on this.
Well, so what I love about this is transparency, it then gets us to a place of having communications, setting expectations, understanding what candidates want, what employees want, and us being able to basically right-size, and not just right-size once a year or once every couple of years, but right-size in more or less real time and making sure that, listen, we can all admit and understand that we have pay inequities. We get on TV tonight and say, “Listen, there isn’t a company on the planet that does have pay inequities. Okay. Here’s what we’re doing to fix our inequities. We’re not saying no. We’re not looking the other direction, and we’re not going to think it’s going to just fix itself. Here’s the actions that are helping us fix and this is our plan.”
I think that’s what most people want to hear. They want to hear the truth. So the transparency part, I think, is really important because they want to hear like, “Okay. You recognize you have a problem. Okay. Fantastic. You got a plan. Fantastic. Let’s check in in a couple months and let’s see if we’re making strides to get to where we eliminate inequities. Done.” Two questions, one of them is who should be using OpenComp on the enterprise side? Should recruiters, HR… I know comp, obviously comp and benefits folks, I know they’re going to be in it all the time, but who else should be using OpenComp?
Yeah. I think leaders and investors, board members, et cetera-
Oh, that’s nice.
… should definitely be using it. I mean, look, the reality is on an individual basis, every hire that gets made is generally that relationship is a hiring manager, a recruiter and candidate, and generally HR gets involved once that that person is established, but it is within the organization. But if you’re all working off of a framework, meaning you have a comp philosophy and you’ve got goals as it relates to diversity, you’ve got goals as it relates to gender, and you’ve got ranges that you know are tied to your market positioning and the relative market that’s relatable to the talent that you’re hiring, i.e. your size of company and your financial disposition and everything else, then it’s going to be a much easier process, right?
But I got to tell you, that’s the most complex thing to get organized and started when you’re building out your company, because everybody is coming from a very different knowledge position, right? When you think about companies that are building and growing, you’ve got a lot of different leaders. Some of those leaders have never had mature operating experience. Some of those leaders have had super mature operating experience, public companies and 20-year operating model experience. When you get those all combined and have different expectations and you don’t have a good compensation framework or a good data model, it is [crosstalk 00:15:51].
You can’t get there. No matter what your good intentions might be, you just can’t get there. You know what it’s interesting, I use this term, it’s diversity, inclusion, belonging, equity, and equality, and it encompasses what really diversity and inclusion is, but I want the other emphasis on some of these other things, and equality, it’s a board concern. I think some of the positivity that’s come out of some of the political movements is that there are people, well, obviously candidates want more transparency. They want to understand what you’re doing to make things better for people, et cetera, but equality, pay equality, this is basic. Again, you can’t fix it unless you have a clear understanding what the problem is and then how to fix it. So, I understand that. I want you to explain to folks the concept of compression or pay compression because I understand it because I’ve been through a number of recessions, but most people don’t understand what compression looks like or how it impacts an organization. What have you seen in compression?
Well, I think compression, when you think about pay administration or year over year, there’s always what you would typically see as merit increases or merit cycles, et cetera. To a certain extent, you’ve also got macro economic, like inflation and everything else. So, it’s a way for a company from a pay administration perspective to address pay more from the standpoint of if the role is or if your role isn’t changing from a scope or that composition by that much, you still want to be able to address pay and increase pay through a what we’ll call merit cycles, right?
So in a situation where you’re in a type of role that somewhat doesn’t change, whether it’s non-exempt or exempt, and this is the FLSA definition of different types of roles, whether you are eligible for overtime or not, but there are qualifications in terms of the scope of a job that you do and the autonomy or the supervision of that job. Now, in jobs where you are non-exempt, there’s a tendency to run into a lot of compression, meaning you’re asked to do the similar things, but you start to get into this rate of pay, where you’re spending a lot and you’re reaching a compression point before you move on to the next job or naturally get promoted, right?
That’s why a lot of people change jobs.
That’s exactly right. So they can elevate or get into another pay band at another organization or look to increase their pay. So a lot of times, that’s a big forcing function for what we’ll call just natural attrition or churn in that, because a said company may not be willing to pay this at a certain level of pay rate, so they just cap you out and you get into this compression until you actually pick up and determined that you’ve got to find another job.
It’s interesting to think about it from a retention perspective. I think a real-world scenario for folks listening is director of demand gen job pays $80,000 and you go through a recession, pick your favorite recession over the last 20 years or whatever, and there’s pays frozen for three years. And then all of a sudden, the economy comes back. It’s coming back with a vengeance. You’ve got a director of demand gen. You need to hire another one. They’re not taking that job at $80,000. That’s now $120,000 job. So you hire that new person at 120, and the person that’s there at 80 is doing the exact same job and they’re getting paid $60,000 less, $40,000 less. And so, how do you right-size that is something that all companies deal with, by the way. They’ll deal with it in different ways, but they’re always trying to navigate to equality and getting those inequities out of the way.
Thanh, last thing before we roll out is favorite customer story. So I know you got a bunch of them, but just maybe even your most recent favorite one, just something where a customer used OpenComp and just you’re like, “Man, that’s a fantastic use of way of OpenComp.”
Yeah. Look, I think you’re right, there’s a bunch of them, but one of the things I want to go back to is what you just talked about. Whether it’s the recession or not, we’re seeing that in this post-pandemic. That same scenario that you’ve talked about, it’s a huge whipsaw. Now, everybody, this is the most dynamic hiring time I’ve ever seen. The same job, as you said, pre-pandemic is now 20 to 30,000 in some situations. Look, you’ve got to be really astute with the data that you’re using-
That’s a good point.
… and really be sharp with how you’re profiling that data. Otherwise, you’re going to be at a loss, either you’re not going to hire that person, or you’re going to hire the wrong person. Either way, it’s a detriment to your business, right? So I just want to really hone in on that, because that is what we’re seeing right in front of our faces today. We’re trying to stay on top of it and really work with our customers to show them that the data is really important. Really going through this whole customer journey and understanding your pay equity within your own organization is going to be the difference between companies that really accelerate and companies that are really going to drop out post-pandemic because-
I liked that you mentioned journey. You’ve done it a couple of times during our conversation, because everyone’s on their own journey. It’s just like all the other parts of diversity and inclusion. There’s no judgment here. Yeah. We all know that we have problems. That’s stated and covered. Now, it’s a matter of what do you do? Your company might not be exactly like the company right next to you or a competitor that’s right next to you. You might be on a different journey, but you’re on a journey and you’re dealing with either the pay inequities and equality part of this, but everyone’s on their own journey.
Yeah. It’s recognizing the problems or the gaps, right? So we always talk about this rate of change, right? Everybody talks and delivers a lot lip service around pay inequity and gender, and then the geo. But at the end of the day, you need to fundamentally understand how you are performing and what your gaps are and how is it related to the comparable cohort companies that you compete for talent against. If you understand that, then you can actually build a plan to improve your own self. When I say self-care, this is self company care to improve, right? Because [crosstalk 00:24:17]-
Yeah. Yeah. Or you’re going to go through a massive amount of attrition.
… “Oh, we’re going to do this and we like this,” and you don’t actually measure your own state or your own wellbeing on it, then it’s just lip service.
Right. Which is historically what pay inequities has been.
Exactly. [crosstalk 00:24:36].
All right, my friend. Well, listen, thank you so much for coming on the Use Case Podcast. I loved our conversation and I just appreciate it.
I appreciate you. Thank you for having me on and sharing all the things that we love to talk about.
Awesome. And thanks for everyone listening to the Use Case Podcast. Until next time.
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William is the President & Editor-at-Large of RecruitingDaily. At the intersection of HR and technology, he’s a writer, speaker, advisor, consultant, investor, storyteller & teacher. He's been writing about HR and Recruiting related issues for longer than he cares to disclose. William serves on the Board of Advisors / Board of Directors for 20+ HR technology startups. William is a graduate of the University of Alabama at Birmingham with a BA in Art History. He also earned an MA in American Indian Studies from the University of Arizona and an MBA from Case Western Reserve University.