IQ Hurts Sales? The Data Scientist Who Burned Down Old Hiring | Regina Chou

When I did develop the prediction model for Mercedes-Benz, what I found was IQ actually has a negative correlation to the number of cars that were sold.

Wow.

And I thought, oh my gosh, so it hits— so the IQ goes up to a certain level and then it drops off. So why is that? So for flight risk, for example, one of the common threats, so across different industry and different job roles, there's one common trait that we found, and which won't be surprising to everyone, is actually loyalty and commitment. Yeah, emotional resilience is a big one as well. Our goal is to become the biggest database of human data, and the application of that is absolutely huge. That was always the, the vision when we had building the company, is to aggregate all this data, and because It's all scattered everywhere. So once you do that, you're going to have this insight of human potential that just have unlimited applications.

Hey, hey, welcome to Capability Amplifier. This is Regina Chow, and I'm Mike Koenigs, your host today. So I want you to forget everything you know about traditional hiring science. The assessments you currently trust are based on models that are over 30 years old. And my guest today is the reason those old systems are now irrelevant. So imagine a journey, a brilliant mind emerging from a rice paddy in Taiwan where she had no running water and no electricity. She became the first in her family to attend college. And grew into an amazing IO psychologist and data scientist. We'll find out what IO means in a moment. So Regina Chau is the co-founder and chief scientist behind the algorithm that powers the performance machine. She didn't just tweak old hiring methods, she burned the old model down. Regina analyzed 450 human psychographic traits and built blueprints that predict human potential and flight risk, similar to how insurance companies use psychographic data to predict risk. Now, our predictive engine known as the Reggie Blueprint, hmm, imagine that, delivers results within 15 to 20% pre-hire accuracy. And she helped clients like Mercedes-Benz predict car sales volumes and scaled CrowdStrike from its founding to a $2 billion IPO. So her mission is simple. Connecting humans to work they're uniquely suited for. The technology you're about to see is the result of her dedication to turning guesswork into data-backed certainty. And she is a remarkably sweet, fun human being to spend time with. So how are you doing?

Thank you, Mike. I'm feeling fantastic. It's so good to be here.

Oh, I just— I love it already. So, well, let's begin with your journey from a rice paddy in Taiwan without electricity and to become the chief scientist behind this hiring algorithm. So what were the experiences you had growing up that led to what you're doing right now and predicting the potential of what a human being can become?

Well, I guess so where I grew up, I grew up in poverty where we literally, we had nothing, no running water, no electricity. But so that's, I guess that's when I, you know, learned resilience and hard work. So I think it is how do we unlock human potential regardless of the environment we are in? And that is really what started my curiosity into looking into how to elevate human potential.

Okay, so you were the first to attend college in your whole family. And so how does that translate? I mean, work growing up in poverty to your passion for predictive analytics and organizational psychology. Bridge the gap for me.

Attending the college was actually my grandfather's wish. So we were, you know, 3 generations of rice farmers. And if you know anything about rice farming, if you want to be poor.

Yeah, yeah, no.

And so it's actually been my grandfather's dream. And I literally, so it's a promise I made that I wanted to do that for him. But of course, then I had the privilege. My father was, he also was determined to get my family out of poverty. So even though he didn't finish high school, it's something I actually didn't discovered much later on, but that's a different story. But he built a very successful oil sale company in Taiwan. And so that's when he, he always had this dream to move to Australia.

Okay.

So he achieved his dream.

That's where you got the slightly strange accent.

My Chinese Aussie accent.

So great. It's so great. So I want to know, bridge the gap here. So Taiwan rice paddy farming to Australia and then the psychographic, like what was the first breakthrough moment you had that bridged science and psychology and like your first really big deal that started the snowball that probably led you to right now, right here?

Thank you. Okay. Yes. So, I've always been fascinated about human people and that led me into psychology.

Not the other kind of people. Not the aliens, not the aliens.

Well, if I do have an opportunity, I would not pass on that. So I was actually working with a Mercedes-Benz dealership in Sydney, Australia, and we were just measuring traditional traits. You know, their IQ, their emotional intelligence. And when I was at the dealership presenting the result back to the business owner, he basically said to me, so, okay, how does this help with my business? So what if someone scored this IQ or have this certain level of emotional intelligence? And that's when it really hits me because that's where the traditional assessment fell short.

And what kind of assessments, for example, the ones that you were like, well, these are obsolete compared to the data I've discovered?

Yeah, but then the fact is for business owner, they, the fact that how much they score on different traits actually serves no meaning to them. What's meaningful to them is how will someone actually perform? So in his case, how many vehicles can his salesperson actually sell? So, you know, that's when I start to ask, so can I actually know how many vehicle— can I actually predict how many vehicles someone can sell before you actually hire them? Because that's where you can deliver the value, you know? And so that's when I try to solve that problem. Can I actually— because if I can actually solve that problem, find a solution to that, I can help many, many business in different industry. And it also means connecting people to meaningful work because that means connecting the right people with the right traits into meaningful job. So I started working on that and that's when I actually find out, well, if I can measure enough psychological traits and then I can actually get the actual performance data, so in this case, how many vehicles they actually sold each month, I could actually figure out what psychological traits are driving the actual sales performance. And then you can use the same algorithm, apply on people actually applying for the job. You'll be able to predict how many vehicles someone can sell.

Right.

So that was my aha moment. And that's when I thought, this is pretty amazing. This, if I can do that and put it into a software.

Yeah. So what that, translates into when you go downstream a little bit is— I'm going to jump ahead a little bit, but I want everyone watching, listening to understand the magnitude of, of this work— is you're able to look at any kind of a KPI or a performance indicator in any kind of a job. Um, you've got over 450 psychographic traits that you've analyzed. You've built up, I think it was like over 5 million data points. Is that accurate? Okay? And that means you can determine how well a person's going to function in their job, kind of no matter what it is, and whether or not they're going to run away. They're a flight risk, their probability of success. And what I've learned is there's some things we all assume are most important, like IQ, and it turns out they aren't necessarily relevant, uh, all of these factors. So can you bridge the gap between the weird non-obvious things that we all think are important that aren't and the ones that matter? That was a big compound question.

But in fact, yes. And there's so many biases that we have even actually within industrial psychology. There's always been a belief over the years that IQ is actually the most predictive. It has the highest predictive power in predicting how someone would perform in a job. So the belief is that the the better you're going to do a job. And it's actually believed that applies across different industries and different job roles.

And what have you found to be true?

That is absolutely not the case.

So IQ isn't the main predictor?

Absolutely not. But this is only because, so you can't actually look at anything in isolation. So that's one thing that's important. You can never look at IQ on its own because humans are so much more complicated than that. What else do we have? You can name it, thousands of different attributes that's within all of us at different levels. So try to imagine if you combine someone with high IQ, low work ethic, or someone with high work ethic and low IQ. And that's just a simple, you know, 2x2 combination. But imagine combining thousands of different traits. That's how complicated we are. In fact, the first time when I found out, so because of that, with all the prediction model I try to develop, when I go into every corporation, so I try to, IQ was actually always in there because apparently that's one of the most reliable predictors. But when I did develop the prediction model for Mercedes-Benz, What I found was IQ actually have a negative correlation to the number of cars that was sold. Wow. And I thought, oh my gosh. So it hits, so the IQ goes up to a certain level and then it drops off. So why is that? It's basically, so it teaches, so it was a very obvious lesson. So being a car salesman, the IQ benefit only reaches to a certain point. It no longer serves the purposes. And one of the dealers, dealership principal actually says to me, oh, you mean when they got to a certain point in IQ, they actually sell the car back to customers. They're too smart for their own good. I said, okay, if yes. Okay.

That's pretty interesting. Well, it's like some of the smartest people I know are also the most stuck. They spend too much time staring at their belly button to get to heaven. You know, it's like it doesn't work. So I'm gonna ask you another question. Hope that all made sense. So you've got— so there's IQ and then there's 450 other psychographic traits. So what are— if you had to cast a wide net and say, here are the ones that matter most, that consistently show up, that mean someone's going to be a hard worker, ethical team player, um, you know, the things that matter most matter most to them. Is there such a thing?

There is actually.

And the non-obvious ones are always the most interesting to me, but also, yeah, what are they?

That's right. So as opposed to IQ, and in fact, one of the common threads I continue to find across different industry and different jobs is actually hope and optimism, which is very, very surprising. But then if you look—

Is that easy to determine and easy to measure and easy to detect? Absolutely, it is. Okay, so hope and optimism, all right.

Yes, and I guess that's what's driving the resilience in people.

And that's a measurable trait too, resilience? Is it something that you can detect?

Absolutely. Okay. In fact, everything is measurable. I know that sounds, but that has always been a challenge of mine. And, but what I've, something what I've learned is that if you understand it enough, Everything is measurable. And when you have the data, you— there is always insight and answer within that.

Okay. Okay. So let's, let's move on to another case study, which is Diamond Asia Capital. And tell us about how that work translated into the product, because at first I assume you were just gathering lots and lots of data and probably crunching a bunch of spreadsheets. But at some point it became possible to create the product that became WhoHire. So what was involved in that and how did, how did, you know, like how many big lessons did you have to learn and how much data to crunch until you're like, I've got a predictable model I can adapt to just about any business type?

See, along the way over the years, there's so many valuable lessons. In fact, you know, every prediction model that we try to develop, huge lessons across different industry from, you know, when we're talking about, you know, janitors, cleaners in the hotel, all the way to C-suite executive management leadership prediction models that we've worked on across different industry. But it all started with actually a philosophy of I've always believed it's not the education, not the past work performance that's going to predict future performance. It's actually someone's potential and their suitability to a job. So with Diamond Asia Capital, what we— so the founder and I, we have this, he actually had the similar philosophy as me. That's when we started talking. And, you know, he always— so, 'cause in Asia, obviously you have to graduate from top pedigree university in order to be in the banking and investment fund jobs. And so he always thought he got lucky that his resume was put in a different, in the wrong pile because he didn't graduate from top university. So he wanted to, because he's been so successful, he wanted to give back to the society. So what can we do? And so we decided to run a social experiment where we actually open up applications for people to apply for hedge fund investment manager apprentice. But one thing we did was we didn't allow them to upload resume. And also we actually masked their first name and last name so we don't So we take out all the biases, their ethical background, nothing else. We didn't want to know anything about them other than looking at their psychological DNA. And so we had over, you know, 3,000 applications because it's not heard of in Singapore. You mean you're not going to look at my work experience and education? And that was, that was you know, really fascinating. So we had applicants from not graduating from high school all the way to having PhD, multiple PhD degrees. And we ended up hiring 8 of them and most of them have been successful and continues to be.

How many years later?

5 years later. Okay.

And so the, when you look at the common threads amongst all of them, they all had what were the common threads? I mean, this is a hedge fund manager, but is that translatable into almost all other businesses and jobs too?

So with every industry, and in fact, let me give you this example. Even with Mercedes-Benz, we did, so we did the research in Australia, Singapore, and Malaysia. And so even same brand selling the same vehicles, You look at the prediction model, the psychological makeup of each country, they're actually vastly different.

Wow.

Even selling the same luxury vehicle. So, but that really explained to you how different environments are suited for different people.

Yeah. So every culture, a language, it's just like there's all sorts of very subtle nuances. Absolutely. In every environment, every business type. And that's why you've got effectively a roadmap or a blueprint that can be applied to a business. You look for those, you can quantify 'em, you can measure 'em, you can predict them.

Yes.

And that would be impossible as a human, especially if you're dealing with large volume or at very low probability at the speed that you're doing it now with software, I presume.

That's right. And so for flight risk, for example, one of the common threads, so across different industry and different job roles, there's one common thread that we found and which won't be surprising to everyone is actually loyalty and commitment. So, you know, and that's something, you know, emotional resilience is a big one as well. That applies. So any job that actually requires a lot of emotional labor, you really need that.

So you can't have criers apply. No. Remember, I don't know if you remember the movie, There's No Crying in Baseball. Might be too distant of an American reference. It was a Tom Hanks movie. But anyway, that didn't land. That didn't land. It's okay. I want to move on to another one here, which is, see, it just goes to show I could have used a predictive model and known that that joke wouldn't land. Mei, don't run it. Past an old movie reference that's 30 years old with someone from Taiwan and Australia. Moving right along. And it's baseball too. Terrible idea. So next one. So you got Mercedes-Benz, CrowdStrike hedge fund, but then you've got Andy Elliott who is in effectively sales training, personal development, personal growth. Anyone who's been exposed to like a Tony Robbins-like environment, for example, would resonate. He's used this model. Doubled his revenue all the way up to $100 million. You got Tommy Mello, he's in the garage door business, so in the trades, and he sold his company for like half a billion dollars, a chunk of it. He's about to have a billion-dollar exit now. And they've taken turnover from 200% down to single digits. They've been able to predict and grow and scale, which became a predictive mechanism for figuring out when to exit. Now, this all equates— this is just incredibly complex roadmap you're navigating. So just talk about other predictive models, other case studies, or what, again, the distinctions between these businesses and industries.

Right.

Again, a huge compound question, but I wanted to give you some time to digest and think about because I'm, I'm looking for the shortcuts here. Like, what are the shortcuts?

Okay. That's okay. I'll give you an example of the model that we developed for Tommy, his garage door technicians. Because, so going to the research work, so in understanding Tommy's value, and so then one of the things that I measure with these garage door technicians is called greed. And I know that sounds But greed is essentially measuring someone's aspiration of material goods. And it turns out someone that score higher on aspiration for material goods actually performed much better. And that's when, you know.

So in this case, greed is good. It's a motivator. And it doesn't, it's not associated with one of the capital sins. It's actually good in a team and a business environment when it's applied properly.

It's a motivational driver. And when Tommy saw the result, he thought, you know, I've always, always felt that's the case. And that is actually the culture and the value I want to drive my company. But now, not only I have the data to back it up with, I also have a tool to be able to measure it.

Yeah.

Pre-hire.

That's so cool. That's so cool. That's so cool. I was going to use another distant movie reference, but Minority Report, Tom Cruise was in it when they could predict if someone's going to commit a crime in the future, right?

It's actually very, very similar philosophy. Exactly. Moneyball applies the same principle as well. Yeah. Yes.

That's great. A Brad Pitt reference. Okay, next one here. I want to go on. I have a Gen Z. We have a Gen Z in the room right over here. And these, let's see, I don't know what exactly where the cutoff is right now, but it ends at around someone who's 27, right? Okay-ish. 28. All right. And what I want to know, like, they feel like a completely different breed of human. They're the value systems, how their motivations, how they absorb content, but also how to drive and motivate them. It's a— I remember when I was hiring millennials at first, And holy cow, so distracted, entitled, hard to motivate, hard to be team players compared to— I'm— what the hell am I? I'm in between baby boomer. What is it? Gen— Generation X. What the hell am I?

Generation X. I can't remember.

Born in '66. Is that what I am? I'm a Gen X. Okay. But I didn't get it. It took me a long time to be able to figure out, oh yeah, they're just motivated by different things. So when you're finding and hiring, talk about the generational shifts, the gaps, and attracting and retaining that audience. What's different? What's changed? What patterns do you see? Or is that an illusion?

You know what? Yeah, I actually think every generation actually has similar problems or they are, say, the same problem. Um, we all want to find meaningful job.

Yeah.

So it's a matter of that they actually found the right environment that helped them to thrive, to be the best, um, version of themselves. And that is something each generation is seeking for. I think Gen Z just be more upfront and be more vocal about it. But it's no different. Every generation faces the same challenge. And that is one thing actually doesn't change. And so I think as employers, you know, we business owners, we also need to step up in helping the younger generations in achieving that. I think it's also our responsibility in helping them to do that.

Yeah, finding meaning.

Yeah.

So how about— so I'm just thinking through, let's say I've got an environment, a business environment. Is there a way to take your tools, your assessment, and determine, first of all, what are the best humans in that kind of a job, or what I need to change so I can attract and retain better? And is there a distinction between different generations, or does it just come down to a certain fingerprint of the right psychographic traits in a particular environment? Can you talk a little bit about where's the crossover between environment and people and either adapting one or the other? Again, I don't know if I'm asking that question properly.

No, well, that is actually our mission. That's what we're trying to do by, you know, bringing— so bridging that gap between the two parties. Is where we're going to achieve success. So it takes, it takes both parties to work towards that. Yes, so not only we need to find out what they're most suited for in these jobs in order for them to achieve potential, but what else we can do in helping them to achieve that. And that's actually, that would be what our legacy is.

Okay, so let's go back to You're queen for a day or a week or a month or a year, and you get to give founders who are struggling to find and hire and scale their teams, which is the number one indicator, the compounding effect so you can get to freedom. We want to grow our business without having us have to be there or to get to an exit to achieve financial freedom while you're creating meaning for these team members. So again, that's a complicated formula. So you're queen for a day, you've got access to the entire audience of business owners. What's your advice for the struggling founder who's trying to make sense out of this and get the best of the best?

Start using data for hiring just like you do in every aspect of your business in terms of your product, your— that's one thing most business owners don't do. When it comes to people really rely on gut feeling, they think, well, that's, you know, we'll give them a try. We are not so sure. Human complicated. If they don't work out, next. And no, that's not the way to do it. It's a much smarter way, which is data and technology, and then combine that with heart, then you have a winning formula.

Wow. And those, when I hear, okay, heart, How do I measure that? How do I predict that? That's, again, these are so nuanced. Like, what's predictable about determining heart?

Like everything in life, I think it's a combination of science and art. And you need both. So it's never just one or the other. That doesn't work. You need a good balance of both. And that's where we come in actually. So when we developed the science and technology, we didn't just say, you know, this is where, you know, you cut these people out just because they didn't meet this benchmark. No, that's not what we are advocating at all. No, it, yeah. So bring the, so the heart is about your, How do you actually build your culture? And that's something we want to help company to do as well. So you can't, you know, that's what you need to build your identity, your culture, and then that's when you bring the people on onto the same team to achieve the same dream.

So good. So that's going to bring us back to who hires a data intelligence engine, and can you talk a little bit about the value of your aggregated data and how it's useful beyond hiring people, hiring people, giving them meaning, finding the right people for the right job that has a high likelihood of success. Just talk about, again, you've got this huge map of information that very few or no one has that's quite like you.

Yes. Well, our goal is to become the biggest database of human data. So we are not— we're talking about, you know, human potentials and the application of that is absolutely huge. That was always the vision when we had building the company is to have this, to aggregate all this data and because it's all scattered everywhere. So once you do that, you're going to have this insight of human potential that just have unlimited applications.

Okay. So now you have a really cool business partner, Jonathan Wisman, and he's sitting in the room with us right now. But talk a little bit about your relationship and where his unique abilities have merged well with yours and what you're doing in your combined vision. Think about your legacy for Who Hire and where you guys are 100% on the same page. Talk a little bit about that business relationship, how you evolved over years, and how you've zoomed in on this core goal and what exactly that is.

Interesting you ask that, actually. We actually—

You don't look at him. You look at me. Look at me. You can't see his reaction. Only I can. I'm putting you on the spot. I know it.

The moment I actually knew I found the right business partner was actually, he actually showed me a video he made about elevating human potential. And every time I watched that, I had tears in my eyes. And because that is exactly the vision I had when I started the company. Yeah, it's been amazing, an amazing journey we've been on, and I'm so excited about the future that is unfolding in front of us. And yes, it is all about unlocking human potential and how do we elevate that. That's great.

Well, how will you know? How are you gonna measure? 'Cause you're all about measurement. How will you know when you've arrived? Do you have an idea, a concept, a number? Like, is it a number of people? Is it like, Is it money? Is it— what would you define as being, I know we're there when X happens?

When the business owners or the companies no longer judge people by their past behaviors, their educations, or their past work experiences.

Keep going. Keep going. I love this. And what else?

So that's, that's our mission, you know, so that, yeah, because we all have these huge potentials, it is about providing them the opportunity to do so. So when companies no longer look at resumes when they are hiring, I think we've achieved our goal.

Wow. Can you say that one more time all the way through? Because that is a profound, I think it's a profound mission, vision, and North Star.

Thank you, Mike.

Yeah, you're welcome.

Thank you so much. Yeah.

I know I'm putting you on the spot, but that was like, that is one of the best visions I've heard in decades.

Thank you. Thank you for this opportunity. I've had so much fun with you in the last few days, and it's been so much, such a pleasure getting to know you and your team, and everyone's been so fantastic, and it's been such a wonderful experience. Thank you.

You are so welcome. I'm so glad I get to participate in this. Good.

It's history in the making.

I really do. I think it takes just this recipe of people and technology and vision and heart and values. It— there hasn't been a better time for this.

Thank you.

Yeah, so good. All right, so where do people go to learn more about you and what you're doing right now, and, and how do they start a relationship with Who Hire?

Well, can visit whohire.com. So, and my business partner, Jonathan Wiseman, thesalesboss.com to learn more about us.

All right. So they just got to go there, fill out a form, ask for a demo, line up and find out if this is right for them. There's lots of extra materials they can go visit and download and start a relationship with someone who could change the course of your life and the people who are in it.

Yes. Wow. Absolutely love that.

Good job.

Thank you, Mike.

You're welcome. All right. Well, I'm going to end this with— now you've met the heart. Another part of the heart of the organization. If you haven't watched the interview with Jonathan Wissmann, you must do that. It is— I think they're really on the right mission with the right technology at the right time. And they're good people who care. So my name is Mike Koenigs. This is Capability Amplifier. This is Regina from WhoHire. And it has been a pleasure spending time with you. It's been a pleasure spending time with you.

Thank you so much.

Yeah. And we'll see in the next episode. You did a great job. A great job. This is your first time on camera, right?

Absolutely.

All right.

You did. What do you think? It's my debut.

That's a good job. All right. Yeah. Make sure you post great job, Regina. And then we're going to see in the next episode. So let's say goodbye. See you. Thanks for watching.