Kerry Goyette, founder, and president of Aperio Consulting Group, is today's guest on Be Customer Led with Bill Staikos. Aperio Consulting Group is a corporate consulting firm specializing in developing high-performance cultures via workplace analytics and training. Moreover, Kerry is a keynote speaker, an award-winning author, and an expert in behavioral science. In today's episode, Kerry discusses her thoughts, knowledge, and expertise with workplace analytics and the future of work.

 [01:04] Background – Starting the conversation, Kerry recounts her journey thus far. 

[03:03] Aperio Consulting Group – Kerry discusses how Aperio help its clientele work at their best.

[05:31] Workplace Analytics –  Kerry discusses how analytics technology has influenced her work with corporations. Moreover, she explains how organizations are using these capabilities to strengthen their cultures and teams. 

[09:47] Artificial Intelligence – Kerry discusses how artificial intelligence may be used to uncover opportunities and assescs cultures or teams' strengths and weaknesses.

[13:17] Aperio’s Approach – Kerry shares with us how her firm approaches exploring raw, messy data to find valuable information using data mining techniques and artificial intelligence. 

[21:34] Great Resignation – Kerry outlines what she sees as the factors influencing the ‘great resignation’ and why there’s more to the story than it being just another effect of the pandemic. 

[26:59] Future Work – Kerry discusses her perspective for the new normal and how our work culture may evolve as people begin to rethink the way they work.

[30:00] Motivational Research Institute -  Kerry talks about her work in the Motivational Research Institute and why she founded it. 

[34:14] Inspiration – Kerry reveals where she finds inspiration.

Resources:

Connect with Kerry:

LinkedIn: linkedin.com/in/kerry-goyette/

Website: aperiogroup.com

Be Customer Led - Kerry Goyette Talks About Workplace Analytics & The Future of Work

Welcome to be customer lad, where we'll explore how leading experts in customer and employee experience are navigating organizations through their own journey to be customer led and the accidents and behaviors of lawyers and businesses exhibit to get there. And now your host of Bill's staikos.

We're going to get into all that in more detail, Carrie, welcome to the show. I'm so excited to have you on.

[00:01:03] Bill Staikos: The stuff that we're talking about today, folks is so topical. So strap in, put your seatbelts on, get your pen and paper out. You're going to be taking notes.

this is going to be a great, great conversation. So Carrie, we always ask every guest to start with. Just a little bit of background and context, just share your journey and feel free obviously to go back as far as you would like, but you have this outside of running your business for over 10 years, you also have a really interesting background.

[00:01:32] Kerry Goyette: Yeah. So it's, it's interesting as you kind of look back at, and I'm sure a lot of people could say the same thing, like look at our journey and you kind of realize, wow, how did I end up here? But yeah, so my post-graduate studies, So psychometrics, I'm a certified forensic interviewer, and I just am passionate about studying human behavior and became even more passionate about studying human behavior in the workplace.

And so I love business. I love to see systems and kind of just analyze systems and how they work. what better place to do that then the, the workforce. And so I just became passionate about it and it's just, it's kind of, I know everybody can say like, yeah, we are going through hard times and I don't want to minimize it.

But from a behavioral scientist, I can say I am rolling up my sleeves and I just am so excited because there's a lot going on. there's a lot that's yeah. Coming to play and it's just a great study in human behavior.

So just, what do, what a typical engagements look like? How do you work with clients? Let's just start.

So it can be problems or it can be just, Hey, here's what we're trying to achieve. You know, we're kind of struggling, trying to, hit these great goals. And so we step back and really analyze it from it from a human behavior standpoint, there's lots of stuff out there on tools and, and whatnot.

And what I've found over the years and looking at, human behavior is we often look at human behavior from like, from an isolated lens. Like, like, like humans are in a vacuum. And so when you talk about potential, like, oh, I have these grades. Yeah, I bet those great strengths can only be leveraged and only be seen in the right environment.

And so the environment has such an incredible impact. And so depending on what's going on in your organization, who else is on your team and how are the personalities working, working together that it's just not as simple as saying like, okay, we need to hire this person that has these great strengths.

Yeah. That may be true. But there are like, Factors. And so that's what, where I really like to dig in. And, and when you do that, I will say my clients, why, my firm kind of, started at 10 years ago and thought, yeah, this is starting a business is way harder than I thought. And I just thought, gosh, where's this going to go?

And. My business got built by referral. And so I say that because that means a lot to me because that means we do good work and we get good results. And so, and I think what my leaders are realizing is that when you really focus on, engineering teams and getting people coordinated and working well together, you can outperform.

Any other team with higher IQ and more experienced. And so, so there is tremendous business value to do this. Sure.

How has analytics technology shaped the work that you're doing with companies or even how organizations themselves are applying these capabilities in a way to improve? Cultures teams, et cetera. Yeah,

So they were so disappointed when I wanted, I wanted to study the brain. But I think that objective approach really looking at validated research is just part of my DNA. And so that's really, I would say like a core value of our firm. Let's rely on empirically validated evidence data. How can we use this to support what we're doing?

And when we do that, and human humans are hard, we didn't study as leaders or business owners. We didn't necessarily study how to, how to lead human beings. And so it is challenging, but when you, when you base it on theory and, and validate. Research, I will tell you the results are very consistently positive and predictable.

And so that's what we do, but, but academic and business don't talk very well. So what we do is we gather kind of all of that, all of the academic research, and then we translate it to the business world and something that's practical, pragmatic, and actionable. And so that's kind of what, like where my history has been an.

I saw this trend coming and, and, a lot of people are like, gosh, you, you do, you wrote a book on emotional intelligence, you have a Ted talk on motivation like AI, like what, but it actually fits perfectly because AI is just kind of new math. If you will. I, I think we build it up to something really dramatic and it is awesome and very powerful.

But it's just an, it's another objective measurement tool that is at our disposal to use. And so when I've worked with leaders, whether it was, lean or six Sigma in the eighties, we all, we all like get so excited and gravitate towards something, but then it's like, oh shoot, human beings are in love involved.

So now we need to have lean and six Sigma leadership training. And it's like, okay, here we go. Like, I saw this. Like here we go with AI and data. Like, everybody's so excited about it and it's going to be like, the crystal ball, no, it's not. Humans are involved. It's going to be really messy. So, so three years ago we, we dug into AI because we wanted to figure out, we wanted to figure out this synergy.

I had a hypothesis. And that hypothesis turned out to be true, but I will be honest when I went into it, I thought, okay, I have no idea. And how that is as an entrepreneur, you kind of like, all right, I'm going all in, but I have no idea how this is going to turn out and, and let me also say, I'll just be completely authentic.

I knew zero about AI, so it's not like, yeah. Talk about bringing out all my vulnerabilities. But we dug into it because I had this hypothesis was seeing other trends that okay, humans are involved. And so that's where the problem is going to be. And so sure enough, you look at AI initiatives, 90% of AI initiatives fail.

It's been due to the, the human machine interaction. And so my art quest has been, this is great

technology that of course can be used for bad or used for good, but how can we figure out how to partner. To really help the human and also help the organization in a way that's beneficial to both.

And I think it's possible. And I think we've demonstrated that, I think it's messy and I think a lot of people are trying to engage with it, which I think is a good thing, but it's going to be messy for a while.

think about it and the right with the right use cases, et cetera. When you think about measuring. Whether it's strengths or weaknesses around cultures around teams, high performing, low performing. How does a perio in your organization, how do you approach that measurement? And then how does AI play a role in that and identify opportunities?

The problem now is we're in the knowledge era and. I have leaders all the time. Like, how do I know my employees are working their virtual? Like, how do I like, and then, but, but here's the thing I will bring up then nobody is talking about bill. Do one of the reasons why employees these days are so stressed out and burnt out?

Because they don't know what drives success. So they are like, like, okay, I don't know what the performance metrics are necessarily. So I'm going to do twenty-five million things. And at the end of the day, they don't even feel like they're really adding value and how demotivating to the human spirit is that.

So that's a part of the conversation I'm really passionate about because yeah, we don't have the right metrics and we know we have all this data, but most of my clients are like, we have all this data. We don't know what to do with this. Our data scientists don't know how to speak English that that the executives can understand and the executives don't know how to speak data science language.

So there's just all of these, kind of things that are there that are going on that are, that are really kind of, causing some of this environment. And I, again, that's where I think if we take a behavioral science approach, What not, what can we get from the data, but what does a human need to know?

What does the leader need to know to make a better decision? And how does that need to be served up to the leader? And then with employees, like, we need to better, we have this data, we have capabilities. How can we better start to tell them, like, here's truly what drives performance and let's cut through the chaos.

Let's get rid of all these things. And if you can focus on these three things, this is where you're going to drive the most value. Your, this is where your. The biggest impact. And now all of a sudden they feel like, okay, I know what I'm supposed to do. They have more clarity because back in the industrial revolution, it was very clear.

I got to get this many widgets. Now it's less clear. And so that our brains hate uncertainty so we can provide some clarity for them. And now they can really focus on what matters and they can see how it's driving value to the company. And that, that's what, that's what we ultimately want to do. We want to feel like our work matters and when we feel like it doesn't matter, then all of a sudden it's like, well, why am I doing it?

Even in past lives. I've heard, let their, how and CEO saying, like, how can we make our employees more productive? Who cares if I produce a hundred widgets, if our customers want to buy one of them. I've wasted 99% of my time. Right. So how do we think about efficacy and what does that mean, et cetera, like, so how are, are there measures or are there different ways to be thinking about this, counsel that you can provide to, to listeners, and how they might be able to approach similar

Yeah. So this is, this is what we have done. So I'll just kind of walk you through and share our approach and how it's worked for clients. But, number one, the first step is, is exploratory. most of my organizations have raw, messy data. Right. So it's

[00:12:25] Kerry Goyette: yeah, exactly.

I mean, we literally had a conversation the other day and there was like, no, we don't have that data. And we just ask the question three different ways. And finally somebody goes, oh, well, we do, but it's over here. So it's like, okay, the data exists. It's just over here. first of all, I would just say every client who comes in with like, I'm so embarrassed, our data is a mess.

Number one, just don't every, everybody says that. So if you're feeling that, that that's the norm that's okay. But the first step is exploratory, meaning that we have to raw messy data. has a lot of golden nuggets in it, but it requires careful processing. And so our firm, we use a data mining approach and machine learning.

And so what we do is we take this messy data, like, let's say, we're looking for, one client, we looked at sales. So we took all of their messy data from their CRM. and so our algorithm is able to, to, to, to really scan that dataset and, and isolate those key variables that are driving that performance.

So sales is easy because we have a good performance metric, revenue, or if we're looking at retention. And so this is where I will say technology can be our friend, because what AI can do. That helps humans is that it can see patterns and it can work with tons and tons of data. So we just tell clients, give us all your data and it's messy and it's unruly, but then the algorithm just cuts through.

And it will say, okay, so for sales for you for this organization, here's what really matters. And so now we can get really focused. And so for this organization, for the VP of sales, it was groundbreaking because it sounds like, like something like, the sales VP of sales should have known, but, but the data was able to identify this little hidden pattern of customers that they had in year one.

They had a retention issues with, customers that had bought from them once. And so it was a $22 million opportunity that nobody was focused on and it highlighted to the VP and the sales VP. And so it was like, oh my gosh, like, oh my gosh. And so it can pull out little nuggets for you. Second. I mean, that's the beauty of it.

And so that's where, I think that AI can be your friend. It comes through lots of data. It sees all these weird, it can look at a path, look at data in all different ways and then it pulls out what's important. And then more importantly, it can, it can also be. What does it matter now because we're, we focus on behavioral science on the, for this particular client, we had assessments.

And so we were able to look at the sales people and look at their data, their assessment data. And we found that there was one critical factor that led to success, literally correlated to revenue in a one-to-one relationship. And it was an optimal level of perseverance, not too high. Interestingly, it looks at it on a spectrum.

So not too high because that Salesforce rigid not too low or they wouldn't follow through enough, but then the data from that, from that we can see. Okay. So what does perseverance mean? The data can spit back and tell you here's what perseverance means. They're staying on calls 22% longer. So it's not perseverance, which is something very.

Conceptual. And that's what I've struggled with up until AI is like, well, it's perseverance. And then they'll be like, well, how do I train that? Now? It's like, oh no, we can, we can figure out how to train it. So not only does it help in identifying what truly matters, but it reveals. hidden risk factors or reveals opportunities that you didn't realize.

And more importantly, it cuts through the chaos and the noise. And I will just say one other important, important side benefit that people don't realize everybody's trying to collect data, but nobody knows what to do with it. Or they're not really good at figuring out what to do with it. It's expensive to collect a lot of needle and, data and it's expensive to keep.

And so the benefit of doing this is you see what data do you need? And what data doesn't matter, let's just stop. Like let's get rid of it, let's collecting it. And so for us, like, it's just a great way to, it just, it just checks the box of so many things. Now, again, it took us, we've been working on this problem for three years.

This was not an easy problem to solve, but it can be solved.

at a, at a prior company I worked for, we were a Microsoft shop, so we had workplace analytics. And one of the things that we were looking at was our hypothesis. Was certain behaviors, around customers led to better experiences for that customer, which obviously would leave higher revenues, et cetera.

of data, if, where do you start? Like, if, if you're like, I don't even know if I have anything that's using. Like, how can you start small in this space?

So that's, that's the exploratory part. And honestly, that. Isn't really that hard for a data scientist. So that's not a difficult problem to solve, send your data out, have them spit out an analysis. the second step that we've built that's predictive is, creating an algorithm so that we can now predict.

So it can now notify ahead of time. Like, Hey, VP of sales, we see a dip in this. You need to, you need to be on this. And here he had hears like what it's seeing so that now the VP can go like, oh, great. Oh, awesome. Yeah, I know what that means. Like, they've got the subject matter expertise. They can go deal with it.

That step is something only right now that I know of that we have built and that's, that's the three, but that's the easy part. But I would say first step is the exploratory part. Let's just get your data analyze. See what you have asked that data scientists. Is, are we collecting, based on what, what, what you see are we collecting the right data?

Should we be collecting other data? And it's messy and, but it's not, it's just, it's messy. And I would say that that's the hardest part. it's messy, but you can do it and just send it out to a professional to do. And it takes time.

And they were like, we need to do this. Like in three months it was like not going to happen. It doesn't work then. Right. but, let's, let's talk a little bit about, like, you're doing all this work globally and I want to bring it back to your point around employees, not being able to understand what a success look like, and they're doing a million different things as a result, trying to figure out kind of like what's going to stick against the wall.

I mean, that clearly is leading to people to look elsewhere, pick up the phone, talk to recruiters. Are you seeing other things that are shaping sort of the great resignation? Like what else are you kind of, I'm just curious to hear your perspective, given you work with so many clients. What else are you seeing kind of shape that shape?

The kind of environment around the train?

Yes. It added some punch to it. And so, yes, it's not just about millennials and gen Z. Do they have some kind of interesting traits about them? Yeah. How we grow up our context, what happens in our environment matters, but no, it's, and it's also not the technology. It's not school, it's all of that and, and more, and so it's just, they're there, it's kind of.

Well, a perfect storm. You could call it a storm in a negative sense, but it's just, it's just a lot of these factors. That are playing a part of that, but I've been talking about this for years because the birth rate rent, it went down after the industrial revolution, it went down simply because people realize kids were economic liabilities, and now we have to pay for childcare to go to work instead of, having them tend the farm.

Helping us. So the birth rate went down, they just became more expensive and harder and, and two women started entering the workforce. So it really masked some of the, the, from the workforce. It kind of masks that the birth rate had gone down because women were entering it. But now what happened with COVID the Pantex.

Women left the workforce. Why? Because they had just so many pressures of, of kids in school and, and, and other factors that they left in droves. And so now it's becoming apparently obvious because males, went down significantly. And so, so now. It feels like a bigger punch, but, but really that, that, that's not new.

It's just being highlighted right now. And then on top of that, because of technology, now we have a skills gap and so we're having to be retrained and, and, technology is picking up some of the rote routine things that honestly, a lot of humans didn't like to do. So in some ways that's good, but, but there's a skills gap.

And so now you've got the gen Xers and baby boomers. That are really having to step up their game on technology. And again, we've got an issue there because now kind of, we always try to like, fix things that maybe require a. Scalpel with, with a sledgehammer, but we're now like, okay, we just need younger people.

Let's hire a bunch of younger people. And now we've got a lot of pages I'm going on and really gen X-ers are good. Like they're good managers, they're hard workers. It pays, there was some re recent research that came out. It pays to upskill them, get them up-skilled on technology. And, and so that's where we're having to do.

Reverse mentoring gen Z or is mentoring the gen Xers and baby boomers. So there's a skills gap there and organizations, we just, haven't always been great at training and up-skilling, so we're having to get better at that. Immigration is at a record low, I've got one organization that they are struggling finding drivers, and they're having to recruit out of Eastern.

I mean, we're just these problems that we're having. We're having to get really creative and how to, how to solve it. And then of course, the boomers, which is our largest generation is now exiting the workforce and we knew it was coming. They kind of held on during the recession. and now it's like, boom, boom, boom.

They're, they're exiting like crazy. And so it's multiple factors. And so I think we really have to look at that and, and, and not just assume it's like, well, I think a lot of people want to bring blame the pandemic. And just kind of hope when things go back to normal, like hope is not a strategy and know it's not going to go back to how it was.

So we need to think differently. We need to think about our cultures, those organizations that focus on their culture pre pandemic have better retention rates than those that didn't. And then two, during the pandemic people inherently, it goes back to my point of people in here. I feel like their work matters.

And so law firms are, are, are really hitting a real low. I mean, they're getting hit hard because, we drove them hard. If you want to be a partner, you work 80 to a hundred hours a week. And now people are like, why? Like what is what I'm doing really matters. And so I think we have to rethink, and I think there's a way, so, employees now are in the driver's seat.

They hold more. And, organizations are being forced to think that way. So it just, it's why I have a Hartford leadership. It's why I've always worked with them. This is not an easy job and it just got harder. Hm.

Leveraging analytics, artificial intelligence, machine learning culture overall. What do you think that new normal is going to be? It feels like certainly the balance has shifted from business centric to employ, or I shouldn't even say employ more like work, workforce centric, right. Because people outside of employees work at companies, what do you think.

Next normal might look like if this, if that, if I could even use that term,

I think we're going to make mistakes. And I know a lot of people are really concerned about people analytics. We don't want to have bias. And I absolutely agree, which is one of the reasons why we got into this space is we care deeply about honoring the human. And so, we're going to make mistakes, but ultimately it's kind of like.

when you think about our, child development, when you're one and trying to walk you fall down, you make mistakes. And I think it's going to be rough for awhile. I really do. I think it's going to be rough, but it's a good thing. We've been working on automation cause we're struggling trying to find people to, get bodies in the door and.

I think everybody was fearful of it, but it kind of came at a good time because now we need help. We need help. And we want to put people in, in positions where they, they feel like they have purpose and meaning, I keep going back to that, but I cannot say how important that is. And I think we can use data to get, I mean, this is the great thing is I can see through all this mess that we're going someplace good.

So artificial intelligence, it just feels, but if you think about it as, super math, I would just say engage with it. It's okay. That you don't know much about it. I haven't worked with one leader. That's like, oh, I know exactly what that is. They're like, I'm tired. They're in fact, they're tired of hearing about it.

So I would just say, engage with. It's going to happen. If you don't, you will get left behind because there's great D we need it to help us make sense of the data. And so, no matter what data you're collecting, there's something I can almost guarantee we haven't yet found a data set where there wasn't something valuable in there.

And so you've got some valuable things. We just have to kind of put on, our mining hats and, and mind through it and, and, let it be messy for a little bit and then find the gold nuggets and then focus on there. Don't, don't bite off more than you can chew, but find the golden nuggets focus there and then you'll just, you'll just get better and better.

And why did you start up the Institute? and, and what is that doing for organizations or even maybe private. Yeah.

And so, one of the big thing, being a business owner and a woman at that, starting a business. It was hard. I mean, I always equated to, I felt like I was standing on a stage naked and everybody's looking like it just like, there's nowhere to go. I can't blame my failures on anybody.

And so it was, and I just kind of have a heart for innovation. We need a lot of innovation in this, in this world, I think we have some pretty big problems we need to solve. And so it's not easy being an entrepreneur. And so we studied entrepreneur entrepreneurs and we partnered with an angel investment group and we wanted to dig in what, what, how important was the human factor.

And can we study that? And then can we use that information? To better help people make better investments. Number one, because we see that women and underrepresented, races are not, are not getting invested in. And so we wanted to study what is the human factor? And sure enough, we found that 78% of the success was due to the human factor.

And so we looked at 120 different psychometric. True. we use the algorithm to really make sense of the data. And so now we're, I'm using it to how can we better equip entrepreneurs and investors? cause we have the same mission, both investors and entrepreneurs want them to succeed for different reasons, but, but still, and so let's figure out a way that investors can better help them.

And entrepreneurs can, can understand what, what is going to hold me back because we are social beings by nature. And when you're an entrepreneur, you kind of feel like you're on this island by yourself. And so we as humans, we're messy and we have what we, what I call de-railers. It's a kind of, probably the most talked about a chapter in my book, but when you're put into an environment like entrepreneurial.

Ooh, it amplifies it. And so we have to be really careful. And so that's what we found. it was just so interesting, but that's what we found from the research. It was the derailers that, that significantly predicted their failure.

All the potholes. So we're going to give you the road. We're going to give you sort of the street roadmap ahead of time. Make sure you Dodge these as you're driving in your ideal.

I was, interviewed for a documentary and on mergers and acquisitions. And so I think it's two, a way to highlight that, there's. Underrepresented groups that, that have a lot of potential, but, but they may not have the resources that other groups do. And so, how can we start to highlight them?

And again, when we, when we start focusing on other factors than just, the typical, factors that investors focus on, I think it starts to bring in and be more inclusive to the community in a whole. And then ultimately, we can use this to better, better equip entrepreneurial.

Where do you get your inspiration from? that like that now likes talking for the last year. I'm really curious to hear your answer.

I'm just instantly going to be inspired. and I feel as over the years, that's not how inspiration works that I have to be intentional and it's really a full-time job. And so. I am most inspired when I get to think strategically to solve a problem. and I love to dig into the research, look at the data and then find a solution that's practical and works that that's like inherently.

So it may be different problems or, in different, use cases, but that's what creates inspiration. But here's the little flip side to that, that I've learned that there's a cost to that. And I think that that's. That it took me a long time to learn this, but, we have good into, I kept the saying like, oh, well, yeah, I know I love doing that, but I had to do this this week.

Oh. Or this happened, or a client asked for this, there was always a reason. I finally stepped back and took a hard look at it and realized if I'm really honest. There's like, To creating space for inspiration. And so that means I have to turn off my slack. I now have a practice where I take one day off a month and it's my, it's my inspiration day.

And I go away, I'm off the grid and nobody can talk to me and I think, but I had to get over that and I tell people, do you have a need to be liked? Do you have a need to want to impress others? Or do you have a need to prove yourself? And that's, what's going to keep. From designing inspiration into your life because you will respond to that slack message or let email just take you away.

And so I think we just have to be honest and say, there's a cost to it. Am I okay with it? And then how can I kind of set XP expectations? And I let people know that. I want to deliver great value. So I need some thinking space. And so then people are like, oh yeah,

[00:33:18] Kerry Goyette: I literally had a conversation today with my team saying, okay.

I know we're doing this once a month. Is there any way we could get to like every other week? So yeah, T two beats just determined bill. So I'll let how that goes, but we'll see.

Like each one of them has a hundred different things that can kind of pull us away. So, giving yourself the freedom, having the courage to be able to step away from all that as well is critically important, just from a broader wellness perspective, but also, being able to find that inspiration.

Okay. This has been a great conversation. Thank you so much for coming on the show and sharing some of your insights and knowledge with

I

Maybe for another time, maybe have you on the, on the show another time. And, thanks everybody for joining us this week. We're out. Talk to you

Leave us feedback on how we're doing or tell us what you want to hear more about until next time we're out.