Is what you’re doing in HR impacting your bottom line? If not, why are you doing it? Pat Acheampong is the founder of Ahumka Digital. He is a self-proclaimed Tech Junkie, a Would be Philanthropist, an Entrepreneur, Bon Vivant, and Aspiring World Changer.
With over 15 years of experience working in global businesses in the UK, North America, Australia, and Asia, he is currently head of Employee Experience and Service Delivery at Zurich Insurance in Hong Kong.
Any tech-geek is a friend of mine! So let’s dive into the episode and learn about using the employee experience – profit loop to boost your organization’s bottom line.
[00:01 – 05:17] Opening Segment
- Dwight and I welcome and learn more about Pat Acheampong
- Today’s topic – The Purpose is Profit
[05:18 – 12:31] What’s Broken in HR Data Collection Today
- Lot’s of data, little insight into the employee experience
- What’s missing in employee experience and how to improve it
- Keep people in people analytics – profit loop
[12:32 – 20:57] What You Should be Producing Analytics On
- ‘So What?’ analytics – placing value on a people asset
- Practical evaluations
- Finding the right turnover
[20:58 – 29:17] What to Measure and How to Do It
- It doesn’t have to be contained into a fixed field
- Quality data is key, get specific with priorities
- The right people focused on the right places
[29:18 – 31:30] Closing Segment
- Summary of our conversation and final points
Resources:
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“It’s the why, why are we doing this? If I’m doing something in HR, what is the impact on my organization’s bottom line, how do I measure that, and if there isn’t an impact on the bottom line – why am I doing it?” – Pat Acheampong
Announcer:
Here’s an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what’s happening in people analytics today. Give them the technology to connect, hit record, pour their discussions into a beaker, mix thoroughly. And voila, you get the HR data labs Podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, but count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here’s your host, David Turetsky.
David Turetsky:
Hello, and welcome to the HR data labs podcast. I’m your host, David Turetsky. Like always, I try and find people, fascinating people inside and outside the world of HR to bring you interesting trends and what’s going on in the world of HR data analytics. Today, I have with me my great friend, Patrick Acheampong. Patrick. Hey, great to meet you also called Pat, by the way. And I also have Dwight brown from Turetsky Consulting. Hey, Dwight, how are you? Hey, David. I’m great. How you doing? Very good. Thank you. So for those of you who do not know, Pat, pat has been inside and outside the world of HR for almost 30 years. And by the way, if you saw him You wouldn’t guess that he looks like he’s 18 years old. It’s a new hat. So Pat’s done security. Pat’s done HR technology inside the world of the financial services industry and outside, right, Pat?
Pat Acheampong:
Yeah, absolutely. I’ve been practicing oil and gas and also industrials as well, just to see what life is like outside financial.
David Turetsky:
You’ve really been pretty well rounded. I’ve tried, I’ve tried. And so one thing if you don’t know, Pat that and even if you do know, Pat, that you may not know, and I was actually there. So I guess that pat had worn a kilt, and we were both at the same party and we both wore kilts. But unfortunately, on his way home, he was mistaken for a flasher. Pat What happened?
Pat Acheampong:
It was a long night, but it’s this is back in the UK it’s freezing cold in the in the winter, so fools everyone’s got their long coats on. So if you can picture it, where you’ve got socks that come up just below the knee, and then you’ve got a three quarter length coat that covers you just up to the knee. So if you’re looking at that, which I wasn’t until I actually got it looks like you’re wearing a pair of socks and nothing else except the kilt. And so there were a few strange looks on public transport on the way home that night.
David Turetsky:
Okay, so again, full disclosure, I was also wearing a kilt that night, and I did not get accused of being a flasher. I think I may have left my long coat at home. But we were all wearing sporran, which are the traditional kind of murse or man purse that you were on top of the kill. So I think I’m just gonna leave it there, Pat. Yeah, I think we need probably best Yes. Yeah. The good news is you were not arrested, though.
Pat Acheampong:
Oh, no. Luckily, otherwise, there would have been even more red faces. And I’d have missed the next party. So you couldn’t have?
David Turetsky:
No, we could just could not. So Pat, our topic for today is the purpose is profit using the employee experience profit loop to boost your organization’s bottom line? That is a fascinating topic. Do you want to explain it in a couple minutes?
Pat Acheampong:
Definitely is and it’s it’s something where, sort of over the years, I’ve done little bits of, essentially what it is, is, instead of looking at what we’re doing HR, just as looking inwardly and just thinking about it from an HR point of view is basically the why why why are we doing this? So if I’m doing something in HR, what is the impact on my organization’s bottom line? How do I measure that? And if there isn’t an impact on the bottom line, then why am I doing it? That’s essentially what it is. And it starts from great employee experience leading to really engage employees will be paid employees are more engaged with the customers provide a better service, which means you get less customer churn, which means customer acquisition is lower. The cost of customer acquisition is lower, customer churn is lower, revenues are higher costs, costs are lower. So that’s and then that feeds them back into back into the employee experience. So that’s, that’s that’s what the mean If you see that,
David Turetsky:
excellent. So Pat, we have a ton of HR data. And getting back to the employee experience and the profit loop, what is broken about all the data that we’re collecting today?
Pat Acheampong:
Right? I think where we’ve got to is, you and I know from, from where we started out, there were very few HR systems. And now we have more and more and more. And basically, what they’re doing is collecting data, what we’re not so good at, is getting that data out and turning it into meaningful insight. So the three stages of the data, which is, you know, what’s happened in the past, we give it that, you know, how many people do we have? And then we look at, okay, they throw big wire analytics in it and say, you know, with, with these people, where they based, what are they doing, and very rarely do we then go into the insight to say, with all these programs that we’re doing is it isn’t making any difference. And that’s, that’s what’s missing, we’ve got lots of data, not as much insight. So, you know, it’s what’s been done, not what the value of what’s being done is. And I think that’s why.
David Turetsky:
And so I guess the question is, you know, what are the things that you’ve seen that kind of lead to an understanding about how do you improve that employee experience you were talking about before? That what are the kinds of things that were missing in there?
Pat Acheampong:
I think what’s what’s missing is basically, that there are there are measurements that that tell you everything that you need to know about the overall performance, there’s employee engagement, you’ve got how that leads to customer satisfaction, and how that leads to profit. So basically, it’s it’s how do you energize your employees or engage your employees so that they want to provide that great customer experience, which is a bottom line is what what people are supposed to be there working for? So it’s taking, if we, for example, provide training to people? Do we measure how many hours we’ve provided? We measure how much that training has cost? Or do we measure? Well, since we started providing status, technical change to employees, what we’ve noticed is that we’re producing a better product that customers want, or if we’re providing top notch customer service training, has that now translated into a lower churn rate, because customers are now getting better customer service. And so they’re looking at the company, and, you know, staying and referring others, etc. So we don’t go beyond the water we provided into what’s been the impact, we just do what we want to be provided.
David Turetsky:
So it’s really looking at those outcomes of that, what is that training actually buying us? What is the return on that investment, we’ve had some conversations before on HR data labs about being able to get to that closed loop, unfortunately, what happens is, is that, because HR doesn’t really have a good handle, or doesn’t actually have access to a lot of that customer acquisition, customer churn data, it’s hard to actually make those or draw those conclusions, because we only have half of the data. So I think what you’re saying is, is that we need to not only have the HR side of it, but consider the customer or the product or the product. Well, yeah, the end product side to be able to say, is the training I’m investing in is the dollar or pound or nickel or whatever training I’m doing for employees, is that generating any benefit to the end product? So that I can see where should my trainings actually be focused? And where should I spend more, you know, which areas should I spend more or less?
Pat Acheampong:
Absolutely, and it’s not just training, it’s things like, you know, the new beer pong table that we put up every Friday, you know, should we be doing that? Or is that actually more? And I think a lot of it is because we’re sitting in the HR silo, and with the best will in the world, and some companies are very good at it. And that design, we have HRM This is what we’ve done have our data scientists who say well, let’s look at this from a big picture point of view. Rather than just looking at this bucket why not look at Well, what’s the impact on the end product which the end product where the business is the bottom line and connected too.
Dwight Brown:
You know, I think this really gets to a key point people analytics and that is how do you keep people in people analytic Yeah, and you know, the idea that you talked about with reaching across lines reaching across segmented boundaries to go from HR to whoever has a customer data. And while at the same time measuring that employee engagement, it makes a lot of sense. And I love that loop that you talked about what’s getting to the profit and, and whatnot. And, and the fact that that is a continuous loop because it’s not something that you do as a single project. And you’re done. You’re You’re continuously going and going and going. Yeah,
Pat Acheampong:
absolutely. And I think one one other things, it takes HR, not completely, but to large extent, part of being seen as just a cost center. Because right now, if you’re just providing things at a cost to the company, that’s always seen as if you can’t then demonstrate how that is of value, then guess who’s the first cost cuts? When companies?
David Turetsky:
Exactly. So, Pat, you talked about data scientists in the world of people analytics, I just wanted to comment on that. Because, unfortunately, and I’ve seen this happen too often, that data scientists try and boil the ocean and overthink the people analytics problem, where the things you’re talking about really don’t require necessarily don’t require data scientists, they require a people analytics person who has the ability to kind of bring things together, or at least to ask their colleagues in the other areas, Hey, can we sit down and talk about how the investments we’re making impact the things that you’re doing?
Pat Acheampong:
Absolutely. And it’s, you know, it’s trying to, you know, take each lever as it comes and says, What’s the impact of this level, rather than taking every single program you’ve got, and then trying to measure all of that, because then what happens is, no one can make any sense out of it. So then people give up and say, well, it’s impossible to do
David Turetsky:
But I mean, really boiling the ocean.
Pat Acheampong:
Yeah, absolutely. Yeah. And we try and make it too complex. You know, you’ve got to start simple. It’s a bit like lean startup, where you know, you do something you learn from it. And by that learning, you do the next one.
David Turetsky:
So that brings up our next question, Pat, which is, so what should we actually be producing analytics on? You know, simply, you know, where do we begin? What is the right way of actually starting in HR to produce analytics?
Pat Acheampong:
Good question, I think what we tend to do is retreat into the safe world of so what analytics? So that’s what I call it say, What? Where it’s a case where we’ve done X amount of training, or we have X number of people or, you know, the usual HR piece, which is we’ve, we’ve promoted the summer people that’s kind of so what what is, what does it give me? Well, as I said, Before, we should be looking at, you know, the information in our systems, what is it actually telling me about value? And as long as every time we look at it, and think value, rather than just I will go for the easiest analytics piece that I can find, then, you know, that makes sense. So my point that I always make it easy so many organizations that say, our employees are our greatest asset are our employees are our most valuable things to the organization. And I’ve yet to see any company, maybe there are some that can actually tell you how much that value is exactly how much it costs, but they can’t tell you what its values. And it’s their biggest asset.
David Turetsky:
Yeah, but I think that that’s troubling, right? It’s troubling, because you’re right, that it’s hard to place a value on a people asset. I mean, we have various ways, and there’s earned value. There’s EDA, there’s, there’s a lot of ways of taking the word capital, which means you know, the equipment, machines, resources and putting a value on it. Because accounting has text data on this, there’s rules about how you actually do that. But for people, there really isn’t a good way of actually assigning a value, there’s no way of being able to understand what why is, what the value of y is in this equation. Because people aren’t that easy to to value, right? I mean, you can figure out what their market value is from, how much do I pay them? That’s easy. We have surveys for that. But it’s not an understood thing to say, what’s the value of their output? What’s the value of them being here? What’s the value of their innovation in their minds in the way they think and the way they talk? right?
Pat Acheampong:
Exactly. Yeah. And even the market value, I would argue that’s their cost, not their value. Totally agree.
David Turetsky:
Totally agree. Yep.
Pat Acheampong:
And I wonder how much of that is because traditionally, they’re not challenged to do that, because I’m sure if the challenge came down and said, Let’s stop telling me how much it was costing, start telling me what the value is, people will find a way to do it. And I know certainly at one point I was I was looking into that. And there are various sort of research papers in there that have started looking at, you know, what is the value of the output of employees to the company, right. And so it’s doable, it’s just not something that it will take work, it’s just not something that was pioneered, because we’re the best one of the world’s most companies haven’t really thought about it that way. which is surprising when you consider 60% of your costs are people costs.
Dwight Brown:
Well, and being able to quantify that in some way or another is a challenge too, because if you look at the value equation with the from a people side of things, so much of it is is really what you get that feeling through conversations that you have, and it’s a lot of, you know, you’re combing through unstructured text to be able to get the to get the data that you need. It’s hard to create solid endpoints. I mean, there are definitely endpoints there. But I would say that’s also an additional challenge and to that measuring value piece.
David Turetsky:
But you can certainly look at how many inventions people have made patents. You can you can look at how innovative people are. And by the way, even to your point, Dwight, there is network analysis, you can look at that does value who people talk to what level they’re talking to? What frequency they’re talking to those people? Are they adding value? Are they just responding? So network analysis actually does do something like that today, I’m placing a value on people in an organization. But I think what you’re saying is it’s just even one piece of it right? That’s not that’s not the larger piece that I think I think Pat’s talking about. But that’s really hard to get started, because no one’s actually thought of that. Pat, is there? Is there some? Is there something more practical, that we should be evaluated?
Pat Acheampong:
I think I think the practical way comes into it. We’ve talked before about avoiding the ocean. And I think generally what happens is, we look at it and we think, oh, wow, that’s this huge, scary thing, we’re never going to be able to do it. And so we never start. Whereas if we started finding the pieces that go into it, and you know, eventually you start off, he say, Hey, we’re going to measure this part of value, and then we’re going to add this. So an analogy is when a lot of companies started measuring turnover, and then moved on to what’s the cost of this turnover, we now know we have you know, churning at 20%. That, really, what’s that costing? And before no one ever asked that question. That’s awesome. Now, great, you find increasingly there are more and more sophisticated ways of measuring how much that turnover was costing. So it started off with just measuring people’s salary. Now it’s gone on to measuring Well, how much productivity Am I losing? While that person is out? How many people is it taking to then bring another person in? Am I paying over and above what I paid so that the different variables got added to it? rather than starting out? Right? We need the end point was the total cost people started and then they then they added to it? And I think this is this is the same thing. When you talk about value. You talk about what what do we know that we can measure? And what can we add?
David Turetsky:
And I think at the end of the day, what it tries to do is it tries to give managers and leaders and understanding if you’re providing it to them in the right way to say Listen, this is a precious resource. Yeah, there is a cost if you lose them. Sometimes we have to lose them. Sometimes there is more opportunity cost in not letting someone go than in letting someone go. And I think you brought it up before an engaged employee is someone who you want to hold on to potentially exempt unengaged or disengaged employee who is causing disruption. And they’re in a position where you have people who are willing to take on that role or should be taken on that role, you’re actually missing out on the opportunity to increase your productivity, increase your the engagement of others around that person. And let’s face it, not all turnover is bad turnover. Sometimes you need to let someone go at a level to be able to promote other people to be able to get new ideas. And so that’s that’s good. And so that that’s not necessarily in the cost equation of turnover. But it’s other things that we have to be able to add to the story about turnover in order for that leader to make the right decision.
Pat Acheampong:
Absolutely. And also what what is the right type of benefits, as you’ve said, there’s turnover that’s needed, in which case that’s a net positive that you would add to the equation rather than take it as a cost of turnover. You know, are we are we losing a poor performer versus a, you know, a good performer? That’s to me is a different cost of turnover because The cost of them staying is higher than the cost of government. So then it gets more and more sophisticated. And I think the ability to churn through big data that we have these days that we haven’t had in the past that definitely provides opportunities. And I think with things like big data, the technology and the various ways of grabbing polls, surveys, etc. What we haven’t quite transitioned to in HR yet, is when we talk a lot about digitization, but we haven’t transitioned to what we do with all the data that comes out of the digitization. That’s very different.
David Turetsky:
And I think that brings us to our third question,
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David Turetsky:
The third question is what and how do I measure to be able to make all this happen? And so you talked about pulse surveys? And there are other pieces of data that we can bring not just in HR systems? Where does all this come from? And how do you actually measure it?
Pat Acheampong:
Right? Good point. And, and this is where we start thinking about data lakes. So instead of having the walls between the various departments, and like, alluded to that as well, you have your corporate data lake, and you have people who understand what’s in there and make the connections between them. And that’s where your data scientists come into it, because it’s not your standard data analyst. So if there’s a real incentive to do this, I think the insights that you can get from that, which you know, and then you can surround around structured data. So, you know, pulse surveys, there’s a lot of work that’s being done around texts, analysis and sentiment analysis, it doesn’t have to be, you know, just everything that’s contained into a text field in your HR system. I know, moment, I’ve seen a lot of work being done around text analysis, which is really, really good and predictive analysis that goes with it. So it is there. It’s just not something that is that is currently being considered a priority within HR, necessarily, because it’s kind of thought of as this is a financier.
David Turetsky:
Yeah, but I think the problem is more what we talked about just a little while ago, which is the How do I get started? Because in order to be able to use a data lake and be able to get a person who’s trained to be able to look across finance and HR, you first need to establish the beachhead of what is HR trying to accomplish? What are we going to do with this data? What is the general business problem we’re trying to solve? And what I’ve heard from a lot of the clients that we deal with in Turetsky Consulting is that it’s two stories. First of all, my data is in rough shape, I need help fixing it. Yeah. And the second one, and that goes just beyond HR. But it’s it’s mostly an HR help me fix my data. And the second problem is, where do I begin? Because the problem I’m dealing with is not that I have a ton of data, I have a ton of data. But it goes back to the point of what am i measuring? How am I measuring it? And then how am I distributing it to which people?
Pat Acheampong:
Yeah, absolutely. I think the first part, I mean, obviously, the quality of the data is key. And if you haven’t collected that, basically, there’s not much you can do about that. But what you can do is start by saying, Well, how do I want to start measuring it slowly? Because then that tells you Well, what they need to do to start fixing first. So then you don’t have to go out and buy a multimillion dollar HR system to fix everything you say, right? We need to fix, we need to know this piece of information. So what is it that we need to do in order to get this information? And we’ll fix the others. We’ll get to it. But we’re not going to do it right now. And so you start with that?
David Turetsky:
Yeah, I think you start in one place. You mentioned it before, start one place, and then radiate out from that center. Right? You talked about how we need to have a good handle on one thing first and make incremental improvements from there. Yeah, to me, it starts with the employee object. And then you go to the job object, and then you go to location object and you go to all the other things in the HR domain that enable us to understand more about our people. Yeah, and it starts with the smallest unit first, and then it goes out from there. And as you’re radiating out, then you start to say, Okay, well, I’ve solved the problem of understanding and being able to be clear on who an employee is, yeah, cuz that’s not actually clear anymore, right. I mean, there’s, there’s a lot of differences even this year versus last year about who’s an employee who’s a contractor who’s a gig worker that’s been evolving and a lot of the HR systems were built before even the concept of a gig worker exists. Exactly. So as we start radiating out from that we solve that problem of understanding the data, then we can say, What business problem Am I trying to solve? Like we were talking about turnover? What is turnover? mean? We could probably exclude the people like gig workers or temporaries. From that, because they’ll cause noise in the system, because they come and go as we have projects. Absolutely fine. Not really. Yeah, exactly, exactly. By Design, we don’t want that cost hanging around. So that’s not that should not be part of our turnover equation. Well, just then we’ve talked about something that is pretty revolutionary, you’re having a turnover analysis, that turnover analysis shouldn’t include seasonal and non traditional workers, because that’s not what we want to measure. Once you’ve done that, then you can start thinking about something else. So starting isn’t as hard as people actually think it’s just you got to get that momentum.
Pat Acheampong:
Exactly. Exactly. And you need to have people, logical people, who can focus on that as well. I mean, first of all you’ve got to start forming a hypothesis. What you think New something? So, you know, does does training means performance? Or does it have no impact at all? Does foosball, you know, move engagement? Or does it have like, you know, what is your hypothesis? And how can you test it? And again, that you’re not going to get a generalist to be able to do that. So it’s having the right people focused on it. Because I understand how to connect the dots.
Dwight Brown:
And getting that question of, can you even test it? That’s the other hard part. Exactly. Do Do you have what you need to be able to test the hypothesis? If not, you know, we may have to go back to the drawing board with that.
David Turetsky:
I would say though, to that point, Dwight, you can find signals. Remember, this is an art as much as a science, you can find signals that can help you to test those hypotheses, if you’re willing to assume and you’re willing to make that guess. And as a good scientist, you’re looking at those observations around your hypothesis to say, can they actually tell me, you know, is that is there enough data there? Is there enough insight there to be able to provide me with that understanding? And if not, how do I collect those other signals? You know, Pat, you mentioned the generalist, yeah. generalist is a really important voice in understanding the context. But they may not be the best voice in being able to put the data together, they may be a great voice to add, to be able to talk about the problem and the context of the problem. And even to Dwight’s point to find the points to be able to measure, they may have ideas about where is this data stored? Or how is it stored and maybe even have an understanding about the process that we don’t understand? Absolutely, I think it takes the team, a cross functional team, looking at the data lake and saying, what are those signals I can utilize.
Dwight Brown:
I was just gonna say, and are we comfortable making those assumptions? And is our customer comfortable making those assumptions? That can be a true back and forth? But I agree, you know, you take those little signals and yeah,
Pat Acheampong:
absolutely. And once once you start to crack it, I mean, like you said, it’s a joint team, because they’ll have different skill sets. So the generalist is there to say, is what I know is what I think that the data scientist is there, this is there to say, okay, based on what you’ve said, here’s how we put it together. Right? And I think once you start to crack it, and once you start to show real value in this stuff, it suddenly becomes something that has a real sort of real world application. And as you start seeing what moves, what moves employee experience to then impacts your your bottom line that actually has commercial value, as opposed to just just guessing at it, right.
David Turetsky:
So we talked about the employee experience, and how we can add analyses to be able to provide a good feedback loop to those employees so that we can keep the loop going and focus more on the value of the employee. And what kind of insights can we do? Or can we bring to leaders to provide more feedback for them? We talked about what is the data that drives that we’ve talked about what analytics can we use to drive those kinds of value discussions? And we’ve talked about how do we measure them? Where does the data exist to measure them? Pat, is
Pat Acheampong:
there anything else you wanted to add? Before we close? I’ll just gonna say that what we need to do to start making this work think differently about HR and people analytics and make sure that we’re part of people that really understand it. And also that we look at it from a point of view of what value does that data bring to the business as opposed to doing all implementing programs for their own sake? Right.
David Turetsky:
That’s great. Pat, thank you very much. This was great. We’re gonna talk more. Thank you do I know thank you safely harmed in the making of this podcast? Thanks. Thank you for listening. Thank you, Pat. And thank you for listening. And we hope that you enjoyed this episode. If you did, please hit that subscribe button. And also, if you have a friend who you might find this conversation valuable, please forward it to them. If not, please give us some feedback at Turetsky Consulting dot com slash podcast. Thank you very much for paying attention and listening and take care and stay safe.
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