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Join us in this HR Leaders episode as we explore the transformative power of integrated data in enhancing skills development and employee engagement.

Top experts delve into how leveraging people data is essential for boosting ROI, from improving talent acquisition to strengthening employee retention and development strategies.

Discover actionable insights on using data to make more informed decisions and foster a consistent employee experience, whether in the office or remote.

🎓 In this episode, you will learn:

1. The role of people data in driving ROI.

2. How to build a data-driven talent management strategy.

3. Key methods for aligning employee development with business goals.

4. Best practices for ensuring a consistent employee experience, on-site and remote.

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Chris Rainey 0:06

Hey everyone, good morning, good afternoon, good evening, depending where you're tuning in from. My name is Chris Rainey, co founder here at HR leaders, and I'll be your host for today's live panel discussion where we'll be talking about how to use integrated data to drive skills development and employee engagement. With that being said, let me introduce you to our amazing panelists. Both. We've got Luciano palestri, is the global talent director, medius, we've got Khalifa Oliver, good friend who's back again. Nice to see you again. Khalifa, who's a global director of employee experience analytics strategy at Ford Motor Company. We've got Matt Fenton, who's a senior vice president of customer strategy and advisor at beamerie, and last, but certainly not least, we've got Adam Marsala, who's the head of people analytics at JLL, nice to see everyone. Smiley and faces again, Matt, we're going to go straight into a meaty question. No no, no icebreakers. Today, we're going straight in. How can we leverage integrated people data and to leave significant increase in ROI? You know, whether it's employee engagement, productivity or even retention, it's a big question, but we're gonna get going. Yeah,

Matt Fenton 1:15

yeah. Let me. Let me dive in. I think to start, what's helpful to discuss is a little bit of why data, and I think specifically why people data. And so when I think about kind of what companies have been most successful the past few years, there are a few that come to mind for me. So it could be Tesla in terms of paving the way for an electronic car revolution. It could be Nvidia, whose once graphic cards have now proven valuable for AI driven applications. Could be Google, because I use it 100 times a day to answer innocuous questions because of how quickly it's able to address them. So I think for these companies, two things are they have in common. I think one is around really being data centric, and two is they have incredibly talented people. These people come at a cost, and that cost is generally 60 to 70% of operating expense. It's a huge number. And so for those three companies I mentioned, you know, we're talking about over $100 billion a year in expense. And so if it were me, I'd want to manage those costs like I do other aspects of your business, which is with data. And so using that sort of people data, great. We need to manage it effectively. And so when I think about increasing about increasing return on investment, I generally think about two vectors, or at least I'll share two there. There are more, and I'm happy to catch up offline with anyone. So I think the first is around, you know, really using that data to find and hire high quality talent faster. For me, this is really a play on both efficiency of of your HR processes, as well as retention, which is a massive cost burden for from any organization. And so this really is about being more data driven with talent decision making, which means, you know, reducing gut feeling and intuition as drivers of hiring. It doesn't, however, mean that there's a program that's out there that's going to tell you who to hire, how to hire, however, it can shorten the time and really narrow your focus to find only the talent that is really best fit and has the right skills. So from there, it's your job to then take that talent right, create a validated assessment approach and ensure individuals have the right attributes for the roles. But really it does that data helps to make much more efficient, and ultimately, the talent that you hire will stay for longer because they're better match for that role. The second that I'll mention, and then I'll let others define, is is around creating opportunities to grow and develop, which really engenders engagement. I think McKinsey did two research pieces fairly recently, one was a few years ago, which said that, basically, the main drivers of retention are one relationship with manager and two creating opportunities for development. A second study found, this is more recent, found that the median cost for Fortune 500 company is two, $50 million a year on disengagement and attrition. And so factoring those together, this people data helps us understand what are the opportunities for growth. Where can people go and how do you mobilize that talent more effectively? So I mean, at the end of the day, if you want to drive ROI, use that people data to find talent faster and to mobilize it and strengthen the connective tissue which drives development, engagement, ultimately, retention. No, I agree

Kalifa Oliver 4:14

with everything that you say, Matt, one of the things that I always think about is when you think about companies and they're putting a value proposition, and you think about what somebody's actually buying into when they apply for a job, that's a product, and that's why we need to take a product centric approach to the way that we look at and measure people. We've relegated people and people data so much to it's on the wrong side of the balance sheet for so long that we don't really see it as a long term investment. But if it's something that's building your product and carrying your product and helping your product and interfacing with your customers, that means the people who are applying for your jobs are buying something, and that's that proposition that you gave them at a time, and when they get in and they realize what they bought is a. A dud. It's just like any other customer come back, right? Customers will come back and sabotage your store, or they will go and write your really bad Yelp review or any other review, and that's exactly what it is. I think the mistake a lot of leaders make is not understanding that investing in people is just a longer term investment. You do not see the returns as fast, and that's even more our reason as to why you need to use the data the return on investment is there. It's just a longer term investment that both sides are making, one into the product that you offer them, and the other into the exchange of your ideas and your work to produce the revenue generating product.

Adam Rasala 5:43

And I'll add to that, that unless you're in technology or manufacturing, people are going to be your primary source of revenue. So they are going to be the ones that retirement investment is going to be really counting the most as well. That's

Chris Rainey 5:58

really important. Really cool point, Adam, because, you know, people in it sounds, it sounds weird to say people are your product, but in cases that they are to your point, right? And that's often overlooked just, you know, you put a lot of that investment into your products, if you're manufacturing, or some of the other examples that you just mentioned, but we kind of forget about that one are, are speaking to a CEO of a very large creative agency that you'd all know of, and he was saying the same thing recently. He's like, people are our product in the advertising industry, so if we don't invest in our people, then how do we expect to retain them, let alone deliver a good experience to our customer on the other end? So I love that. Sorry. Luciana, I cut you off.

Luciano Pollastri 6:43

No, I was bouncing on what Khalifa was mentioning. I believe that data also allows you to check that consistency between the product that you are selling the product that you are actually delivering once you have the employee with you, and even more, when you are thinking that most of the things were happening in the office in the past. So you had that interaction that was happening physically in the past. But now I believe that the data is allowing you to compensate for all these interactions that were not happening, that are not longer happening in the office, and to recreate a sense of understanding of what is happening in the mindset of the the people and the and the employees that you have. So that's also a way to ensure consistency between the initial advertising employee value proposition that you're given the actual things that the employees live in, both at home and in the office.

Chris Rainey 7:30

Yeah, that's a big challenge right now. I'd love to hear from you, Kali for how you're navigating that with the employee experience part of your role. How are you making sure there's a consistent experience both the people in the office environment versus people operating remotely, data

Kalifa Oliver 7:50

nerd, data right? I think for me, I focus a lot on listening, but I take a multi prong approach to listen that we were accustomed to listening to what people tell us, right? And so that's typically a survey, but people tell us things in multiple different ways, right? So there's movement signals that are sending us. Are they leaving the job? Are they leaving a manager? Are they leaving a company? All of these are pieces of data that we get to try to understand, are people who are working remotely more likely to leave the company? Are they staying? Are they performance? That AI, productivity, numbers changing. All that are things we could look at, we can look at, and this is really big when you think of remote working versus in office working in that debate, organizational network analysis, right by people being remote, or having a remote team or having a remote manager, is that changing? Anything about how these how the network structures are set up, are some groups being often, is the connections being broken like it really helps you to understand the underlying structures that you typically will not think of, because we often think of the hierarchy, but organizations are like societies, right? They there are these networks that form, and these networks are what created culture, right? And often, like you see something like of network analysis, you realize there may be somebody who they're remote, but they are so central to functions, everybody goes to that person for all the information. So them being remote is not the problem, right? It might just be leaving. It might be at risk for leaving, because you just have not promoted this person for years. They are central, and you break the notes. So I think it's us really not just looking at data, because, of course, I love data, but the rule I put on my wall, the rule of thumb, the thing I get on my soapbox to say all the time, is that when we look at data in HR, every data point is a human it's a human being, and because we we have this, this responsibility, we have to always look at context with that data. So everything in HR, unfortunately, is not black and white. It is, what is the context, what is the complexity, what is the change? Because I can't be somebody to honestly tell you that the data. Data is just going to tell you all of the answers, right? Because there were lots of people who said that in 2019 and as you can tell, every forecast was wrong in 2020 every last book was wrong, right? But navigating location with experience is really about letting the data also guide you, using that and using context and using every form of data that you have, what they're telling you and what they're not telling you, and that's how you really hold the full ROI of your data and your people, the people organization,

Matt Fenton 10:32

yeah, building on that side, I completely agree. I think, in my experience, it's the the data actually isn't the problem, the capturing of data. Think the most difficult part about data data analytics, driving ROI isn't actually the data itself, but rather, what's the right question to ask to be solved with the data. And so being more hypothesis led thinking about what is the context, I think, to your point, Philip, given the various data points, what are hypotheses that we can test, and how do we then use data to test them? And I think that sometimes organizations fall into this trap of, I need to capture data. We don't have enough data. We need more data. There's a ton of data. And if you want to do email and calendar metadata analysis, figure out how your people are spending their time. If you want to do informal network analysis to figure out where are those influencers in your organization, how do we make sure that they're they're recognized and surfaced? We can do that, but those are the underlying point is that I had a hypothesis. We don't know where these people are, or I don't know how my people are spending their time, which might mean that I think that they're spending it inefficiently. So as much as there's an exercise of what data can we capture, how do we piece it together? That's important. I think that the thing that I would focus on a bit more is, what is the question that we're asking that we can solve with data, not just the mass gathering of that information?

Chris Rainey 11:48

Yeah, right now I love, I love the context bit, going back to what Cooper said, because that's the part that you often miss. You can have all the data points, but what is the context, or that the individuals context of their circumstance as well. When you a lot of that you can't capture just with data. So it's important to keep the human side. Like you mentioned, Khalifa is someone that you always go back to on that. Matt, you started off by talking about, with the research, the need for employee development initiatives as a retention tool for some of the research. Adam, what are some of the things that you're doing around the employee development initiatives, using data analytics, that are having the most impact that you could share with everyone? I

Adam Rasala 12:31

don't think you can really decouple your development initiatives from your business strategy. So to me, I guess this is like, there are two parts to this exercise. I think one is really trying to understand the business and their priorities. I think most of us will struggle without knowing, you know, what are some of the skills or abilities that are going to be relevant to the to the success of the business? And they're not always going to be the same. You're going to have different business lines, business units, different leaders that will be at a different time in the economic cycle, that will have different priorities, and they will be growing, or this will be shrinking. And I think it's really important to to really meet the business where they are at. So I think to that end, you know, having people partners is really going to be a critical part. I think we're investing a lot of time in art, in equipping people partners, with enough tools to be able to really carry on with those conversations with the business at the meaningful level. I think it's really important to make sure that those those faults are connected to the rest of the organization being connecting them to the to your people analytics organization, to your talent teams, is super critical, because these are your These people are your property, source of that information. So you can't really do much meaningful development without knowing what the business is trying to do. In some cases, your mature people analytics teams, they'll do that for you. They will reach out a little fire about this. But I don't think anything can replace a senior strategic people partner and their ability to actually carry on, carry a meaningful conversation with the business. So I think the good starting point in trying to unpack some of the business strategy into you know, what we would understand where the HR lens is going to be, making sure that a data analysis is not just some post employment afterthought. You really need to be thinking about the metrics. It needs to be integrated throughout the design process. You need to make sure that you are building the meaningful criteria for success from the very get go. So, you know, it's difficult at the beginning, but once you've done that for a while, you kind of you are able to draw on your experience some of the things that you've done in the past. And I think it's really, really critical. And one of the reasons why we think about this when we think about developing talent. End is that there are elements of data that we are measuring within people analytics are very difficult to move the needle on, and sometimes they result from the lack of connection between what the business wants and what we're driving from the HR perspective, one example that I can give you is tying development programs to strategic business priorities will help you move the needle, things like belonging, things like sense of belonging, or understanding the mission of the company. And I do think that those are really difficult to start, to start moving the needle on without really understanding what the business strategies is really about. But I think that's just the one part of it. In reality, there is a lot and lots of data that we have access to from the to assist with a bottom up approach. A good starting point here is obviously going to be your attritional retention metrics. There are going to be lots of data on engagement that you're going to have if you have a continuous listening strategy. Those are some things that you can use to start taking in the employee feedback and looking for irregularities, looking for areas that you can improve and you can have, you can have your HR leaders have a conversation with your people analytics team and try to look for them to give you a starting point to initiate the conversation so that from the top down, you need to really try to translate the strategy into between meaningful development opportunity. And then from the bottom up, you need to be proactive about letting business know about some of the things that you're finding when you're wrong. And I think you know that second part is where you're going to, where you're going to probably be spending a lot of time. You want to be proactive about this. You want to make sure that you're pulling in data from as many sources as you can, and then finding those stories that you're going to that you're going to bring to the business. So all in all, I think the reality on the ground is that there is a very important role for you to play in making sure that you build meaningful relationships, and then investing a lot of time and actually being proactive about bringing the data to the business so that they can react to something, if you're lucky, some of the business lines, some of the business leaders will you know, will be very vocal, will be well, we need you to know what the problems are. But some of them, they are usually very busy people, and you need to be you need to be approaching them with a with a kind of game plan already. You thought, well, thought through ahead of the time, just to make sure that you're not wasting their time.

Chris Rainey 17:50

Yeah, I love the part that you added, that you maybe overlooked. As you said, bring the story to them, and not just the data. Khalifa, we spoke about this before.

Kalifa Oliver 18:02

I think we've got we were, and this is, I blame a lot of people in my field, right, including myself. We we do so much work with the data. We see so much data. We're like, Where's all this data? We throw data at you, right? Like, look at all the smart stuff we did. And I always get back to that thing. And my husband always jokes and says that people don't really ask you about their labor. They just want to see your baby, right? They just always want to end product. And most leaders, when they ask you for data, they're not really asking for data. They're asking you for a solution, right? They're asking you for the story, right. To your point, like, one of the rules I use is that when you think about creating solutions, especially when you try and put data in there for leaders, you have to assume that in person is one of two things, busy or lazy. And so how do you boil it down into the easiest thing that they can digest? Right? I think we have built a culture with a lot of leaders where they will not tell you that data is intimidating because it is right. Data is a very intimidating thing, and a lot of us who are really comfortable with data forget how intimidating data can be, right? And then we've we've confused analytics in many cases, which reported. So we think, if I throw a dashboard at you, we don't think about that leader sitting there just trying to cut and slice and nice, and it's just data overload because we have not provided any insights. And definitely, to your point, Adam, we have not provided a solution, and so we're in there. We have all these fancy data. Why aren't they using it? Why aren't we getting any action? And action, of course, is the key to any successful analytics program, and to get and to get that ROI, right? But they're not using it, because we just threw data at them. And I think we're finally coming to that point where we're saying, Okay, well, what does that tell me? What is the story? Because once I know the story, it's because I understood at the beginning of the data process, what were you trying to solve? What was keeping them up at night? Most people. When they ask you for data, that is not what they're asking you for. You know, that's what I remember asking for data. And so often I reframe the question that when you know somebody will say, hey, I want discount acquisition data. I say, but what do you really want to know? What are you really trying to solve? And I find when I do that, it helps really streamline how the data is being used, what data I need to prioritize, and then what story I tell and what language I need to use to tell the story. Because if I'm talking to the CFO, that CFO does not care about their feelings, they don't they don't care about engagement, but they want to know about ROI. They want to know, how does this affect their product development? They want to know, how does that affect their productivity and their metrics? But if I'm talking to the CHRO, I'm using very different language from talking to the CEO, I'm using very different language. And I think it's a skill that you learn over time where you realize that you can ground yourself in that data and create the story to the audience that you're delivering it to. And I think if you could bring it to life, and you leave your voice in the room when you walk out of it, that I think, is what makes that very successful. In terms of it,

Luciano Pollastri 21:14

that's quite fun, because I have a totally different reality. So I'm in front of a full data engineering community, and they tell me, stop the story. Start with the data at the end, one way or another, depending on who you are in front of you. And I have to say that the need to catch up. From the HR perspective, people in cultural auto values, we have to catch up in this and and be able to connect. For me, data has been more of a bridge and the capacity to connect with my customers, internal customers, than a distance that I was putting with them. So I need to make a more of an effort on grounding my story on data, than creating the story that I already had in mind. Okay, because otherwise they don't believe it. So it's fun to see that depending on where you are and what kind of audience you have. You have a different reality in there.

Chris Rainey 22:03

That's interesting that you just said that. Yeah, it depends on your audience, like Khalifa said as well, right? But I think if you start that conversation from the beginning, then you can understand that, right? I think it's sitting down, but some of the most successful people next leaders I speak to just are really good at building in individual relationships with each each leader in the business and understanding their specific needs and what is keeping them up at night and then working backwards from there. So there's very clear on what the problem we're trying to solve in the first place is. You don't want to go start off on being confused about that part as well. You mentioned your own team. What are some of the ways that you're using people analytics within your own team, and some of the HR initiatives that's helping build sustainable improvements and business performance?

Luciano Pollastri 22:53

So we have been integrating data and data analytics really, since, let's say, really, three years ago, okay, the our main problem was attrition at one point in time, like all tech companies during the pandemic and we were not getting to the bottom of what were the reasons of attrition, the exit interviews were not providing with more insights than were enabling us to understand it. So all of a sudden, I started having a conversation with a data scientist, and he has created a machine learning modeling of understanding through the data, 10 years of data in our ERP, HR, EAP, trying to understand what would the data point at in terms of the drivers of attrition within the company, and very counter intuitively, Louis County is The name of the data scientist that helped me. He pointed at the fact that the people that were staying more in the company were the ones that have been changing more managers during their lifetime alternatives. And he started to have a totally counter intuitive analysis on why those guys were being there longer in the organization where they were changing more and more of managers even more than changing jobs. Okay, changing manager was making them renew their vows with the organization, with one person, and that commitment to one person was making be them, be out of the market and not listening to job offers from the outside. Then it created, boom. Everything was data related, and I started to really look into the data. From that moment, while I was having a clear understanding, a clear explanation, how we could support my analysis more than threaten what I was was my initial belief, that was the moment where we started you really using that analysis in HR, within our modules, from more than just a reporting perspective, more than a pure data perspective, really trying to predict and to use machine learning into clusterizing the comments of our people during the engagement surveys, trying to understand them more from a predictive analysis than from, let's say, reactive situation. And now we embed that almost in everything that we do, we have. Started a new global recruitment process last year. Everything that we do is data driven. We have been analyzing, for example, in the pipeline, what are the exact moment that the people that we converted to employees are applying to our positions. So we know that the maximum conversion rate is the people that have applied between seven and 11 days from the moment that we have been posting the the job vacancy. So we start there, when we start screening, because we believe that the data is pointing at the fact that this is where we have the higher conversion, higher quality of the conversion. So what I'm what I'm mean by that, is, all of a sudden it was not that one day from another we we use data, is that we understood that data was not threatening us, but was helping us to make a better job out of it, and that has created the culture of integrating data in everything that we do. As soon as my team understood that I was not going to replace them with data analytics, with bots and etc, but I was making them better professionals from there, and I was making myself also better professional out of it. It changed radically the way to see, to see the data, and also it has been embedding everything that we do from a data perspective, rather than from a nutrition I believe perspective.

Chris Rainey 26:15

I love that story, by the way, because we forget sometimes many HR and talent teams see data as a threat, right? And we're seeing the same thing with AI right now. AI is going to replace but in rally, what we're seeing is actually empowering yourself and the team to do more valuable work, more meaningful work, more strategic work, and removing a lot of the admin stuff that you were doing. So now your team's seeing it as an enabler, as a part, rather than a threat. And it's cool to hear you share your own journey of seeing it that way, and then actually, again, wow, these are I'm seeing. You're seeing all these insights and the value just coming out of that. And I feel like everyone's kind of still going on that journey to some extent, and it's constantly going to evolve and change and end, yeah, gone. AI is

Luciano Pollastri 27:02

a typical example that, yeah, there's nothing that there's nothing that you can do if you don't start practicing right? So you have the fear until you start practicing it, and you see the value that it can bring to you, and you can still exist above the AI. And then all of a sudden you understand that you can focus yourself on the most human part of the thing. So while you were believing that AI was going to robotize everything, you understand that it's freeing you up time to do the most human part of the job, but the most interesting one, and the one that in reality, we are all thriving for, right? I would like to not to do any administrative task and only only focus on the most human part of it. And I believe that once you start not even mastering practicing AI, practicing the data. You understand that it automates many things that were you were spending a lot of time in things that were boring, administrative and not bringing any value from an intellectual perspective, and it helps you to even connect even more with the people. And that's once you start practicing both a generative AI, data analytics, etc. Once you have a little bit of trust in what you are doing, that's what it enables, mid and long term for the people, and immediately you see a learning curve that goes huge, huge, huge on the progression side,

Kalifa Oliver 28:15

yeah, I think, I think one of the things is people see data as magic, which is something possibly the problem they think is magic. And that's why I think they think this, AI is sentient. It's like, No, we got a lot of, a lot of you know, things that we got to do before we get there. I think that's the first thing. But what do you think? I think data represents in HR, well, across the company, but in HR is accountability, and I think the is perceived as a threat, often because of the idea that it's like it creates this accountability, if I know this thing, then I probably should do this thing. I no longer can say I do not know this thing exist and it's happening, right? And so what I found I work a lot and more sentiment data when I look at it and you tell I mean, you say, well, x, y, z is the case. So now I'm accountable for that thing, because I know that thing, and I think that's where you find a lot of that push back against analytics and not really wanting to invest in some of the technology the way they can, because they just, if I don't know that it doesn't exist. And they were in a process of trying to get companies away from

Matt Fenton 29:27

from that mindset, yeah, I think, I mean, it's a bit of Pandora's box, right? Knowledge is power, and use it wisely. So, you know, with great power comes great responsibility. So to speak. I think to quote Spider Man,

Chris Rainey 29:43

I know that great Spider Man. Quote, there. Love it. Yeah.

Matt Fenton 29:47

I think if we also connect kind of this dialog to the opening question, which was about ROI, and how do we perceive ROI, how does talent, data and analytics coming together provide some of that? I think the perfect example. Shown what you're sharing about attrition. I think attrition is something that organizations monitor closely, and perhaps they you know, many organizations over the past couple of years have thought about it in the sense of, there have been rifts, there have been layoffs, been particularly challenging, but over the course of kind of organizational history, attrition has actually been a tremendous driver of cost and and I think about this just to apply kind of like a simple construct, you think about an organization that is 10,000 people, and imagine 1% of people leave each year, not a huge amount. 100 people are departing, and that's a very low level of attrition. Now, if you take those people, and you assume each person, on average, is a fully loaded cost of $100,000 the cost of that attrition to the organization is generally between three and $5 million a year. It's three to 50% of the salary cost. And so when you think about this in the context of Lee, show notes, point around looking at attrition. How do we understand and predict attrition? Build archetypes or do cluster modeling on top of our attrition analysis and truly understand who's most likely to attrit and to proactively address that. We get stronger engagement. We spend less time on the tactical stuff. We spend less time training new talent that's just subsequently going to leave and in turn, we're driving tremendous implied value. Now is that value something that you can take out, extract $3 million and then spend it somewhere else, not necessarily, no, but it is something that's increasing importance, and I think that organizations are more and more paying attention to that. And the value of retaining your top or high performing individuals, of having reduced regrettable attrition, and thinking about how that then that kind of talented knowledge core that we talked about, that so many organizations are founded upon, it remains in the organization. So I think tremendous ROI and analytics is an enabler asking the right questions, having the right context, building the right story, helps that kind of self fulfilling prophecy, or rather that flywheel effect that, in turn, positively reinforces the need and the importance of having data and analytics in the first place. So

Luciano Pollastri 32:01

to bounce on what you're saying Max, for me, it's important. I'm going to give a very concrete example on how we modelized the cost of attrition for the company. So we took the overall EBITDA, we divided by the number of employees that we had attributed a certain criteria to the contribution of each of them. And so more or less, we're saying, Okay, this is the amount that each single person contributes to the organization was. So this person living Okay, is going to stop producing that for the organization until we replace it, or the person that is replacing it reaches to the level of production that is required. So without entering into more details, the objective is that each single organization is to make this assessment and understanding what is the cost of attrition to make the business case of the investment on talent. Because it's a no brainer. As soon as you enter into that one is a no brain, but it goes very, very fast. But if you don't do that, that explanation, and you don't, don't objectivise the conversation with your CEO, with your business that they don't see, it's just about me thinking that it costs a lot, versus you thinking that is not that difficult, etc. If on top of it, you take a holistic approach in which you show that there are certain talent pools that are more difficult to reach. So if you lose someone in one place, it's going to cost you more than someone else in another place. That's why you need to invest in your ATS system and your skills, anticipation, AI anticipation, your sourcing part, etc. I'm now making a little day advertising of math in one way. But what I'm saying is, at the end, you cannot just take a one to one approach. Is everything is contributing to the same thing. A new cost on attrition is not only is the person living, is the knowledge that is not being filled for years, and that also is something that is very difficult to object device. So once you get your model, and people agree on the model, is way easier to convince them, on the investment, on the people. We were trying to convince the people and making them understand that an exit interview is just providing you the real mirror, the real mirror in the during the driving system, you look at the rear mirror, you see the guys that have already left. Okay? And my objective is to go to the front window and look at the ones that I can identify, because I know that the data is telling me these guys are risk. These guys are at risk that before they are start thinking of being vulnerable to an offer from outside that address their issue. And that's where data is really, really interesting, because data doesn't like it, just gives you an objective approach to it, and you can still make the decision to address it or not. You don't lose the control or the power because you have data, you are just more insight into making a decision. And that was very well with my customers in an engineering company like we are.

Adam Rasala 34:37

And I wanted to, I wanted to add that you always want to be careful about you shouldn't really put an equation sign between investing in more analytics and realizing more benefits on retention, because it's a very complex topic. So it helps you to understand and every like most leaders at this point, will, will will be very well over. Aware of your HR value chain. More development means more engagement. More engagement means better productivity. Better productivity leads to better outcomes for individuals groups, and then that translates to at the end of that chain into a business value most of the most of the leaders will know that. I think the problem here with attrition and modeling and making the case for it is that we in HR do not control certain factors that lead or significant factors leading into ultimate decision for employee to leave. And I think that's this is how you know some of our training modeling exercises have been challenged originally. So I'm really interested in how you're dealing with, you know, we have all of this work taken care of. We are able to project, you know, the relative risk of some employees or employee groups leaving, but then the leader will be very vocal about telling you, hey, I'm sure that's great, and all this data is fascinating. I really would like to do something in this space. But how can you guarantee that you can prevent or lower, in fact, guarantee that you're going to lower attrition through those investments? It's a part I'm really interested in you here, hearing you how you're addressing this. Okay,

Luciano Pollastri 36:22

so in what we have been doing, the first item was to try to define what were the what was the data pointing at, and then we try to confirm it to our engagement service. And what we have seen is that there was a huge correlation between what the data was pointing at and the intention to stay of our people and the actual attrition when we were anticipating and predicting it. Okay, so what I'm saying in there is obviously it's not a direct correlation, and I don't pretend that we have the solution for attrition in the world. That's not what I mean. But it allowed us to have some areas to work on, and it allowed us to determine some to qualify the things that were repellent versus the ones that were retainers, we were a little bit confused about the things that are making people stay in the company versus the ones making people live in the company. And they are different in nature. Okay, of course, compensation one that you don't get it right, people live, okay, that one is an obvious one. But there were other things that were not as obvious, in what are the things that are making people stay? And that's what we focused on. And it was very clear, for example, that for us, flexibility was one of them, also the with the pandemic and with the working from homecoming, remote work, distributed workplace and etc. All of sudden, it allowed us to identify that we were flexible by nature, but we were not promoting that enough, so people were not conscious about it. So for us, the working item out of the data was, if people are saying that flexibility is the one of the main drivers, having them staying in the company, let's make sure that they understand what is our value proposition in terms of flexibility. Okay? And I'm taking one example out of them, the same for learning and development. That was the second item that was making them stay. If we don't get learning and development right, people live, but if you get it right, they stay more it's more sticky than the compensation assets. Compensation is something people leave if you don't get it right, but they don't stay if you get it right. So our objective was really to work on those items that would make people stay as such, and that's how we related it, and we got really good results. Just to express what was our value proposition, and insist on those drivers that were making people stay. And our attrition went down, and our tuition, we have an attrition that is more or less half of what the industry is traditional. Okay, so we don't have a huge platform of attrition as such, but to keep it that way, we had to make a new effort and to make our employee value proposition alive. And we made all those decisions based on data, not based on what we believed or based on on what people thought they wanted. It was based on the actual fact that people were living when were they telling us? I don't agree with this. Okay, that's the way that we correlated it, and obviously it's not 100% I'm not going to pretend that there is a solution. Otherwise we all will have it already applied. I

Kalifa Oliver 39:21

don't want to underline one thing that you're saying that I think needs to be kind of shouted from the rooftops, that attrition and retention are two very different things. And when you work with data long enough, there are too many people who look at it as a continuum, that that these things just the opposite. They're not they exist in two different URLs, and they're often driven by very different things. They may be overlapped, they're similar, but I think that is a huge thing that we've learned in new world of analytics, that we have to do retention studies and nutrition studies as different things, and if there are key metrics that can work together to. Look at most of those things. That's really, really important.

Chris Rainey 40:06

Yeah, Adam, when you was describing it, were you talking about the, sometimes, the lack ability to control some of those things affecting employees, and therefore it's make it hard to change. I'll give you an example. I was speaking to a VP of employee experience recently, and he said, The only reason I took this job, Chris, is that the operations, finance and marketing had someone reporting into me so I can affect change in employee experience, because I have the ability to do that across all of these functions. And if I didn't do that, which he had in his previous company, it was very difficult to make meaningful change when you're in a silo, when you're not embedded in the rest of the functions. It's kind of what you was talking maybe I just took the top context Adam.

Adam Rasala 40:57

What I was referring to is there are so even the best attrition models are going to still be there are not going to be 100% accurate, obviously. So what I was talking about is the the effect size for most of the attrition drivers that you do have access to as a HR department, so all the data points and their the strength of those correlations, there are going to usually be maybe 30% 30% of decisions to be able be explained by the things that you control. So that leaves the remaining 70% which are going to be anything from you might be wanting to be you might be ready to leave, but there is no opportunity there. Or you were looking to leave, but you were poached. And I think that reduces some of the predictive power, the predictive strength of that model. And I think that's what we we have we have seen challenges to our approach, like it's great to understand some of the drivers, but it's also very important to understand that the things that you control as a company, those can those. Those can be very important, like Luciano said, like, if your conversation not right, like no amount of development is going to keep you. There are. There's so much, so many things outside of our control. Relying too much on nutrition modeling can maybe hide some of the other important things that we are focused on and people under the selecting development, data driven or evidence based decision making. I think those are all important, but in the in a broader context of everything else that we do in HR, and necessarily want to make it all about attrition, I think, is what I was trying to say, yeah,

Chris Rainey 42:46

no, we went on a we went on an attrition route as well. But it's important. I'm glad. I feel like we need a part two to get into some more of the session. I'm getting told we have to wrap up getting told off, but we cover quite a lot. So I think given everything we discussed, based on what we discussed in the conversation, what would be your parting advice for everyone listening right now? So I think they can take away or focus on because it's a lot to take in of what we just spoke Khalifa, you want to kick us

Kalifa Oliver 43:17

off? Oh, data is your friend. Don't try to back data into your decisions. Let the data drive the decision that would be my my key advice,

Chris Rainey 43:27

amazing Adam,

Adam Rasala 43:30

keep talking to your business partners. Keep talking to your leaders. Make sure you understand what it is that they want and bring in data people and analytics professional to the table as early as you can amazing.

Chris Rainey 43:41

Luciana,

Luciano Pollastri 43:44

the data is your friend. I believe I'm going to just paraphrase Khalifa, because I would say people practice, practice with the tools, practice with the data, with the AI, with everything, so you get comfortable with it, and you make out the most out of it, instead of being threatened by it. Love

Chris Rainey 43:59

that last one, at least? Matt,

Matt Fenton 44:01

sure, yeah, I think this conversation has reminded me of, there's an advertisement not too long ago. I'll be brief here, but it was a two pager. Left hand side had a picture of the night sky. Right hand side had a picture of the or, excuse me, left hand side picture the night sky. It said this is information. Right hand side said had the same night sky and it had constellations drawn on that nice guy. So that this is knowledge. And I think the idea here being that start with the problem statement, so that you can get closer to the knowledge. Data can be overwhelming, that left hand side information, there's a lot. So what is the question you're trying to solve? And then use data versus trying to figure out what data is trying to tell you without any hypotheses.

Chris Rainey 44:40

I love that we need to find that I need to find that picture and post it on LinkedIn. No, I love that. And everyone listening. Thanks so much for tuning in from all over the world. I can see like so many different continents and countries represented. So thank you for tuning in. Thank you to our amazing panelists for sharing their lunch and experience and and special thanks to our friends at beam, wherever. Are in the world. Enjoy us today, and we'll see you again soon. Bye, everyone, thanks so much everyone. Bye, bye.

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