How to Uncover Your Hidden Talent Management Problems

I am very excited for this webinar specifically because I'm a data nerd and because I do believe people analytics is extremely valuable, especially in today's kind of workplace environment. Uh, and so our goal today is to help you uncover your hidden talent management problems. And my name is Dan s Shaba. As Zach said, I'm the managing partner of workplace intelligence at thought leadership and Research agency. I've led, you know, over 70 research studies, so I'm very involved in the data space across all aspects of hr, some of which we're gonna be talking about today. Uh, and I'd love to introduce all of you to Chris Moore. Chris Moore is the c e o and founder of Zeroed in a data management company that brings people and technology together to improve the profitability of business. And that's a lot of what we're gonna be talking about, the relationship with people and technology and how you need both. And in Chris's role, he focuses on innovative workplace and predictive analytics that give people, managers and their HR counterparts the insights they need to make timely and accurate decisions about their workforce. As a 25 year veteran, Chris has designed, implemented world-class analytics solutions for clients, including TD Ameritrade, c v s, Caremark, McKesson, US Department of Defense, and Williams Sonoma Brands. Uh, he's widely recognized in the industry, uh, for his work in the areas of people, analytics, performance measurement, and talent management, all these really key HR areas that you, uh, care about and, and focus on as part of your daily job. So, Chris, it's a pleasure to have you here. Yeah, thanks Dan. Uh, I'm connecting in from the Bay Area, but on our side of the country, it's the Chesapeake Bay area, so, uh, just, uh, north of Annapolis, south of Baltimore. So, uh, welcome everybody. Happy to be with you today. So to kick this off, I'm curious, and I'm sure other people are as well, what your inspiration was was for starting Zeroed in back in 2004, which feels like forever ago. Like everything before Covid feels like 50 years ago. Yeah. And how has the company evolved since? Yeah, it's, uh, it's been 19 years and I'm getting all my LinkedIn congrats, you know, 'cause they, they all see your, your work anniversary there. But, um, uh, prior to Zeroed in, I was the chief technology and founder of a talent management company in the nineties, a company called Training Server. And, um, we, we, we built that solution, uh, uh, to, to really help, you know, our clients get to better, you know, uh, better handle on their, on their training, their compliance, uh, developing their workforce, and ultimately trying to meet their business goals. And back then, you know, the key measures around learning and development and talent we're all, all driven around Kirkpatrick models of evaluation, you know, maybe Phillips r o i models, brinkerhoff. And, um, you know, as, as those that solution evolved and, and, um, I began looking to, you know, what am I gonna do next in my career? Um, I I really found that human capital management systems, including the one that, you know, I built, was data rich and information poor. Uh, but clients were, were just thirsty to get more, better insights, particularly around the impact that their initiatives were having. And, you know, vendors including us, we'd always push them towards third party tools, whether back then it was Crystal Reports today, you know, then it was business objects and SaaS. And, you know, today it's a, a myriad of other tools available, uh, but to get better information. So it was looking at ways, you know, where, where we could build a platform that, that integrated with, uh, sort of these, you know, disparate ecosystems, you know, within the human capital space and really provide a complete picture to help managers make better business decisions. And as an industry veteran who has studied the workplace for many, many years, what do you see right now are the biggest issues facing today's workplace? Well, um, lemme, I got a couple slides I'm just gonna talk to, just kind of give, give some context. Um, you know, one of, one of the areas that, that we find that we hear particularly post pandemic is, you know, all of our, all of our employees are working from home now. And, you know, one of the biggest problems is accountability, right? How, how, how can we, how can we keep, we used to be able to keep track of them in the office, right? We could log their hours correctly, we could monitor what they're doing, we could peek around the corner. Um, you know, the, the whole work from home model has, uh, sort of, uh, internalized the gig economy for businesses, right? You know, you've got, uh, you know, a lot of skilled talent that's just kind of a bit disconnected from the workforce. Um, and as a result that, uh, the other challenge that both the employees and the employer face is this, is that disconnect where, you know, the employees think that, well, management's not listening. They're not solving our problems. Um, and, you know, management thinks that, you know, the, the, uh, the workers are, you know, out on coffee breaks all the time, or just not, not as engaged as, as they'd like them to be. So clearly they're, the, the, the challenge is, is the return to return to office model. And, you know, of course, from the workforce's standpoint, uh, they, they're wanting to drive this, this level of engagement, right? They want, they want the individual to be as motivated and and empowered around the goals and visions and business drivers of, of the organization, but they want to do it in a, um, uh, a good social what's, what the right word is. But, um, in a, in a manner that the, the employees feel good about it. They're, they're, they're meeting their own goals too. Um, we see a little bit of connection or, or disconnection between engagement and what you might think of resilience, right? Where, where engagement is about that alignment to the business and areas of resilience. And, and companies measure resilience. Look at factors such as stress and burnout. Uh, and it's really individual employee focused. So those are some of the challenges off the top. And then at, at the, at the worker level. And then from the technology side, you know, there's just, there's just never enough access to the right data for, for the decisions that need to be made. Um, I mentioned disparate ecosystems, right? So, uh, many, many organizations have 2, 3, 5, 10, 15 different, you know, people in tech systems within their environment, and they just don't all talk together. Um, and then where, where the, another big challenge comes in is, is getting access to the business data so that you can actually see the impact that your hr, your talent, your performance management, your comp initiatives are driving. So that's a, that's a challenge. And then lastly, it, it's really understanding what the story, you know, what story the data's telling, right? And, you know, what impact is that driving? And then, you know, at the end of the day, you have to say, so what? Right? So, so what if we measure, you know, so what if our turnover is, you know, 6.5%? What, what does that mean in relation to what we're trying to accomplish? Is that good? Is it bad? So you really have to put a lens on that and, uh, begin to tease out and, and, and, and look closely at the metrics that you're collecting that you're, that you're wanting to, uh, monitor and, and make sure they're the right ones that are, that are really, can be influenced. Because there's many things that you can measure, but you can't influence it. So how is that gonna change your outcomes? I agree with all those challenges. We're seeing it too is actually a study that I saw this morning that backs up the first challenge that you explained about back to work. Mm-hmm. And the study found that 80% of bosses regret their return to office plans. And part of it is based on what you were saying about maybe they're not using the right data, maybe they didn't ask the right questions, and they just said, oh, let's just do this. Uh, and so that, you know, there's a lot of pushback from the employee side because of that. And then based on what you just said, yeah, there's a, there's a lot of maybe data that's being collected, but is it actionable? Is it actually being used? And even if it is used to employees or employees aware of it. So it's kind of a lack of transparency too. Yeah. There's, there's some, you know, there's some creative solutions out there that, that, uh, organizations are employing that, you know, particularly on the, on the listening side, you know, we talked about the gap between the, you know, the workers and management and, you know, enabling survey platforms, you know, to do pulse surveys and to really, um, you know, touch, touch the workforce in a way that, that, uh, just, just can gain that, that sentiment and that feedback, you know, feedback back and zeroed in is, is relatively unique in that we're able to combine the qualitative and quantitative information together, you know, to give that complete story. We have a survey platform, we have our analytics tools, and, you know, together that helps build that complete picture in a, kinda a, in a routine, in a routine way. So, you know, surveys can go out rule-based, but it's, it's really about, you know, uh, listening to the workforce. And that's, uh, we, we see a lot of that happening, uh, more and more with our, uh, Yeah, we call this employee voice. And, you know, especially during covid, employee voice got louder because the employee voice needed more help, especially during a tough time. And the employee voice is more decentralized with all that remote work. And so I, I think that we have noticed that there is this transition of more regular surveys and data collection. You know, for instance, performance reviews, as you know, historically it's been one performance review per year, but now the workforce, especially the younger workforce, demands more frequent reviews Sure. And, and all that. So that's a, a pretty big change as well. Uh, from a data perspective, what types of data have you found that are most useful when solving HR and workplace issues? Well, certainly the se sentiment, um, is, is key. Um, you know, looking at, again, we talked about, you know, where, where it's resilience, looking at stress and burnout, which can be driven through apte, absenteeism, tardiness. Um, you know, it's, that's, it's easy to track on an, on a, you know, an hourly workforce. You know, the, the salaried workforce is a little more difficult there. Uh, but looking at, looking at the stress levels, the burnouts, and just really understanding, you know, the, the overall workloads that are being assigned to people and what's expected of them. And then lastly, the business data, right? You have to look at the business data to see, you know, are we, are we accomplishing what we set out to do? And if not, you know, again, what are those levers that we can, we can pull to, to influence? No, I like the employee engagement data too, because it's all this like, juggling act and balance between, you know, a company wanting more productivity and higher engagement, and then burnout and, and, uh, you know, issues with mental health, right? And so it's, it's that whole juggling. So for instance, you know, during covid we noticed that, you know, as productivity increased people working remote, that affected work-life balance and led to burnout at the same time. So what might be good in terms of productivity could actually be a huge issue with people being burned out and then leaving. Sure. And you, one of the things you evangelize is this four step process for leveraging people analytics data to address these workplace issues. Can you kind of go over that for us? Y Yeah, sure. So, you know, the first, the first piece is really understanding that understanding your current state, right? If you don't, if you, if you can't get a picture of where you are today, how do you know where you want to go? So, you know, the best way to do that is, is through, uh, descriptive analytics. And if you're, some of your listeners may, uh, familiar with the, with the different, uh, uh, stages of, of analytics descriptive is, is typically at that, at that first level. And, and it's, it's tracking what happened in the past. What, what do we look like today? What happened in the past? It uses historical data. Um, this is, this is important on the workforce planning side. So you get a current, you know, a current view of your workforce, and then, you know, up from there, it goes to, you know, the, the next levels, which are more diagnostics, why things happen the way they did predictive, what could happen in the future based on the past. And then the ultimate goal is prescriptive. What should happen in the future, and how can we influence that, right? So that's where, you know, being able to influence what you're, what you're measuring is so key. Um, but technology helps, helps drive all that. But that, that first stage is really understanding your current state, getting that complete picture so you know, um, you know, you know where you are. And, and then the second step is really uncovering gaps or outliers. And, and we actually do this through you looking at the data, looking at anomalies, looking, looking at the outliers, looking at change, and then determining what the root cause is. Um, because without understanding the root cause, uh, and we'll talk about the next step, but there's no way to change, you know, there's, you don't know what, what needs to be changed. So you've, you've, you've heard about, you've, you've heard the slogan, or, you know, what gets measured gets changed, right? Well, it's also true what gets measured gets funded. Um, because when you can look at the root cause and you can understand the impact that that's having on the business, you can monetize the impact. And once you monetize and put dollars to the, to, you know, to, to the impact, you know, of what's occurring as a result of your current processes, then you can actually build a business case for change that the financial people within your organization will understand. And that's how measurement terms to funding turns to change management programs. So that third step is actually designing and implementing change. And, you know, sometimes this takes a lot of time and effort. These change initiatives are not always quick and easy. Uh, they can be very lengthy and very complex. Uh, and as a result of that, that fourth step is really the measurement side. It's, it's identifying, you know, what are we gonna measure during these change programs? How are we gonna hold the people who have to have to enable the change and have to adopt the change How, you know, sometimes it's people, sometimes it's process technology, you know, how do we hold them accountable? How do we track it, how do we adjust it? And it's just cyclical. It's that, uh, wash, rinse and repeat model, so to speak. And, you know, that's always been pretty important, um, in, in the process itself. So It's a very good, logical way of looking at it. Right? Yeah. So the four steps, understand the current state, determine the root cause, implement your change, and, you know, hold people accountable, track and measure, and, you know, just cycle through. Yeah. And from more of a practical standpoint, how has this process specifically helped you work with clients? Can you share in a, maybe a client example? Yeah. Uh, I mean, I think there's a couple examples. Um, uh, probably the best is to use an analogy like, you know, there one of the best ways of, you know, you can look at individual measures, but you can also create indices, you know, what you would call a key performance indicator, that that key performance indicator may be a composite of, of measures. Um, a good, a good, uh, uh, example of a key performance indicator is, is, um, like the, the, on the hi, on the hiring side, you know, whether you look at, um, you know, time to fill would be individual measures, cost of higher individual measures. Quality of hire, however, is a composite metric, right? It's made up of multiple underlying measures. So that really becomes, uh, you know, an indicator of sorts based on the factors flowing into it. Um, you'll, you know, you know the concept of a dashboard, right? But there's also, you know, the difference between a dashboard and a scorecard. So, you know, from a measurement perspective, you know, a dashboard is gonna provide you the, the information to change quickly. So you have a change initiative. Your dashboard may provide you some, you know, in, in the short term, um, in, you know, identifiers that can help you, you know, quickly shift, quickly change. Um, but the scorecards are a little more longer term. So, a good example or analogy is, you know, an airline industry, you know, Southwest, Southwest Airlines, right? Your pilots, they have a dashboard, right? They're gonna change based on their flight, it's point in time. They have to make adjustments by watching their, you know, their, their gauges on their dashboard. But the flight operations crew, they don't have a dashboard, they have a scorecard, right? They're measuring everything from, you know, on time arrival to, um, you know, all, all of, you know, all of the various indicators that show success from a flight operations. So there's a, there's a difference in, in scale in that the, you know, the dashboard's a little short term focus, maybe a micro view. And the scorecards are a little longer term goal oriented with a macro view, uh, against the strategy of the organization. That's a great example. And we talked about when we first opened this up, uh, about the importance of not just technology, but people, and why is it important to not just focus on all this modern technology, but also people when trying to improve employee experience, increase retention, and grow your business. Sure. Um, you know, can technology replace people? Sure. Um, can people replace technology? Sure. But somewhere in the middle, you, you've gotta have process to help, help bring the two together. So, uh, it, it's by, by being able to focus, you know, you need the process to help put focus on the people, because the people have to execute the process around, or with, in conjunction with the technology. So there's, there's a couple examples of this. You know, with, well, it's zeroed in, we have a, what we call people would think of like a white glove service, right? We can give, uh, you know, our, our customer a set of tools, you know, just like, you know, you know, they can adopt Tableau or Power bi, but it's just a tool that without a process in place to collect and clean the data, to organize it, to structure it, to quantify it into particular facts, um, you know, that that whole, that whole process is something that, you know, an employer or the employees, you know, on a people analytics team have to do. So being able to help them in that process enables them to use the technology more efficiently, because they're not spending their time doing the, I hate to say mundane, but you know, the dirty work. Uh, they're spending their time looking at the data, analyzing the data, and using it to make business decisions. So, you know, that's, that's one example. We were talking about engagement earlier. Um, with our survey platform, uh, we've adopted, um, a process, uh, by a company called SECOR Consulting. The eight factors of engagement, and the, the difference between this model is that there's, there's sort of an employer and employee side of, of engagement. And in traditional engagement surveys, yes, you're, you're obtaining the feedback from the employee, but it's about the employer. And what the eight factors of engagement do is they empower the employees to actually change and think, um, how they can actually affect, uh, uh, affect the business and improve their own life as, as part of it. So it has a, a more personal approach. So again, that's, you know, it, it uses the process of the model to, to bring both components together. Uh, and then also back to, you know, with our survey tool, we, we use it a lot for 360 leadership assessments and multi-rater assessments. But at the end of the, at the end of the process, traditionally it's been a report that identifies, you know, skill gaps, competencies, um, bench strength and weakness within, at, at the individual and organizational level. And then the individual that maybe the leader goes off and says, okay, I'm gonna, I'm gonna select these, you know, three or four skills. Maybe it's something I'm weak on, maybe it's something I just wanna strengthen even more. Um, but there's little in that process to hold the people accountable again. So it goes back to accountability. So you've taken these, you check these three that I'm gonna work on, you know, how is that, you know, how are we measuring the accountability and the improvement of that? Because there's little, there's little pulsing as an example that goes with, with the 360 side. So those are some of the areas that, uh, that we're, we're focusing on the people side, but through the process that links 'em to the technology, you know, tech, tech. Yeah. Go ahead. Yeah, I was gonna say, for the companies that are investing just overly in the technology and forgetting about the people, what are some of the consequences? Well, losing touch, you know, not listening to the workforce, um, doing the wrong things and trying to correct something that's not, that's not aligned. Um, you know, technology's the accelerator, but, you know, people are the judge of success. You know, the, the tech, today's tech has reasoning it didn't, you know, back as much as it does today, right. Through, you know, uh, through AI and other, other, um, other methods. But, but the people have the emotion. They know what's right and what's wrong, uh, for the most part. So that's, that's, that's one of the consequences is, is not, not empowering the people and listening to the people and getting their feedback as a result of the, of the tech. Yeah. A lot of the time now, we're saying about how tech is augmenting the human experience and the partnership between humans and machines, and how that can yield higher input or output and, uh, productivity success for the organization. Mm-hmm. Uh, one of the things you talk about that I really like, and I haven't really heard many other people talk about it, is this whole idea of the single source of consistent truth when dealing with tech implementation. Can you explain what that means and why it's important? Yeah. Um, you know, there's, there's lots of ambiguity in data, right? And, and large organizations, um, have multiple disparate business units much, you know, in addition to the multiple technology systems. But those different business units all calculate something as simple as headcount or turnover, you know, di different ways. And again, it leads to ambiguity because one, you know, one person's measuring something that they think is, is, is perfect for them, but there's no way to benchmark it and compare across the organization. So by, you know, enabling a single point of truth, you know, it's that, you know, that that single source of bringing data together from, from your disparate systems, it's a data warehouse behind the scenes. But it's modeled in a way that, that, that, that pre, pre, pre-calculate, pre configures these different facts that are, you know, the, the quantifiable elements of measurement so that they can be quickly retrieved and filtered and compared, uh, from that single source with without ambiguity. It seems like one of the big challenges too, kind of connecting what you said earlier is, you know, there's probably all sorts of data spread across your organization, and that could be globally different business functions, et cetera. That is a massive challenge to be able to collect that in the process by which you do that. Is that something zeroed in helps with? Yeah, it is. You've, our, our participants may be familiar with the concept of E T L extract, transform load. There's various tool sets that, um, you know, you can reach out, you can either pull or have clients push data to you to, you know, to, to centralize. But yes, that, you know, many of the more, more modern systems have open APIs to, uh, to provide access to that data. Sometimes those APIs are not about extracting data in bulk, though. They're more transactional, transactional, where, you know, one system has to, you know, initiate a transaction in the payroll system or vice versa. Um, so from that perspective, there's sometimes there's other approaches that can be taken rather than the enabled APIs to get large streams of data out so that it can be, you know, warehoused and organized and then kept up to date on a recurring basis. But yeah, absolutely. It's something we do for our clients. It, it's, it's really one of the, the key values that, or the differentiators that they find with us is being able to organize the data. You know, our clients are mature. They've, they've built, you know, dashboards and scorecards with Power BI and Tableau and Click View. Um, but they found that they, in order to do that, they have to do the dirty work, and, and it's very, it's very challenging and they don't get enough time to spend on the business. Plus all the ad hoc reports that come in, uh, from, from the business into a people analytics team is just, you know, hundreds, you know, if, if not more on a monthly basis. So they need to be able to, to respond to that, uh, in a, in a timely manner. And having that single point of truth is key to be able to do that. Yeah. One of the big hot areas right now that you briefly touched on is the importance of people analytics as it relates to, you know, succession planning, having a strong bench mm-hmm. You know, skill development, you know, pipeline, all these, all these, uh, talent management and, uh, kind of talent attraction, recruiting aspects of a, of a business in hr. Uh, can you give an example of how people analytics can help organizations better utilize their talent? Yeah, sure. So, you know, we, we work with the, with the City of Detroit and their, their hr, um, HR group. And, you know, they've, they've are combining their budget data with their people data, uh, from a, you know, I think we're pulling from, uh, five or six different systems at City of Detroit, including their legacy, you know, mainframe systems for historical data so that we could look at trends and patterns. But they're bringing in their budget today, uh, to enable, you know, better insights on the workforce planning side. And their, their overall goal and their direction is to actually include talent management and talent acquisition in that process. They've, they have some key, um, their key goal is to, to really build an internal kind of talent marketplace first, um, to give their emplo their existing employees the opportunity to step up or, or move up in positions that they're qualified for, uh, before they go outside and look at, look for external talent. So, you know, being able to enable that, you know, the first thing they need to do is that, you know, the descriptive analytics to get a handle on what they have, where are their skills, where, you know, in what are their key job roles and, you know, city, the city's using zeroed end to help them with that effort. Very impressive. Um, but a lot of HR people kind of fall short or don't have the training when it comes to people analytics. Uh, what are, what do you see as their biggest challenges and what can we do to, you know, enhance their skills and set them up for success in this new digital, you know, people analytics world? Yeah, so part of it is, is is, you know, being able to, you know, well, clients have, one of the shortfalls is that they, they remain quite tactical, right? They, they continue to, you know, everybody wants to try to do things themselves. I do it myself first until I reason realize I can't do that. That's outta my skillset. So, um, uh, a a lot of organizations, you know, stay in that mode and, you know, you know, a HR tends to have to sometimes live with the tool sets that a, that it gives them. And there's always been this kind of, um, not the, not the greatest, uh, relationship between HR and, and it, I think it's, it's strengthening now with, um, with the focus that, that HR is getting, uh, you know, from, from the, you know, the H C M technology side, there's lots of investments going into those, those solutions as you mentioned. So, you know, being able to step back and, you know, leverage, leverage partners to help where they can is, is very key. And, you know, not trying to do everything themselves. So Yeah, I'm, people have people, people are doing it better out there. Um, and other organizations can, um, benefit from that. Yeah, that's what we're, we've been seeing over the past several years about like, you know, all the stakeholders involved with employee experience and, and everything with it, HR, communications and marketing and facilities management. And I think especially now with like hybrid working, the importance of it and hrs relationship continues to grow and become more important. Mm-hmm. Um, you know, looking into the future, you know, since you've been involved in the space so long and you have a, this whole historical lens on how it's developed, if you were to, you know, guess like what, what the future's gonna evolve in, and you probably know because you're kind of working on features and improving mm-hmm. You know, zeroed in everything you're doing there. Uh, how do you think it's gonna evolve to be maybe more accurate and useful to organizations? Well, certainly predictive analytics is, is, you know, and, and artificial intelligence is, you know, working its way quickly. And, and vendors are, are adopting it quickly in a variety of ways, including the generative ai, ai, the chat G p T type models. Um, you know, some of 'em are just dropping a chat, you know, uh, a search box to the real chat G P T onto their screen. Okay, that has some value, but it doesn't let chat G P t learn from their, from a client's, uh, confidential information, right? So there are, there are ways to implement, you know, generative AI such that, you know, it can, you know, it can actually learn from the confidential information without disclosing that confidential information to the rest of the world to be able to answer questions. So that, that's currently, that's currently evolving. Um, but even on the, on the more basic predictive modeling, right? What you, so one of the, one of the most common predictive models that we implement for clients is, is a flight risk indicator. Um, it's a, you know, it's a, it's a decision tree model. It's what they call supervised, uh, learning. It, it looks at the past to predict the future, right? And it can all be done manually, but it takes so much time and so much data to do that. We can teach a computer, you know, with artificial intelligence much in, in a model, uh, the ability to make decisions much quicker. So it's, um, that said, you know, if, if a predictor or a probability score is tied to, you know, to Dan and it says, Dan's a high risk, you know, a high risk individual, you know, somebody needs to take action on that. So what's gonna happen? So, uh, well, the model, what we use is, is what's called explainable ai. It's part of the model that allows us to see the inputs and see exactly which inputs were attributed to Dan to calculate that probability score. So we would know that maybe it's part of your pay, maybe it's your manager, maybe it's your schedule, um, based on the inputs to the model. So with that, something comes out of that. So what action needs to be taken? Okay, are we gonna increase your pay? Are we gonna have a stay interview or some type of intervention? Are we gonna change your schedule? Um, those things get recorded in other systems, but it's not that quick for them to flow back into the model. So, uh, I believe there needs to be a way to, you know, record, uh, annotate, if you will, what, you know, okay, we're gonna make this, we see these, these issues, here's the change that we're gonna make. Here's when we're making it, here's how long it's gonna take for the, you know, for it to trickle down. And then this is when we have to measure it again to see if that change actually kept Dan with the company. So I don't think that that's, uh, we don't, we don't see that rarely being done enough. I really like this example because it really backs up what you said about humans plus technology, so mm-hmm. You know, tech helping identify which employees have a flight risk, but then humans kind of being the interv intervention and, and being able to act on, you know, using some of the things that you said, some of the ways. Yeah. And, uh, I also think it hits on part of your process about collecting and measuring and testing the right data over time. Yeah, that's right. Why do some companies not prioritize investing in their people, and what can be done to change that? Do you think that there's a role for people analytics to be able to make that happen more? Yeah, again, it goes, it goes to that initial lack of insight that they have today and the inability to build a business case because they don't know what's changing. They don't know the impact it's having. They can't monetize it. So, you know, that's, that limits that, that window of an, you know, ability to invest. Um, and you know, the, again, the, you know, h HR is a cost center. Um, it's not a, it's, you know, it's not a profit center. So, um, that, that also lends a challenge. So you really have to identify what the, you know, what they're, what they're losing financially as a result of their current processes and, and, um, use that to, to build the case for investing. Yeah. I, someone who's been to a lot of HR conferences and spoken to a lot of HR executives and professionals over the years, you rarely hear the word monetizing. Yeah. So when you said it, when you said it earlier on, I'm like, oh, wow. Like that is definitely something that will hopefully trigger some people in the audience to kind of rethink their own roles and how they're kind of measuring and trying to improve and justifying, uh, a lot of these, these talent management efforts. Yeah, Exactly. Um, you know, we work worked with William Sonoma, um, um, early on in our, in, in our company phase. And, uh, we measured associate productivity reporting for them. And, you know, a lot of the data came from HR systems, right? Their hours worked, um, their schedules, you know, about the people, you know, what stores they reported to, but it linked to all of their point of sale data too, and tracked sales per hour against sales per hour goals. So, you know, they were able to very quickly identify their top producers at the store level so that the store managers could give those top producers priority scheduling, right. And, you know, where people weren't meeting their sales per hour goals, they were able to engage them in, you know, training initiatives to build the talent that they needed, you know, to drive the business results. And, you know, that's a, that's a great example of how, you know, how our clients use their HR and business data together, you know, to in improve business outcomes. Yeah. So you're basically enabling them to produce more and make them less of a flight risk, which are both measurable and in a sense, monetizeable. Yeah. Yeah. You're giving them the confidence to succeed. I love that. And I like how you think of about this whole space, and I think, I think really what's happening, and tell me if I'm wrong, but it's something I've, I've, a trend I've seen for years is, at least from the HR perspective, a lot more HR people are being hired from the business. So they're a lot more adopting your kind of mindset as kind of, I guess, an entrepreneur business person and, and someone who's involved in kind of operations and everything. Are you seeing that as well? We are, yeah. There's a, um, uh, you know, a lot, a lot of site, you know, um, site cycling and, you know, between, between different jobs, rotational jobs, if you will. Right. Rotating in from, from the business and, and across. So Yeah. And you gave a great example about how organizations can use people analytics to improve talent outcomes. Is there any anything else that really stands out in the work you've done and, and, and the relationships we you have with clients that you'd like to talk about? Uh, those were most of the key ones. Um, you know, it's, it's, it goes back to the, our, our clients are, have tried, are trying to do this themselves, and they're just, again, struggling with the data in getting it organized in a way that they can use it for ad hoc reporting, that they can then use it for better insights for decision making, and then they can step back and, and really think about how do we want to enable predictive analytics in our, you know, in within our business, within hr, within our business. And then, you know, the service that, that we have with our, uh, you know, our implementation team, you know, they're there on a kind of a concierge service to help clients enable the technology more and more and more across their workforce. Well, that's great. And I'm sure there's gonna be a lot more advances to come, as you said, especially in the area of ai, which is one of the hottest topics in all things work right now. Uh, we'd love to take any questions if anyone has it. This has been a lot and, uh, you know, a lot of great information and, and data and insights and real life examples. So if you have any questions that you'd like Chris to answer for you, I'm sure that ev you know, everyone comes different companies. One of the things I'll, I'll do before we do, do that, or, you know, I can give some advice on getting started if that's, you know, works and, you know, one, one of the key areas is just on the communication side, you have to communicate what's possible to the workforce. You know, we talk about a data-driven culture, right? So you, you have to build a data-driven culture, and you ha in order to do that, you have to communicate what it means to the business. What are our goals and what we're trying to accomplish. Um, I remember back in, uh, mid 2000 early, I went to, um, Accenture, uh, I visited Accenture, and they had a big training center in Chicago or outside called, um, the Q center. And in the, in the lobby of the Q Center, they had, I guess it was, they had a light in the center of on a table, and that light was a, was an indicator of their stock price. Oh, it was green or it was, you know, yellow or it was red if it was going down. And I mean, I don't, I'm not advocating that co public companies do that because it can really set the mood when people walk into work and they see that the, the light is red. Maybe it makes 'em work harder, but, um, You know, or they, yeah, I would, I would, uh, definitely put stress on a lot of people. Like there's all these studies about like, you know, like McDonald's is red and yellow and it makes you go in and out faster. Mm-hmm. So it communicates like, uh, fast food. Yeah. As Margaret said in the Chat Q Center, she used to work at Accenture. Okay, maybe she remembers the light, maybe she remembers the light in the lobby. You probably don't forget it. Yeah. Um, so communication is really just understanding, you know, what's our vision? What are we trying to accomplish? That's, that's one of the ways of getting started. And then it's, you know, don't procrastinate, right? Just, you know, start with what you have. Everybody has, everybody starts with the same amount of data, right? Um, it evolves over time, but you have to start with a baseline. You have to start building this, um, this trust in your data because without trust, there's, we talked about it, we got ambiguity, you got people questioning it. Um, and in order to start, and, you know, you have to go through this process of, yeah, there's some data cleansing, all the data is useful, right? Um, even dirty data is useful, but over time it can be cleaned, it can be transformed, it can be reorganized, and you just have to evolve it, you know, over time. And then of course, lastly, that, that last point is, is getting into that one, that one point of truth that, you know, that's really what, where you want to end up. Um, so that you can, you can use that as your, you know, Azure source for, you know, answering the important questions that you need to, When you think about your clients and who you work with, um, from an HR perspective, who, what area of HR is most interested or are using this the most? Is it people who have titles, like people analytics within organizations, or is there others? Um, we, we do find that, you know, people analytics, uh, aligns under to, it's a lot under total rewards. So, you know, looking at, you know, the, the workforce as a whole from a, you know, from a compensation, from a development side, um, that's key. Talent acquisition is also really keen on because ta, you know, talent acquisition is somewhat of a silo, right? They work with the people on the front end coming through the door, and they lose track of 'em by the time they're in the organization. So there's not a lot of, of messaging back from the HR system once they're onboarded, uh, to understand, to get to that quality of hire metric or, or indicator that they, they would love to be measuring and looking at. Because if they have a higher quality of hire with certain, you know, certain sources, you know, uh, certain industries, certain competitors, you know, that, that's really keen. So being able to, again, get, get the data to flow back and forth, and with that single point of truth, when you can bring your applicant data, your candidates, you know, the whole hiring process together with the new hire process and the onboarding, you begin to paint. You have that, that complete story of what's working and what's not. What about l and d? I could see this being very useful for that group. Yeah, no, it always has been. That's where we started, you know, mentioned that, you know, there's, they're still measuring Kirkpatrick and Phillips model for, you know, um, but working more on the impact side and trying to get, trying to get to impact. Uh, but they do a lot of, uh, you know, compliance reporting and just, you know, kind of the more of the descriptive analytics so that they understand who's had what training, when did they have it, you know, um, when is it needed. Uh, but moving forward, it's, it's really gonna be focused around skills in the talent marketplaces, and we're seeing that with, you know, you know, a number of HR tech players coming on, uh, with that, those capabilities. Yeah, we are too. It's massive. We, we have a, a lot of research coming out on skills. It's a really big area, especially because of all these new skills that, you know, a lot of people need to lead, you know, AI skills like we were mentioning before. Mm-hmm. And then the lack of skills, a lack of soft skills with new people coming into organizations and coming out of remote work. There's, there's been a lot on that as well. So there's a lot of disconnects. And then organizations need to know, like what skills they need for what positions and updating those skills. And so there's a lot of opportunity I feel, when it comes to o and d, especially as you were saying before in terms of understanding the bench and, and, you know, session planning, all these things of making better decisions about who goes where within the organization. Yeah. That's, that's, that's key. And then again, you know, people are, are not gonna lose sight of engagement, right? So, you know, because the workforce is disconnected. And, and that's where I think I mentioned the, the eight factors of engagement that, um, it, it really actually, one of the first factor starts with accountability. So there's, the eight factors are, it's accountability, it's purpose, it's agility, um, it's recognition, it's, um, it's care, um, it's trust, it's development, and it's, and it's resources. So, and those, those factors that I rolled off are, are much more employee leaning than employer leaning, right? Some of 'em are, you know, are are employer base, but it's really empowering the worker to be accountable for themselves, to giving them purpose, to giving them the ability, you know, to be agile. You know, recognizing them on their successes, caring for them when they, you know, when it's needed, um, giving them trust and allowing them to trust the organization, giving them the tools for development and, you know, giving them the resources they need, you know, around that. So those eight factors are key, and it's, and it's all about measuring, right? Whether it's measuring the individual, uh, rolling it up into indices, comparing it, benchmarking it, and then developing and monetizing change initiatives to, uh, to improve. Excellent. Well, it doesn't seem like we've, any questions? Does anyone have any questions for Chris, or we're all analytics experts, which I highly doubt. We'll wait a few seconds. Yeah, sure. Anyone else? Right? Well, we appreciate the opportunity to, uh, you know, share our journey. It's zeroed in and, um, it, it's only been 19 years, so, uh, we've, we've got a few more, uh, uh, decades left here. So Jill says, uh, you know, this was excellent. Thank you for your time. So, and Sarah as well says, thank you for this. I think you supplied so much rich information, Chris, that like, how do you, how do you even ask a question, right? Yeah. I feel like you've answered so many questions that could pop up. I mean, you've certainly answered all of my questions and I had a ton for you do. So I really appreciate you joining us. Yeah. I, I have a question. Do I, do I get SS h RMM credits for presenting too? That's, I've always wondered that. I've always wondered that. Uh, I don't know. I don't think so. No. Thanks For the, thanks for the opportunity. Um, uh, I do have my contact information, uh, if anybody wants to reach out to me, uh, you can find me on LinkedIn or directly through email. And we're at zeroed in.com. So if, uh, we can help you with your people analytics journey, uh, we'd love to give you, uh, lend you our expertise. Yeah, be sure to follow him, connect with him on LinkedIn, follow Zeroed in, and you can go to zeroed in.com to learn more about their people analytics and data management solutions to help your organization. Chris, again, thanks. It's a pleasure. Honestly, I felt like I, it just got smarter by listening to you, which is always a good thing. And I hope this has benefited the audience and we really appreciate it. Great. Thank you all. Have a great afternoon.

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