Transcript for "jeff kuzmich - Migrate_HR_Analytics_10012025_5055583":
Hello, and welcome to our webinar event, Connecting the C Suite and Beyond. First, I want to cover a little housekeeping. Please note that the primary audio connection for today's session is streaming via your computer. There's no dial in option. So, just make sure you check your speaker volume, make sure your audio is turned on and set. Also, you may need to refresh your webinar browser. If so, you can just use the key commands we have here on screen for you. So, let's take a closer look at some of the other important features. To download and print a copy of today's deck for future reference and to access additional resources, refer to the files and resources window at the top right of your console. It's available at any time during the event. Also, we'll be sending a recording afterwards, so you can access everything that way as well. If you have any questions during today's event, you can send them via the Ask Us a Question window. To submit a question, simply write in your question and click Submit. We'll have some folks in the back end helping answer those questions directly. However, time might not permit us to respond to each and every one. So just know that the questions are important. They help us to drive more important content that'll be useful to you in the future. And please note, this presentation does not constitute legal advice. It's for informational purposes only. And now, I'd like to introduce our presenter. Joining us today is Jeremy Kessel, the Director of Platform Strategy at Paychex. Jeremy has more than fifteen years of HCM experience in service, user experience, and product management. As a data driven leader, he leads the evolution of digital platforms, embracing technology and analytics to push boundaries and craft innovative customer experiences. Welcome, Jeremy. Let's turn this over to you. You can walk us through what we'll be learning today about HR analytics. Thanks, Rob, for the introduction. I wanted to start by talking about the topics we'll cover today. We'll focus on discussing strategic decision making through data and analytics. We'll discover ways to improve collaboration between HR and the C suite. Whether you're the CHRO or an HR leader on your team, sometimes understanding the intent behind the question is half the battle. And then we'll take some time to explore a case study with Paychex Flex and what we're doing with our technology to improve the lives of our customers. When I work with a lot of HR leaders and just within my experience, words that come up a lot of times with regards to data and analytics is around complications, intimidation, misleading, and all that's accurate. It's hard to work with data. It's hard to understand how best to use it to produce a result that's meaningful and helps you make an informed decision. When it comes to the misleading component of HR analytics, a lot of this is just around the quality of the data. Garbage data in means garbage data out. And that's a term that's often thrown around, but it's true. Your outputs are only as good as your inputs. So you need to really focus on that. And we'll talk more about that later. But there is a brighter side. And when we talk about where AgriAlytics can help you, it's through a full gamut of HR responsibilities. And at the end of the day, it's all about making data driven decisions. And so that's what we're going to focus on for today, is how we can improve the way that you think about the questions that you're being asked from your C suite and the best way to respond to those. We'll focus on five topics, starting with labor costs, and we'll work through this list, and we'll give you a few examples of how this can work. So when you look at the C suite and put yourself in the role of the CFO, what might they ask when it comes to labor costs? Why are personnel costs climbing? Why is overtime trending up? How do we lower costs without cutting essential services? Questions could be asked in a variety of ways, but they all boil down to one sentiment. Are we staffed in the most efficient way to protect margins? So how could you as the HR leader respond in this scenario? You could analyze cost per employee, taking a look at the full wages that they're paid. You could look at insurance coverage and retirement match and so forth. You could review your overtime trends, understanding how those are going up or down and what's the cause behind those. And you could break that down by department. And you could calculate productivity output per labor dollar. In this scenario, it really just means how much revenue are you producing and how much are you spending on your staff to produce that. And what that could break down into is a response like this. Last quarter, labor costs grew 12%, but productivity only improved 4%. Overtime in operations is 30% higher than industry peers. By adjusting schedules and cross training, we could reduce overtime by 15%. Now the data doesn't really matter here. This is actually a real world scenario, but we anonymized it for the purpose of discretion. The point is, it's attacking the tenets of analytics as a whole. And there's four tenets. We can look at descriptive, diagnostic, predictive, and prescriptive. We'll focus on the first three for the portion of today though. When you think about descriptive, you're defining what happened. When you look at the diagnostic analytics, you're defining why. And then the predictive is what can you do about it? So when you actually break down this statement, it's hitting those three tenets as it pertains to the response. So when you go back to your leadership, this is a way of constructing it in a meaningful manner that tells that story and shows that there's data behind the response itself, giving you more confidence and clarity about what can be done. So if we look at turnover and retention, what might your CEO ask? Are we losing high performers? Is our turnover higher than market? Which roles are we losing the fastest and why? Again, all this is boiling down to how do we retain top talent without overpaying? So where could you respond here? Let's take a look at a few data sets. You could identify attrition patterns by tenure, role, or manager. This gets a little bit into flight risk analysis, understanding what's actually leading toward company churn. You could calculate replacement costs. What is it gonna mean to onboard your new talent? And link turnover to business disruption. Again, in this scenario, you could be looking at it as you losing subject matter expertise. What is it going to take to replace that knowledge within the business and how long is that going to impede your productivity as a result of that? You could turn that data set into a response that tells you that 40% of your voluntary resignations are within the twenty four months, costing you 50,000 per employee in lost productivity and replacement. If you can improve early career development, you could reduce attrition by 10%, translating into those savings and onboarding costs. Again, the data points aren't the important part here. It's about how you're constructing the response and how the data can get you to that goal. Let's take productivity. Again, what might your CEO ask in this scenario? Where are those productivity bottlenecks? Which teams over perform and why? How much of lost productivity is tied to disengagement? In the end, where can we improve productivity without increasing headcount? All these scenarios are ultimately boiling back to one common theme, which is how do you maximize the output of your business? How do you get the most out of your talent in the organization? How do you improve the work life balance of your employees without sacrificing quality of output for the business? And so if we look at that from your leadership role, if you are again in the HR responsibility, how could you respond? You could conduct a survey and understand the engagement across the organization. You could review team output metrics, measuring sales and service and support and what are they producing in their role. And you can analyze process bottlenecks. Again, what are those red tape scenarios that are preventing your team from being most successful? And how does that translate back to your output metrics? By compiling that data, you could perhaps come with an insight that shows that higher engagement scores delivered more client projects per quarter. And that if you continue to invest in targeted workflow automation in those lower engagement teams, you could actually save money by not actually adding any additional headcount. So again, you're starting to get to a point where you're blending the data with where you could actually pair that with automation and not sacrificing quality. If we look at workforce management, your CEO might ask, do we have the right skills to deliver our goals? What roles are most vulnerable to automation? How do we grow without scaling at that same pace? And particularly in that last one, you're looking at, we don't wanna pay more payroll while we continue to grow as an organization. And that really, again, is all about maximizing output and can we grow revenue efficiently without overspending on staffing? So in this scenario, you could respond by evaluating skills gaps. You could quantify cost of skill shortages, and you could identify roles where automation offsets hiring. And in this example, we can look at support scenarios where support tickets for your live agents have risen by 20% year over year. And instead of adding the headcount, by introducing AI assisted ticketing, you can handle 40% of those lower level issues, saving on hiring costs and allowing for the more complicated and subject matter required issues to percolate up to the talent that you have in your company. We'll take a look at one more scenario here. What might the CFO ask? Is our training investment paying off? What people related spend is not essential? What would happen if we reallocated half of our training budget? And again, this is all boiling down to our people programs driving measurable returns or should we cut the costs? So in this scenario, you could respond by reviewing sales performance and quotas. Understanding, are they hitting their goals? Are they behind or are they exceeding? Review sales team retention. How much turnover are you seeing in your sales organization? And track training program participation. How many of your sales reps are actually participating in the training programs that you offer? By compiling that data, you may be able to understand employees who completed the sales training program closed 15% more deals in six months, and that the uplift equals incremental revenue, about 11 times the cost of the program. So you could see that by investing into your talent, and for those who've participated in the training that you offered, they're more productive, more effective. So really the goal here is less about cutting the training and more about getting more people to take the training that you offer. In all these scenarios, you have to consider a few things. Do you have the right tools? Do you have disparate systems that you have to stitch together? Is the data behind a bunch of layers that makes it difficult to access? You really wanna strive for simplicity. If you have to stitch all this information together, it makes it more complicated going back to the top of the call. Is your data clean? In this scenario, you got to look at it from two aspects, completeness and quality. If you have partial data or it's incomplete or it's not inaccurate, or if it is accurate, excuse me, then you're gonna be in a scenario where you're not gonna have the outputs that you're expecting. Your insights are gonna be partial and that's going to lead to bad predictive models in the future. But assuming that's all good, then you really gotta focus in on your top down support. If you have the organizational structure to make sure that you're successful, that they're investing in the data strategy, that they're giving you the tools to access that data, then you're well primed to be effective in this role. So now let's take a look at PageX Flex HR analytics and some case studies in this space. We wanna kind of show what we've done to make customers happier and more comfortable working with the data for their company. Starting with retention insights, we built a proprietary predictive analytics model that's based on 35 unique data points across millions of records that predicts employee risk of leaving. This is seamlessly integrated throughout Paychex Flex. And so clients have a quick way to go into the application, understand where those flight risks lie, and drill into any one of their employees to understand what they could do to help improve the possibility of retaining that talent. What we've seen with this solution is that up to 20% reduction of predicted employee turnover for clients who leverage the tool compared to like clients who don't. That's a material difference for those that are actually taking advantage of the data at their disposal and investing in the data within the application that we offer. Diving deeper into Paychex HR analytics, we've opened up a variety of analytics solutions for our customers around turnover, diversity, pay, performance management, and employee movement, and that list continues to grow. The way that we broke it down is by a series of guidebooks that explain all those themes and the guidebooks are actually broken down a layer further that makes it very easy to get to the insights that you're after, whether that's compensation planning, benefits overview, total rewards, or budgeted comp. We even offer compensation benchmarks so that clients can see how they compare against the market competition. If we step back, we can look at retention and turnover. Looking at this, going a layer deeper into the guidebook, each one's presented in a way in a journalistic fashion that helps make it easy to understand the data that you're looking at. So they all start with an explanation. They show you the trending that you can dive into each of these visuals for a layer deeper. You can look at involuntary, involuntary turnover, you can look at retirement trends. You can continue to go further into the insights and understand the use case and get analyst tips about what you're actually looking at. Sometimes whenever someone looks at a visualization for the first time, it again can be a little bit intimidating, but having the words to describe what you're looking at helps the adoption and makes you more comfortable and more informed. And as we continue to go down through the list, it keeps going for a variety of scenarios around resignation rate. But perhaps this isn't the most comfortable way for you to interact. And that's where AI Insights comes into play. So we've invested a lot into the AI capabilities of the platform, and you could ask a basic question through a Q and A framework. So when we look at this, you can always start off with a series of quick questions that are common across companies that are like you. But perhaps you have a specific question. There's no longer a need for that middle person to play that role of trying to get that insight for you. You can just simply ask the question, in this case, show me the breakdown in resignation reasons for the past six months. And AI Insights will provide you with a quick response that explains what that is with a trend that goes along with it. Do you have the opportunity to break down that trend further and save it for future reference? Or perhaps this isn't enough information for you. You want to go a layer deeper. Now that you know what the last six months look like, what is the turnover impact on labor costs for the next six months? Here, you can get another detailed visualization, remembering all the history from before, to be able to inform you about how you're actually performing in the company and where you might be trending over the next six months. And so that's gonna be key in terms of making informed decisions, especially around budget planning season. All these scenarios show the value of the AI insights. And when we look at utilization in the first quarter of launch, we saw that increase by 400%, which showed that those who've used it saw the value and continue to come back. So, key takeaways for what we discussed today is really around ensuring that HR analytics can foster strategic alignment, collaboration, and efficiency within your leadership. Using the data always makes you appear and seem and feel, and in fact are, more informed. Don't be intimidated by working with the data. Become comfortable working with it. It'll help you make informed business decisions. And the future is now. AI is going to make it easier and easier to access your insights. The most important thing again, is making sure that the data is of quality and the AI will make the accessibility that much simpler. All right, folks. And now is your opportunity to respond. Would you like to speak with a paycheck sales professional about maximizing your workforce insights with AI powered HR analytics? A quick poll will just come up, and you can answer yes or no, and we'll be able to contact you and walk you through all this great information and how you can leverage it. So, thank you once again for joining us today for Connecting the C Suite and Beyond. As a reminder, you can access a printable copy of the presentation deck in the files and resources window on your console, along with other related resources. If you can spare a few moments as we close, we welcome your feedback on a brief survey that will pop up. I want you to understand that your responses help us improve future resources like this to support your business. Thanks Thanks again, and have a great day.