Hiding in the Average: Why Human Capital Metrics Must be Disaggregated to be Effective Management Tools By Andrea Kropp

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1 Hiding in the Average: Why Human Capital Metrics Must be Disaggregated to be Effective Management Tools By Andrea Kropp MEASURING THE HETEROGENEOUS WORKFORCE We ve all heard the joke that when Bill Gates walks into a bar, the average net worth of the patrons rises by a few billion dollars, but that certainly doesn t mean that the average bar patron is a billionaire. In general, averages conceal more than they reveal. Why then does most of the HR profession settle for average measures of their most expensive asset their workforce? In many cases, the answer lies with antiquated systems and manual scorecard creation processes. That tide is changing. More and more HR scorecards and reports reflect the reality of a heterogeneous workforce. New, automated data sourcing options and Web-based dashboards with unlimited drill-down are changing the very core of human capital measurement and delivering the truly actionable workforce insights that HR executives have sought relentlessly since the introduction of enterprise-wide systems. INTERNAL VARIATION ABOUNDS: THE EXAMPLE OF VOLUNTARY TERMINATION RATE For most organizations there is greater internal (than external) variation for any given metric. Metrics from Promotions Rate to Absence Rate to Employee Engagement differ more by lines of business, pay grades, recruitment sources, and tenure or job categories within a company than they do across companies, even in vastly different industries. This should be welcome news. It means that organizations are largely in control of their own fate so long as they can de-average to identify laggards and bring them up to standard. Take the case of the widely used metric Voluntary Termination Rate. According to CLC Metrics benchmarks, in 22 half of all companies had annual Voluntary Termination Rates between 8 percent (25 th percentile) and 17 percent (75 th percentile) with a median value of 11 percent (5 th percentile) 1 (see Table 1). The fact that half of all companies have a total Voluntary Termination Rate that falls within nine percentage points of one another (17% - = 9%) can deceive one into believing that there is little variation. However, when we look inside companies and break down their workforces by pay grades, recruitment sources, tenure, gender or job categories we see tremendous variation. Continuing with Voluntary as one example, cutting this measure by Organization Tenure and EEO Job Categories Table Voluntary Total Company. Percentile 22 Voluntary 1 th 5% 25 th 5 th 11% 75 th 17% 9 th 29% Table Voluntary By Organization Tenure. Percentile Workforce Segment 22 Voluntary 5 th <1 Year Tenure 27% 5 th 1-2 Years Tenure 15% 5 th 2-3 Years Tenure 1 5 th 3-5 Years Tenure 9% 5 th 5-1 Years Tenure 7% 5 th 1-15 Years Tenure IHRIM Journal January/February 24 13

2 Table Voluntary by EEO Job Category. Percentile Workforce Segment 22 Voluntary 5 th Officials and Managers 5 th Professionals 7% 5 th Office and Clerical 13% 5 th Sales 1 reveals ranges of four-27 percent and six-13 percent, respectively, at the median (see Tables 2 and 3). Although Voluntary Termination Rate appears on most HR dashboards, it is only one measure among many. Each key performance indicator (KPI) must be similarly broken down. Here the cuts are Organization Tenure and EE Job Category. For measures such as Training Investment per FTE, better cuts might be job grade or performance rating, while for Promotions Rate it may be gender or ethnic background. Regardless, it is clear from conversations with HR executives worldwide that few organizations effectively de-average even their most critical human capital measures. Therefore, they miss the opportunity to focus on pockets within their workforce, spreading resources thinly across initiatives aimed at up-skilling, engaging, or retaining all employees, and wondering why they are not achieving the results they expect. A total company value, much like a nationwide average high school test score, doesn t tell you what to do to bring up the average. Should you invest in inner city schools, in students for whom English is not their native language, or elsewhere? Similarly in HR, the total value can tell you if something needs to be done, but is not helpful in telling you what should be done. Where companies are able to drill down deeper, it is typically by line of business or geography. This is how organizational reporting structures are typically organized and mirrors the way budgets are typically allocated. In addition, there is a long history of reporting by line of business, product, geography, or cost center from the Finance and Sales functions. These breakdowns are just as important to the HR executive, but since they are far from novel, they won t be discussed here. Rather, we turn attention toward breakdowns impossible in other functions, namely, breakdowns by various employee or job characteristics, and illustrate the actionable information this approach can generate through a case study. CASE IN POINT:COMPARING VOLUNTARY TERMINATION RATES IN THE HEALTH INSURANCE INDUSTRY We have established that companies must be able to expose intracompany variation (internal benchmarking) in order to manage the macro-level indicators on their human capital dashboards. That said, external benchmarking can be hugely valuable for contextualizing one s own performance and for setting stretch targets based on results others are () () () () known to have achieved. However, to be effective management tools, benchmarks too need to be disaggregated. The combined power of disaggregated company results and disaggregated benchmarks is highlighted by the case of four health insurance companies and their Voluntary s by tenure. The four health insurance companies in this case example are all based in North America and employ between 9, and 35, employees. Figure 1 shows each company s difference from the benchmark (company result minus 5 th percentile benchmark result) for 22 Voluntary. Values above zero indicate turnover above benchmark; values below zero indicate turnover below benchmark levels. One could argue whether the 5 th percentile is the right target for Voluntary. Most companies would probably prefer to be at the 25 th percentile. For the purposes of this case, the choice is arbitrary and not particularly relevant. Setting a different standard would simply shift all the bars up or down by an equal amount. The variation across and inside the companies would remain. Looking at individual results, we note that Elm Company suffers from relatively high voluntary turnover in all tenure bands. In contrast, Jasper Company s high turnover is confined to the first five years of tenure, after Figure 1. Difference between Company and Benchmark Voluntary for 22 by Tenure Band. Elm Company Ivy Company () () () () Fir Company Jasper Company 14 January/February 24 IHRIM Journal

3 which its relative performance improves. Ivy Company has a very isolated first year turnover problem. Meanwhile, Fir Company excels in the first year category, but has difficulty retaining employees in the three to five year band. These are major differences revealed as we de-average. The results begin to suggest different strategies different possible answers to the What should I do question. Ivy Company might investigate whether changes to the recruiting or onboarding process were having an adverse effect. They may discover that early tenure terminations originate principally from one recruiter or from a handful of frontline managers. Conversely, Fir Company might convene a focus group of employees in their second or third year to understand what they are seeking in their professional lives and whether they believe they ll be able to find that at Fir. In many cases, metrics raise more questions than they answer, but they are remarkably effective at getting to the source faster. Just as Ivy s next steps would be wasted effort for Fir, Fir s root cause investigation plan is inappropriate for Ivy. It is important to note that all four companies had the same pattern for absolute Voluntary by tenure highest for <1, second highest for 1-2, third highest for 2-3, etc. exactly as seen in the benchmark figures. Absent the benchmark information that allows them to assess relative performance, each of the four companies may have attacked first year turnover since it is the highest. In the case of companies such as Fir, knowing that < 1 year Voluntary Termination Rate is high for all companies and that their result is actually superior allows them to focus more appropriately on 3-5 years of tenure where their relative performance lags behind. Organization Tenure is one of many cuts we could have chosen for the analysis above. Ideally, an organization would cut by 5, 1, 15 dimensions looking for the greatest variance before they committed resources to a specific plan. However, the Organization Tenure cut alone has another very useful application in workforce planning. With some basic assumptions about workforce growth, hiring, and termination patterns, these values can be used to project the number of new hires required each year. They can also be used to model the impact of specific reductions in Voluntary. For example, if the six health insurance companies assumed that their Voluntary s remained fixed and if they hired new people today, it is a simple task to calculate the number that would remain after one year, two years, three years and so forth. Figure 2 shows how the four companies would compare to each other. After three years, the number still employed varies from 614 at Fir Company to 367 at Elm Company. After 1 years, the range is a high of 343 at Fir Company and a low of 182 at Elm Company. One could also ask the question, At what point in time have half the new hires left? For Elm Company this is near the one and half year mark; for Ivy and Jasper, it occurs at around three years. Fir Company retains half its new hires for a full five years. Presenting business leaders with this alternate perspective on workforce turnover can sometimes provoke a conversation that an aggregate figure would not. Based on what we now know about Fir and Elm, it is hard to believe that their total 22 Voluntary s across all tenure bands are 9.7 percent and 12.6 percent, respectively. This difference seems hardly worth discussing. So far, the health insurance case has only probed a single dimension Organization Tenure. Despite that, it has already been instructive in identifying next steps for HR and possible targeted retention strategies. The ability to cut data by a single dimension in routine reporting represents the 9 th percentile of practice in human capital measurement. Few companies have layered multiple dimensions into their reports and Figure 2. Ten Year Retention of 1 New Hires Assuming 22 Voluntary s ty Tenure Band. 25 Comparison of Four North American Health Insurance Companies Jasper analyses, but those that have are able to pinpoint HR intervention opportunities with great precision. Continuing with the Voluntary by tenure analysis, we add EEO Job Category as a second layer. We know from benchmarks that Voluntary varies with EEO Job Category nearly as much as it does with tenure (see Tables 2 and 3). Combining the two allows further pinpointing of retention issues and therefore development of more targeted action plans and interventions. For Elm and Jasper Companies, a Voluntary Termination Rate was calculated for each of 24 combinations of Organization Tenure and EEO Job Category. Fir Ivy Elm IHRIM Journal January/February 24 15

4 X 6 Tenure Bands 4 EEO Job Categories 24 Employee Segments This list can be sorted in descending order or compared to benchmarks as before to highlight relative strengths and weaknesses. However, as we apply more dimensions it becomes increasingly important to keep track of the number of employees in each segment to separate out employee segments that are major turnover cost drivers high headcount, middle to high turnover from those that are niche problems low headcount and exceptionally high turnover. Clearly a Voluntary of 15 percent among a population of employees is a greater cost driver than a Voluntary of 25 percent among a population of 1 employees with 15 versus 25 actual terminations, respectively. Plotting Voluntary against the percent of all employees falling into each of the 24 categories helps visualize the business impact that turnover in each segment is having (see Figure 3). Applying this visualization method to Elm and Jasper reveals several interesting similarities and differences between the two. Similarities include: 1) Each company s single highest Voluntary is Office and Clerical employees with less than one year of tenure. 2) Of the four, the Office and Clerical job category has the widest range of Voluntary s by tenure. 3) Voluntary s for the Professional and Office and Clerical categories decreases with every tenure band. Rates for Officials and Managers and Sales do not follow this pattern. 4) Each company s highest Voluntary in the Sales category is in the two to three year tenure band. 5) The lowest, less than one year, Voluntary is in the Sales category. 6) The Officials and Managers and Sales categories are weighted toward longer tenured employees. Notable differences include: 1) The highest Voluntary segments are spread across more job categories at Elm than at Jasper. At Jasper, the six highest values are mixed equally between Professional and Office and Clerical. At Elm, the six highest cut across Professional, Office and Clerical and Sales. 2) There is greater internal variation by job role for the early tenure groups, especially less than one year. Using the Sales category as a baseline in each case, Elm s Office and Clerical rate is seven times higher, and the Professional rate is almost three times higher. Conversely, at Jasper, the Office and Clerical rate is three and half times Sales and the Professional rate is two and half times Sales. 3) Officials and Managers constitute a much greater portion of the workforce at Jasper than at Elm Looking again at the hypothetical case of 1 new hires starting today and terminating at the 22 rates, we see in Figure 4 the cumulative impact at Elm and Jasper. At Jasper, the longterm retention of Office and Clerical and Professionals workers is nearly identical, while at Elm these two groups differ substantially. At Elm, a new Sales hire has a 5-5 chance of staying about four years, while at Jasper they have a 5-5 chance of remaining eight years. This type of information can then be fed into workforce planning scenarios to forecast everything from recruiting to succession planning needs. Figure Voluntary versus Percent of Workforce for 24 Employee Segments. 16 January/February 24 IHRIM Journal

5 Figure 4.Ten Year Retention of 1 New Hires Assuming 22 Voluntary s by Tenure Band for 4 Job Categories. Elm Company Jasper Company Offi cials and Managers Professional Sales Workers Offi ce and Clerical CALL TO ACTION: A NEW DAWN FOR HUMAN CAPITAL MEASUREMENT Clearly two layers of analysis Organization Tenure and EEO Job Category are more instructive than the single layer analysis using Organization Tenure. For large organizations, one could argue that three or four layers might be required to get to a highly actionable level since an organization with 5, employees divided into 24 segments still has roughly 2, employees per segment. In addition, the two cuts used in the case are not appropriate for all measures. One would expect Compensation per FTE (full-time-equivalent) to vary by Organization Tenure and EEO Job Category, so applying those cuts to that metric is misguided. Each organization must determine for itself the right KPIs and the right data cuts based on strategy, institutional knowledge, data validation studies and a bit of trial and error. However, once any initial set of measures is in place, the next goal should be reporting at a sufficiently disaggregated level to allow for interventions targeted at hundreds of employees not thousands, or tens of thousands. Without this level of analysis, HR organizations run a high risk of spreading resources thinly across all employees thus diluting the impact the function could have. When human capital measurement was in its infancy, there was a lot of focus on selecting the right metrics to measure. However, a scorecard of all the right metrics reported only at the total company level does little to further an organization s goal of improving performance on those metrics. Progressive organizations have realized this and are deploying new technology tools that make dynamic drill down and data cuts inside their HR scorecards a reality. Organizations just beginning the measurement journey should build with the end in mind. An actionable measurement program that truly allows HR to manage by the numbers requires the ability to disaggregate data and find out exactly what s hiding in the average. ENDNOTES 1 All benchmarks cited in this article are derived from raw transactional data supplied by CLC Metrics member organizations. CLC Metrics calculates metrics on the member s behalf, applying consistent rules and formulas and publishes benchmarks quarterly. The sample for Voluntary benchmarks without filters and cut by Organization Tenure is 41 organizations with a median employee size of 1 and average of 24,. The sample for Voluntary benchmarks by EEO Job Category is 29 organizations. Andrea Kropp is a project manager at Washington DC-based CLC Metrics, a membership program of the Corporate Executive Board providing human capital reporting and benchmarking solutions. Her responsibilities include management of human capital data mining engagements for member companies, development of new CLC Metrics program offerings and authorship of membershipexclusive research studies on human capital trends and benchmark findings using the world s largest transactional human capital data warehouse comprising more than 2.5 million employees from over 15 North American, European and Australian organizations. She holds a bachelor s and master s degree in chemistry with a focus on modeling complex systems. Ms. Kropp can be reached at Written correspondence can be addressed to kroppa@executiveboard.com or Andrea Kropp, CLC Metrics, 2 Pennsylvania Ave. NW, Suite 6,Washington DC 2, USA. IHRIM Journal January/February 24 17