Making HR Measurement Strategic
This book will help you better understand how to analyze, measure, and account for investments in people. However, although data and analysis are important to investing in people, they are really just a means to an end. The ultimate purpose of an investment framework is to improve decisions about those investments. Decisions about talent, human capital, and organizational effectiveness are increasingly central to the strategic success of virtually all organizations.
According to 2010 research from the Hay Group, businesses listed in Fortune magazine as the world's most admired companies invest in people and see them as assets to be developed, not simply as costs to be cut. Consider how the three most admired companies in 64 industries—firms like UPS, Disney, McDonald's, and Marriott International—managed their people during the Great Recession, compared to their less-admired peers. Those companies were less likely to have laid off any employees (10 percent versus 23 percent, respectively). By even greater margins, they were less likely to have frozen hiring or pay, and by a giant margin (21 points), they were more likely to have invested the money and the effort to brand themselves as employers, not just as marketers to customers. They treat their people as assets, not expenses. Perhaps the most important lesson from the 2010 World's Most Admired companies is that they did not launch their enlightened human capital philosophies when the recession hit; they'd been following them for years. Once a recession starts, it's too late. "Champions know what their most valuable asset is, and they give it the investment it deserves—through good times and bad" (p. 82).1
It is surprising how often companies address vital decisions about talent and how it is organized with limited measures or faulty logic. How would your organization measure the return on investments that retain vital talent? Would the future returns be as clear as the tangible short-term costs to be saved by layoffs? Does your organization have a logical and numbers-based approach to understanding the payoff from improved employee health, improvements in how employees are recruited and selected, reductions in turnover and absenteeism, or improvements in how employees are trained and developed? In most organizations, leaders who encounter such questions approach them with far less rigor and analysis than questions about other resources such as money, customers, and technology. Yet measures have immense potential to improve the decisions of HR and non-HR leaders.
This book is based on a fundamental principle: HR measurement adds value by improving vital decisions about talent and how it is organized.
This perspective was articulated by John Boudreau and Peter Ramstad in their book, Beyond HR.2 It means that HR measurements must do more than evaluate the performance of HR programs and practices, or prove that HR can be made tangible. Rather, it requires that HR measures reinforce and teach the logical frameworks that support sound strategic decisions about talent.
In this book, we provide logical frameworks and measurement techniques to enhance decisions in several vital talent domains where decisions often lag behind scientific knowledge, and where mistakes frequently reduce strategic success. Those domains are listed here:
- Absenteeism (Chapter 3)
- Employee turnover (Chapter 4)
- Employee health and welfare (Chapter 5)
- Employee attitudes and engagement (Chapter 6)
- Work-life issues (Chapter 7)
- External employee sourcing (recruitment and selection) (Chapter 8)
- The economic value of employee performance (Chapter 9)
- The value of improved employee selection (Chapter 10)
- The costs and benefits of employee development (Chapter 11)
Each chapter provides a logical framework that describes the vital key variables that affect cost and value, as well as specific measurement techniques and examples, often noting elements that frequently go unexamined or are overlooked in most HR and talent-measurement systems.
The importance of these topics is evident when you consider how well your organization would address the following questions if your CEO were to pose them:
- Chapter 2: "I see that there is a high correlation between employee engagement scores and sales revenue across our different regions. Does that mean that if we raise engagement scores, our sales go up?"
- Chapter 3: "I know that, on any given day, about 5 percent of our employees are absent. Yet everyone seems to be able to cover for the absent employees, and the work seems to get done. Should we try to reduce this absence rate, and if we did, what would be the benefit to our organization?"
- Chapter 4: "Our total employment costs are higher than those of our competitors, so I need you to lay off 10 percent of our employees. It seems "fair" to reduce headcount by 10 percent in every unit, but we project different growth in different units. What's the right way to distribute the layoffs?"
- Chapter 4: "Our turnover rate among engineers is 10 percent higher than that of our competitors. Why hasn't HR instituted programs to get it down to the industry levels? What are the costs or benefits of employee turnover?"
- Chapter 5: "In a globally competitive environment, we can't afford to provide high levels of health care and health coverage for our employees. Every company is cutting health coverage, and so must we. There are cheaper health-care and insurance programs that can cut our costs by 15 percent. Why aren't we offering cheaper health benefits?"
- Chapter 6: "I read that companies with high employee satisfaction have high financial returns, so I want you to develop an employee engagement measure and hold our unit managers accountable for raising the average employee engagement in each of their units."
- Chapter 7: "I hear a lot about the increasing demand for work and life fit, but my generation found a way to work the long hours and have a family. Is this generation really that different? Are there really tangible relationships between work-life conflict and organizational productivity? If there are, how would we measure them and track the benefits of work-life programs?"
- Chapter 8: "We expect to grow our sales 15 percent per year for the next 5 years. I need you to hire enough sales candidates to increase the size of our sales force by 15 percent a year, and do that without exceeding benchmark costs per hire in our industry. What are those costs?"
- Chapter 9: "What is the value of good versus great performance? Is it necessary to have great performance in every job and on every job element? Where should I push employees to improve their performance, and where is it enough that they meet the minimum standard?"
- Chapter 10: "Is it worth it to invest in a comprehensive assessment program, to improve the quality of our new hires? If we invest more than our competition, can we expect to get higher returns? Where is the payoff to improved selection likely to be the highest?"
- Chapter 11: "I know that we can deliver training much more cheaply if we just outsource our internal training group and rely on off-the-shelf training products to build the skills that we need. We could shut down our corporate university and save millions."
In every case, the question or request reflects assumptions about the relationship between decisions about human resource (HR) programs and the ultimate costs or benefits of those decisions. Too often, such decisions are made based on very naïve logical frameworks, such as the idea that a proportional increase in sales requires the same proportional increase in the number of employees, or that across-the-board layoffs are logical because they spread the pain8 equally. In this book, we help you understand that these assumptions are often well meaning but wrong, and we show how better HR measurement can correct them.
Two issues are at work here. First, business leaders inside and outside of the HR profession need more rigorous, logical, and principles-based frameworks for understanding the connections between human capital and organization success. Those frameworks comprise a "decision science" for talent and organization, just as finance and marketing comprise decision sciences for money and customer resources. The second issue is that leaders inside and outside the HR profession are often unaware of existing scientifically supported ways to measure and evaluate the implications of decisions about human resources. An essential pillar of any decision science is a measurement system that improves decisions, through sound scientific principles and logical relationships.
The topics covered in this book represent areas where very important decisions are constantly made about talent and that ultimately drive significant shifts in strategic value. Also, they are areas where fundamental measurement principles have been developed, often through decades of scientific study, but where such principles are rarely used by decision makers. This is not meant to imply that HR and business leaders are not smart and effective executives. However, there are areas where the practice of decisions lags behind state-of-the-art knowledge.
The measurement and decision frameworks in these chapters are also grounded in general principles that support measurement systems in all areas of organizational decision making; such principles include data analysis and research design, the distinction between correlations and causes, the power of break-even analysis, and ways to account for economic effects that occur over time. Those principles are described in Chapter 2, "Analytical Foundations of HR Measurement," and then used throughout this book.
Next, we show how a decision-science approach to HR measurement leads to very different approaches from the traditional one, and we introduce the frameworks from this decision-based approach that will become the foundation of the rest of this book.
How a Decision Science Influences HR Measurement
When HR measures are carefully aligned with powerful, logical frameworks, human capital measurement systems not only track the effectiveness of HR policies and practices, but they actually teach the logical connections, because organization leaders use the measurement systems to make decisions. This is what occurs in other business disciplines. For example, the power of a consistent, rigorous logic, combined with measures, makes financial tools such as economic value added (EVA) and net present value (NPV) so useful. They elegantly combine both numbers and logic, and help business leaders improve in making decisions about financial resources.
Business leaders and employees routinely are expected to understand the logic that explains how decisions about money and customers connect to organization success. Even those outside the finance profession understand principles of cash flow and return on investment. Even those outside the marketing profession understand principles of market segmentation and product life cycle. In the same way, human capital measurement systems can enhance how well users understand the logic that connects organization success to decisions about their own talent, as well as the talent of those whom they lead or work with. To improve organizational effectiveness, HR processes, such as succession planning, performance management, staffing, and leadership development, must rely much more on improving the competency and engagement of non-HR leaders than on anything that HR typically controls directly.
Why use the term science? Because the most successful professions rely on decision systems that follow scientific principles and have a strong capacity to quickly incorporate new scientific knowledge into practical applications. Disciplines such as finance, marketing, and operations provide leaders with frameworks that show how those resources affect strategic success, and the frameworks themselves reflect findings from universities, research centers, and scholarly journals. Their decision models and their measurement systems are compatible with the scholarly science that supports them. Yet with talent and human resources, the frameworks that leaders in organizations use often bear distressingly little similarity to the scholarly research in human resources and human behavior at work3 The idea of evidence-based HR management requires creating measurement systems that encourage and teach managers how to think more critically and logically about their decisions, and to make decisions that are informed and consistent with leading research.4
A vast array of research focuses on human behavior at work, labor markets, how organizations can better compete with and for talent, and how that talent is organized. Disciplines such as psychology, economics, sociology, organization theory, game theory, and even operations management and human physiology all contain potent research frameworks and findings based on the scientific method. A scientific approach reveals how decisions and decision-based measures can bring the insights of these fields to bear on the practical issues confronting organization leaders and employees. You will learn how to use these research findings as you master the HR measurement techniques described in this book.
A decision framework provides the logical connections between decisions about a resource (for example, financial capital, customers, or talent) and the strategic success of the organization. This is true in HR, as we show in subsequent chapters that describe such connections in various domains of HR. It is also true in other, more familiar decision sciences such as finance and marketing. It is instructive to compare HR to these other disciplines. Figure 1-1 shows how a decision framework for talent and HR, which Boudreau and Ramstad called "talentship," has a parallel structure to decision frameworks for finance and marketing.
Figure 1-1 Finance, marketing, and talentship decision frameworks.
Finance is a decision science for the resource of money, marketing is the decision science for the resource of customers, and talentship is the decision science for the resource of talent. In all three decision sciences, the elements combine to show how one factor interacts with others to produce value. Efficiency refers to the relationship between what is spent and the programs and practices that are produced. Effectiveness refers to the relationship between the programs or practices and their effects on their target audience. Impact refers to the relationship between the effects of the practice on the target audience and the ultimate success of the organization.
To illustrate the logic of such a framework, consider marketing as an example. Investments in marketing produce a product, promotion, price, and placement mix. This is efficiency. Those programs and practices produce responses in certain customer segments. This is effectiveness. Finally, the responses of customer segments create changes in the lifetime profits from those customers. This is impact.
Similarly, with regard to talent decisions, efficiency describes the connection between investments in people and the talent-related programs and practices they produce (such as cost per training hour). Effectiveness describes the connection between the programs/practices and the changes in the talent quality or organizational characteristics (such as whether trainees increase their skill). Impact describes the connection between the changes in talent/organization elements and the strategic success of the organization (such as whether increased skill actually enhances the organizational processes or initiatives that are most vital to strategic success).
The chapters in this book show how to measure not just HR efficiency, but also elements of effectiveness and impact. In addition, each chapter provides a logical framework for the measures, to enhance decision making and organizational change. Throughout the book, we attend to measures of efficiency, effectiveness, and impact. The current state of the art in HR management is heavily dominated by efficiency measures, so this book will help you see beyond the most obvious efficiency measures and put them in the context of effectiveness and impact.
Data, Measurement, and Analysis
In a well-developed decision science, the measures and data are deployed through management systems, used by leaders who understand the principles, and supported by professionals who add insight and expertise. In stark contrast, HR data, information, and measurement face a paradox today. There is increasing sophistication in technology, data availability, and the capacity to report and disseminate HR information, but investments in HR data systems, scorecards, and integrated enterprise resource systems fail to create the strategic insights needed to drive organizational effectiveness. HR measures exist mostly in areas where the accounting systems require information to control labor costs or to monitor functional activity. Efficiency gets a lot of attention, but effectiveness and impact are often unmeasured. In short, many organizations are "hitting a wall" in HR measurement.