The "LAMP" Framework
We believe that a paradigm extension toward a talent decision science is key to getting to the other side of the wall. Incremental improvements in the traditional measurement approaches will not address the challenges. HR measurement can move beyond the wall using what we call the LAMP model, shown in Figure 1-3. The letters in LAMP stand for logic, analytics, measures, and process, four critical components of a measurement system that drives strategic change and organizational effectiveness. Measures represent only one component of this system. Although they are essential, without the other three components, the measures and data are destined to remain isolated from the true purpose of HR measurement systems.
Reprinted by permission of Harvard Business School Press, from Beyond HR: The New Science of Human Capital by John Boudreau and Peter M. Ramstad. Boston, MA, 2007, pp. 193. Copyright © 2007 by the Harvard Business School Publishing Corporation. All rights reserved.
Figure 1-3 Lighting the "LAMP."
The LAMP metaphor refers to a story that reflects today's HR measurement dilemma:
- One evening while strolling, a man encountered an inebriated person diligently searching the sidewalk below a street lamp.
- "Did you lose something?" he asked.
- "My car keys. I've been looking for them for an hour," the person replied.
- The man quickly scanned the area, spotting nothing. "Are you sure you lost them here?"
- "No, I lost them in that dark alley over there."
- "If you lost your keys in the dark alley, why don't you search over there?"
- "Because this is where the light is."
In many ways, talent and organization measurement systems are like the person looking for his or her keys where the light is, not where they are most likely to be found. Advancements in information technology often provide technical capabilities that far surpass the ability of the decision science and processes to use them properly. So, it is not uncommon to find organizations that have invested significant resources constructing elegant search and presentation technology around measures of efficiency, or measures that largely emanate from the accounting system.
The paradox is that genuine insights probably exist in areas where there are not standard accounting measures. The significant growth in HR outsourcing, where efficiency is often the primary value proposition and IT technology is the primary tool, has exacerbated these issues.4 Even imperfect measures aimed at the right areas may be more illuminating than very elegant measures aimed in the wrong places.
Returning to our story about the person looking for his or her keys under the street lamp, it's been said that "Even a weak penlight in the alley where the keys are is better than a very bright streetlight where the keys are not."
Figure 1-3 shows that HR measurement systems are only as valuable as the decisions they improve and the organizational effectiveness to which they contribute. That is, such systems are valuable to the extent that they are a force for strategic change. Let's examine how the four components of the LAMP framework define a more complete measurement system. We present the elements in the following order: logic, measures, analytics, and finally, process.
Logic: What Are the Vital Connections?
Without a proper logic, it is impossible to know where to look for insights. The logic element of any measurement system provides the "story" behind the connections between the numbers and the effects and outcomes. The chapters in this book provide logical models that help to organize the measurements, and show how they can articulate useful decision frameworks. Examples include the elements of turnover costs, the conditions that determine the value of enhanced selection, and the connections that link employee health and vital organizational outcomes. Missing or faulty logic is often the reason why well-meaning HR professionals generate measurement systems that are technically sound, but make little sense to those who must use them. With well-grounded logic, it is much easier to help leaders outside the HR profession understand and use the measurement systems to enhance their decisions.
For example, recall Figure 1-1, which shows how finance organizes its measures of return on equity to reflect the logic that equity is used to purchase assets, which are used to generate sales, which, in turn, produce profits. The logically derived measures include leverage (assets divided by equity), asset productivity (sales divided by assets), and margin (profits divided by sales). You can directly calculate return on equity simply by dividing profits by equity, but that would obscure the logical connection points that are vital to make decisions about equity, assets, and sales effectively. The power of the framework is to embed the measures within a logic that enhances decisions.
In the field of human resources, there are many logical frameworks, including salary structures, workforce-planning models, and even labor contracts. All are useful, but they are not sufficient to connect decisions about investments in HR programs to strategic outcomes. In contrast, some authors have proposed a "service-value-profit" framework for the customer-facing process. This framework calls attention to the connections between HR and management practices, which, in turn, affect employee attitudes, engagement, and turnover; which, in turn, affect the experiences of customers. This, in turn, affects customer-buying behavior, which, in turn, affects sales, which, in turn, affects profits. Perhaps the most well-known application of this framework was at Sears, which showed quantitative relationships among these factors and used them to change the behavior of store managers.5
Measures: Getting the Numbers Right
The measures part of the LAMP model has received the greatest attention in HR. As discussed in subsequent chapters, virtually every area of HR has many different measures. Much time and attention is paid to enhancing the quality of HR measures, based on criteria such as timeliness, completeness, reliability, and consistency. These are certainly important standards, but lacking a context, they can be pursued well beyond their optimum levels or they can be applied to areas where they have little consequence.
Consider the measurement of employee turnover. There is much debate about the appropriate formulas to use in estimating turnover and its costs, or the precision and frequency with which employee turnover should be calculated. Today's turnover-reporting systems can calculate turnover rates for virtually any employee group and business unit. Armed with such systems, managers "slice and dice" the data in a wide variety of ways (ethnicity, skills, performance, and so on), each manager pursuing his or her own pet theory about turnover and why it matters. Are those theories any good? If not, better measures won't help. That's why the logic element of the LAMP model must support good measurement.
Precision alone is not a panacea. There are many ways to make HR measures more reliable and precise. An exclusive focus on measurement quality can produce a brighter light shining where the keys are not! Measures require investment, which should be directed where it has the greatest return, not just where improvement is most feasible. Organizations routinely pay greater attention to some elements of their materials inventory more than others. Indeed, a well-known principle is the "80-20 rule" that suggests that 80 percent of the important variation in inventory costs or quality is often driven by 20 percent of the inventory items. Thus, although organizations indeed track 100 percent of their inventory items, they measure the vital 20 percent with greater precision, more frequently, and with greater accountability for key decision makers.
Why not approach HR measurement in the same way? Employee turnover is not equally important everywhere. Where turnover costs are very high, or where turnover represents a significant risk to the revenues or critical resources of the organization (such as when departing employees take clients with them or when they possess unique knowledge that cannot be re-created easily), it makes sense to track turnover very closely and with greater precision. However, this does not mean simply reporting turnover rates more frequently. It means that the turnover measurements in these situations should focus precisely on what matters. If turnover is a risk due to the loss of key capabilities, turnover rates should be stratified to distinguish those with such skills from others. If turnover is a risk due to losses of clients with departing employees, turnover rates should not focus on skill differences, but instead should be stratified according to the risks of client loss.
Lacking a common logic about how turnover affects business or strategic success, well-meaning managers draw conclusions that might be misguided or dangerous. This is why every chapter of this book describes measures, as well as the logic that helps explain how the measures work together. For example, Chapter 4, "The High Cost of Employee Separations," deals with turnover.
Analytics: Finding Answers in the Data
Even a very rigorous logic with good measures can flounder if the analysis is incorrect. For example, some theories suggest that employees with positive attitudes convey those attitudes to customers who, in turn, have more positive experiences and purchase more. Suppose an organization has data showing that customer attitudes and purchases are higher in locations with better employee attitudes? Does that mean that improving employee attitudes will improve customer attitudes? Many organizations have invested significant resources in programs to improve frontline-employee attitudes based precisely on this sort of evidence of association (correlation).
The problem is that this conclusion may be wrong, and such investments misguided. A correlation between employee and customer attitudes does not prove that one causes the other, nor does it prove that improving one will improve the other. Such a correlation also happens when customer attitudes actually cause employee attitudes. This can happen because stores with more loyal and committed customers are more pleasant places to work. The correlation can also result from a third, unmeasured factor. Perhaps stores in certain locations attract customers who buy more merchandise or services and are more enthusiastic. Employees in those locations like working with such customers, and are more satisfied. Store location turns out to cause both store performance and employee satisfaction. The point is that a high correlation between employee attitudes and customer purchases could be due to any or all of these effects. Sound analytics can reveal which way the causal arrow actually is pointing.
Analytics is about drawing the right conclusions from data. It includes statistics and research design, and then goes beyond them to include skill in identifying and articulating key issues, gathering and using appropriate data within and outside the HR function, setting the appropriate balance between statistical rigor and practical relevance, and building analytical competencies throughout the organization. Analytics transforms HR logic and measures into rigorous, relevant insights.
Analytics often connect the logical framework to the "science" related to talent and organization, which is an important element of a mature decision science. Frequently, the most appropriate and advanced analytics are found in scientific studies that are published in professional journals. In this book, we draw upon that scientific knowledge to build the analytical frameworks in each chapter.
Analytical principles span virtually every area of HR measurement. In Chapter 2, we describe general analytical principles that form the foundation of good measurement. We also provide a set of economic concepts that form the analytical basis for asking the right questions to connect organizational phenomena such as employee turnover and employee quality to business outcomes. In addition to these general frameworks, each chapter contains analytics relevant specifically to the topic of that chapter.
Advanced analytics are often the domain of specialists in statistics, psychology, economics, and other disciplines. In fact, HR organizations often draw upon experts in these fields, and upon internal analytical groups in areas such as marketing and consumer research, to help augment their own analytical capability. Although this can be very useful, it is our strong belief that familiarity with analytical principles is increasingly essential for all HR professionals and for those who aspire to use HR data well.
Process: Making Insights Motivating and Actionable
The final element of the LAMP framework is process. Measurement affects decisions and behaviors, and those occur within a complex web of social structures, knowledge frameworks, and organizational cultural norms. Therefore, effective measurement systems must fit within a change-management process that reflects principles of learning and knowledge transfer. HR measures and the logic that supports them are part of an influence process.
The initial step in effective measurement is to get managers to accept that HR analysis is possible and informative. The way to make that happen is not necessarily to present the most sophisticated analysis. The best approach may be to present relatively simple measures and analyses that match the mental models that managers already use. Calculating turnover costs can reveal millions of dollars that can be saved with turnover reductions, as discussed in Chapter 4. Several leaders outside of HR have told us that a turnover-cost analysis was their first realization that talent and organization decisions had tangible effects on the economic and accounting processes they were familiar with.
Of course, measuring only the cost of turnover is insufficient for good decision making. For example, overzealous attempts to cut turnover costs can compromise candidate quality in ways that far outweigh the cost savings. Managers can reduce the number of candidates who must be interviewed by lowering their selection standards. The lower the standards, the more candidates will "pass" the interview, and the fewer interviews that must be conducted to fill a certain number of vacancies. Of course, lowering standards can create problems that far outweigh the cost savings from doing fewer interviews! Still, the process element of the LAMP framework reminds us that often best way to start a change process may be first to assess turnover costs, to create initial awareness that the same analytical logic used for financial, technological, and marketing investments can apply to human resources. Then the door is open to more sophisticated analyses beyond the costs.
Education is also a core element of any change process. The return-on-investment (ROI) formula from finance is actually a potent tool for educating leaders in the key components of financial decisions. In the same way, we believe that HR measurements increasingly will be used to educate constituents and will become embedded within the organization's learning and knowledge frameworks.
In the chapters that follow, we suggest where the HR measures we describe can be connected to existing organizational frameworks and systems that offer the greatest opportunity for using measures to get attention and enhance decisions. For example, the accounting and finance systems in organizations currently pay a great deal of attention to escalating health-care costs. The cost measures discussed in Chapter 5, "Employee Health, Wellness, and Welfare," can offer additional insights and more precision to such discussions. Moreover, starting by embedding these basic ideas and measures into the existing health-care-cost discussion, HR leaders can gain credibility to be able to extend the discussion to include additional logical connections between employee health and other organizational outcomes, such as learning, performance, and profits. What began as a budget exercise becomes a more nuanced discussion about the optimal investments in employee health, and how those investments pay off.
You will see the LAMP framework emerge in many of the chapters in this book, to help you organize not only the measures, but also your approach to making those measures matter. Our next section illustrates how some alternative measurement frameworks can help us understand the benefits and limitations of several of today's most popular approaches to HR measurement.