When it comes to developing and analyzing metrics it is helpful to view them from two perspectives: macro and micro. The basic difference between two is this: Macro-level metrics are the overall organization or cross-functional metrics used to drive strategy; and micro-level metrics are those measures that support the improvement and management of a particular project, program or initiative.
Macro-level metrics provide a view across the entire organization or function. A macro set of measures is reflected in the company scorecard or executive dashboard. The scorecard shows the performance of the organization or function in a variety of different categories. Perhaps the most common categorization of macro-level metrics is the balanced scorecard, which was developed by Robert S.Kaplan and David P. Norton in The Balanced Scorecard: Translating Strategy into Action (Boston: Harvard Business School Press 1996). There are four categories or perspectives described in this classic scorecard, including financial, customer, internal business processes, and learning and growth. These categories are reflective of a variety of measures across the organization. Some are obvious, such as sales, profits and customer satisfaction. Others are less obvious. When the scorecard process is moved from the top of the organization to the tactical functions in the organization, its usefulness begins to wane, as success at the departmental or functional levels is typically reported using different types of measures.
Classic examples of measures owned (or perceived to be owned) by the human resources function include absenteeism, employee turnover, cost of training, accidents and so forth. Today, human resources has become responsible for an even greater variety of measures.
Figure 1 shows current human capital measures taken from a study of progressive organizations. Although categories are listed in the figure, each category would have a specific measure that is being tracked or monitored by the human resources function. In essence, these measures provide a macro view of performance with measures that may not be captured in a strategic scorecard.
Common Human Capital Measures
1. Innovation and Creativity
2. Employee Attitudes
3. Workforce Stability
•Turnover and termination
•Tenure and longevity
4. Employee Capability
5. Human Capital Investment
•Human resources department investment
•Total human capital investment
•Investment by category
8. Workforce Profile
9. Job Creation and Recruitment
•Recruitment sourcing and effectiveness
10. Compensation and Benefits
11. Compliance and Safety
•Complaints and grievances
•Charges and litigation
•Health and safety
12. Employee Relations
•Absenteeism and tardiness
Micro-level metrics measure and track the success of particular projects, programs or initiatives rather than report the status of strategic objectives. They represent data directly connected to those projects, offering decision makers data they can use to allocate or resources they can reallocate, as well as programs for future implementation they can improve.
Some micro-level metrics are obvious. For example, if a new recruiting process is implemented a typical measure might be recruiting effectiveness indicating the percent of candidates selected from that particular recruiting source. When a selection process is changed, the measure of effectiveness could be the actual time for someone to start. This measure is the number of days from the date the requisition for a new recruit is approved to the time he or she is actually on the job. Another example is when a new employee suggestion system is installed and the number of suggestions submitted is a tracked metric. In these cases, the measures are specific to the new project or program being implemented.
The most common set of categories for micro-level measures are taken as a project unfolds. These measures, 1) reaction, 2) learning, 3) application and 4) impact, show program value from a variety of perspectives. More importantly, they provide sufficient data to make improvements as the program is implemented. These measures essentially track the success of a project or program through a value chain. As employees react to a program, they learn what is necessary to make it successful, and they apply what is needed for success, which ultimately results in an important impact. Figure 2 shows this chain of impact and describes the definition of each measure. The figure also indicates the current status of use in many organizations with which we work. Five-year goals represent the target use based on best practice organizations.
Two types of analysis occur when working with macro-level measures: process improvement analysis and correlation analysis. For process improvement the macro measure is monitored against an objective. For example, if lost-time accidents are monitored, a specific target is set and as the safety performance moves higher than the target, an effort to improve the process is the initiative. This often results in trying to understand the causes of the problem and implement of a particular program to improve it. This is a combination of monitoring the data, making adjustments with particular solutions and improving the measure.
The second type of analysis, correlation analysis, considers relationships between various macro-level metrics and other organizational metrics. For example, there may be a correlation analysis between job satisfaction and employee turnover. Each is measurable, and the correlation will show the relationship between the two metrics. Another example is the potential correlation between employee engagement and gross productivity, both of which may be monitored by the human resources team. Correlation analysis attempts to show the connection between the soft measure, employee engagement, and a hard measure, revenue per employee. There are a variety of different correlation analyses that show if we invest more in people there will be a corresponding output in productivity, quality, profitability and share price.
Micro-level analysis tracks the success of a project or program through the value chain shown in Figure 2. This analysis not only describes program success through the value chain, but it also describes the barriers and enablers to success. This analysis can be extended to include ROI by isolating the effects of the human resources program on the impact data, converting that impact data to money and comparing it to the cost of the program. Along the way, the measures that could not be converted to money are considered intangibles. So this level of analysis captures reaction, learning, application, impact, ROI and intangibles all linked to the program.
It is helpful to consider metrics in terms of macro and micro. Both types of metrics are important. The macro view describes how the organization is doing overall. The micro view shows us the success of a particular project or program, supporting decisions that lead to resource allocation and program improvement. Though both views are important and somewhat different, a future column will show how macro-level metrics and micro-level metrics can be integrated. Until then, let us know your thoughts and how you use macro-level and micro-level metrics.