Can you give me an example of a measure that represents the total value an employee adds to the organization?
Admittedly, this is a trick question. Everyone knows perfection doesn’t exist.
We want to think that our performance measurement system is really good—after all, we use it to make all sorts of decisions, including pay. On reflection, however, it’s clear that any measure of employee performance is deficient in that it doesn’t capture the entire value that the employee contributes. Any measure is contaminated by things beyond the employee’s control. Finally, any measure of an employee’s contribution is imprecise in that it isn’t really capable of making precise distinctions, either between people or between different aspects of a person. There is always an element of error, statistically speaking.
A good example is sales performance. Total sales may seem like a perfect measure until you consider the many factors that influence the measure. Any measure of total sales will be deficient because it doesn’t represent the good will associated with honestly representing a product. “Total sales” is short-term. Good will affects future sales. Every measure is deficient in that it doesn’t tell the whole story.
As a measure, “total sales” is also contaminated by factors beyond the sales representatives’ control—factors such as marketing, competition, and the quality or value of the product. Any seasoned sales representative will tell you that the territory will affect a salesperson’s ability to sell. In addition, all measures are imprecise. Consider two sales people: Both had a great year, and their annual sales were within a few thousand dollars of each other. Can we really differentiate between the skill, effort, or success of the two? No.
In practice, the myth of a perfect measure leads to misunderstandings about performance and capabilities. Here’s an example.
A call center manager was concerned about the employee taking the fewest calls. Clearly, this long-term employee was starting to slide. His performance had been in a slow decline.
The manager called the employee into his office. Being a sensitive boss, he asked the employee how things were going and if anything had changed in his life. Finally, he got down to business and confronted the employee about his performance—knowing there were no external factors affecting performance.
The employee’s response was unexpected: “I think I’m totally on top of my game. All my peers appreciate me, and I’m resolving the toughest issues. My productivity is very high!”
After a bit more discussion, the situation became clear. Informally, other call center representatives were escalating the difficult calls to the “problem” employee. If the employee is helping out a team member, he’s obviously contributing both experience and capability. This contribution, however, wouldn’t show up in his productivity measurement data. But his productivity was clearly high, considering that he was handling the most difficult calls.
In this case, the measure was deficient because it represented only quantity, not the quality or difficulty of the task. The employee’s performance was much broader than what was measured. Meanwhile, the other employees’ measurement data was contaminated—their productivity measures were a representation of the more experienced employee’s work. I would also bet this measure is imprecise—that it’s affected by random events, like the call-center computer going down.
This example shows that there can be great value in unmeasured aspects of performance, and that measurement can’t tell the whole story. In the best-case scenario, this call center manager would learn that it’s not possible to rely entirely on a measure that represents only one aspect of employee contribution.
The Myth of Perfect Measurement in ActionOrganizations often act as if their measures are nearly perfect. As a consultant, I’m often asked to develop measures or provide measure-based guidance on important questions, such as “What value did we get from training?” It’s important to set expectations carefully. Without a perfect measure, there will be no perfect answer.
Early in my career, I spent months, and considerable amounts of a client’s money, searching for a good measure of technician performance for a regional phone company. In one sense, we had very good measures of contribution: the number of repairs completed successfully and the number of phone installations (this was in the era of copper lines and touch-tone phones). But applying these measures in real-world context, even using strong quasi-experimental designs, we ran into many complications. In the process, my client and I learned, or relearned, a lot:
- Employees "game measures"
- Teamwork matters
- Organizations are multilayered and complicated
- Employee performance is multidimensional
- It’s difficult to isolate the contribution of an individual to the organization
- It’s impossible to perfectly quantify the effect of an intervention (such as training) on an individual’s contribution.
Wouldn’t it be great if there were a perfect measure of employee performance? You could do all sorts of analyses and run organizations in a perfectly rational manner. Unfortunately, the perfect measure doesn’t exist. In fact, I’m not sure you can ever calculate the ROI of a training program, because there is never a perfect measure of employee performance—and that’s what training is supposed to affect.
Beyond this sort of program evaluation and decision making, many talent decisions are based on imperfect measures. As in the example of sales performance above, even sales commissions are based on measures that deficient, contaminated, and imprecise.
Consider More Subtle OutcomesIn a general sense, phone technicians have clear outcomes: new phones are installed or repaired phones work correctly. Many, in fact most jobs, have much less clear outcomes.
Consider a teacher in our public schools. Is the outcome of successful teach a grade on a test, success in life or something else. Measuring the full contribution or value of these jobs is nearly impossible because the outcomes is so complicated. Even if we could define a clear outcome, its measure would be contaminated and deficient.
What Can We Know?Even though all measures are deficient, contaminated, and imprecise, they can still be useful tools to support decision making, if we use them wisely. What’s critical is that we never assume that measures are perfect.
If we make this mistake—if we accept measures as having any degree of perfection—we shut down authentic conversations and prevent the exchange of meaning between managers, employees, and different parts of an organization. In the case of the call center described earlier, for example, relying on a measure without communication, and without the value of human judgment, could have resulted in the organization losing one of its most valuable employees.
Paradoxically, if we accept the inherent weakness of measurement, we can use measures more effectively. When we understand that measurement is an inherently simple approach that will never adequately describe the complexity of employees, or untangle the nuances of employee performance, we can compensate.
One way we can compensate is by relying on multiple measures—while accepting that even this solution is imperfect. Using multiple measures provides the benefit of triangulation, and is often adequate for making decisions, providing feedback, and even supporting human resource decisions.
Ultimately, the weaknesses of measurement provide an opportunity for discussion and learning. Authentic and productive communication that acknowledges these imperfections enhances learning about an organization and its complexities.