Everywhere they turn these days Compensation practitioners are finding themselves bombarded by a repeated self-help message; you need to establish compensation metrics for your organization. You need to do this.
If you’re collecting data for compensation metrics, congratulations.
But no one seems to have completed the recommended strategy by explaining what you should do with all this data that you’ve collected. It’s as if the answer is somehow intuitive, that everyone would just know.
Believe me, they won’t. Capturing internal compensation metrics data is not the same as using that information.
Use of Metrics
In order to gain value from the use of compensation metrics data, you must use that data in a meaningful way. Data collection should not an end-in-itself, but an ongoing process that can assist you in developing a storyline that not only explains what is happening with your reward programs but can help steer your organization in the right, corrective direction.
Here’s a useful foundation precept you should keep in mind when speaking with senior management; they like to hear a story. And that story becomes more powerful, more compelling when peppered with facts and figures. Maybe even a graph or two.
Examples
What can these metrics tell you, and how do you convert raw data into a story that grabs the attention of senior management?
As you’d expect, there is no straightforward answer, no pathway to correct action other than “It depends.” Because there are numerous subjects on which you can collect and monitor data points, and each one can present possible storylines.
Each metric is likely to have a baseline figure, your target goal, or a traffic light (green light, yellow and red), and as your data (the dashboard pointer) moves right or left of that baseline, your story is created for you. It can be good, bad or indifferent. You only have to tell it.
Below are three standard examples to illustrate how metrics could be used.
- Average Age: The cumulative age of each employee, divided by the number of employees.
- Do you have a young or old workforce? Do your managers have to deal with Baby Boomers, Millennials, Generation X or a hodgepodge of attitudes and interests?
- An older workforce likely creates more medical plan charges, more instances of retirement, and may display a different (better?) work ethic.
- Younger employees tend to be less experienced, more likely to job hop, and less likely to appreciate your non-cash benefit offerings.
- Do you have hiring profiles? The age of an employee can usually tell you something about their likely attitudes toward work environment, pay levels, other benefits, working conditions, and career prospects.
- Over time, is the pointer showing a workforce that is aging, or growing younger? Consider the ramifications. Prepare for them.
- Average Pay: The cumulative annual cash pay of all employees, divided by the number of employees.
- You may want to mix this category with others (e.g., age, sex, and grade, among others) to gain a more precise read.
- The external marketplace is a constantly moving target and is expected (perception) to always move upward. How does your average pay compare to that market, to assigned grade midpoints, and within key functional areas?
- Is your average pay growing, remaining stable, or possibly declining? In either case, you need to know why.
- Is the metrics pointer going in the direction you want? Why not?
- Sex: For the sake of simplicity let’s look at Males and Females, though this category seems to be evolving within the workplace.
- How many of each do you have? Do you have concentrated pockets of employees (e.g., engineers, clerical workers, hourly staff, accountants) that might skew the overall results? Perhaps the lower grades have more females while the higher grades show the opposite. Oh-oh.
- Combine this statistic with others to see whether an adverse impact is appearing elsewhere (males paid more, concentration (or absence) of females in certain jobs/functions.
Of course, every metric could be further refined (sorted) by grade level, functional area, length of service or any other slice of data that would provide you with meaningful information.
The takeaway here is that metrics can tell you a story if you’re reading them. They can be a smiley face, a cautionary flag, or a skull & crossbones. But you won’t know that unless you work with the collected data in order to your educate your senior management.
Chuck Csizmar CCP is founder and Principal of CMC Compensation Group, providing global compensation consulting services to a wide variety of industries and non-profit organizations. He is also associated with several HR Consulting firms as a contributing consultant. Chuck is a broad based subject matter expert with a specialty in international and expatriate compensation. He lives in Central Florida (near The Mouse) and enjoys growing fruit and managing (?) a clowder of cats.
Creative Commons image, "Employee," by Southern Methodist U.
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