More and Better Performance Metrics Must Be Developed

Sabermetrics. It’s a great word. No doubt a consulting firm would be using the term had not baseball already laid claim to it. Sabermetrics is the search for objective knowledge about baseball using mathematical and statistical analysis. Yes, after a century and a half, new metrics have been developed for this, the most measured of our pastimes.

Performance metrics are also about the search for objective knowledge using mathematical and statistical analyses. Baseball metrics help team management to make the best decisions about players and ultimately optimize the team’s performance. While most organizations are more complex than a baseball team, a primary purpose of performance metrics is the same: to make the best resourcing decisions in order to optimize organizational performance.

There are no baseball metrics for strategy, player management practices, or player engagement. Even before sabermetrics, baseball statistics have always measured performance outcomes or results, as should any organization’s performance statistics. Performance metrics should be associated with measurable objectives. You can’t—and shouldn’t try to—measure strategy or practices or engagement.

You’d think that the baseball stats that we’ve been using for generations have to be the best, but the data supporting sabermetrics is incredibly compelling. Michael Lewis’s Moneyball: The Art of Winning an Unfair Game is a must read book for anyone involved in business or any aspect of human capital management. It’s a compelling story about using these new metrics to build a championship-caliber team in the face of 100 years of accepted scouting practices. In spite of the data, sabermetrics have been slow to gain wide spread acceptance. The Wall Street Journal’s Carl Bialik reported that “. . . self interest compels veteran writers and broadcasters to reject the idea that stat heads have a better handle on the game than they do.”

In business, it’s the consultants and pundits who are struggling to hold on to the most overrated and useless of conventional stats. In spite of saber-sharp data, many insist there must be meaning in the metrics because they have been around for so long. The ratio of Human Resources employees to all employees is a good example. Probably the oldest HR metric, it is based on the premise that comparing the number of HR employees to the number of total employees that they support was an indication of HR efficiency, if not performance. A ratio of 1:100 was considered the standard. Like many common metrics, it may sound logical but was flawed from inception. What if two HR organizations had the same 1:100 ratio but:

• One paid its HR staff twice as much as the other?

• One had a budget that was more than twice as big as the other?

• One supported demanding physicians and research scientists at a dozen locations around the country, while the other supported office works in one location?

• One was detested by the rest of the organization and the other was highly valued?

These are the problems with any headcount-based metrics. And yet this ratio is how many organizations manage their HR function—and HR outsourcing organizations sell their services.

The nature of functions like manufacturing and distribution fostered the development of meaningful metrics, but that is not the case for HR and human capital intensive operations indicators and performance. This perspective from major consultancy Watson Wyatt Worldwide indicates how convoluted approaches to measuring human capital indicators and performance have become:

. . . many organizations have difficulty focusing on the “right” measures. They might measure a factor generally related to financial performance, but it may not be the best one considering the business and strategy. For example, organizations know that employee turnover is related to financial performance; and since turnover data is readily available, a company might choose to measure turnover. But if the company’s turnover rate is very low, it most likely does not strongly affect the business performance.

This suggests that a metric is important only if the data it measures is poor. If a metric is valid, it is always valid regardless of what the answer is. If turnover was a valid metric and the turnover was very low, it still should be measured. If a team measures batting averages and runs batted in, they do it regardless of whether or not the results are good or bad.

Incidentally, there are many documented cases of organizations with very low turnover that significantly and adversely affected business performance. Low turnover can be a bad thing. Retaining the wrong employees, fostering complacency, or enabling a culture that drives out change agents and high performers may result in low turnover but is deleterious to any organization. Regardless, metrics associated with turnover and cost-per-hire are as meaningless a human capital metric as the win-loss record is for pitchers. Retention—retaining the staff you want to keep—and recruiting efficiency are far better HR metrics for organizations to monitor.

Although it is often difficult to evaluate the impact of outsourcing operations because of the lack of a “before” benchmark, outsourced operations tend to be measured more than those that are not. This is because after an organization outsources work, they are more apt to ask, “What is it costing us now and what do we get for our money?” We have documented both efficient and incredibly inefficient outsourced operations. The only consistent difference between the two is that the best performers aggressively measure and report their own performance.

There is need for more and better performance metrics in every category of outsourcing but it is particularly great for human capital intensive operations. Under the auspices of Staffing.org, leading outsourcers, consultancies, academics, professional associations, and end-user organizations are working together to establish standard metrics and metrics templates to improve the measurement of human capital intensive performance. The Metrics Roundtable includes Veritude, Hewitt Associates, Saratoga, PricewaterhouseCoopers, and Watson Wyatt. These organizations are taking a leadership role in developing critical approaches and metrics for all organizations.

Until more and better performance metrics are established, keep in mind that valid metrics should:

• Be limited to no more than two to four for each outcome or result.

• Make sense to everyone associated with them.

• Be easy to understand and to measure.

• Drive continuous performance improvement.

• Be based on requirements established before initiating work.

They shouldn’t be:

• Strategic—you can’t measure strategy.

• Complex.

• Numerous.

• Exclusive.