Making Sense of HR Metrics

By themselves, HR metrics do not convey insight.

Take hires, for example. The only way to interpret whether 50 hires are good or bad is in context. Depending on the goal, 50 means very different things. Were you planning on hiring 10 people, 50 people, or 100 people? Hire count is, therefore, a neutral metric. Hiring can only be evaluated relative to a specific goal. The value of the hiring goal, relative to other HR objectives, should also be understood within business context. 50 general hires in a time period of modest growth represent one thing, 50 sales producing hires in a time period of rapid market expansion and conquest represent something else entirely.

This neutrality principle applies to many, if not all, other conventional HR metrics. Take the Attrition Rate. (Attrition Rate could also be referred to as Exit Rate, Turnover Rate or Termination Rate). Is 15% Attrition good or bad? 15% Attrition may be a fantastic accomplishment for Customer Service Representatives in an inside sales center, but 15% could be double the average rate for Managers or some other role. Also, you must consider that if a business is shrinking, employee expense may need to come down - in that case, a 15% Attrition Rate may not be enough. However, if you intend for headcount to grow, then a 15% Attrition Rate could be more than you expect.

Here are several ways you can turn neutral HR metrics into insight:

   *  Compare segments to each other: Seeing how different parts compare to each other can help you determine what is normal and abnormal, what is good and bad, and what changes make a difference.

      Suppose that you measure time-to-hire, and one division consistently has a time-to-hire of 30 days while other divisions have a time-to-hire of 55. You may not know the reason for the discrepancy at the outset, but at least you know that 30 days isn’t outside the realm of possibility. With this information, you can set the bar higher for other segments until you can get them to 30 days as well. You can also study the differences between the divisions to try to understand why they are different. Maybe they should be different. Maybe they shouldn't.

More on segmentation in Finding Useful Insight in Differences and Segmenting for Perspective

   *  Correlate the metric to another measure that matters to you: For example, if you’re in a retail business, you might run statistical analysis of store performance to correlate metrics like Time-to-Start & Quality-of-Hire with Customer-Satisfaction. If what you really care about is Customer Satisfaction, associating these other metrics may help you determine whether Time-to-Start, Quality-of-Hire, and whatever other metric is a good thing, a bad thing, or a neutral thing as it relates to Customer Satisfaction.

   *  Trend the metric over time: See how the metric is changing over time to provide perspective on how it is changing and what is normal and what is abnormal over a larger field of vision. If you see that this metric is hovering around the same place as before, and it has never caused any business problems for you before, you probably have nothing to worry about now, either. If the metric is increasing or decreasing, you can evaluate whether that’s a good thing or bad thing for you in the context of what you’re trying to achieve. If you have no clear insight about it, ignore the metric — don’t try to use a metric you don’t understand just because everyone else is using it, or just for the sake of using something. Drive towards metrics that matter. If you don't have a strong story, move along.

   *  Compare with other companies using benchmarking sources: You can turn to a number of credible paid sources when you want to make use of HR Metric benchmark data. Here are some better-known options:

      PWC: www.pwc.com/us/en/services/hr-management/people-analytics/benchmarking.html

      SHRM: www.shrm.org/resourcesandtools/business-solutions/pages/benchmarking-service.aspx

      CEB (now Gartner): https://www.gartner.com/en/documents/3913492/hr-budget-and-staffing-benchmarking-suite

      The Hackett Group: www.thehackettgroup.com/hr-metrics

Keep in mind that the companies you’re comparing to may be different in ways that cause their numbers to be different as well.

Here are some ways that companies vary, which impact the gamut of HR Metrics:

      *   They may be different sizes and structures.

      *   They may have different growth rates.

      *   They may have a different job family distributions.

      *   They may have a different tenure and age distributions.

Because many behind the scenes factors can affect almost all HR metrics, the average company, or any other company, may not be an apples-to-apples comparison for you.

Benchmarking can provide you with a perspective about where your numbers are relative to average, and what is in the range of possibilities, but it doesn’t tell you where your metrics should be (the target). For the target, I would return to a careful analysis of the other points of reference I suggest above: goals, segments, correlations, and trends.

I believe external benchmarks can be useful, however, I also believe they are frequently used naively as a target, which is misleading, ineffective, and dangerous. You have to weigh whether you want external benchmarks around at all, with the possibility of misuse looming.

Conclusion

Conventional HR Metrics offer almost no value with superficial observation. By themselves, conventional HR metrics do not convey insight. To make sense of HR metrics you need context and contrast. Context is determined by strategy. Contrast is provided through comparisons. Comparisons can be made to expectations determined by goals, past experiences, correlation to other outcomes and benchmarks.

Therefore, all conventional HR Metrics require analysts for situational design, guidance, for interpretation. In theory, any, or all, conventional HR Metrics can serve as inputs to models, which at some future stage may require less work from analysts, but the model, the model's inputs, and the model's outputs require analysts for design and validation too. There is probably no way around the fact that to use HR Metrics effectively you need an analyst.

This is an excerpt from the book People Analytics for Dummies, published by Wiley, written by me.

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