Proprietary Metrics — the Next Big Thing in Talent Management

The idea that you can create a template that will work forever doesn’t happen in any business … There’s some really, really bright people in this business. You can’t do the same thing the same way and be successful for a long period of time. — Billy Beane

Screen Shot 2014-04-10 at 8.57.20 AM I am a strong advocate of what I call “parallel benchmarking,” which is borrowing the proven best practices from completely different industries and functions. This article advocates the borrowing and the adaptation to talent management of what are known as “proprietary metrics” from the baseball industry. Proprietary metrics get their name because they cover metrics that are so powerful that they are “owned” and their components are therefore not shared. In baseball, there are dozens of proprietary metrics, while in the corporate world of talent management, they are surprisingly rare. Corporate examples of these proprietary metrics include Google’s “retention metric” for predicting which employees are about to quit and its “hiring success algorithm” for predicting the characteristics that lead to new hire success on the job.

Baseball Has the Most Advanced Metric Model to Learn From

You might not know it, but baseball metrics (which are known as Sabermetrics) are literally years ahead of the metric practices in talent management. Most talent metrics are calculated but once a year and they merely inform the user about last year’s results. In direct contrast, most baseball metrics are provided in real time on the scoreboard for all players and managers to see precisely when they need right as they are making a decision. Many baseball metrics are also “predictive talent decision metrics” that accurately guide executives in important talent decisions including who to hire, how much to pay, and how long a player will continue to add value. Even “old-school” baseball managers now realize that the use of metrics for talent decisions can result in more productive hires, increased revenue, and significantly more wins.

The Value Gained by Not-sharing Your Metrics

Another critical lesson to learn from baseball relates to the value of sharing or not sharing the details behind your metrics. During the early Moneyball era in baseball, metrics were open and commonly shared by all teams. While this universal “open-source” sharing made it easy for teams to compare their performance against each other, the fact that every team used the same metrics meant that no individual team could gain a competitive advantage. It took a few years, but eventually baseball executives realized that increasing performance above that of your competitors was a critical goal. So in an attempt to develop a competitive advantage in metrics, the best teams and some vendors started to develop what are now known as “proprietary metrics” (examples include WAR, Ultimate zone ratings, and StatRank).

Proprietary metrics in both baseball and talent management by definition are unique and valuable, so the data used, the methods for collecting the data, and the components in the metric formula are all treated as valuable secrets. This exclusive or limited use allows executives using the proprietary metrics to make better talent decisions than their competitors.

A List of Proprietary Talent Management Metrics to Develop

If you have weak metrics, keeping them secret obviously doesn’t by itself add much value. What is needed are advanced talent decision metrics which provide such measurable insight and value that you want to keep them secret long as possible. Whether you are a corporation or a vendor, you should be constantly striving to develop these “proprietary metrics” that when used correctly, significantly improve talent management decisions and results. In order to have a large impact, proprietary metrics in most cases have to be developed in areas where no current metrics exist. Some areas where I suggest that proprietary talent metrics should be developed in the corporate world include:

Recruiting

The factors or algorithm that predicts candidate on-the-job performance and retention
A metric that shows what the level of competition for external top talent will be 6 to 12 months into the future

Retention

A risk metric that shows which employees have a high probability of quitting within six months
A metric that predicts what the turnover rate by manager will be in 6-12 months into the future

Development

An algorithm that successfully identifies leadership potential in team members with less than two years at the firm
A leadership algorithm that predicts a leader’s success over the next two years based on the actions that they take
Calculating in which cases moving and retraining existing workers has a higher return on investment than externally hiring new ones

Productivity/ innovation

A metrics process that identifies the job-related factors that increase employee productivity and innovation
A metric that accurately identifies innovators among candidates and recent hires

Compensation

An algorithm which shows which reward and recognition factors have the greatest impact on improving employee productivity
A metric which accurately determines which employees are under or overpaid
Predicting into the future how many years an individual employee will remain productive and “worth their salary”

Business case metrics

Calculating how much the value of a replacement new hire is above (or below) the value produced by the average current employee.
Calculating the increased dollar impact for each percentage increase in new hire on-the-job performance.

The proprietary metrics mentioned above might seem far-fetched to many talent management leaders, but some of them are already being used for improving baseball talent decisions.

In a Competitive World, Metrics Must Also Be Continually Improved

Another lesson to be learned from baseball is that no matter how good your array of metrics are initially, they will eventually be copied and even exceeded by your competitors (as baseball guru Billy Beane stated above.)

Keeping your best metrics proprietary will work up to a point. But in order to remain competitive, you must have a process for continually upgrading your talent metrics, so that your organization is continually in the lead in understanding the factors that cause current performance and that reveal future performance. Next-step metrics for most talent functions start with the development and use of real-time metrics to help managers make decisions based on today’s data. And at some point, talent decision makers will begin to demand predictive metrics that tell you in advance how you must act today in order to ensure superior future results.

And last but perhaps the most important metric frontier is the development of business-case metrics, which show you the direct value-chain connection between improving talent management results and the subsequent improvement in business results. This last step is essential because nothing increases funding and credibility more than quantifying and showing your direct dollar impact on corporate strategic goals.

What Exactly Should Be Kept Secret and Proprietary?

In baseball, some will reveal the name and the even the value of their proprietary metrics. But unless you are going to sell them, in the corporate world I wouldn’t even reveal those two factors because the mere knowledge of their existence and success will encourage others to develop similar metrics. You should also strive to keep secret the following metric-related components:

The data needed to calculate the metric
Where, when, and how that data is gathered
The elements and their weight in the formula for the metric
What is a passing and failing score on the metric
Which talent decisions are improved by the metric
Common problems involved in using the metric
The models developed as a result of the metric

Final Thoughts

After decades of work in metrics, I have found that both corporate recruiting and talent management are literally years behind in the adoption of all forms of advanced metrics. Google of course is the lone exception, with a variety of proprietary algorithms and its employee research lab. Google is also internally focused, so it avoids the use of benchmark comparison metrics with other firms.

A handful of ERP and talent management vendors have actually developed some proprietary metrics, but for the most part, I can’t honestly say that I have found them to be worthy of being kept secret. Instead, what is needed are bold corporate talent leaders who are not afraid to study and learn about the type of talent decisions that are currently made in baseball. Corporate leaders should then proactively identify new talent areas where a metric that explains why things are happening, what will happen in the future, and the correct actions to implement in order to take advantage of that future because these actions would add significant business value by increasing revenue, productivity, and innovation.

Obviously advanced and proprietary metrics are more difficult to develop, but the dollar business impact may be up to five times higher than using existing “copycat” low-value metrics like cost per hire or the number of training hours provided. So the last step is for leaders to stop worrying about benchmark comparisons with other firms and instead to focus on metrics that provide quarterly and year-to-year double-digit improvement in their own talent results.

Some of the Related Conference Sessions at the ERE Recruiting Conference in San Diego:

Using Big Data to Drive Measurable Recruiting Results: Getting Past the Hype, Wednesday, April 23, 4:15 p.m.
Stop Slowness from Killing Your Recruiting Department, Thursday, April 24, 2 p.m.