When companies invest in employees with a certain level of compensation and benefits, it is important to know whether they are paying the right amount and for the right results. However, this is not always as straightforward as it sounds.
In our last post about data analytics and pay, we talked about the challenges of using data analytics. Now, let’s look at what a company can accomplish by leveraging data analytics in their compensation and total rewards programs.
At their best, more advanced data analytics can help organizations understand what they are actually rewarding versus what they think they are rewarding or want to reward. For example, regression modeling can identify the elements that drive compensation decision making, such as individual employee characteristics, experience, performance, time in the job, or place in the organization.
If individuals working in certain parts of the organization are generally paid more, the organization cannot determine if the situation is problematic until it knows what is driving that trend. In some cases, this could be something as straightforward as the local labor market. If the labor market is more or less competitive in certain geographic areas, more advanced analytics can help separate those factors to identify what is going on at the decision-making level and to determine whether pay decisions are consistent with the company’s pay philosophy.
In some cases, this is as straightforward as determining whether the people with the highest performance ratings are the ones most likely to see their pay increase and receive promotions. This allows the company to get an objective take on what it says it is doing regarding pay and what it is actually doing regarding pay, says Wendy Hirsch, a principal with Mercer’s workforce strategies group in Milwaukee.
Hirsch notes that one large company wanted to change its total rewards philosophy in order to allocate rewards in the way that would have the greatest positive impact on profitability. “We found that a number of its facilities could have a very significant positive impact on profitability by reallocating 5% of reward budget dollars to base pay rather than to benefits,” says Hirsch.
In this case, many of the company’s field facilities were located in large urban markets with very competitive labor markets. “The market for specific skill sets was particularly tight, so companies could pay a little bit more to get a higher caliber employee from a competitor across the street,” thereby increasing the profitability of those facilities, says Hirsch.
“Data analytics can be used as a way to determine the best way to reconfigure and reconstruct current total rewards dollar in ways that had the best potential to increase profitability,” says Hirsch. However, this must occur on multiple levels. For example, the company would have missed out on additional profit-increasing changes if it had treated all of its facilities the same way.
Data analytics revealed that another set of facilities responded more favorably to investments in benefit programs rather than base pay. In this case, the facilities had a large number of employees who valued benefits and were more likely to stay with the company when they were enrolled in and taking advantage of those benefit programs. With higher retention and productivity, these facilities would become more profitable following this change.
Next time, we will focus on the impact CFOs can have on compensation data analytics.