While Microsoft (News - Alert) CEO Satya Nadella recently suggested “karma” to close the gender gap, smart money says that new technology in the hands of adept managers is better equipped to do the job.
Nadella reignited the long-smoldering gender pay gap debate by saying women who don’t ask for raises will receive “good karma” and eventually get their due. Karmic intervention aside, the reality is that a confluence of technology and new thinking about talent management stand a much better chance of leveling the playing field in ways that ensure everyone – regardless of gender – has a fair shot of getting paid what they are actually worth.
Specifically, new talent management technology that relies on predictive analytics and big data can not only help managers to better assess employee performance, but also aid in making more fair and fact-based compensation decisions. The implications of these new solutions reach beyond the gender pay gap, which according to a recent report issued by ADP is actually worsening. Specifically, these new approaches hold the potential to significantly enhance an organization’s ability to retain top performers by generating a side-by-side estimate of what it would cost to replace an employee compared to the projected cost to retain him or her.
So, for instance, using these tools, a manager could clearly see that for a high-performing specialized engineer making $115,000, it would likely require a $20,000 raise to retain the employee, while it would cost upwards of $80,000 to replace her after calculating recruiting, onboarding, training and lost productivity costs. Armed with such information, along with insights generated through other performance evaluation methods, a compelling case can be made to invest the $20,000 to keep the top performer happy and delivering strong results for the team.
Yet most organizations rely on compensation strategies and models that are holdovers from the industrial age. That includes a merit pay increase matrix that, in truth, rarely rewards merit effectively and accurately, and ends up treating everyone pretty much the same regardless of actual performance. That may keep average and marginal employees in their seats, but in a world in which high performers increasingly have access to information to effectively gauge their value, it’s hardly a formula for growing an organization adept at attracting and keeping the best talent.
Consider that under a merit pay matrix, a top performer who is a flight risk might get a 5 percent to 6 percent salary increase. That’s top of the scale, and likely the best a manager can offer. Yet what if the reality is that, based on that employee’s skills and performance, he really should get a 25 percent increase? And based on the plethora of information and recruiters readily available, he knows it.
With new talent management technology, the guesswork is taken out of the equation, allowing managers access to fact-based data and assessments in ways that will help support the case for fair and relevant compensation adjustments, which, in the long run, benefit everyone. The employee is happy that she is working for a company that recognizes her worth and is willing to pay for it. The manager now has a top performer who is re-energized. And the company saves the cost of replacing an employee while getting the added value of an experienced and motivated veteran.
Over time, this creates the framework for building an organization increasingly populated with great performers who are fully engaged and satisfied that they are being fairly valued and compensated for their contributions.
These new talent management tools are leading to what I like to call the personalized enterprise, where technology will allow for a more customized experience for employees at every level of the organization. And in the process, they just may help to close the gender pay gap by relying on hard data on what it really costs to retain good employees.
Adrienne Whitten is vice president of product marketing for Saba Software (www.saba.com).
Edited by Dominick Sorrentino