Wouldn’t it be nice if you could forecast how long an employee was going to stay before moving on to the next company – or even your competitor? Don’t you wish you could go into an interview knowing how likely a candidate was to be successful at your company? Well, as it turns out, you can. Predictive analytics is available for human resources, and if you’re not making use of it, you might as well throw money away.
Every time you hire a new employee, you invest not only time but resources into finding them, interviewing them and training them. The Society for Human Resource Management (SHRM) found last year that the average cost-per-hire is $4,129.00, and it takes 42 days to fill each empty position. If the position you’re recruiting for requires a higher salary than average, or if you’re looking for a specialized, educated or trained employee, that cost will be higher.
This means that if you have high employee turnover, you’re only losing money through the loss of productivity that naturally happens due to lowered productivity in their last month, and continues on until their replacement is trained and fully onboarded, but also the actual dollar amount tied to hiring. All together, it’s not unusual for these costs to amount to tens of thousands – especially if the turnover rate is high enough to lower morale. Low morale means employees that only do what it takes to not be reprimanded.
So how can you fix what can easily turn into a visual human resources cycle? With predictive analytics software, such as SAP’s SuccessFactors platform. While predictive human capital management solutions may seem like an investment at first, the long term ROI will be multiples of its cost – if you know how to make the most if its capabilities. Here’s how:
Increase Engagement In The Workplace
A big reason why employees leave workplaces is because they either feel overwhelmed with their tasks, or underutilized given their level of skill. Both of these problems can be solved with human capital management software like SuccessFactors, which will give managers into which employees are struggling and could benefit from training (before it’s an emergency), and which employees are ready for a promotion, or would be happier and more fulfilled with a lateral move to a different position (before they leave on their own).
By watching for each of these scenarios, you’ll be able to increase your employee engagement levels and create a much better workplace culture – all before your staff could have even realized that they were dissatisfied.
Watch For Data Indicating Unhappy Employees
There are few things that managers dread more than a valuable, highly productive employee blindsiding them with a resignation from a position that they seemed engaged and happy with. Except, really, were they blindsided? More often than not that employee has made their needs, concerns or other causes of unhappiness known to the manager – but either the employee failed to make it clear that it was an ultimatum, or the manager failed to listen properly.
While it’s tempting to take out these problems on managers, they are, after all, only human. What you can do, however, is fix that problem – by taking out at least some of the human factor in employee satisfaction ratings. While an unhappy and unmotivated team member may be putting on a brave face, the right predictive analytics solution will be able to use an algorithm to compare their current salary, level of productivity, overall rating, and time since their last promotion (as well as many other data points) and judge how likely they are to be dissatisfied. If you can catch a dissatisfied employee before a recruiting firm or competitor does, and right what’s wrong, you’ll likely save the company close to their entire year’s salary – and have an even more loyal member of your company to boot.
Human capital management software can make all the difference in your employee turnover rates – and it can also improve overall productivity and satisfaction. Invest in this resource today to watch your company grow at an accelerated rate tomorrow.