With January right around the corner, this is the time of year when employers need to be proactive with their employee retention strategies. Recent data from Glassdoor specifically calls out January as the month when more employees are likely to leave.
Now is the time to find out which of your best performers may be calling it quits in the new year. Data-driven organizations use workforce analytics to identify the employees who are most likely to resign and more importantly, why, so the right levers can be pulled to stem the tide of employees rushing for the exits.
The cost of employee turnover
Employee turnover is the single most prevalent HR metric. PricewaterhouseCooper’s 2017 annual survey found 77 percent of CEOs are concerned that key skills shortages could impair their company’s growth. It’s also a very costly problem.
According to Bersin by Deloitte research, the average voluntary turnover rate is 13 percent. If, for example, an organization has 30,000 employees and an average voluntary turnover rate of 13 percent, the potential cost to the organization is a staggering $427.7 million in one year.
It’s important to note that not all voluntary turnover is bad — like the loss of the employee with a negative track record for productivity or the team member who clashes with the workplace culture. Rather, turnover becomes a problem when organizations struggle to retain their very best talent and this negatively impacts the bottom line.
More than ever before, business leaders need strategic insight and the ability to model how turnover trends impact revenue and profits — quickly and accurately.
It’s not always a question of pay
In one case, the HR team knew from past experience that an across-the-board pay raise was the wrong thing to do. It was an expensive way to fix the problem, and worse, it was unlikely to lead to fewer resignations. The problem was that HR had no data to prove it.
The same Glassdoor study also found that people don’t always leave because of pay. Dissatisfaction towards their managers or a lack of sense of connection and meaningful contribution towards the company are also key reasons voluntary turnover occurs.
Because of all the different factors that affect turnover, it’s important to look at your resignation metrics in-depth so you can focus on the right areas and not just to see what happened, but understand why it happened, what will happen next, and how to adapt your retention strategy to align with company objectives.
How to reduce employee turnover
So what should companies be looking for to reduce voluntary turnover? Here are few telltale data points that all companies should be measuring.
Take stock of the damage: Determine what’s leading to higher turnover by first assessing what damage has already been done. It’s not uncommon in a single organization for turnover to be calculated a number of different ways — meaning there is a lack of ability for meaningful comparison across the organization. Companies should start by calculating resignation rates the same for all departments and locations.
Identify who is resigning: It’s important for businesses to take stock of who is actually resigning. Is it top performers? Senior managers? When many of the employees who leave are the best and brightest, they take all their skills, knowledge and connections with them, putting the organization at a disadvantage.
Analyze the causes of turnover: Rather than jump straight into giving raises across the board, dig deeper to determine how resignations are affected by factors such as compensation ratio, promotion wait time, tenure, performance, and training opportunities that employees may be seeking. This insight supports better decisions around changes to pay, benefits, and professional development in order to manage costs, while retaining the right people.
Determine who can be saved: Once you have determined which general groups are experiencing high rates of turnover, implement a retention program targeted at the key employees with the highest-risk of exit.
Data demystifies employee churn. The patterns vary: it could be a bad manager, a remote department that feels disconnected, or employees who have a long commute time. Workforce data identifies and addresses the biggest patterns we hadn’t previously considered through advanced AI and machine learning.
If companies aren’t able to look at their workforce through a data-driven lens and accurately predict employee behavior such as voluntary turnover, they’re at a disadvantage in terms of retaining top performers, as well as keeping people-related costs to a minimum.
By combining these identified patterns with the basic knowledge of organizational behavior, companies can implement systems and programs that truly incentivise employees to remain at their positions come January and beyond.