How Can You Reduce Voluntary Turnover in your Workforce?
Nobody likes to terminate an employee — it hurts productivity, organizational culture and can make future hires more difficult. Still, as undesirable as termination can be, it can often be far less harmful to an organization than voluntary employee turnover, in which employees leave on their own accord. Though it creates less strife, voluntary turnover is also less easily controlled and can be indicative of systemic problems that could very well cause further departures, and further deficiencies.
The ADP Research Institute® (ADP RI) white paper, Revelations from Workforce Turnover. A Closer Look Through Predictive Analytics, lays out a systemic look at the issue of voluntary turnover. The results show not just the shape and scale of the problem, but recommend some remarkably easy-to-implement solutions, as well.
The Power of Predictive Models
An approach to the complex problem of why people choose to leave their current job is to leverage the power of modern predictive analytics, using computer models to turn past experience into future insight. To achieve this, ADP RI looked at monthly anonymized payroll database for more than 41,000 organizations of 25 or more employees for a two-year period. In all, this let ADP study data from about 12.5 million employees — more than enough to begin to derive some useful insight.
In the past, analysis of voluntary turnover has simply correlated certain employer attributes with the likelihood of leaving, but this can only be so useful to any one business trying to make specific policy decisions. The study states that it's "one thing" to map out these correlations, but that it's quite another to "map out an array of influences and point to combinations that are driving turnover in a specific company."
The Most Important Contributors to Employee Turnover
Right now, 60 to 70 percent of all employee turnover is voluntary, and ADP RI's predictive models have broken this group down to its component parts. The big challenge was categorizing all employees as being high, medium or low-risk to voluntarily leave the business, crunching the raw data from millions of employees to construct an industry-specific profile for employees most likely to leave. When tested, these categories held up well, with designated high-risk employees actually leaving at much higher rates than mid-risk employees, who were in turn more likely to leave than their low-risk co-workers.
There are certain generalities about employee preference that remain reliable across the board, but for the most part worker needs vary from industry to industry; high-tech industries with ample salaries but meager time-off will see very different employee reactions than one with a relaxed culture and below-average pay. Compensation and seniority are almost always near the top of employee concerns, it's true, but there are important exceptions. There are always important exceptions.
Every Organization, and Employee, Is Unique
One of ADP RI's more interesting findings came from a case study of one particular business, again showing how simple statistical insight must be seen through the lens of a particular business before they can be truly useful. The report found that at 29.4 percent impact, the most important determining factor for employees at this business was security and "tenure" relative to the organizational-wide average.
More interesting, however, was the fact that the studied business had a counter-intuitive emphasis on commute time; for this business, time spent getting to work was a bigger motivator on employee turnover (7.3 percent impact) than even the frequency of wage increases (4.2 percent impact). That's a powerful insight that would not have been possible without statistical tools.
As ADP RI puts it, before big data tools, HR had to "turn to employee surveys, industry opinion and whatever information they could muster." Now, sophisticated statistical modelling allows a more graceful approach, one that can conform to the unique attributes of a particular business.
The key to such gains is a systemic, data-derived understanding of the workforce. In the modern era, the traditionally gut-based processes of talent acquisition and maintenance are quickly becoming more science and art.
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