Impersonal or not, it turns out that big data analytics are better than armies of HR reps at reducing the cripplingly high cost of employee turnover.
Big data is revolutionizing virtually every corner of the financial world, but in many cases HR has stood out as a lone, stalwart opponent to technological change. The conventional wisdom was that workers are unpredictable human animals that can only be understood by the intuition of an equally unique HR professional.
But new research from the ADP Research Institute® (ADP RI) into the dynamics of "voluntary turnover," shows that's likely untrue. According to the paper, "Revelations from Workforce Turnover. A Closer Look Through Predictive Analytics," the gut-based approach of the past is losing ground to the raw effectiveness of big data analytics, and financial leaders could see big gains as a result.
Traditional Solutions Create Traditional Problems
Businesses have always been able to rely on certain dynamics in employee turnover. For instance, like most other industries, finance sees the highest turnover in September, owing mostly to the beginning of the post-secondary school year. For a long time, that was the high watermark in the analytics of employee turnover, since going beyond that level of insight meant conducting a large number of interviews and surveys. Not only are these expensive and time-consuming, but they also tend to simply reinforce the conventional wisdom of the time.
Remember that unlike involuntary employee turnover (firing and layoffs), the dynamics of voluntary employee turnover (quitting) are not directly within the employer's control. That's a problem when the majority of all contracts are ended by the employee, rather than the employer. According to the ADP RI, workers in the finance and insurance sectors leave on their own 64.9 percent of the time — about 5 percent more than their peers in the professional, scientific and technical services industries.
The impact of such unplanned departures can be profound — starting with the financial cost of employee turnover. So not only does uncontrolled quitting require finance leaders to hire and train on a less than ideal schedule, but it dramatically impacts the cost of doing business. "Besides the direct costs of identifying, hiring and training replacement workers," says the ADP RI paper, "studies have attributed other, less obvious costs to turnover such as loss of productivity, reduced time to market and lost institutional knowledge."
Big Data to the Rescue
What if all these potential problems could be avoided through more sophisticated analysis of employee data? ADP RI identified 40 personal and professional attributes — which range from job experience to commuting distance — relative to the job itself that can best predict the likelihood that any one employee will voluntarily leave their job in the coming year. Of course, pay and promotion were lead drivers across categories, but the weight of certain attributes vary across industries.
If it turns out that concerns over compensation are driving high voluntary turnover, your accountants may just find it's cheaper to assign raises than rehire and retrain those positions at an elevated rate. But if the issue turns out to be commute time or something similarly divorced from pay, analytics can keep finance leaders from wasting resources on unnecessary raises and instead focus on the actual origins of employee dissatisfaction. From there, finding ways to retain your employees will be much easier now that you know the root causes.
On the other hand, organizations looking to trim some fat could just as easily use these insights to intentionally create voluntary turnover and minimize firings.
As the ADP RI report says, "If attrition is the goal, now you know how to make that happen faster."
Analytics for All
Analytics of internal data is becoming a requirement for firms of every size, and thankfully a diverse array of solutions has cropped up to compensate. There are a number of cloud-based services that can do robust employee sorting and behavioral prediction based on easily collected data. This means that any organization, who wants to keep pace with their sector, needs to be at least planning their analytics strategy..
Larger organizations with more wide-ranging needs to analyze their own data may find it more cost-effective to hire and maintain their own analytics department; for the cost of just a few extra employees in IT, you could reduce the overall cost of employee turnover. At this point, most finance firms should be developing a stable of experts in data analysis for all kinds of tasks both inward- and outward-facing, so why not include turnover prediction as one of this team's core goals? And remember, finance is not alone; 60 to 70 percent of turnover across industries is voluntary.
The extra predictive power for any organization could be that last extra factor that ensures your most vital employees are there when you need them most.
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