Although the business press enthusiastically touts the benefits of analytics (and the related field of analyzing Big Data sources), they share far too few specifics about what that means for most companies. So this week, let’s take a look at one of the more common problems in workforce management: measuring and understanding employee turnover — then hopefully preventing it from happening in the future — with analytics.
Why is employee turnover so important?
Recruiting and training employees is expensive. Losing the good ones is a bad thing and holding on to the not-so-good ones isn’t great either. Moreover, turnover brings with it a loss of skills and knowledge, diminished productivity and a larger workload for employees left behind, which can decrease their job satisfaction and increase their chances of joining the exodus.
Companies benefit when they can minimize turnover and maximize its opposite — retention. While some manage to do so by sheer luck, others need to work at it and that’s where analytics comes into play. Using analytics you can find answers to big questions such as “Why do employees leave?” and “Why do employees stay?” — then use that information make changes that cut turnover and improve retention.
Understanding Employee Turnover and Increasing Retention
Step 1: Clean up your HR data.
This seems like it should be the easy part of the exercise, but companies that don’t have a dedicated human resources department can find themselves in the middle of a period of high turnover with no clear idea of who’s leaving, what those people have in common, how much the firm has invested in them, and who might be next.
Before you can find the answers to those questions, you’re going to need to start with a good set of reports. Here’s some of the data you might need:
A list of employees with organizational and tenure information.
Recruitment, hiring and termination information.
Employee salary information.
Employee exit interviews.
Employee satisfaction, engagement and performance surveys.
Industry and job salary comparisons.
General economic data.
Step 2: Figure out who’s leaving.
Once you’ve got a good set of reports in hand and everyone agrees on who’s leaving, it’s time to think about what those people have in common: Is your attrition rate highest in a particular geography, within a specific job or working under a specific manager? Are you losing your top performers?
This is the point in a project where I usually introduce a few statistical methods to flush out correlations that might not otherwise be easy to spot. The good news is even a quick reading of industry research will yield a list of factors that might impact an employee’s decision to leave – things that we can consider in our statistical models:
Is the job monotonous?
Are mentors and career-growth opportunities available?
Are working hours reasonable?
Do employees feel excessive stress and burnout?
Do employees feel their jobs are secure?
What factors did they list on their exit interviews?
Are other jobs readily available at similar or greater salaries?
With any luck, we can spot an actionable trend, such as “attrition among mid-career engineers in our Baton Rouge office is 25 percent higher than for mid-career engineers in our Lafayette location.”
Step 3: Figure out which high-value employees might leave you next – and take steps to make them want to stay.
Finally, what we really want is some way to figure out which of your top performing employees are going to leave and to step in before they do. The good news is any good model that explains who has left you also can be used for predictive purposes.
Once you know which employees are most likely to leave and what factors are likely to make them want to do so, you can make changes to management, working conditions, compensation, benefits and other factors that will increase the odds of them sticking around.
Download our guide to learn more about analytics for human resources:
DataClear is a Baton Rouge-based data analytics consulting firm. Contact us for a free 30-minute consultation and discover how your company can profit from data-driven decision making using tools that won’t break your budget.