In a previous article, we discussed linking exit survey responses to employee turnover data analysis for the purpose of identifying drivers of attrition. Exit surveys, however, are not the only tool available for better understanding employee turnover statistics.
Engagement surveys—in particular, census engagement surveys—are powerful because they capture many different perceptions across different facets of the employee experience throughout the organization. Data about employee opinions regarding recognition, growth and development opportunities, the direction of the company, and many other aspects can be gathered at the same time, giving a snapshot of employee perceptions across the entire company. This data can be used for measuring engagement, but it is also useful for linking to different talent or business metrics to understand how employee perceptions and opinions impact various business outcomes. In this article, we’ll look at how engagement surveys can be used to analyze employee turnover—and give leaders information about what matters most to employees and the actions they should take to reduce attrition.
Employee turnover is an issue for most organizations because it’s expensive and disruptive. Hiring and training new employees costs thousands of dollars on top of the productivity losses businesses face when employees—particularly top talent—leave. Most companies have the desire to reduce turnover to avoid these costs.
Engagement surveys offer a great way to understand how employees view the current work environment and can highlight where things are going well—as well as the aspects of the experience that are not working for employees. Comparing engagement survey data to termination data can reveal areas of the employee experience that need improvement, ultimately helping to reduce attrition. (Tweet this!)
Typically, six to 12 months after the engagement survey is administered, termination data from employees who left the organization after completing the survey is compared with those employees’ engagement survey responses. This allows an analysis of how employees who ultimately left the organization viewed the employee experience shortly before leaving; their perceptions can then be compared to the perceptions of employees who have remained with the company. This comparison can be used to analyze the reasons for employee turnover and suggest interventions to reduce it.
Often this data analysis looks at the biggest differences in perceptions between those who stayed and those who left. Typically, the number one driver of attrition is intention to leave. When asked “Do you intend to remain in your job for the next year?” often there will be a 15–30 point difference in scores between employees who left the company within the year after the survey and those who remained. Other attrition drivers can be unique to the company, the job role, or the length of tenure. In particular, factors related to tenure can vary in relation to where the employee is in his or her journey with the company. For example, employees with less than three years’ tenure might express dissatisfaction with resources or work/life balance, while those with more than three years’ tenure might rate opportunity for growth and development as unsatisfactory.
Employee turnover analysis also focuses on unfavorable impressions, to determine where employees who left were most unhappy. What aspects of the job or the company were lacking? Analysis of comment themes between employees who left versus those who stayed is also important, and often reveals that employees who departed experienced the work environment very differently than employees who stayed.
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In many organizations, employee turnover data analysis is targeted at the most critical areas—job roles with the highest turnover, positions that are most difficult to fill, or those most important to the company’s profitability. For example, in a food distribution company, delivery drivers may have a high turnover rate. Because reliable delivery is the basis of profitability for the organization, retaining drivers is critical to company success and reducing turnover in this job role offers the biggest dividends. A technology company might be most interested in retaining product designers crucial to their competitiveness—and difficult to replace when unemployment is low. Now, during the COVID-19 pandemic, health care organizations are likely concerned about retaining nurses and other medical professionals needed to fulfill their mission of providing care for patients.
As noted previously, targeting can also extend to job tenure. Attrition data may reflect a spike in turnover at certain points in particular job roles, showing that employees who have been in the job for three years are more likely to leave than those who have a shorter or longer tenure. Targeted analysis may reveal differences in the employee experience for employees who left versus those who stayed in the job. Many organizations also focus on employee turnover statistics for new hires. Having gone to the effort and expense of finding and training new employees, they want to address negatives in the experience that may cause new hires to leave.
By focusing on the most critical and high-risk segments of the employee population, a company can establish internal employee turnover benchmark data and correlate this attrition data with engagement survey responses. Tracking the benchmark year-over-year can reveal how the experience has changed for better or worse, if the reasons employees are leaving are the same or different, or if the pattern or time cycle for departures has changed.
In all of these analyses, it’s important to define voluntary attrition. Employees leave for a variety of reasons that can be divided into push and pull categories. A push represents dissatisfaction with some aspect of the job or organization, while a pull is due to external factors—a move for a spouse’s job relocation, a family illness, or other reasons unrelated to the employee’s satisfaction in their job. Narrowing the focus to employees who were “pushed” from the organization keeps the spotlight on factors within the company’s control. Specifically, it’s important to get granular, detailed information about those who chose to leave for other employment. What does the new employer offer that was lacking in the experience of the employee in the organization? This is particularly important when the labor market is tight and there is a lot of competition for talent.
The current disruption of the COVID-19 pandemic poses a different problem, as businesses may struggle to hold on to front-line employees who, in different circumstances, would not be difficult to replace. For these employees, competition is not the issue so much as safety. Our research has shown that the key to retaining front-line employees under the new conditions imposed by the pandemic is to communicate and demonstrate support for their concerns about safety and health.
Correlating engagement survey results with employee turnover data can give leaders insight into how to not just engage employees but also retain them. (Tweet this!) It allows engagement results to be leveraged in a different way to answer a different business question.
By establishing internal employee turnover benchmark data, leaders can also gain some predictive capabilities; if the benchmark shows that 40% of software engineers who express dissatisfaction with growth and development opportunities leave within a year, leaders have an opportunity to intervene to keep more of those employees on board in the future.
Exit surveys are another potential source of data. Exit survey data can be compared with engagement survey responses to see how the employee’s perceptions changed over time. How were employees who departed feeling during the last engagement survey? Were they also unhappy then? Many times the answer is yes; correlating exit and engagement survey data may yield additional predictive capability about attrition risks and opportunities for intervention.
Organizations have many reasons for wanting to avoid or reduce employee turnover—cost, disruption, loss of institutional knowledge, loss of productivity, and impact on remaining employees (stress from picking up the slack for others, burnout, and uncertainty about the company’s direction). Engagement survey data—when linked to termination data—offers leaders the ability to predict turnover risk. More importantly, correlating these two sources of data can give leaders insight about the actions they should take to improve the specific aspects of the employee experience that are the biggest factors in driving attrition. Using survey data to address and get ahead of attrition problems frees up leaders’ attention so they can see the way forward—and frees up organizational resources for a greater focus on strategic objectives.
At Perceptyx, helping companies identify and reduce attrition risks is one of our goals. With custom surveys paired to our people analytics platform and expertise in all aspects of survey design, strategy, and communication, we can help you increase employee engagement and reduce turnover. Get in touch and let us show you how.