Analytics

Is Regrettable Turnover Hurting Your Business?

By April 23, 2018 No Comments

In today’s rapidly changing environment, the development and retention of critical talent are one of the most significant challenges faced by CEOs. Various sources estimate it can cost anywhere from 30 to 400% (the average is 150%) of an employee’s annual salary to replace them, depending on their level, experience, skill set, etc. It is essential for you to develop a system to understand why people leave your organization. Understanding the drivers of turnover trends across different employee segments will lead you to develop retention policy adoption or changes.

Over the years when I have talked with HR professionals about their organization’s turnover, they’ll typically share data that point to overall percentages. However, best practice indicates that not all turnover is undesirable or regrettable. Unfortunately, in many organizations, this number is often not tracked or reported.

Your first priority should be to standardize your organization’s turnover vocabulary and subsequently turnover calculations. Many times, I have seen in the same organization there are different turnover calculations among departments. Once you have this standardization, organize your overall turnover data into Un-regrettable and Regrettable turnover buckets.

Un-regrettable turnover is when the employee is asked to leave the organization. The reasons could be due to poor performance, perhaps a bad fit, some type of reorganization or downsizing, or something else.

Regrettable turnover is when an employee voluntarily leaves the organization for reasons such as retiring, going to a competitor, is in a key position, or may have critical jobs skills. The reasons are often a combination of several factors and finding the causes is sometimes difficult. But if not managed proactively, the consistent loss of key people can have a damaging effect on an organization.

Regrettable turnover should now be your focus. It is calculated as the % of employees who left the organization in a specific time period and are replaced.

Using the Regrettable turnover data, mine the demographics of this population to determine overall patterns that contribute to turnover in your organization. Demographics can include personal data such as gender, ethnicity, job band, critical job role, exempt and non-exempt; organizational data such as division, location, size of a team; and there might be other demographics such as office vs. home, permanent contractor, etc. In addition to this quantitative data, you will need to gather qualitative data most likely through surveys (engagement, pulse, etc.) and some type of exit interviews from those regrettable employees.

The data mining needed for this analysis (Descriptive analytics) requires the skill-set of a data analyst who has highly developed pattern recognition skills and generally likes Stars Wars. The most common tool used in this work is Excel pivot tables. Many companies get started borrowing this skill-set temporarily from another department, such as accounting or marketing, for their early projects. Others get started hiring a local University professor or student on a project-by-project basis.

By combining and analyzing these data sets, you most likely will uncover what groups of employees are experiencing high turnover. From this list, you can investigate what groups have the highest impact on business outcomes. You can also determine what groups are the highest risk to leave based on the severity of the findings. This helps you prioritize your retention efforts to the groups who need your attention the greatest.

This analysis allows you to discover the groups of employees most likely to leave and the reasons behind their leaving. Generally, the reasons fall into the following categories: lack of career advancement; cultural issues such as work rules, bias, atmosphere, or their team; relationship with their manager; workload overload; lack of education and/or training opportunities; lack of coaching or mentoring; and sometimes compensation.

Understanding the turnover drivers per group allows you to custom tailor your retention strategies to keep your key employees. When the regrettable turnover data is analyzed properly, and the resulting retention remedies are thoughtfully applied to specific groups, everyone wins. Key people stay longer resulting in higher productivity and lower turnover costs, and the halo effect improves general employee engagement.

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