Human resource metrics are profoundly critical in measuring the value and effectiveness of human resource initiatives. They help compare different data points that are implicit in human resource policies. On the other hand, human resource analytics help evaluate the reasons behind the occurrence of a particular trend and its impact on employees in the workplace (Kassick, 2019). Whereas metrics do not say anything about a cause, they help measure the difference between data points.
Many organizations today have problems retaining their employees for a long time; therefore, several metrics can help alleviate this issue.
To test this hypothesis, HR metrics that comprise the following will be used:
- Cost of Voluntary Turnover
- Turnover rate
- First-year turnover rate
Cost of Voluntary Turnover
This is one of the most perceptible metrics that can help analyze employee retention in any organization. It is implicit in the average cost required to replace an employee who just left an organization voluntarily. In essence, this metric evaluates the average direct monetary costs associated with a position that had been left vacant and is now filled. The cost of voluntary turnover comprises smaller costs such as separation pay, payables to contractors, and staffing costs for replacement hiring. This metric is effective because it provides a good evaluation of an organization’s costs as it transitions from an employee’s resignation to hiring a new employee. Moreover, this metric predicts how a company is affected by costs associated with employee turnover. As a leader, one could apply this metric to track turnover costs and make room for necessary changes.
The cost of voluntary turnover is calculated by summing up the total employees’ projected salary cost and the complete voluntary separation of employees. Remarkably, the cost of voluntary turnover can be used in different analytic stages to guide human capital management (Armstrong, 2019). For instance, the human resource manager can use the data drawn from this metric and apply it in descriptive analytics. For example, if 12 people in a company decide to quit voluntarily, we can assume that the company responds by hiring ten people. In this case, a human resource manager can leverage the cost of voluntary turnover to make inferences about the company’s costs due to employee resignations.
In the diagnostic analysis stage, this metric can help to show why the costs are being incurred. In predictive analytics, the voluntary turnover cost can help assess how the costs will impact its financial position. In the last stage, which is prescriptive analytics, HR managers can rely on the data obtained from this metric to implement measures that will cut down voluntary turnover costs. Data sources required to calculate this metric comprise the total costs of separation and vacancy replacement. These sources can be housed in the human resource record books, which provide a detailed analysis of all staffing costs.
The turnover rate refers to the percentage of employees leaving an organization within a particular period. This metric is profoundly vital in measuring a company’s human management position (Armstrong, 2019). High turnover rates imply that an organization is losing a lot of employees. Admittedly, this trend has enormous cost implications on the side of a company. This metric is applicable because it can help determine where and when an organization has the risk of losing talent that it did not intend to lose. The turnover rate results are helpful in guiding human resource practitioners in a company on how to mitigate the risk of losing employees within a given period.
To calculate the rate of employee turnover, we divide the number of employee separations during a certain time by the average actual number of employees and multiply the result by one hundred. The formula is; (Number of separations during a particular time period/Actual number of employees during the time period) X 100. Multiplying the quotient of these two numbers with 100 will give us the turnover rate.
As noted earlier, the data sources required to calculate this metric are the number of separations and the average headcount. The number of separations is gotten by combining voluntary separations and involuntary terminations. This data source is housed in the human resource records. In the descriptive analysis, we can describe what happened by talking about the number of employees who left the organization. In the diagnostic analysis, we can assess the reasons why the employees left the organization. Some may have left voluntarily, and others involuntarily. In the third stage, which is predictive analysis, we can address the implications of the organization’s turnover. In prescriptive analytics, human resource personnel can come up with ways to control the turnover rate.
First Year Turnover Rate
This metric measures the workforce percentage with tenure of one year or less who resign before the end of their tenure. Ideally, the formula applies to any tenure group. Hiring a new employee is common in most organizations. However, a certain percentage of employees leave their jobs within the first year of employment (Armstrong, 2019). This metric provides great insight into the hiring trends of an organization. To calculate the first-year turnover rate, we take the number of resignations within a tenure of one year or less and divide it by the average headcount of employees hired within the same year.
The main data sources used are the total of employees who depart the company in the first year after they are employed and the sum of employees recruited within the same year. Admittedly, this metric can also apply to all four stages of analytics. In the first stage, we can describe what happened by outlining the number of people who quit in their first year of employment. In diagnostic analytics, we can assess the reasons why the employees quit their jobs in the first year. For predictive analytics, we could make presumptions of what this trend implies to our organization. In the last stage, which is prescriptive analytics, we can develop measures that we can take to alleviate this trend.
In conducting the research, it is paramount to communicate with the organization’s management to inform them of the hypothesized problem. It is also important to liaise with the human resource team during the research. Ideally, the HR team has most of the information required to conduct this research. It would be nearly impossible to work without getting data sources from the human resource department. Additionally, it is important to communicate with some employees to get insight into the existing turnover trends.
The communication strategy would be majorly implicit in topic shifting. Since the research would be centered on varied issues, it is imperative to shift topics to engage listeners and get crucial details occasionally. The use of specific and descriptive feedback to give opinions will be essential when required to do so. The strategy would also entail the use of written information, for instance, providing questionnaires to employees to get their insights regarding crucial topics.
The selected metrics will be effective in alleviating issues that organizations face in retaining their employees. For instance, the cost of voluntary turnover helps an organization predict how the expenses associated with employee turnover affect it. The turnover rate helps an organization determine the number of employees that it loses within a particular time. Lastly, the first-year turnover rate helps an organization determine how many employees leave their positions in the first year of their employment. However, communicating with the stakeholders is crucial in ensuring that the objectives are achieved.
Armstrong, M. (2019). A handbook of human resource management practice. Kogan Page Publishers.
Kassick, D. (2019). Workforce analytics and human resource metrics: Algorithmically managed workers, tracking and surveillance technologies, and wearable biological measuring devices. Psychosociological Issues in Human Resource Management, 7(2), 55-60. doi:10.22381/PIHRM7220199