Data Analytics in HR: Employee Turnover and Engagement

Introduction

The relevance of deploying data analytics in human resource management cannot be overstated. Data analytics can serve as a crucial aid to decision making, and a data-driven approach to HR seeks to ensure that organizations are guided by factual input rather than guesswork or mere intuition. In the words of Waters, Streets, McFarlane, and Johnson-Murray (2018), "HR analytics can improve the credibility of the HR function by showing the linkage between people and business outcomes" (p. 9). This paper examines several scenarios in which analytics can be deployed from a workforce perspective. More specifically, the workforce areas addressed are employee turnover and employee engagement.

Employee Turnover: Overview and Business Question

Employee turnover — also routinely referred to as the employee turnover rate — can be defined as the rate at which employees leave a firm over a specified period of time, particularly those whom the organization must replace. It is for this reason that Dessler (2018) defines the employee turnover rate as "the loss of talent in the workforce over time" (p. 152). The employee turnover rate is a metric that no organization should ignore, as it enables an assessment of how effective the company's HR policies and practices are.

When the employee turnover rate is high, the company incurs significant hiring costs. These can include, but are not limited to, advertising costs, administrative costs associated with selection, and training costs. A high turnover rate also brings with it the associated cost of productivity loss — both the actual productivity loss when a position becomes vacant and the loss incurred while a new employee is being oriented into a new role. Yet another often-overlooked consequence of high employee turnover is decreased morale among remaining employees, who may be required to work overtime to cover departing colleagues' responsibilities. Given these downsides, the relevance of deploying analytics in this context is considerable.

Business Question: Has the turnover rate of the organization been on an upward or downward trend over the last five years?

Leading Indicators for Employee Turnover

Overall turnover rate: This metric helps make sense of data relating to the number of employees who leave the company during a specific year. To compute the overall turnover rate, the number of employees who leave the organization in a given year can be divided by the average number of employees — calculated as the total number of employees at the beginning of the year plus the number remaining at the end of the year, divided by two. The resulting figure is then multiplied by 100 and can be compared against the industry average.

Voluntary and involuntary turnover rate: It is useful to distinguish between employees who leave willingly and those who are terminated. The voluntary turnover rate — arguably the more representative indicator of underlying HR issues — can be computed by dividing the number of employees who depart on their own volition within a given period by the average total number of employees, then multiplying by 100. The involuntary turnover rate is computed in the same way, substituting the number of terminated employees. Understanding this distinction allows organizations to target their interventions more precisely.

Average employment length: This indicator provides insight into how long employees typically remain with the company. If employees tend to leave after a short period, this may signal deep-seated HR issues that require resolution. Average employment length can also be compared against the industry average for additional context.

Business Impact Metrics: On this front, the aim is to establish whether the employee turnover rate over the period in question had an impact on productivity — one of the key business impact metrics identified by Pease, Byerly, and Fitz-enz (2013). If turnover is found to correlate with productivity, relevant interventions can be designed, given that "HR interventions are designed with business outcomes in mind" (Pease, Byerly, and Fitz-enz, 2013, p. 32). Productivity can be measured in terms of: (i) company profitability (i.e., net income); and (ii) a comparison between input (total labor time) and output (e.g., the number of manufactured products).

Conclusion

It is clear from the discussion above that the relevance of data analytics cannot be overstated, particularly when it comes to efforts to gather, analyze, and report crucial HR data. In the present discussion, data analytics has been applied to gain insight into two workforce issues: employee turnover and employee engagement. Data gathered in these areas will enable organizations to formulate better-informed decisions. More specifically, the organization will be able to determine whether the issues highlighted affect its ability to remain competitive — in terms of both productivity and profitability — going forward.

References

Dessler, G. (2017). Human Resource Management. Precision Higher Education.

Pease, G., Byerly, B., & Fitz-enz, J. (2013). Human Capital Analytics. John Wiley & Sons.

Waters, S. D., Streets, V. N., McFarlane, L., & Johnson-Murray, R. (2018). The Practical Guide to HR Analytics: Using Data to Inform, Transform, and Empower HR Decisions. SHRM.