As companies emerge from COVID-19, attracting and retaining top talent and reducing operating costs is expected to become of paramount importance. Forward-looking companies are exploiting their global rewards data to generate evidence-based insights and make decisions that can mitigate employee retention risk and unlock new advantages for the entire business.
Global organizations are discovering that data analytics can help them identify employees who pose the greatest risk of leaving the business. Strategic data-driven analysis of total rewards, employee profile data, and historical workforce trends, delivers deep insights into the current and potential performance of individual employees by appraising their value to the business and responding with appropriately designed reward programs and policies.
According to a study from Work Institute, 41.4 million, or 26 percent of US employees voluntarily left their jobs in 2018. As today’s business leaders know, every employee’s voluntary departure is extremely costly to the organization in terms of disruption, recruiting, onboarding, and retaining replacements. It is estimated that the cost of entry-level position turnover is 50 percent of annual salary; mid-level at 125 percent of salary; and senior executives at over 200 percent of salary. Retention of even relatively low numbers of employees who would otherwise voluntarily terminate employment can result in significant cost savings to an organization.
Many companies today continue to rely on traditional tools such as market surveys or benchmarking data, and annual performance reviews, when designing long-term incentive compensation programs and establishing award levels for employees. Unless an organization is also tapping into the complete spectrum of employee – and business – data already available, it is not fully leveraging its potential predictive powers to enhance employee retention.
Doing so positions businesses to deliver incentive compensation specifically designed to retain high-performing employees who have the potential for future success and who might otherwise be at risk of leaving the company. The same analysis can also inform and enhance decisions on reward programs for individuals who are not high performers or are deemed to lack the potential to become high performers or leaders in the company.
While today’s global rewards programs and practices often focus on retaining the company’s strongest performers, applying a data-driven lens on overall employee retention is an increasingly important challenge as well. A study by the National Association of Corporate Directors noted, “labor market scarcity is believed to be one of the three most-pressing governance issues facing directors in the coming years”.
As companies regain their footing and align their workforce with their strategic business needs in response to COVID-19, there may be increased competition in the labor market for qualified resources. Current employers must be proactive in assessing their post COVID-19 talent needs and retaining key talent in the event that reductions in force become a necessity.
Performing a retention risk analysis on your employees, requires looking beyond the obvious to include historical data that the company holds both for the current population and employees who have left voluntarily. Honing in on data to explore historical patterns and trends among voluntary departures can unlock characteristics that can be applied to the current workforce to help identify each employee’s level of risk in leaving the organization. Such data typically includes organizational roles, location, annual salary, incentive compensation, tenure, years to retirement and more. While individual data points are informative at any point in time on their own, e.g. performance rating, looking at all data points over a period of time provides additional insight.
Using a statistical backed approach to determine the data points most impactful in predicting whether an employee will terminate, a retention risk analysis will calculate a score for each employee that represents their risk of voluntarily leaving the company.
The accuracy of this score is validated by running the analysis on the full historical employee data set to determine if the analytics model assigned higher scores to those employees that did, in fact, go on to voluntarily terminate. The assigned scores allow the user to determine the level of statistical accuracy of the model, refine it to reach the desired level of accuracy, and generate risk retention predictions.
|Categorizing employees enables an understanding of each employee’s contribution to the organization in total, across all data points analyzed.|
|Using a statistical backed approach to determine the data points most impactful in predicting whether an employee will terminate, a retention risk analysis will calculate a score for each employee that represents their risk of voluntarily leaving the company.|
Coupling an employee’s retention risk score with performance and career potential data, the retention risk analysis will classify each employee into one of four categories:
Categorizing employees enables an understanding of each employee’s contribution to the organization in total, across all data points analyzed. With this information, the organization is then better positioned to retain those employees who will likely provide the highest probability of achieving overall strategic and business objectives.
The Retention Risk dashboard illustrates the four categories of employee classification. Each point in the quadrant represents the intersection of an employee’s average performance score and calculated retention risk score. Visualizing a retention risk analysis allows a company to easily view the number of employees that may be at risk of voluntarily terminating. (Source: KPMG in the US, 2020).
It should now be clear that companies failing to take a strategic, data-driven approach in today’s remarkably tight global labor market face a higher risk of seeing valuable talent walk out the door ― perhaps directly to a competitor who has potentially taken the time to generate valuable new workforce data insights to attract that employee.
Taking a highly customized approach to compiling and analyzing data is critical. There is no ‘one-size-fits-all’ approach that can simply be applied to every business. Optimal results will require precise consideration of key factors that essentially define how each business operates. The use of data to generate useful insights must be specific to each organization based on its culture, compensation policies, reward policies and more.
|Taking a highly customized approach to compiling and analyzing data is critical. There is no ‘one-size-fits-all’ approach that can simply be applied to every business.|
|The use of data to generate useful insights must be specific to each organization based on its culture, compensation policies, reward policies and more.|
Companies can gain a much deeper understanding of their retention risk by developing an analysis of their employee and total rewards data that:
A comprehensive analysis that’s customized to be business specific will ultimately provide a holistic view of the organization and help to clearly identify where to focus efforts in the journey to minimize retention risk. Using data strategically to deeply understand your organization in these new ways will position you to make informed decisions on how to retain employees and provide appropriate reward packages.
This information is critical in today’s rapidly evolving and highly competitive environment. If your business is not taking advantage of the remarkable ability of data to generate competitive advantage, be assured that your competitors almost certainly will – and that a valuable employee and future leader could walk out the door because you failed to take the necessary steps to retain them appropriately.
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