Progressive organizations increasingly view the ability to predict employee behaviors as critical to achieving their business objectives. However, HR leaders need better access to analytic techniques, modeling frameworks and data from a variety of sources, both internal and external, to build predictive solutions that actually link employee behavior, characteristics and performance to desired business outcomes.
Most organizations know when employees leave and who made the decision. But understanding the reasons for departure, beyond what a simple exit survey can provide – and using that information to keep other critical employees from leaving – is a different, more complex challenge. It is critical to not only know who left the organization, but also understand why they left and then be able to predict the attrition risks of current employees, especially those in pivotal roles.
We work with HR and business leaders to understand and model the root causes of employee attrition, and then predict and visualize attrition risks at both employee and aggregate levels. Aon’s Predictive Attrition Risk Modeling uses a 212 hypotheses framework and helps organizations predict factors behind employee attrition behavior and identify who is likely to leave in the next 6 to 18 months.
Reducing voluntary turnover, especially among employees in pivotal roles, brings significant economic advantage to any organization. Focusing on planned vs. unplanned attrition allows leaders to identify both where in the organization employees are unexpectedly leaving, as well as where in the organization headcount is being rationalized according to established plans. Healthy turnover is often a mix of voluntary and involuntary actions. The critical information for a business leader is whether or not that turnover is being executed in a way that is anticipated and in line with the business strategy.
After recovering from the economic downturn, a $35 billion financial services firm needed to attract top talent to meet its growth objectives, but was consistently losing out to other firms. Due to its challenges attracting talent, the client was particularly concerned with retaining pivotal employees in the organization. We developed three predictive attrition models for the chosen employee segment and scored current employees with attrition risk probabilities. Outcomes and accompanying dashboards allowed the organization to implement targeted action plans and segmented retention strategies, and their top performer separation rates quickly fell from 18% to 8.7% in one year.
The demand for quality hires is increasingly acute as departing and retiring employees leave big holes to fill. At the same time, with increased voluntary turnover, organizations face growing pressure to hire quickly, often resulting in a greater number of poor hires. One of the biggest challenges to landing high-quality hires is the inability to pinpoint the characteristics of a successful employee, as well as identifying the key aspects of a work environment that lead to success for any given role.
Aon’s Predictive Quality of Hire Analytics uses statistical modeling techniques to increase recruiter efficiency, bringing fact-based hiring profiles and behavioral assessments to find new hires perfectly suited to their respective roles. We use our proprietary frameworks to build predictive models using data on performance, engagement, HRIS characteristics, post-hire assessments and candidate preference models.
Organizations can vastly improve the effectiveness of recruiting by understanding who within their current workforce is successful and why, and the sources of talent that produce better hires. Additionally, organizations that use modeling outputs to customize their pre- and post-hire assessments, especially for people in pivotal roles, see reductions in both overall turnover and first year of service attrition.
A large technology company wanted to quickly recruit large volumes of quality employees in their inside sales organization: personnel who would stay with the organization and deliver consistent performance. Several role-specific predictive models were built that tested 73 pre- and post-hire characteristics such as education, commute time, prior experience, manager quality as well as 18 external factors such as industry growth and inter-industry mobility. Using the model, the company gained insights on predictors for employee success, and built employee profiles to be used in the recruitment process.
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