Employee Insight: Predicting, Visualizing, and Mitigating Intent to Leave
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Abstract
Beyond informing human resource (HR) policies and practices,information gleaned from predictive analytics, visualized via dashboards,can increase awareness and prompt employee and management actionsbased on identified variables often related to intent to leave andemployee wellness. While considerable research, relevantmeasurements, and tools are available within the field of humanresource management that focuses on measuring retention andmitigating turnover, there is minimal application of such tools in highereducation outside of the early alert systems used in student retention.Analytics, particularly those with predictive abilities, can contribute to anincreased understanding and improvement in the retention of theeducation workforce.Higher education institutions have the potential to proactively impact thehealth and well-being of employees and foster a culture of wellness. Forexample, risk stratification using objective and subjective data can beutilized to assign risk levels to employees. In addition, risk visualizedthrough real-time dashboards allows management to be alerted to atrisk employees so that management can proactively mitigate burnoutand other wellness concerns via early intervention.This article provides an overview of established metrics, exploresanalytical system design, and outlines practices related to creation andimplementation of a dashboard model that can provide at-a-glance viewsand risk stratification of key performance indicators (KPIs) (e.g.,compensation data, workload, wellness, etc.) relevant to the predictionof employees’ intent to leave and overall wellness. This proactiveapproach will allow employees and management to collaborate to enableearly recognition and engagement, improve educator retention, andminimize intent to leave.
