Employee Insight: Predicting, Visualizing, and Mitigating Intent to Leave

Beyond informing human resources (HR) policies and practices, information gleaned from predictive analytics, visualized via dashboards, can increase awareness and prompt employee and management actions based on identified variables often related to intent to leave and employee wellness. While considerable research, relevant measurements, and tools are available within the field of human resource management that focus on measuring retention and mitigating turnover, there is minimal application of such tools in higher education. Analytics, particularly those with predictive abilities, can contribute to an increased understanding and improvement in the retention of the education workforce. Higher education institutions have the potential to proactively impact the health and well-being of employees and foster a culture of wellness. For example, risk stratification using objective and subjective data can be utilized to assign risk levels to employees. In addition, risk visualized through real-time dashboards alerts management to implement early interventions that mitigate burnout and other wellness concerns for at-risk employees. This article provides an overview of established metrics, explores analytical system design, and outlines practices related to the creation and implementation of a dashboard model that can provide at-a-glance views and risk stratification of key performance and predictive indicators (KPIs) (e.g., compensation data, workload, wellness, etc.) of employees’ intent to leave and overall wellness. This proactive approach will allow employees and management to collaborate to enable early recognition and engagement, improve educator retention, and minimize intent to leave.