Using Machine Learning to Predict Employee Turnover

Organizations continue to face ongoing employee retention and recruiting challenges, which have become even more acute due to the COVID-19 pandemic. In today’s unstable economy, employee retention is still one of the hot button issues facing many HR managers. Employee turnover has cost organizations billions of dollars each year. To address these ongoing challenges the HR community is turning to machine learning, which is the science of discovering and communicating meaningful patterns in data and supporting the development of actionable plans. A HR database consisting of 1,470 employee records was examined using both decision tress and neural nets, which are two of the more popular machine learning models. The analytical results, which were based on employee demographic, preference, and performance data, suggests that machine learning-based predictive models can provide automatic and timely employee assessments, which allow for both the identification of employees that may be planning to leave and the implementation of appropriate amelioration initiatives. Specifically, the positive predictive value for both models was on the order of 80 percent. Job engagement, work satisfaction, experience, and compensation are but four of the factors found to be closely aligned with an employee’s decision to leave. The primary purpose of this presentation is to highlight how machine learning can reduce employee turnover through early detection and intervention. IBM artificial intelligence can predict with 95% accuracy which workers are about to quit their jobs - Eric Rosenbaum. Keywords: Machine learning, human resource management, employee turnover, actionable knowledge discovery, intervention strategies