This algorithm can predict when workers are about to quit--here's how

In a recent article for Harvard Business Review, Professors Brooks Holtom of Georgetown University and David Allen of Texas Christian University describe the results of their latest research. Using big data and machine-learning algorithms, the two developed a real-time indicator to measure two main indicators that an employee is about to quit. The first was "turnover shocks," which are events that prompt workers to consider leaving an organization. This could be a change in leadership or major acquisition, for example, and was measured with events including news articles about a company, changes in stock value and legal action taken against the firm. Researchers also measured "job embeddedness," or how deeply connected a worker felt to their organization, based on publicly available data like number of past jobs, employment anniversary and tenure, skills, education, gender and geography.