This algorithm can predict when workers are 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.

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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.

When put to the test, the algorithm identified that those marked as “most likely” to be receptive to a new opportunity were, in fact, 63% more likely to be in a new job by the end of the three-month study period.

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