Our latest research has focused on using big data and machine-learning algorithms to develop a turnover propensity index for individuals – a real-time indicator of who is likely thinking about quitting. Based on our assessment of these turnover factors, we used machine learning to classify each individual as unlikely, less likely, more likely, or most likely to be receptive to new job opportunities. We sent e-mail invitations to a smaller sample of 2,000 employed individuals who had been identified by our algorithm as unlikely, less likely, more likely, or highly likely to be receptive to an invitation to view available jobs tailored to their specific skills and interests. Those who were rated as “Most likely” to be receptive opened the e-mail invitation at more than twice the rate of those rated as least likely. Second, to look at the ability of the TPI score to predict actual turnover, we used the remainder of the sample of 500,000 individuals. Over a three-month time period, those identified as “Most likely” to be receptive to new opportunities were 63% more likely to change jobs, as compared to those who were “Unlikely” to be receptive. Those identified as “More likely” were 40% more likely to quit.
Read more on bit.ly/2HvpaDn.