Machines Make Better Hiring Decisions than Humans
Predictive analytics – the process of using advanced statistical techniques from modeling and machine learning to analyze past events and behavior in an effort to predict the future – is one of the disruptive business innovations today. Myriad companies make liberal use of predictive analytics to make decisions about what products to buy and sell, where to make investments, which components are likely to fail – and when – in complex machines and, recently which applicants to hire.
Bloomberg Business recently published result of a comprehensive study by the National Bureau of Economic Research (NBER). It surveyed over 300,000 hires across 15 companies and discovered that predictive algorithms made better hiring decisions than human hiring managers. In this study of low-skill, service sector jobs – like contact centers – it found that predictive algorithms reliably identified applicants who were more likely to be retained longer.
As the leading provider of predictive talent analytics solutions, particularly for customer service roles, HireIQ’s real-world experience mirrors these findings. Our findings reflect those in the NBER study – “green” (high potential) candidates perform better than “yellows” (moderate potential), who perform better than “reds” (low potential). Clients report significant improvements in key service and performance metrics such as a 60% improvement in 90-day retention rate; a 56% increase in first call resolution; and a 28% improvement in customer satisfaction.
Does this mean companies can rely exclusively on these algorithms and completely dispense with the human element? Of course not. But it does beg a modest change in recruiting’s overall approach to evaluating candidate potential. Using predictive analytics to prioritize applicants for further review improves the efficiency and effectiveness by focusing first on the green candidates and leaving the reds for later.
The overall value of a company’s recruiting team is significantly increased when quality-of-hire is improved. Predictive selection algorithms are a valuable addition to the hiring manager’s toolbox.