Big data and hiring
How software helps firms hire workers more efficiently
THE problem with human-resource managers is that they are human. They have biases; they make mistakes. But with better tools, they can make better hiring decisions, say advocates of “big data”. Software that crunches piles of information can spot things that may not be apparent to the naked eye. In the case of hiring American workers who toil by the hour, number-crunching has uncovered some surprising correlations.
For instance, people who fill out online job applications using browsers that did not come with the computer (such as Microsoft’s Internet Explorer on a Windows PC) but had to be deliberately installed (like Firefox or Google’s Chrome) perform better and change jobs less often.
It could just be coincidence, but some analysts think that people who bother to install a new browser may be the sort who take the time to reach informed decisions. Such people should be better employees. Evolv, a company that monitors recruitment and workplace data, pored over nearly 3m data points from more than 30,000 employees to find this nugget.
Some 60% of American workers earn hourly wages. Of these, about half change jobs each year. So firms that employ lots of unskilled workers, such as supermarkets and fast-food chains, have to vet heaps—sometimes millions—of applications every year. Making the process more efficient could yield big payoffs.
Evolv mines mountains of data. If a client operates call centres, for example, Evolv keeps daily tabs on such things as how long each employee takes to answer a customer’s query. It then relates actual performance to traits that were visible during recruitment.
Some insights are counter-intuitive. For instance, firms routinely cull job candidates with a criminal record. Yet the data suggest that for certain jobs there is no correlation with work performance. Indeed, for customer-support calls, people with a criminal background actually perform a bit better. Likewise, many HR departments automatically eliminate candidates who have hopped from job to job. But a recent analysis of 100,000 call-centre workers showed that those who had job-hopped in the past were no more likely to quit quickly than those who had not.
Working with Xerox, a maker of printers, Evolv found that one of the best predictors that a customer-service employee will stick with a job is that he lives nearby and can get to work easily. These and other findings helped Xerox cut attrition by a fifth in a pilot programme that has since been extended. It also found that workers who had joined one or two social networks tended to stay in a job for longer. Those who belonged to four or more social networks did not.
There is no point asking jobseekers if they are honest. But surveys can measure honesty indirectly, by asking questions like “How good at computers are you?” and later: “What does control-V do on a word-processing programme?” A study of 20,000 workers showed that more honest people tend to perform better and stay at the job longer. For some reason, however, they make less effective salespeople.
Algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs. But they must be designed and used by humans, so they can go horribly wrong. Peter Cappelli of the University of Pennsylvania’s Wharton School of Business recalls a case where the software rejected every one of many good applicants for a job because the firm in question had specified that they must have held a particular job title—one that existed at no other company.