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.
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