Machines Gauging Your Star Potential Automate HR Hiring
By Aki Ito - Oct 10, 2013 9:01 PM PT
They can drive cars, win Jeopardy and find your
soon-to-be favorite song. Machines are also learning to decipher the most human
qualities about you -- and help businesses predict your potential to be their
next star employee.
A handful of technology companies from Knack.it Corp. to
Evolv Inc. are doing just that, developing video games and online
questionnaires that measure personality attributes in a job applicant. Based on
patterns of how a company’s best performers responded in these assessments, the
software estimates a candidate’s suitability to be everything from a warehouse
worker to an investment bank analyst.
Welcome to hiring in the age of big data, an ambition
marrying automation with analysis in the race to better allocate talent. Having
people work at what they do best would make them more productive, bolstering
the economy’s capacity to expand, according to Erik Brynjolfsson, a professor
at the Massachusetts Institute of Technology in Cambridge.
“People are our biggest resource, and right now a lot of
them are mismatched,” said Brynjolfsson, who specializes in research on
information technology and productivity and is an advisor to Knack. “If you put
the right kind of person in the right task, it’s good for that person and it’s
good for the company.”
The advent of the Internet has been both a gift and a
curse to recruiters, who now can access a greater pool of potential workers yet
also get inundated with too many applications to process. The problem has been
a lack of tools to quickly, cheaply and accurately sort through that deluge in
an economy that has seen almost five years of above-7 percent unemployment.
Jobs Unfilled
Some 3.7 million U.S. jobs went unfilled in July, even
though more than 11 million Americans were looking for work, according to Labor
Department figures.
“You have this enormous pool of people that’s being
missed because of the way the entire industry goes after the same kinds of
people, asking, did you go to Stanford, did you work at this company?” said
Erik Juhl, head of talent at Vungle Inc., a San Francisco-based video
advertising startup, and formerly a recruiter at Google Inc. and LinkedIn Corp.
“You miss what you’re looking for, which is -- what is this person going to
bring to the table?”
Online Game
To aid that search, Juhl this month will begin using an
online video game designed to track, record and analyze every millisecond of
its players’ behavior. Developed by Knack in Palo Alto, California, Wasabi
Waiter places job-seekers in the shoes of a sushi server who must identify the
mood of his cartoon customers and bring them the dish labeled with the matching
emotion. On a running clock, they must also clear empty dishes into the sink
while tending to new customers who take a seat at the bar.
Using about a megabyte of data per candidate, Knack’s
software measures a variety of attributes shown in academic studies to relate to
job performance, including conscientiousness and the capacity to recognize
others’ emotions. Knack’s clients will also see a score estimating each
applicant’s likelihood of being a high performer.
In a study last year, Knack piloted its technology with Royal
Dutch Shell Plc (RDSA)’s GameChanger, a program that invests in entrepreneurs
to develop their ideas into new products for the energy sector. Hans Haringa,
an executive at GameChanger, wanted to see if Knack’s video games could predict
who pitched the ideas that turned out to be successful.
Innovative Talents
“Knack built themselves a calibrated model with the
capacity to predict innovative talents,” said Haringa, who added that
GameChanger is considering adding Knack’s tool to select the right people in
whom to invest. “It’s early days for the technology but it clearly has upside
and potential.”
Home to a more widely-used human resources machine is
Evolv, which specializes at evaluating candidates for hourly positions at
companies including Xerox Corp. (XRX) and Harte-Hanks Inc. (HHS) The San
Francisco-based company administers an online questionnaire to applicants on
behalf of its clients. A computer model translates those results into a traffic
light for hiring managers so they can decide whom to interview: green for
high-potential, yellow for medium-potential and red for risky.
Evolv’s advantage is the oceans of information it has
tracked on the survey results and those candidates’ real-life outcomes if they
got hired: how well they performed on the job and how long they ended up
staying with the company. In the way that years of experience informs a veteran
recruiter, terabytes of data teach Evolv’s algorithms to see who has the
makings of a good hire.
Debunked Assumptions
The patterns gleaned since the company’s founding in 2007
have debunked many of the common assumptions held by recruiters, Evolv
executives say. For example, a history of job-hopping or long bouts of
unemployment has little relationship with how long the candidate will stay at
his or her next job, according to Evolv’s analysis of call center agents.
“As human beings, we’re actually pretty bad at evaluating
other human beings,” said David Ostberg, vice president of workforce science at
Evolv. “We’re making sure people are using the right data, instead of the
traditional methods that were previously thought to be valid but big data’s
showing are not.”
Cognitive Abilities
New York-based ConnectCubed has also developed software
to determine the personality and cognitive abilities of job applicants that, at
its largest clients, is tailored for that specific company. ConnectCubed has
existing workers at those businesses complete its video games and
questionnaires so the behavioral profiles of the star employees serve as a
benchmark for who managers should hire in the future.
“When new people apply, you can say, wow this guy has all
the makings of our top salesmen,” said Michael Tanenbaum, chief executive
officer and co-founder of the service. “These are things that are impossible to
measure from a resume, especially with educational backgrounds that are often
more determined by socioeconomic status than your innate ability.”
To be sure, Knack and ConnectCubed, which say they can
predict high-performers across a broad set of workers, haven’t been around for
long enough to track, over time, whether their technologies actually are
improving the quality of the employees their clients hire or those businesses’
bottom line.
“My concern is, with only a 9.5-minute sample of
behavior, is that really enough?” said Frederick Morgeson, a professor of
management specializing in personnel psychology at Michigan State University in
East Lansing, referring to Knacks’ video game assessment. “Are we sampling
enough of those behaviors to be confident that we’re capturing what the person
might do in the totality of their complex behavior?”
Amassed Evidence
Evolv on the other hand has amassed evidence of results
for its clients. San Antonio, Texas-based direct marketing company Harte-Hanks
found call center agents selected by Evolv’s software had a 35 percent lower
30-day attrition rate, reported 29 percent fewer hours of missed work in the
first six months and handled calls 15 percent more quickly than those hired
through the company’s existing recruiting services provider at the time.
Still that success may be harder to achieve among
higher-skilled professionals. There’s a reason Evolv has kept its focus on
evaluating hourly workers, Chief Executive Officer Max Simkoff said.
For “our largest telco customers, a single
percentage-point increase in any customer experience metric they track, they
can correlate to additional percentage points in subscriber base,” said
Simkoff, who is also co-founder of the company. “The performance data is not
there yet” for employees engaging in higher-level tasks, he said.
Guessing Game
Juhl at Vungle says the algorithms will never replace the
age-old interview altogether, no matter how accurate Knack’s predictions are.
The goal is to experiment with a variety of tools that can offer more
information about each candidate and make the recruiting process less of a
guessing game, he said.
“As we grow in scale, we’re trying to put in the
foundation now so we can measure it down the line,” he said. “What I would like
for it is to gain substantial weight in the process -- as valued as the opinion
of the most senior members of the team.”
To contact the reporter on this story: Aki Ito in San
Francisco at aito16@bloomberg.net
To contact the editor responsible for this story: Chris
Wellisz at cwellisz@bloomberg.net
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