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.
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?”
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’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.
“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. and Harte-Hanks Inc. 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.
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.”
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?”
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.
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.”