Bosses, as it turns out, really do matter – perhaps far more than even they realize.
In telephone call centers, for example, where hourly workers handle a steady stream of calls under demanding conditions, the communication skills and personal warmth of an employee’s supervisor are often crucial in determining the employee’s tenure and performance. In fact, recent research shows that the quality of the supervisor may be more important than the experience and individual attributes of the workers themselves.
New research calls into question other beliefs. Employers often avoid hiring candidates with a history of job-hopping or those who have been unemployed for a while. The past is prologue, companies assume. There’s one problem, though: The data show that it isn’t so. An applicant’s work history is not a good predictor of future results.
These are some of the startling findings of an emerging field called workforce science. It adds a large dose of data analysis, aka Big Data, to the field of human resource management, which has traditionally relied heavily on gut feel and established practice to guide hiring, promotion and career planning.
Workforce science, in short, is what happens when Big Data meets HR.
The new discipline has its champions. “This is absolutely the way forward,” says Peter Cappelli, director of the Center for Human Resources at the Wharton School of the University of Pennsylvania. “Most companies have been flying completely blind.”
Today, every email, instant message, phone call, line of written code and mouse-click leaves a digital signal. These patterns can now be inexpensively collected and mined for insights into how people work and communicate, potentially opening doors to more efficiency and innovation within companies.
Digital technology also makes it possible to conduct and aggregate personality-based assessments, often using online quizzes or games, in far greater detail and numbers than ever before.
A valuable asset
Companies view workforce data mainly as a valuable asset. In December, for example, IBM completed its $1.3 billion acquisition of Kenexa, a recruiting, hiring and training company. Kenexa’s corps of more than 100 industrial organizational psychologists and researchers was one attraction, but so was its data: Kenexa surveys and assesses 40 million job applicants, workers and managers a year.
Big companies like IBM, Oracle and SAP are pursuing the business opportunity. So is eHarmony, the online matchmaking service. It announced in January that it would retool its algorithm for romance so it could examine employee-employer relationships and enter the talent search business later this year.
The penchant for digital measurement and monitoring seems most suited to hourly employment, where jobs often involve routine tasks, but will this technology also be useful in identifying and nurturing successful workers in less-regimented jobs? Many companies think so, and can point to some encouraging evidence.
Tim Geisert, chief marketing officer for IBM’s Kenexa unit, observed that an outgoing personality has traditionally been assumed to be the defining trait of successful sales people. Its research, however, based on millions of worker surveys and tests, as well as manager assessments, has found that the most important characteristic for sales success is a kind of emotional courage, a persistence to keep going even after initially being told no.
The team of behavioral and data scientists at Knack, a Silicon Valley startup firm, uses computer games and constant measurement to test emotional intelligence, cognitive skills, working memory and propensity for risk-taking. Early pilot testers include the NYU Langone Medical Center, Bain & Co. and a unit of Shell, says Guy Halfteck, Knack’s CEO.
Google, not surprisingly, is committed to applying data-driven decision-making to human resource management. For years, candidates were screened according to SAT scores and college grade-point averages, metrics favored by its founders. Numbers and grades alone did not prove to spell success at Google, though, and are no longer used as important hiring criteria, says Prasad Setty, vice president for people analytics.
Since 2007 the company has conducted extensive surveys of its workforce. Google has found that the most innovative workers – also the “happiest,” by its definition – are those who have a strong sense of mission about their work and who also feel that they have much personal autonomy.
“Our people decisions are no less important than our product decisions,” Setty says. “And we’re trying to apply the same rigor to the people side as to the engineering side.”
Evolv, a San Francisco startup, uses data science to advise companies on hiring and managing hourly workers. Evolv is sharing its data from clients – data that are stripped of personally identifying information and demographics like race and sex – with researchers at Wharton, Yale and Stanford.
Hiring as a science
Transcom, a global operator of customer-service call centers, conducted a pilot project in the second half of 2012, using Evolv’s data analysis technology. To look for a trait like honesty, candidates might be asked how comfortable they are working on a personal computer and whether they know simple keyboard shortcuts for a cut-and-paste task. If they answer yes, the applicants will later be asked to perform that task.
Those who score high on honesty typically stay in their jobs 20 to 30 percent longer than those who don’t, Evolv says.
Neil Rae, an executive vice president of Transcom, was impressed with the project’s results and plans to use Evolv in the call centers he runs, which employ 12,500 workers.
In the call-center world, Rae says, 5 percent attrition a month – 60 percent a year – is stellar performance. Dropout rates are calculated at 30-day intervals, and it takes four to six weeks to train a worker. The cost of attrition – for hiring and training a replacement – is about $1,500 a worker, he says.
In the project with Evolv, Rae says, Transcom was able to hire fewer people – about 800 instead of a more typical 1,000 hires – to get 500 workers who were still on the job at least three months later. The big payoff, he says, should come in cost savings and better customer service with less worker churn in call centers.
“This makes hiring more a science and less subjective,” Rae says.