The impetus for the company’s data-based approach to the generally subjective domain of people decisions came from Laszlo Bock, Google’s SVP of People Operations. Bock, who joined the company in 2006 after stints with GE and McKinsey, found a like-minded ally when he hired Prasad Setty to run the company’s analytics department. At first glance, Setty is an unexpected HR leader with his chemical engineering degree and quick admission that he found the softer courses in his MBA curriculum frustrating because people decisions tend to invite temperamental, emotional and subjective choices. Bock challenged Setty to bring the same rigor to people decisions that Google applied to its engineering analysis.
Recalling how Google once tested forty-two different color shades for the Google toolbar to determine which hue optimized click-through rates, Bock put a stake in the ground that has since differentiated Google’s approach. As Setty recalled during our interview, Bock said plainly, “We need to be able to measure, to find out what does and doesn’t work at Google rather than just adopt best practices.”
Over the past six years, Setty has built his People Analytics department into the company’s mainframe. While Google won’t disclose its exact size, estimates are that Setty has dozens of employees including PhDs, hardcore technologists and ex-consultants. As Setty told us, “the combined power of the group is having the business people make sure we’re solving the right problems, the stats folks ensure there’ s rigor in how we do it and the technologists who make the solutions scalable and transparent.”
Tackling Bock’s challenge, the team’s mission statement is for “all people decisions at Google to be based on data and analytics.” While many companies seek to make data-based talent decisions, Google deploys the kind of rigorous, scientific testing and statistical analysis that is more common in university labs. Running real experiments, according to Setty, helps describe “a small but significant percentage of the variance in human behavior.”
But Setty and his team learned early on while working on a project for Google’s engineering department that leaders didn’t want algorithms to replace human judgment. Rather than entrust important talent decisions to black-box calculations, Google’s leaders asked Setty to focus on providing insights that could help decision-makers improve the odds of getting complex decisions right. As Setty explained, the models that result from his department’s experiments explain normal or “average contexts” that won’t apply universally. The goal of People Analytics is to “complement human decision makers, not replace them.”
What Google Learned
Attracting, retaining and developing talent at Google is serious business, as the analytic team has demonstrated that exceptional technologists can have a performance differential of up to three hundred times an average employee. Over the last half-dozen years, Setty’s team has produced significant insights that have:
- helped limit the number of interviews required (company analysis showed that more than four interviewers didn’t lead to higher quality hiring),
- revealed the optimal organizational size and shape of various departments,
- shown how to better manage maternity leave resulting in a fifty percent reduction in defections,
- created on-boarding agenda for an employee’s first four days of work that boosted productivity by up to 15 percent
- and produced an algorithm to review rejected applications (Google gets over two million applications every year) that has helped the company hire some talented engineers its screening process would have otherwise missed.