IT giant uses algorithm to analyse data from departing workers in attempt to identify at-risk employee groups
Google has worked hard to keep employees happy. Keen to maintain a “small company feel” despite a workforce of more than 20,000 in offices around the globe, its headquarters in Mountainview, Calif., for example, feature a variety of cafés, bikes for travel between meetings, massage chairs and volleyball courts.
Employees are also encouraged to share ideas, express themselves openly and devote days to innovative thought. And the company’s efforts have been recognized — Google topped Fortune Magazine’s 100 Best Companies to Work For in 2007 and 2008.
But the little company that grew is facing some retention challenges as several higher-ups have reportedly departed in the past year. In response, Google has intensified its efforts in the area of predictive attrition to “find situations that may increase the likelihood of some Googlers leaving the company so that managers and HR staff can work on avoiding those very situations,” said Wendy Rozeluk of global communications and public affairs at Google Canada in Toronto.
Google is not providing specific details about its analysis, but the algorithm looks at data from employees who have left the company, studying factors such as where people work, team size and compensation, she said. The tool analyses less obvious factors that may contribute to the decision to leave the company but identifies groups versus specific people at risk of leaving. For example, are tenured, high-performing engineers in North America more likely to stay or leave than junior ones in Europe?
“As anyone who has observed Google over the years knows, we’re serious about keeping our employees happy,” said Rozeluk. “What we were looking for was general trends that might indicate an increased likelihood that someone might leave.”
For years people have taken this quantitative approach, giving tests and correlating the effects with people’s success on the job, said Ed Lawler, director of the Center for Effective Organizations at the University of Southern California in Los Angeles. With the greater focus on human capital, HR needs to monetize all its decisions with returns in mind.
“The biggest change right now is companies are beginning to take all that more seriously because labour costs have become such a huge portion of the cost of doing business, particularly as you move away from a manufacturing economy to a service economy and knowledge economy,” he said. “It’s not surprising (Google is) doing a lot of that. Their costs are essentially people and knowledge management, (those are key) to their survival.”
With advances in information technology that can more easily record, log and analyse data on people, their skills and contributions, and the costs of programs, the costs to the company are more visible, said Lawler.
“Companies are beginning to worry about things like, ‘What is the cost of a bad hire?’ or ‘What does turnover cost us and can we identify our best employees and trend towards a more differentiated workforce?’ which means trying to see who are the critical, pivotal employees or positions,” he said. “You can interface survey data with financial data, performance data, customer reactions, et cetera, and, if you’re skillful at it, get quite good insights into what’s going on in the workplace, what could be improved and what’s a worthwhile investment in training, better selection processes, pay systems and so forth.”
Organizations don’t want turnover and if it’s particularly expensive — as it is for Google compared to a business like McDonald’s — it’s important to properly interpret and react to the data, said Lawler.
