Other analytics-based resources and tools are being developed to assist in the hiring process. Matchpoint Careers, for example, provides psychometric assessments of job candidates’ reasoning aptitude, behaviors, emotional intelligence and other factors to identify, at the application stage, those who have the potential to be top performers in a particular job. By comparing the assessment results against a database of profiles and answers from millions of candidates, HR can have greater confidence about the potential of new hires.
Another company, Silicon Valley startup Knack, uses games to help identify recruits with a higher probability of becoming successful employees. Recruiters use the games to look at a variety of capabilities and traits, from cognitive ability to creativity to learning to decision making. One of them, “Wasabi Waiter,” asks the player to assume the role of a waiter at a sushi restaurant. Players take orders and then analyze customers’ facial expressions—such as “happy,” “angry” and “sad”—to decide how to proceed. How applicants play the game indicates how well they read social and emotional signals. Performance is benchmarked against the top performers among a company’s current employees to help identify the most promising recruits.
Beyond these kinds of discrete offerings is a bigger trend, known as Big Data, which is about mobilizing and managing large volumes of data that are internal and external to the enterprise. Big Data analytics enables companies to run analyses of datasets as large as many exabytes and see patterns and causality well beyond what the human brain can process on its own. The HR department can begin with a hypothesis and see if it is supported by the data. But HR can also just say, “Let’s see where the numbers are taking us and what story is being told,” toward the end of extracting hidden, differentiated value. Big Data analytics is key to delivering on the full promise of talent analytics: predict workforce and marketplace developments and take action in advance.
With these tools and technology capabilities, companies are discovering important trends about their workforce. For example, a large technology company performed an extensive analysis on a representative sample of its workforce to study the multiple factors that went into whether an employee stayed or left. The analysis was complex, looking at the interrelationships among approximately 200 different HR and finance factors and more than 100,000 individuals over two years.
The results, not surprisingly, were also complex. In one part of the company, certain factors (such as opportunities for advancement, training offerings and the quality of supervision) made a difference between an employee staying or leaving. But in another part of the company, different factors were at play. Analytics can help parse these complexities into meaningful information for decision making.
An Asian life insurance company used talent analytics to make better decisions about employee recruiting and retention. The company began working with an HR outsourcing provider, primarily to improve the efficiency of its recruiting and onboarding processes. As the relationship progressed, however, the company asked the provider to help with a more serious business problem: retaining people in one of its most critical workforces—managers at its affiliated agency locations. The company was experiencing 100 percent turnover among these employees, with many leaving after only six months—a situation that was impeding overall corporate growth.
Executives had several hypotheses. Perhaps it was about compensation, or maybe it was an issue with the quality of life at the locations. The provider was able to use predictive analytics to determine the actual predictors of success in terms of the performance and retention of these workers. Among the insights: Educational requirements for incoming managers had set false expectations about the kind of work actually involved. The company had been requiring that all new managers have MBAs. In fact, the analysis showed that these recruits were often among the company’s poorest performers. This data enabled the company to alter the job profile, which also opened up the position to a broader group of candidates.
An important lesson of the company’s experience is that the results of an initial talent analytics program need to be revisited over time to continue to validate the findings. One point of comparison had been between managers working at branches within their home state and those working in a state other than where they had grown up. An initial analysis seemed to indicate that managers who had moved around frequently and had previously been forced to establish new networks were performing better. A year later, a more complete analysis of the parameters found that the reverse was actually true: Managers working within their state were outperforming the outsiders.
The results of this talent analytics program were impressive. Six months after the company changed its hiring profile, new-hire performance shot up by more than 100 percent and new-hire attrition went down by 50 percent.