The Power of Predictive Analytics in Hiring

In an ever-increasing corporate landscape, making quality hires is critically important for organizations looking to improve their bottom line. The U.S. Department of Labor estimates that the cost of a bad hire is in excess of 30% of that employee’s first-year earnings.

To reduce the occurrence of bad hires, a growing number of businesses are turning to predictive analytics and big data. Using algorithms to analyze past and current data, these businesses more effectively can predict and adapt to future trends. From the sports world to big-box retailers, predictive analytics in hiring is shifting the paradigm of hiring decisions away from resumes and traditional metrics and towards data-driven analysis and advanced simulations.

The Moneyball Phenomenon

The 2001 Oakland Athletics baseball team enjoyed one of the most successful regular seasons in franchise history. Led by Tim Hudson, Mark Mulder, and Barry Zito, a trio of excellent starting pitchers, the team won 102 games. Their 63-18 record during the 2nd half of the season following the All-Star break today remains the best in MLB history.

Despite the momentum going into the post-season, the A’s faltered in a five-game series against the New York Yankees in the American League Divisional Series. The stage was then set for a tumultuous off-season.

The Oakland franchise and ownership lacked the deep pockets of most of their Major League competitors. As such, the team lost three of their best players to other teams in free agency prior to the 2002 season. 2000 AL MVP Jason Giambi signed with the Yankees, star outfielder Johnny Damon went to the Boston Red Sox, and closer Jason Isringhausen signed with the St. Louis Cardinals.

Facing a daunting task to rebuild his roster on a very limited budget, Oakland general manager Billy Beane used a novel approach to evaluating talent for the upcoming season. Rather than relying on a traditional scouting report, Beane adopted an approach far more focused on sabermetrics and specific statistical categories (on-base percentage in particular) that were largely ignored by other teams.

Despite significant scepticism throughout the baseball world, the 2002 Oakland A’s dramatically exceeded expectations, winning 103 games including a 20-game winning streak late in the season. The data-driven approach to talent evaluation revealed a path for small-market teams to be competitive with teams from larger markets with much deeper pockets. The story inspired a 2003 novel by Michael Lewis, titled Moneyball: The Art of Winning an Unfair Game, and in 2011 was the basis for a blockbuster movie starring Brad Pitt.

Rebuilding a Big Box Retailer by Algorithm

Founded in 1886, Sears is America’s fifth-largest department store company. Facing massive losses, in excess of $1.4 billion in 2013 alone, the company remains in the midst of a massive corporate overhaul.  One component of this overhaul is a company-wide adoption of predictive hiring analytics. With as many as six million applicants per year for anywhere between 140,000 to 160,000 positions, Sears prioritized the incorporation of advanced analytics into their hiring process. An article by Korn Ferry describes the new system ultimately devised by the retailer:

Under the new system, prospective employees fill out an online application, which includes a retail tech simulation test. In this video game-like simulation, an applicant encounters a real, interactive sales scenario and must sell to a variety of customer types, from the angry, impatient customer to the maddeningly indecisive shopper.

The simulations provided hiring managers at Sears with a new wealth of data from which to make hiring decisions. Using predictive analytics to identify the best candidates based on this data, Sears quickly saw compelling results from the process changes, including a reduction in hiring time from a 3-month process to 35 days, and an uptick in service quality and customer satisfaction during the holiday season.

Beyond the retail floor, Sears also devised a leadership model, looking to enhance their talent management at the executive level. Based on an evaluation of 14 to 17 core competencies, the company uses the data to better predict a candidate’s likelihood of achieving success.

Other Benefits of Predictive Analytics in Hiring

There are a broad range of applications of predictive analytics for hiring and staffing. In addition to helping identify the best talent, analytics can be used for talent pipeline planning. By leveraging macroeconomic data, organizations can better allocate resources. For example, a business might use such insights to identify the best geographical locations to invest into recruitment campaigns looking to attract candidates with a specific skill.

Additionally, data-driven recruiting and hiring help overcome bias, one of the biggest flaws in the human element of hiring. Though the vast majority of businesses and recruiters have no intention of exhibiting bias in their decision-making processes, many forms of bias can sub-consciously affect the hiring process nonetheless. According to Laura Russell, CEO of IMI Data Search, forms of recruiting bias that specifically apply to hiring include intuition, confirmation bias, effective heuristic, generalization, and personal similarity. Rooting out and resolving recruiting bias in your hiring process is mostly about the willingness to admit mistakes so that you can implement the correct checks and balances to correct them.

The impact of hiring decisions is extremely significant. Bad hires can result in legal risks, are very costly, and can damage overall morale. By contrast, good hires enhance productivity and are a cornerstone or an organizational growth strategy. Leveraging the power of predictive analytics empowers today’s leading businesses to hire with greater confidence and achieve better results.