Predictive analytics, hiring, and the bottom line
Thursday, January 7, 2016
With over 1.5 million applicants flocking to Foot Locker annually, the New York-based company might be justified in a relaxed attitude towards hiring.
But the footwear retailer knows success doesn’t come to those standing still.
Back in 2010, the company recognized that while it had no difficulty attracting candidates, it also knew things could improve. Like every retailer, it was aware that the quality of new hires impacted business directly. Not only did the right employees sell more, they were also more likely to stay with the company longer, reducing future hiring costs and increasing company expertise. On top of that, hiring – like the internal processes of any enterprise – was not as efficient as it could be. Process improvements could save store managers time and get the right people on board faster.
In facing these issues, Foot Locker was not alone.
Using predictive analytics
Finding good staff in an efficient, reproducible manner is something of a holy grail for all employers, but especially those working in sectors with high staff turnover such as retail and hospitality.
The solution lies in something made possible only recently by a combination of talent science and technology: Predictive analytics.
Predictive analytics adds data-driven clarity not only to the hiring process, but also the management of employees long after they join the company. The result: more engaged employees, staying longer in roles they fit better.
And that stability is something enterprises need as the economy continues to expand. GDP growth is likely to exceed 3% in 2015, up from 2.4% in 2014, which is paradoxically bad news for employers. In a buoyant economy, job-hopping increases. According to Andrew R. McIlvaine (HRE Online), first-year-of-service turnover rates increased from 22.6% in 2012 to 34.1% in 2013, and they continue to rise.
Checking for fit
With 3,400 locations around the globe, Foot Locker is not a small company, and like most enterprises it had established ways of doing things. Predictive analytics offered potentially great benefits, but the existing hiring process wasn’t broken. On the contrary, the company was doing well.
Determined nonetheless to see if predictive analytics could help, the retailer did what any of its customers would do: it tried the process to see if it would fit.
A two-phase pilot spread over 18 months involved 600 stores representative of Foot Locker’s general customer populations.
After six months, the pilot was already showing impact, with double-digit reductions in staff turnover and double digit increases in sales-per-hour. And the efficiency savings were there, too, with managers having to deal with far fewer applicants for each job opening.
These sorts of numbers speak for themselves and Foot Locker’s senior leadership opted to roll predictive analytics out across the chain.
Proven science maximizes data
What lay behind those impressive numbers?
Foot Locker was using a candidate assessment from Infor that took 20 to 30 minutes to administer. Drawing on a bank of 210 items, the assessment measures 39 behavioral, cognitive and cultural characteristics, including such facts of an individual’s Behavioral DNA® as ambition, attention to detail and self-reliance.
Those assessments are compared with the characteristics associated with success in any particular role. These ‘success profiles’ enable scientifically-based hiring decisions, allowing store managers to see how candidates measure up, based on their answers. They can fall into one of four categories: recommended, recommended with qualifications, recommended with reservations and not recommended.
Running over 14 million assessments per year for many years, at Infor we have honed the process of building accurate success profiles, forming effective assessments, and implementing the best procedures for smooth, error-free implementation.
Does it work? In a word: Yes.
The evidence was compelling enough to change the perspective of the senior management team, says Robert Perkins, Foot Locker’s vice president of talent management. “We quickly shifted from ‘Do we trust the science?’ to ‘How quickly can we accelerate rolling this out to as many of our stores as possible?’”
While we cannot give detailed results for Foot Locker, we are able to say that sales per hour and reductions in turnover have both seen positive double-digit change. With associates now staying at the company longer, there has been a substantial saving in hiring fees.
Better management, too
The impact of predictive analytics, however, goes well beyond the financial: It enables managers to do their jobs better.
For Foot Locker store managers this can mean reducing paperwork and simplifying the procedural flow of hiring. It can also mean helping managers hire better.
Shannon Morris, a 14-year Foot Locker employee, is district manager of Southeastern New York. She has interviewed over 300 candidates since the roll out of the new system. Her take: the questions suggested for managers enable a better, more focused interview – particularly for the less experienced.
“Not every manager has been around for 14 years,” Morris said. “So if we have a manager that’s only been around for one year, it was a lot more helpful for them to have these more educated questions to ask the associates that are in front of them.”
Benefit beyond recruitment
Foot Locker benefited from using predictive analytics for hiring. But the impact doesn’t stop there. Using predictive analytics right at the beginning of an employee’s journey in the company does more than get them into a role where they can be effective. It also provides a road map for their further success within the company, leading to greater productivity faster, and – importantly – longer term job satisfaction.
For more information on using predictive analytics in your organization, download Add data-driven clarity to hiring decisions.
Ph.D. Director, HCM Behavioral Science