The 2014 Retail Equation: Big Data + Talent Analytics = Retention + Performance
Retailers have long valued the impact a strong sales staff can make on customers’ buying decisions. In today's internet age, when consumers can easily shop thousands of online sites instead of physically coming into your stores, providing a positive shopping experience is more vital to company revenues than ever before.
What are the ingredients in a standout retail employee who delivers a great customer experience? First, he/she has been on the job long enough to be knowledgeable about the products. Second, he/she has the mental and emotional assets to sell the products. Let's call the first trait job retention, and the second trait job performance. A retailer needs employees with both traits to maximize store productivity and keep hiring expenses under control.
To gain a competitive advantage through your workforce, you need a strategy to better align your people and processes, and identify the future employees that will add to your greatest asset — your talent pool.
Talent science, which uses predictive analytics to combine a candidate's behavioral traits and historical performance metrics supported by big data, can help retailers to define success connected to specific key performance indicators (KPIs). To start, a single online assessment is used to identify key attributes for success within the incumbent population, and that ideal set of characteristics is used to rank potential job candidates. Store managers can then focus interview and hiring efforts on the top-ranked candidates who are most likely to match or exceed the success levels of existing staff.
The retail industry has always sought to slow the spinning revolving door; trained employees exit and untrained new hires enter. Big retailers were some of the earliest adopters of hiring technology that looked beyond the résumé and into the core behaviors that drive work habits. One such store is Michaels, which brought talent science into the hiring process in 2003. Considering that the average Michaels store contains 40,000-plus different products in a mammoth retail space, the task of keeping over 1,000 locations fully staffed with quality employees is a top priority.
By 2008, Michaels had expanded its use of talent science to evaluate tens of thousands of candidates every month for hourly positions, a task streamlined by technology that quickly and accurately identifies those most likely to be long-term, productive associates in the Michaels store environment.
In a post-deployment study of 7,861 Michaels associates over a 21-month period, hourly associates hired using talent science posted a turnover rate 41.9 percent lower than hires made without the hiring method in place.
Similar results were achieved at Brown Shoe, the operator of over 1,100 Famous Footwear locations. A talent science user since 2006, Brown Shoe has experienced years of turnover reductions among employees. In year one of the rollout, turnover dropped 50 percent over previous years (2004, 2005). In a later multiyear study, employees recommended by talent science continued to have a sizable advantage over their peers, posting a 33.62 percent lower turnover rate at the 30-day interval.
Why does talent science work? These results stem from a combination of finding the right person who "fits" the job and the natural productivity of satisfied employees who perform better than unengaged mishires who weren't compatible with the job.
Whatever results you wish to drive — from reducing turnover to increasing sales to improving customer service — talent science can help. With it you can select the KPIs that hiring managers should focus on to help your workforce stay with the company longer and be more productive.
Dr. Jason Taylor is the chief science officer of Infor PeopleAnswers, a provider of talent science software for employee assessment and job applicant selection.