Here is an excerpt from an article written by Sarah Fister Gale for Talent Management magazine. She explains why adoption of predictive analytics in talent management has been slow, but a few companies have shown how it can positively influence business. To read the complete article, check out all the resources, and sign up for a free subscription to the TM and/or Chief Learning Officer magazines published by MedfiaTec, please click here.

* * *

For many talent managers, 2014 was going to be the year of predictive analytics.

The year was rife with predictive analytics conferences and research reports. Some corporate partnerships — including one between professional services firm KPMG and technology firm McLaren Group — foretold of a business environment in which predictive analytics would radically transform the insight available at talent leaders’ disposal.

And to some degree, it has. Still, as the industry forges ahead in 2015, evidence suggests the prognostications on predictive analytics in human resources may have been too optimistic.

“If predictive analytics were a baseball game, we’d be in the second inning,” said Brian Kelly, president at Vestrics, a predictive analytics firm in Durham, North Carolina. “Everyone’s appetite is quite large, but they are just learning how to do it.”

Despite the slow progress, some companies have shown great strides in using predictive analytics in talent management. These firms, profiled here, have used analytics to identify competency gaps, pinpoint reasons for sales slumps and improved hiring decisions.

What makes these companies successful, said Bill Schmarzo, chief technology officer of EMC Corp.’s global services for big data practice, is that they don’t begin with talent management — they end up there.

“Most predictive analytics efforts start by focusing on strategic goals to do things like create new revenue or monetize insights, vs. building organizational capabilities,” Schmarzo said. “They have to tie talent management back to that strategic goal.”

Sears Gets In the Mood

This is how Ian O’Keefe, head of talent analytics at Sears Holding Co., said he approaches the job. O’Keefe leads a team of analysts at the Hoffman Estates, Illinois-based retailer that has developed a complex analytical process for using people data, sales data, customer data and operations data to predict business performance.

“Sears is a deeply analytical company,” O’Keefe said. Predictive analytics “enables us to understand how things like collaboration, engagement and leadership influence outcomes that our business leaders care about.”

A recent example of this involved tracking retail staff mood and its influence on sales and customer experience. O’Keefe’s team installed mood-tracking systems on all of the company’s time clocks at its stores. When employees punched in and out, a digital screen asks them to rank their mood on a five-point scale, ranging from “unstoppable” (one) to “frustrated” (five) or “not in the mood to answer” (six).

“We collected 80,000 moods per day over 15 months,” O’Keefe said.

Using the data, the data team built a model comparing overall mood ratings to sales numbers, including sales per hour and per store.

Among the more interesting findings: When people come to the store to buy a specific item, like a television, the mood of their salesperson won’t directly affect the sales outcome. But when O’Keefe’s team narrowed the analysis of the data to look at what else customers purchased, a trend emerged. When moods are positive, customers are more likely to purchase peripheral products, like surge protectors, extra cables and protective warranties.

“There is a lot of margin in those items, which leads to a more profitable basket,” O’Keefe said.

Because O’Keefe and the Sears data team can now predict the mood of a particular store on a particular day — busy traffic days tend to have lower moods than slower days — they are now exploring strategies to influence mood on high-traffic days through manager engagement.

For example, the team is piloting an automated email program that sends store managers videos and short training programs on how to motivate people. The email will also send reminders at key times on busy days to walk the floor and encourage team members. This is especially the case near the end of a shift, when moods tend to fall.

“It’s a great example of how we are using analytics to understand the things that incrementally influence sales outcomes in the store,” O’Keefe said.

* * *

Here is a direct link to the complete article

Sarah Fister Gale is a freelance journalist with more than 20 years experience writing about a wide range of topics, including parenting issues, workforce and talent management strategies, sustainability, clean energy, corporate learning, the pharmaceutical industry, food safety, and more. To read more of her articles, please click here.