Charles Duhigg’s book, The Power of Habit, describes tantalizing evidence on how much retail marketers and others can learn from data on customers’ purchasing habits.
In Retooling HR, I suggested marketing frameworks could apply to talent, including talent segmentation, to target employment features to pivotal employee groups, just as marketers use consumer segmentation to target product features to pivotal consumer groups.
More lessons are emerging from marketing, this time from research on habits. Duhigg’s Feb. 19 article in The New York Times Magazine describes a predictive analytics scientist at a major retailer who discovered that shoppers’ purchasing habits are remarkably hard to break. Big-box retailers have lots of customers who shop for large quantities of staple items like paper towels, but do not purchase electronics, groceries or specialty foods, even though they are cheaper than at other stores. This happens because habits become unconscious.
Neuroscience research at MIT and other universities suggests the brain shuts down once the habit is formed to preserve conscious brain space. If you already know where to shop for electronics, why reconsider it?
It’s the same with habits like overeating, with complicated patterns of cues and rewards that may have little to do with hunger. Duhigg describes his habit of visiting the company cafeteria to buy a cookie at 3:30 p.m. each day. Upon analysis, it was a combination of mid-afternoon boredom, getting away from his desk and gossiping. The cookie was incidental to the actual reward, but it was no less a culprit in weight gain.
For talent managers, creating learning and change is as much about changing habits as it is about imparting skills. Like retailers trying to lure customers with low prices, traditional efforts to create organizational learning may be thwarted if employees are not aware of the habits they must first unlearn.
Retail analytics show that there are certain life moments when people open up their habits and are ready to change. The birth of a child is such a moment, but not if you wait until after the baby is born. The second trimester is a key moment when purchasing habits change. Retailers found existing customer data that could reveal with great accuracy when a woman was entering her second trimester, and they could target baby-related advertisements and coupons to her family.
We already see hints of this in human resource management. For example, Google developed a formula that predicts the probability that each employee will leave, allowing the company to “get inside people’s heads even before they know they might leave,” said Laszlo Bock, Google’s head of HR, in Retooling HR.
As in retailing, predictive human capital analytics might reveal when employees are ready for learning opportunities, likely to benefit from a stretch assignment or ready to contribute to a new project.
Of course, this insight comes with equally powerful dilemmas. Duhigg tells of the outraged father of a high school-aged daughter who complained to a store manager that she was receiving coupons for baby-related products. When the father later talked with his daughter, she revealed she was indeed pregnant.
For talent management leaders, what are the appropriate boundaries on insights, even those based on freely-available information? How might employees react to their company using data to predict if they are in danger of burnout or about to leave?
The employee-employer relationship is different from the customer-retailer relationship, but the depth and detail of employment data is increasing, and with it both opportunities and dilemmas like those faced by marketers today. Talent management and HR leaders might do well to get into the “habit” of considering how predictive analytics implications that face marketers today may be a harbinger of the future of employment.
John Boudreau is professor and research director at the University of Southern California’s Marshall School of Business and Center for Effective Organizations, and author of Retooling HR: Using Proven Business Tools to Make Better Decisions about Talent. He can be reached at email@example.com.