In a randomized, controlled experiment at Gap, researchers Joan C. Williams, Saravanan Kesavan, and Lisa McCorkell sought out the effects of more versus less predictable schedules on the productivity of retail employees and the profitability of stores. “The results,” they write at the Harvard Business Review, “were striking”:
Sales in stores with more stable scheduling increased by 7%, an impressive number in an industry in which companies work hard to achieve increases of 1–2%. Labor productivity increased by 5%, in an industry where productivity grew by only 2.5% per year between 1987 and 2014. Our estimate is that Gap earned $2.9 million as a result of more-stable scheduling during the 35 weeks the experiment was in the field. Given that out-of-pocket expenses were small ($31,200), our data suggest that return on investment was very high. (If stable scheduling were adopted enterprise-wide, transition costs might well entail the costs of upgrading or replacing existing software systems.)
Unlike the typical way of driving sales through increase in traffic, the sales increase from our intervention occurred due to higher conversion rates and basket values made possible through better service from associates.
These findings, the authors underscore, contribute to a growing body of empirical evidence that lean staffing practices, with most employees on part-time, unstable, and on-call schedules, are not the money-savers they are often believed to be. It is indeed feasible for retailers to offer their employees more stable and predictable schedules, they add, but employers often overstate the benefits of an on-call system (reduced labor costs) while ignoring its drawbacks (such as poorer customer service and more management time devoted to scheduling).
This research comes at a time when schedule predictability has emerged as a focal point of labor activism and attracted the attention of regulators. San Francisco became the first major city to mandate predictable scheduling with its “retail workers’ bill of rights” in 2014, while Seattle passed a mandate in 2016 and New York City introduced a fair scheduling law for retail and fast food employees last year. Oregon became the first state to enact such a regulation statewide last summer and other states are mulling laws of their own.
Some major retailers have begun to rethink on-call scheduling in light of this mounting scrutiny and negative press. Under pressure from several state attorneys general investigating the use of the practice, six companies agreed to abandon it in 2016, including Abercrombie & Fitch, Gap, J.Crew, Urban Outfitters, Pier 1 Imports, and L Brands, the parent company of Victoria’s Secret and Bath & Body Works. (Gap eliminated on-call scheduling and required schedules to be posted two weeks in advance after an eight-month pretest of the study above in three stores; the full experiment included even more secure scheduling practices.)
These changes are motivated in part by regulatory concerns and HR-as-PR, but also reflect the fact that the labor market is historically tight and retailers have to compete for talent to an almost unprecedented extent. One of the ways a retailer can differentiate itself for candidates is to offer more predictable scheduling, and we’re seeing seasonal employees this year using their increased leverage to negotiate for more control over their schedules.
In many cases, technological solutions are enabling retailers to bridge the gap between employees’ desire for predictability and their need for flexibility in responding to customer traffic patterns. The Gap experiment included the use of a mobile app to let employees swap shifts on their own, without going through their manager. Walmart also revamped its scheduling software in 2016 to prioritize scheduling for peak shopping hours by taking into account foot traffic and sales data from every department in each store, enabling some store employees to have fixed schedules for as much as six months. Startups like Legion are also developing data-driven platforms to make scheduling more predictive (and therefore more predictable).