Retailers have long known that high turnover among frontline employees is expensive, draining both time and money as managers constantly recruit and train new staff. Conventional wisdom holds that scheduling is a major driver of turnover. To reduce churn, retailers are urged to post schedules earlier; create consistent, predictable, and fair work schedules; and ban “clopenings”—schedules that call for an employee to work a closing shift and then an opening one the next day. Those steps can help, but a study we conducted of 280 million shifts worked by 1.3 million employees across 20 major U.S. retail chains found that reality is far more nuanced. Different aspects of scheduling affect each store differently, and only an analysis of data can determine which ones are the most important for a given site, and even to what degree scheduling is the culprit fueling turnover.

To date, most efforts to address turnover have been blunt, uniform, and not informed by data on the local workforce in question. With data-rich workforce systems, managers now have the tools to do much better. They can use analytics to design locally tailored schedules that boost both employee satisfaction and staffing efficiency. In this article we show how to identify, prioritize, and act on the scheduling levers that matter most in each operation. Although our data comes from retail, the same dynamics apply across frontline service industries where scheduling instability drives turnover.

The approach we are advocating doesn’t require collecting more data or building new infrastructure. It just entails having the necessary analytical capability. Nearly every retailer already has the raw data needed to understand turnover at specific sites: timestamps (electronic or physical records of when an employee started a shift, took a break, completed a task, or ended the workday), shift patterns, approvals, and absences. However, most companies use the systems that collect this data only for payroll or to prove they are complying with government laws and regulations. To our knowledge, no organization has fully embraced the data-driven customization our research prescribes. Consequently, the approach we outline in this article offers companies with high turnover among frontline workers a remedy that could quickly have a major positive impact on their businesses.

The High Cost of Turnover

Employee retention varied dramatically across the 20 retailers we studied. Annual rates ranged from just 30% to 73%, with an average of 52%, and median tenure stretched from five to 13 months. By comparison, in many white-collar jobs, annual retention rates typically exceed 80%, and even in logistics or manufacturing work, they often remain above 70%. Persistent churn in frontline retail creates a host of problems, such as chronic understaffing, training pipelines that never fill, and service inconsistencies that customers feel immediately.

The direct costs of high turnover include time and money spent recruiting, onboarding, and training. The indirect costs are more subtle but just as damaging: Sales are lost because there aren’t enough sales associates to keep shelves stocked, assist shoppers, and keep stores organized. Meanwhile, supervisors spend more time replacing workers than coaching existing ones. Widely cited estimates by the SHRM Foundation and Gallup peg replacement costs for frontline roles at anywhere from 50% to 200% of annual wages, depending on role complexity and ramp time (the period it takes for a new employee to become fully proficient)—enough to erase the thin profit margins typical in the service sector.

Scheduling data can act as an early warning system, but the signs are not always easy to read. Stores with erratic scheduling (frequent last-minute changes, inconsistent shift patterns, and limited advance notice disrupt employees’ ability to plan their lives) often experience large fluctuations in turnover, absenteeism, and customer service scores. But operational noise—the small, everyday variations in demand, staffing, or logistics—can make schedules appear unstable even when systems are functioning as they should. The challenge for leaders is to distinguish between normal variability and structural problems that undermine retention. Managers should watch out for signs of weak communication, strained middle management, and a culture that prioritizes short-term efficiency over consistency. If firms monitor scheduling patterns just as they monitor customer satisfaction and inventory turnover, they can spot critical morale and retention risks before serious problems arise.

An analysis of scheduling patterns can reveal not only where problems exist but why. And by drilling deeper into shift-level records, managers can identify the specific mix of factors driving turnover in a particular store or region.