Setting Retail Staffing Levels: A Methodology Validated with Implementation
Abstract
We describe a three-step process that a retailer can use to set retail store sales staff levels. First, use historical data on revenue and planned and actual staffing levels by store to estimate how revenue varies with the staffing level at each store. We disentangle the endogeneity between revenue and staffing levels by focusing on randomly occurring deviations between planned and actual labor. Second, using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. Finally, we implement the results chain-wide and measure the impact. We describe the successful deployment of this process with a large specialty retailer. We find that 1) the implementation validates predictions of the historical analysis, including the use of the variation between planned staffing and actual staffing as an exogenous shock, 2) implementation in 168 stores over a 6-month period produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase, after accounting for the cost of the additional labor, and 3) the impact of staffing level on revenue varies greatly by store, and therefore staffing levels should also vary, with more sales staff relative to revenue assigned to those stores where sales staff have the greatest impact on revenue. Specifically, we found the largest impact of store labor in stores with the largest average basket sizes, located in regions with good growth potential, facing certain competitors (e.g., Wal-Mart), and run by long-serving managers.
