In this paper we use micro-level data on store traffic, sales and labor from 41 stores of a large retail chain to identify the extent of understaffing in retail stores and quantify its impact on sales and profitability. We show how traffic data can be leveraged in making staffing decisions through use of a structural model that captures the relationship between traffic, sales and labor. Assuming that store managers aim to maximize profits, we estimate the contribution of labor to sales and impute the cost of labor for each store in our sample. We find significant heterogeneity in the contribution of labor to sales as well as imputed cost of labor across these stores and across time. Using the estimated parameters, we establish the presence of systematic understaffing during peak hours. Aligning staffing levels with changing traffic patterns can result in a 6.15% savings in lost sales and a 5.74% improvement in profitability. We describe a pilot implementation of our approach at another large retailer where we identify periods of understaffing in their stores and document the impact on conversion rate and lost sales
