Attracting shoppers to stores and converting the incoming traffic into sales profitably are vital for the financial health of retailers. In this paper, we use proprietary data pertaining to an apparel retailer to study the relationship between store traffic, labor, and sales performance. We decompose sales volume into conversion rate (defined as the ratio of number of transactions to traffic) and basket value (defined as the ratio of sales volume to number of transactions) and analyze the impact of traffic on sales and its components. We find that store sales volume exhibits diminishing returns to scale with respect to traffic. We determine that this relationship is driven by a decline in conversion rate with traffic (as opposed to a decline in basket value). Our results show that an increase in conversion rate is associated with an increase in future traffic growth. We also find that increases in intra-day and inter-day traffic variability are associated with a decrease in store sales performance, while an increase in store labor is associated with an increase in sales performance.

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.

M any service firms deliver services via a mix of internally developed and delivered (i.e., insourced) and externally developed and delivered (i.e., outsourced) service processes. Service process outsourcing is especially common in eretailing. Portions of e-retail customer ordering processes and delivery processes can be digitized and contracted to thirdparty vendors. Via outsourcing, service systems change from dyadic to triadic. Prior research examines consumer perceptions of dyadic (consumer to e-retailer) outcomes, but little research considers service co-delivery with outsourcing partners (i.e., triadic systems). Literature also does not focus on joint associations of service process outsourcing and customer traffic with e-retailer operations. We analyze several years of data on North American e-retailers. We first examine factors associated with e-retailer outsourcing levels, for front-end and back-end service processes. We observe customer traffic is positively associated with future outsourcing. We then examine how outsourcing moderates associations between contemporaneous customer traffic and e-retailer operational performance, as measured by numbers of processed orders, website response times, and customer satisfaction. Results suggest outsourcing levels are associated with operational outcomes, yet surprisingly, high outsourcing and high traffic jointly may not benefit e-retailers.

In this article we explore the profitability of different operations models used by online grocers and develop a linear demand model in a competitive setting to better understand the trade-offs made by two competing online grocers in choices for distribution strategy (leverage or direct) and product focus (perishable or nonperishable). We find that the results derived in the duopoly setting are different from those in a monopolistic setting. Specifically, we determine that there is a threshold value for the secondary competitive effects in the demand function that determines how the prices and profitability of an online grocer will be affected by the supply chain length of its competitor. There is also a threshold value for the ratio of supply chain lengths of the two competitors that determines whether product perishability increases or decreases profits. We demonstrate that the existence of this threshold is robust when considering capacity constraints. Further, we show, assuming that supply chain length can be optimized, how the relative size of the infrastructure change cost (when compared with that of the competitor) coupled with the perishability of the product determines the profitability of an investment leading to a shorter supply chain.

The performance of a retail store depends on its ability to attract customer traffic, match labor with incoming traffic, and convert the incoming traffic into sales. Retailers make significant investments in marketing activities (such as advertising) to bring customers into their stores and in‐store labor to convert that traffic into sales. Thus, a common trade‐off that retail store managers face concerns the allocation of a store's limited budget between advertising and labor to enhance store‐level sales. To explore that trade‐off, we develop a centralized model to allocate limited store budget between store labor and advertising with the objective of maximizing store sales. We find that a store's inherent potential to drive traffic plays an important role, among other factors, in the relative allocation between advertising and store labor. We also find that as advertising instruments become more effective in bringing traffic to stores, managers should not always capitalize this effectiveness by increasing their existing allocations to advertising. In addition, we discuss a decentralized setting where budget allocation decisions cannot be enforced by a store manager and present a simple mechanism that can achieve the centralized solution. In an extension, we address the budget allocation problem in the presence of marketing efforts to shift store traffic from peak to off peak hours and show that our initial findings are robust. Further, we illustrate how the solution from the budget allocation model can be used to facilitate store level sales force planning/scheduling decisions. Based on the results of our model, we present several insights that can help managers in budget allocation and sales force planning.