Managing Retail Budget Allocation between Store Labor and Marketing Activities

• 成果类型: Article
• 署名作者: Perdikaki, Olga; Kumar, Subodha; Sriskandarajah, Chelliah
• 署名单位: University of South Carolina System; University of South Carolina Columbia; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Texas A&M University System; Texas A&M University College Station; Mays Business School
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12733
• 发表日期: 2017
• 页码: 1615-1631
• 关键词: retail budgetallocation; Advertising; traffic variability; sales force planning; COORDINATION

摘要

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 budgetallocation 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 budgetallocation 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 budgetallocation 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 budgetallocation and sales force planning.