Balancing Agent Retention and Waiting Time in Service Platforms

• 成果类型: Article
• 署名作者: Musalem, Andres; Olivares, Marcelo; Yung, Daniel
• 署名单位: Universidad de Chile
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2418
• 发表日期: 2023
• 页码: 979-1003
• 关键词: customer dissatisfaction; performance evidence; experience; turnover; centers; queues

摘要

In many service industries, the speed of service and support by experienced employees are two major drivers of service quality. When demand for a service is variable and the staffing requirements cannot be adjusted quickly, choosing capacity levels requires making a tradeoff between service speed and operating costs, both of which depend on worker utilization. However, recent business models have enabled service systems to access a large pool of employees with flexible working hours that are compensated through piece-rates. Although this business model can operate at low levels of utilization without increasing operating costs, a different tradeoff emerges: The service platform must control employee turnover, which may increase when employees are working at low levels of utilization. Hence, to make staffing decisions and manage worker utilization, it is necessary to understand both customer conversion and employee retention, measuring their sensitivity to service time and utilization, respectively. In our application, we study an out-bound call center that operates with a pool of flexible agents working remotely to sell auto insurance. We develop an econometric approach to model customer behavior that captures two key features of outbound calls: time sensitivity and employee heterogeneity. We find a strong impact of contact time on customer behavior: Conversion rates drop by 31% when the time to make the first outbound call increases from 5 to 30 minutes. In addition, we use a survival model to measure how agent retention is affected by utilization (which determined by workload and total staffing capacity) and find that, for more experienced worker, a 10% increase in utilization translates into a 33% decrease in weekly agent attrition. These empirical models of customer and agent behavior are combined to illustrate how to balance customer conversion and employee retention, showing that both are relevant to plan staffing and allocate workload in the context of an on-demand service platform.