Balancing Agent Retention and Waiting Time in Service Platforms

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 outbound 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.

History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science.

Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo (ANID) [Grant PIA/APOYO AFB220003] and FONDECYT-Chile 1181201.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2418.