Balancing Agent Retention and Waiting Time in Service Platforms
47 Pages Posted: 1 Jan 2020 Last revised: 23 Jun 2022
Date Written: June 22, 2022
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 trade-off 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. While this business model can operate at low levels of utilization without increasing operating costs, a different trade-off 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 worked -- 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.
Keywords: queueing, econometrics, service industry, behavioral operations, call centers, online platforms
JEL Classification: C01, C25, C26, C41
Suggested Citation: Suggested Citation