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 in real-time, choosing capacity levels requires making a trade-off between service speed and operating costs. Online service platforms have crowdsourcing of 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 workload, it is necessary to empirically measure the trade-off between customer conversion and employee retention. In this context, we study an online service platform 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: customer time-sensitivity and employee heterogeneity. We find a strong impact of waiting time on customer behavior: conversion rates drop by 33% 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 the assigned workload and find that a 10% increase in workload translates into a 25% percentage 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.