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Balancing Agent Retention and Waiting Time in Service Platforms

Published: 13 July 2020 Publication History

Abstract

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.

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  • (undefined)Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and EngagementSSRN Electronic Journal10.2139/ssrn.3802628
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cover image ACM Conferences
EC '20: Proceedings of the 21st ACM Conference on Economics and Computation
July 2020
937 pages
ISBN:9781450379755
DOI:10.1145/3391403
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2020

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Author Tags

  1. call center
  2. causal inference
  3. econometrics
  4. two-sided platforms

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  • Research-article

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  • ANID Fondecyt

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EC '20
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EC '20: The 21st ACM Conference on Economics and Computation
July 13 - 17, 2020
Virtual Event, Hungary

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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The 25th ACM Conference on Economics and Computation
July 7 - 11, 2025
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Cited By

View all
  • (2024)Hiring Preference and Operational Complexity for Tribal EnterprisesProduction and Operations Management10.1177/10591478241252153Online publication date: 16-May-2024
  • (2022)Queue congestion prediction for large-scale high performance computing systems using a hidden Markov modelThe Journal of Supercomputing10.1007/s11227-022-04356-z78:10(12202-12223)Online publication date: 28-Feb-2022
  • (undefined)Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and EngagementSSRN Electronic Journal10.2139/ssrn.3802628
  • (undefined)Information Design to Facilitate Social Interactions on Service Platforms: Evidence from a Large Field ExperimentSSRN Electronic Journal10.2139/ssrn.3528619

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