Routing and Staffing in Customer Service Chat Systems with Impatient Customers

Tezcan, Tolga; Zhang, Jiheng

doi:10.1287/opre.2014.1284

Cited by 40 publications

(60 citation statements)

References 22 publications

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“…1-19, © 2016 INFORMS 5 or probability of service completion) on system performance. The case-completion-rate function k u in our and approximations has a similar interpretation to the case-completion-rate functions in Tezcan and Zhang (2014) and Luo and Zhang (2013).…”

Section: Literature Reviewsupporting

confidence: 62%

“…We refer to this scheme as the join-the-smallest-caseload (JSC) routing policy. Note that the JSC policy may not be the optimal policy, although Tezcan and Zhang (2014) find that the JSC policy is asymptotically optimal for a similar system. We refer to the baseline system as the system because of this smallest-caseload policy.…”

Section: Definitions and Modelsmentioning

confidence: 94%

“…In both papers, the system is approximated with a processorsharing model; that is, each agent's capacity is infinitely divisible and all customers are served simultaneously. Tezcan and Zhang (2014) examine the optimal routing policy, and they find that under certain conditions the optimal policy is similar to the JSC policy for our system. Luo and Zhang (2013) focus on the transient and steady-state system behavior, given a routing policy.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Models closest to our system are found in Saghafian et al (2014Saghafian et al ( , 2012, Dobson et al (2013), Tezcan and Zhang (2014), and Luo and Zhang (2013). Saghafian et al (2014) model an ED as a case-manager system, as we define it, and disaggregate the analysis to "Phase 1" (similar to our preassignment queue) and "Phase 2" (with repeated testing and interactions with a physician).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The models in Tezcan and Zhang (2014) and Luo and Zhang (2013) are motivated by customer service chat and instant messaging systems in which each agent simultaneously serves multiple customers. In both papers, the system is approximated with a processorsharing model; that is, each agent's capacity is infinitely divisible and all customers are served simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Queueing Models of Case Managers

Campello

Ingolfsson²,

Shumsky

2017

Management Science

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M any service systems use case managers, servers who are assigned multiple customers and have frequent, repeated interactions with each customer until the customer's service is completed. Examples may be found in healthcare (emergency department physicians), contact centers (agents handling multiple online chats simultaneously) and social welfare agencies (social workers with multiple clients). We propose a stochastic model of a baseline case-manager system, formulate models that provide performance bounds and stability conditions for the baseline system, and develop two approximations, one of which is based on a two-time-scale approach. Numerical experiments and analysis of the approximations show that increasing case throughput by increasing the probability of case completion can lead to much greater waiting-time reductions than increasing service speed. Many systems place an upper limit on the number of customers simultaneously handled by each case manager. We examine the impact of these caseload limits on waiting time and describe effective, heuristic methods for setting these limits.

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Section: Literature Reviewsupporting

confidence: 62%

Section: Definitions and Modelsmentioning

confidence: 94%

Section: Literature Reviewmentioning

confidence: 97%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Queueing Models of Case Managers

Campello

Ingolfsson²,

Shumsky

2017

Management Science

View full text Add to dashboard Cite

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Improving customer routing in contact centers: An automated triage design based on text analytics

Ilk

Shang

Góes

2020

J of Ops Management

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We propose an automated triage design for intelligent customer routing in live-chat contact centers and demonstrate its implementation using a realworld data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live-chat, e-mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.

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A behavioral perspective on service center routing: The role of inertia

Schecter

Wowak

Berente

et al. 2021

J of Ops Management

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Research on service center routing has largely assumed either fully codified automated routing, rational routing agents who can optimally route calls, or both. In practice, however, many routing scenarios are not fully codifiable, and human routing agents will likely exhibit biases in their routing decisions. In this paper, we explore the presence of behavioral inertia in service center routing. Inertia in this context is the propensity for routing agents to route issues more frequently to other agents with whom they have experience, rather than optimally routing those issues. We leveraged a unique dataset of more than 70,000 routing decisions in a complex service operations context to examine the presence of inertia and associated performance implications. We find that, overall, inertia hurts performance, but in certain situations can be beneficial. Inertia increases the probability of issue resolution when agents exhibit inertial behavior in routing to other agents with high expertise, and inertia positively impacts (decreases) service time for particularly difficult, complex customer issues. This study contributes to research in service center operations by studying a previously unexplored phenomenon of inertia in routing by human agents and provides valuable insights to practitioners who manage service centers.

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Routing and Staffing in Customer Service Chat Systems with Impatient Customers

Cited by 40 publications

References 22 publications

Queueing Models of Case Managers

Queueing Models of Case Managers

Improving customer routing in contact centers: An automated triage design based on text analytics

A behavioral perspective on service center routing: The role of inertia

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