A framework for balanced service and cross-selling by using queuing science

Demiriz, Ayhan; Kula, Ufuk; Akbilek, Nevra

doi:10.1007/s10845-008-0232-z

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Yet, all interactions are opportunities and should be explored completely. Thus, for example, in inbound, it is highly recommendable to explore situations of crossselling and up-selling as well as claim, opinions, and so on (Demiriz and Akbilek 2009). This enables not only knowledge of the needs and satisfaction level of the user but also an increase in business volume per interaction.…”

Section: Flowmentioning

confidence: 99%

“…Studies point to one million professionals by 2010. In the United States and United Kingdom, contact centers employ around 3% of the working population (Demiriz and Akbilek 2009). This sector has a growth rate of more than 8% a year (Koole and Mandelbaum 2002;Whitt 2002;Avramidis and L'Ecuyer 2005;Whitt 2005;Aksin et al 2007;Holman et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Contact center: information systems design

2010

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The economic sector of contact centers is growing by more than 8% a year. It is a multidisciplinary area in which information systems are decisive to organizations' success. Contact Centers' Information Systems deal with real time requisites and critical business information. A theorybuilding research shows a framework with 12 key design factors to consider, which managers might use to develop projects and researchers may adopt for further investigation in the area of Contact Center design. This work intends to provide a valuable link between the research community and practitioners in industry.

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Section: Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contact center: information systems design

2010

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“…Nevertheless, often 70% or more of their operating costs are devoted to human resources. Well-run call centers adhere to a sharply defined balance between agent efficiency and service quality; to do so, they use queuing-theoretic models (Demiriz, Kula, Akbilek, 2009), as well as simulation technics (Mehrotra et Fama, 2003).…”

Section: Introductionmentioning

confidence: 99%

Improving operational measures in a financial institute call center: a case study

Cohen

Rozenes

2017

BJO&PM

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This article presents a case study that shows the impact of a proposed method of performance measures in a call center service system in one of the largest financial institutions in Israel. It proposes a policy for load-reduction in the peak hours coupled with an improvement in the automated routing of customers' calls. The case study shows the system's current state, and offers improvements to shorten the overall service time while increasing utilization of service personnel in order to decrease the abandon rate (AR) and increase service quality. The article starts by introducing the financial institute and its telephone service structure, the unique characteristic of the current service system, the different types of customers and their characteristics, the utilization of the system and emphasizes the phone call duration and AR. In addition, the article shows a simulation of the current state with an analysis of current service. It compares the current system with an alternative service system that aims at improving the main performance measures including average wait time (AWT), service personel utilization time, and customer abandon rate. The proposed system includes additional "back office" with a callback system to reduce churn rate, and improved the IVR routing. The proposed service system was simulated and all the service measurement was tested statistically. The results indicated a significant improvement in terms of service measurements between the current and the proposed system. Operators' utilization increased, while abandoning rate decreased significantly.

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“…Permissions may be requested from Kalyanam and Zweben 2005;Aberg and Shahmehri 2000]. Very often, the involvement of human agents also translates into opportunities to derive extra value through cross-selling [Demiriz et al 2009]. Live-chat has also been shown to handle more complex transactions than FAQ or email communication, and yet derive higher customer satisfaction ratings 1 .…”

Section: Introductionmentioning

confidence: 99%

Live-chat agent assignments to heterogeneous e-customers under imperfect classification

Góes

Ilk

Yue

et al. 2011

ACM Trans. Manage. Inf. Syst.

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Many e-commerce firms provide live-chat capability on their Web sites to promote product sales and to offer customer support. With increasing traffic on e-commerce Web sites, providing such live-chat services requires a good allocation of service resources to serve the customers. When resources are limited, firms may consider employing priority-processing and reserving resources for high-value customers. In this article, we model a reserve-based priority-processing policy for e-commerce systems that have imperfect customer classification. Two policy decisions considered in the model are: (1) the number of agents exclusively reserved for highvalue customers, and (2) the configuration of the classification system. We derive explicit expressions for average waiting times of high-value and low-value customer classes and define a total waiting cost function. Through numerical analysis, we study the impact of these two policy decisions on average waiting times and total waiting costs. Our analysis finds that reserving agents for high-value customers may have negative consequences for such customers under imperfect classification. Further, we study the interaction between the two policy decisions and discuss how one decision should be modified with respect to a change in the other one in order to keep the waiting costs minimized.

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A framework for balanced service and cross-selling by using queuing science

Cited by 6 publications

References 9 publications

Contact center: information systems design

Contact center: information systems design

Improving operational measures in a financial institute call center: a case study

Live-chat agent assignments to heterogeneous e-customers under imperfect classification

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