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Proceedings ArticleDOI

Intelligent call routing: optimizing contact center throughput

Abbas Raza Ali
- pp 5
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TLDR
In this paper, a new operational model for achieving significantly improved call-outcomes is proposed, where descriptive, predictive and prescriptive analytical techniques can be applied to psychographic and demographic insights; to find the ideal mapping between them.
Abstract
In recent years, most companies now interact with their customers for businesses purposes through contact or call centers. Substantially, customers now perceive a company through their interaction with Customer Services Representatives (CSR's) in their centers. The CSR has become the key role and channel in maintaining brand reputation and ensuring customer retention. Explicitly, in a contact center environment Customer and CSR are the two main transactional entities, and business development depends on their interaction. Contact center management routinely adopts numerous processes to enhance centre services by training their CSRs, call recording for quality monitoring, acquiring customer feedback after the call, and assessing similar factors. However, these factors are often inadequate in advancing the customer experience, due to operational scale and being exclusively focused on the telephone as the medium for interaction. Every contact center strives to maximize its value through, improved customer satisfaction, retention and first call resolution; and minimized communication expenditures, for example, call handling time or talk time. Smart call routing can manage these improvements to enhance overall customer experience, leading to sales and maintained quality of service.The CSR to Customer call-outcome is the critical success factor (CSF) to improvement and optimization. This paper considers a new operational model for achieving significantly improved call-outcomes.A call outcome in contact center environment is most typically random, like flipping a coin, to tell whether a call achieves a sale or not. This random outcome can be made more certain if predicted and optimized by exploiting personal chemistry as a critical factor. Fortunately contact centers are more controlled environments within which to gain psychographic and demographic insights to gauge customer/CSR chemistry.This paper proposes that descriptive, predictive and prescriptive analytical techniques can be applied to psychographic and demographic insights; to find the ideal mapping between them. By using those techniques, the model shows a ten to fifteen percent improvement in call-outcomes.

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Citations
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References
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Introduction to Machine Learning

TL;DR: Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts, and discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.
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The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research

TL;DR: A survey of the recent literature on call center operations management can be found in this article, where the authors identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.
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A Staffing Algorithm for Call Centers with Skill-Based Routing

TL;DR: This paper addresses both routing and staffing problems by exploiting limited cross-training, and finds that minimal flexibility can provide great benefits, and develops an algorithm to minimize the total staff subject to per-class performance constraints.
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Call-type classification and unsupervised training for the call center domain

TL;DR: It is shown that lightly supervised training based on using the output from an automatic speech recognizer in conjunction with supervised labeling of calls by call-type can substantially reduce classification error rates and development efforts when only limited training data are available.
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