Estimation of Contact Center Performance Measures in Case of Overload and Chatbot Implementation

Stepanov, Mikhail S.; Muzata, A. R.; Zyuzin, V.; Kostina, N. S.; Shishkin, M O

doi:10.1109/ieeeconf51389.2021.9415983

Cited by 3 publications

References 7 publications

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A review of natural language processing in contact centre automation

Shah

Ghomeshi

Vakaj

et al. 2023

Pattern Anal Applic

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Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer recommendations for overcoming them, ultimately expediting the pace of contact centre automation.

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