Abstract
AbstractContact 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.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition
Reference152 articles.
1. Larson D, Chang V (2016) A review and future direction of agile, business intelligence, analytics and data science. Int J Inf Manag 36(5):700–710
2. Roscow E, Moore R, Singh S (2020) Contact centre transformation-bring the future forward. Accenture.com
3. Benjamin G, Berg J, Das AC, Gupta V (2019) How advanced analytics can help contact centers put the customer first. mckinsey.com
4. Wong A, Plasek JM, Montecalvo SP, Zhou L (2018) Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacother J Hum Pharmacol Drug Ther 38(8):822–841
5. Mocanu B-C, Filip I-D, Ungureanu R-D, Negru C, Dascalu M, Toma S-A, Balan T-C, Bica I, Pop F (2022) Odin ivr-interactive solution for emergency calls handling. Appl Sci 12(21):10844
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