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Enhancing Customer Service through Natural Language Processing: Extracting Insights from Unstructured Customer Feedback

Author(s) Vamshi Mundla
Country United States
Abstract In today’s competitive business environment, understanding customer sentiment and extracting actionable insights from unstructured text data are critical for improving customer service and communication management. Natural Language Processing (NLP) offers a suite of techniques—such as text summarization, text classification, and keyword extraction—that can transform vast amounts of raw customer feedback, reviews, and surveys into succinct, meaningful insights. This paper explores the role of NLP in enhancing customer interaction and Customer Communication Management (CCM). We discuss various NLP methodologies, illustrate their applications with detailed case studies from the retail and financial services sectors, and analyze the benefits and challenges of deploying these techniques in real-world settings. Our findings demonstrate that a systematic, data-driven approach to processing unstructured text data not only enhances customer satisfaction but also contributes to operational efficiency and strategic decision-making.
Keywords Natural Language Processing, Customer Service, Customer Feedback, Text Summarization, Text Classification, Keyword Extraction, Customer Communication Management
Field Engineering
Published In Volume 5, Issue 1, January 2024
Published On 2024-01-03
Cite This Enhancing Customer Service through Natural Language Processing: Extracting Insights from Unstructured Customer Feedback - Vamshi Mundla - IJLRP Volume 5, Issue 1, January 2024. DOI 10.5281/zenodo.14982331
DOI https://doi.org/10.5281/zenodo.14982331
Short DOI https://doi.org/g8687p
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