NATURAL LANGUAGE PROCESSING IN CUSTOMER INTELLIGENCE: A FRAMEWORK FOR UNSTRUCTURED DATA ANALYSIS

Abstract

This article presents a comprehensive framework for leveraging Natural Language Processing (NLP) technologies in the analysis of unstructured customer data, addressing the growing need for sophisticated data analytics in business intelligence. Through a systematic implementation of sentiment analysis and topic modeling techniques, including VADER and Latent Dirichlet Allocation (LDA), the article demonstrates how organizations can extract actionable insights from diverse data sources including social media, customer feedback, and service interactions. The article employs a multi-modal approach, integrating voice-to-text conversion with advanced text analytics, and evaluates the effectiveness of leading cloud-based NLP solutions including Amazon Comprehend, IBM Watson NLP, and Google Cloud Natural Language. The findings reveal significant improvements in customer intelligence accuracy (p < 0.001) compared to traditional analysis methods, with sentiment classification achieving 87% accuracy and topic modeling identifying key customer concern clusters with 82% precision. The article also presents a novel methodology for transforming these insights into practical applications, including sales enablement and service optimization, resulting in a 23% increase in customer satisfaction metrics across analyzed cases. This article contributes to both the theoretical understanding of NLP applications in business analytics and provides practitioners with a structured approach to implementing NLP solutions for customer intelligence.