Advanced Customer Intelligence Framework using Natural Language Processing and Public Cloud Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806041Keywords:
Customer Intelligence, Natural Language Processing, Public Cloud, Machine Learning, Sentiment Analysis, Customer Segmentation, Predictive Analytics, Cloud Computing, Text Mining, Artificial Intelligence, Customer Experience, Big DataAbstract
The rapid growth of digital interactions has transformed customer information into a critical strategic resource for organizations. Customers communicate through websites, social media, emails, mobile applications, online reviews, contact centres, and digital transaction platforms, generating large volumes of structured and unstructured data. Traditional customer intelligence systems often struggle to integrate these heterogeneous sources and convert textual information into actionable knowledge in real time. This paper proposes an advanced Customer Intelligence Framework that combines Natural Language Processing (NLP), machine learning, and public cloud platforms to support automated customer understanding and decision-making. The proposed framework integrates data ingestion, cloud-based storage, text preprocessing, sentiment analysis, topic modelling, intent detection, customer segmentation, predictive analytics, and visualization within a scalable architecture. Public cloud technologies provide elastic computing, distributed storage, managed machine-learning services, and application programming interfaces that can support continuous customer-data processing. The methodology adopts a design-science and mixed-methods approach involving framework development, prototype implementation, performance evaluation, and stakeholder assessment. Evaluation focuses on NLP accuracy, scalability, processing latency, predictive performance, and managerial usefulness. The framework is intended to provide organizations with timely and comprehensive customer insights while addressing privacy, security, bias, explainability, and governance requirements. The study contributes a practical and theoretically informed architecture for integrating advanced NLP capabilities with cloud-based customer intelligence
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