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Integrating Structured and Unstructured Data for Enhanced Marketing Intelligence through Text Mining and Business Analytics Hashim Salman, Sabreen
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.226

Abstract

In the digital era, the rapid growth of social media and online platforms has led to an explosion of unstructured textual data that holds significant business value. Traditional marketing strategies, once reliant on structured data such as demographics and purchase history, now benefit from insights derived from text analytics and sentiment analysis. This paper explores the integration of structured and unstructured data to strengthen marketing intelligence and customer segmentation. By utilizing text mining techniques and Natural Language Processing (NLP), unstructured data such as customer reviews and comments can be analyzed to extract sentiments, identify emerging trends, and refine customer relationship strategies. The study proposes an integrated framework that combines data extraction, transformation, and loading (ETL) processes with a data warehouse system for unified analysis. Using clustering algorithms such as K-Means and visualization tools, insights into customer behavior, preferences, and market segmentation are revealed. The paper also discusses the challenges of handling multilingual and context-dependent text, ethical and privacy considerations, and the technical architecture necessary for business intelligence implementation. Findings suggest that effective integration of textual analytics with structured data can lead to more informed decision-making, improved marketing strategies, and stronger customer engagement.