INFORMATION SYSTEM FOR INTELLIGENT CUSTOMER CLASSIFICATION: ARCHITECTURE, IMPLEMENTATION, AND EXPERIMENTAL RESEARCH
DOI:
https://doi.org/10.26906/SUNZ.2025.3.159Keywords:
information system, customer classification, machine learning, software architecture, logistic regression, decision trees, experimental researchAbstract
Relevance. In the context of ongoing digital transformation of business processes, there is a growing demand for intelligent information systems capable of analyzing and processing large volumes of customer data. One of the key directions in this field is automated customer classification using machine learning algorithms, which significantly enhances the effectiveness of marketing strategies and decision-making. Object of the research: customer classification processes in information systems using machine learning methods. Purpose of the article: to design, implement, and investigate the architecture of software components of an information system for intelligent customer classification, taking into account requirements for scalability, performance, and classification accuracy. Research results. The article proposes an architectural model of an information system that includes modules for data collection, preprocessing, and customer classification. Several software components were implemented, integrating machine learning algorithms such as logistic regression, decision trees, and support vector machines. Experimental studies based on a real-world dataset demonstrated high classification accuracy and the system’s efficiency under limited computational resources. Conclusions. The developed information system ensures accurate customer classification and can be applied in commercial data analytics platforms. The research findings can be used to further improve intelligent software systems for data analysis.Downloads
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Copyright (c) 2025 Oleksandr Shmatko, Daria Malyshenko, Olena Voloshchuk
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