27.06.2025; Дніпро, Україна: V Міжнародна наукова конференція «Теорія модернізації в контексті сучасної світової науки»
Роботи, що індексуються в Google Scholar

DEVELOPMENT AND EXPERIMENTAL EVALUATION OF AN INFORMATION SYSTEM FOR INTELLIGENT CUSTOMER SEGMENTATION

  • Ihor Bovkun,
  • Oleksandr Shmatko
DOI: https://doi.org/10.62731/mcnd-27.06.2025.003
PDF

Опубліковано 27.06.2025

Як цитувати

Bovkun, I., & Shmatko, O. (2025). DEVELOPMENT AND EXPERIMENTAL EVALUATION OF AN INFORMATION SYSTEM FOR INTELLIGENT CUSTOMER SEGMENTATION. Матеріали конференцій МЦНД, (27.06.2025; Дніпро, Україна), 122–131. https://doi.org/10.62731/mcnd-27.06.2025.003

Завантаження

Дані завантаження ще не доступні.

Авторське право (c) 2025 Ihor Bovkun; Oleksandr Shmatko (Науковий керівник)

Creative Commons License

Ця робота ліцензується відповідно до Creative Commons Attribution-ShareAlike 4.0 International License.

Google Scholar

Анотація

Relevance. In the context of ongoing digital transformation of business processes, the demand for intelligent information systems capable of analyzing and processing large volumes of customer data is steadily increasing. One of the key directions in this field is automated customer classification using machine learning algorithms, which enhances the effectiveness of marketing strategies and decision-making processes. Object of research: customer classification processes in information systems utilizing machine learning methods. Purpose of the article: to design, implement, and evaluate the architecture of software components for an information system aimed at intelligent customer classification, taking into account scalability, performance, and classification accuracy requirements. Research results. The article proposes an architectural model of an information system comprising modules for data collection, processing, and classification. A set of software components has been implemented, integrating machine learning algorithms such as logistic regression, decision trees, and support vector machines. Experimental research was conducted using a real-world dataset, demonstrating high classification accuracy and efficient system performance under limited computational resources. Conclusions. The developed information system ensures accurate customer classification and can be integrated into commercial analytical platforms. The research outcomes may serve as a foundation for further improvement of intelligent data analysis systems.

PDF

Посилання

  1. 1. Kotler, P., Keller, K. L., Brady, M., Goodman, M., & Hansen, T. (2016). Marketing management (3rd ed.). Pearson Higher Ed.
  2. 2. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Springer Science & Business Media.
  3. 3. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1007/978-3-540-87479-9_3
  4. 4. Kumar, S., Rani, R., Pippal, S. K., & Agrawal, R. (2025). Customer segmentation in e-commerce: K-means vs hierarchical clustering. TELKOMNIKA (Telecommunication Computing Electronics and Control), 23(1), 119–128. http://doi.org/10.12928/telkomnika.v23i1.26384
  5. 5. Tabianan, K., Velu, S., & Ravi, V. (2022). K-means clustering approach for intelligent customer segmentation using customer purchase behavior data. Sustainability, 14(12), 7243. https://doi.org/10.3390/su14127243
  6. 6. Zhao, Y., & Zhou, X. (2021, April). K-means clustering algorithm and its improvement research. In Journal of Physics: Conference Series (Vol. 1873, No. 1, p. 012074). IOP Publishing. https://doi.org/10.1088/1742-6596/1873/1/012074
  7. 7. Huang, S., Kang, Z., Xu, Z., & Liu, Q. (2021). Robust deep k-means: An effective and simple method for data clustering. Pattern Recognition, 117, 107996. https://doi.org/10.1016/j.patcog.2021.107996
  8. 8. Jothi, R., & Muthukumaran, K. (2022). Telecom customer segmentation using deep embedded clustering algorithm. In B. Alyoubi, C. E. Ben Ncir, I. Alharbi, & A. Jarboui (Eds.), Machine learning and data analytics for solving business problems: Unsupervised and semi-supervised learning (pp. 85–104). Springer. https://doi.org/10.1007/978-3-031-18483-3_5
  9. 9. Cendana, M., & Kuo, R. J. (2024). Categorical data clustering: A bibliometric analysis and taxonomy. Machine Learning and Knowledge Extraction, 6(2), 1009–1054. https://doi.org/10.3390/make6020047
  10. 10. Lee, Z. J., Lee, C. Y., Chang, L. Y., & Sano, N. (2021). Clustering and classification based on distributed automatic feature engineering for customer segmentation. Symmetry, 13(9), 1557. https://doi.org/10.3390/sym13091557
  11. 11. Kumaresan, S. P., Tan, C. K., & Ng, Y. H. (2021). Deep neural network (DNN) for efficient user clustering and power allocation in downlink non-orthogonal multiple access (NOMA) 5G networks. Symmetry, 13(8), 1507. https://doi.org/10.3390/sym13081507
  12. 12. Xiahou, X., & Harada, Y. (2022). B2C e-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458–475. https://doi.org/10.3390/jtaer17020024
  13. 13. Liu, R., Ali, S., Bilal, S. F., Sakhawat, Z., Imran, A., Almuhaimeed, A., ... & Sun, G. (2022). An intelligent hybrid scheme for customer churn prediction integrating clustering and classification algorithms. Applied Sciences, 12(18), 9355. https://doi.org/10.3390/app12189355
  14. 14. Altameem, A. A., & Hafez, A. M. (2022). Behavior analysis using enhanced fuzzy clustering and deep learning. Electronics, 11(19), 3172. https://doi.org/10.3390/electronics11193172
  15. 15. Yan, X., Li, Y., Nie, F., & Li, R. (2025). Bank customer segmentation and marketing strategies based on improved DBSCAN algorithm. Applied Sciences, 15(6). https://doi.org/10.3390/app15063138
  16. 16. Alshdaifat, E. A., Alshdaifat, D. A., Alsarhan, A., Hussein, F., & El-Salhi, S. M. D. F. S. (2021). The effect of preprocessing techniques, applied to numeric features, on classification algorithms' performance. Data, 6(2), 11. https://doi.org/10.3390/data6020011
  17. 17. Abdulrazzak, H. N., Hock, G. C., Mohamed Radzi, N. A., Tan, N. M., & Kwong, C. F. (2022). Modeling and analysis of new hybrid clustering technique for vehicular ad hoc network. Mathematics, 10(24), 4720. https://doi.org/10.3390/math10244720
  18. 18. Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D. L. R., Thompson, E. B., & Ashraf, I. (2023). A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry, 15(9), 1679. https://doi.org/10.3390/sym15091679
  19. 19. Najeh, H., Lohr, C., & Leduc, B. (2022). Dynamic segmentation of sensor events for real-time human activity recognition in a smart home context. Sensors, 22(14), 5458. https://doi.org/10.3390/s22145458
  20. 20. Domingos, E., Ojeme, B., & Daramola, O. (2021). Experimental analysis of hyperparameters for deep learning-based churn prediction in the banking sector. Computation, 9(3), 34. https://doi.org/10.3390/computation9030034
  21. 21. Saha, L., Tripathy, H. K., Gaber, T., El-Gohary, H., & El-kenawy, E. S. M. (2023). Deep churn prediction method for telecommunication industry. Sustainability, 15(5), 4543. https://doi.org/10.3390/su15054543
  22. 22. Chen, Y. S., Lin, C. K., Chou, J. C. L., Chen, S. F., & Ting, M. H. (2022). Application of advanced hybrid models to identify the sustainable financial management clients of long-term care insurance policy. Sustainability, 14(19), 12485. https://doi.org/10.3390/su141912485
  23. 23. Jiang, W., Song, C., Wang, H., Yu, M., & Yan, Y. (2023). Obstacle detection by autonomous vehicles: An adaptive neighborhood search radius clustering approach. Machines, 11(1), 54. https://doi.org/10.3390/machines11010054
  24. 24. Banegas-Luna, A. J., Peña-García, J., Iftene, A., Guadagni, F., Ferroni, P., Scarpato, N., ... & Pérez-Sánchez, H. (2021). Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey. International Journal of Molecular Sciences, 22(9), 4394. https://doi.org/10.3390/ijms22094394
  25. 25. Eslami, E., Razi, N., Lonbani, M., & Rezazadeh, J. (2024). Unveiling IoT customer behaviour: Segmentation and insights for enhanced IoT-CRM strategies: A real case study. Sensors, 24(4), 1050. https://doi.org/10.3390/s24041050.