Customer segmentation in sales transaction data using k-means clustering algorithm
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Published:
Jun 30, 2024
Keywords:
Clustering, K-Means, Segmentation, Sales, Transaction
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Abstract
Customer segmentation against sales transaction data using K-Means clustering algorithm. The purpose of this research is to develop and validate a customer segmentation model using an optimized K-Means clustering algorithm to enable more accurate customer grouping based on sales transaction data. The methodology used includes quantitative design combined with experimental techniques, quantitative data analysis, and model validation, where rice sales transaction data from Tegal city traditional market is processed to identify customer segments. The results showed the effectiveness of the optimized K-Means algorithm in grouping customers into three clusters based on purchase characteristics, and C4-SUPER rice proved to be the best-selling among consumers. These insights enable the development of more targeted and personalized marketing strategies, enrich the academic literature on customer data analysis, and move towards the practical application of more effective customer segmentation through the use of advanced analytical technologies
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How to Cite
Nugroho, B. I. ., Rafhina, A., Ananda, P. S. ., & Gunawan, G. . (2024). Customer segmentation in sales transaction data using k-means clustering algorithm. Journal of Intelligent Decision Support System (IDSS), 7(2), 130-136. https://doi.org/10.35335/idss.v7i2.236
References
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Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727.
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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.
Appio, F. P., Frattini, F., Petruzzelli, A. M., & Neirotti, P. (2021). Digital transformation and innovation management: A synthesis of existing research and an agenda for future studies. Journal of Product Innovation Management, 38(1), 4–20.
Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM ranking–An effective approach to customer segmentation. Journal of King Saud University-Computer and Information Sciences, 33(10), 1251–1257.
Cui, F., Hu, H., & Xie, Y. (2021). An intelligent optimization method of E-commerce product marketing. Neural Computing and Applications, 33, 4097–4110.
Deshpande, V., & Pendem, P. K. (2023). Logistics performance, ratings, and its impact on customer purchasing behavior and sales in e-commerce platforms. Manufacturing & Service Operations Management, 25(3), 827–845.
Ebrahimkhani, S., Jaward, M. H., Cicuttini, F. M., Dharmaratne, A., Wang, Y., & de Herrera, A. G. S. (2020). A review on segmentation of knee articular cartilage: from conventional methods towards deep learning. Artificial Intelligence in Medicine, 106, 101851.
Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, 104743.
Gao, W., Fan, H., Li, W., & Wang, H. (2021). Crafting the customer experience in omnichannel contexts: The role of channel integration. Journal of Business Research, 126, 12–22.
Griva, A., Zampou, E., Stavrou, V., Papakiriakopoulos, D., & Doukidis, G. (2024). A two-stage business analytics approach to perform behavioural and geographic customer segmentation using e-commerce delivery data. Journal of Decision Systems, 33(1), 1–29.
Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986.
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210.
Kasem, M. S., Hamada, M., & Taj-Eddin, I. (2024). Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Computing and Applications, 36(9), 4995–5005.
Katsikeas, C., Leonidou, L., & Zeriti, A. (2020a). Revisiting international marketing strategy in a digital era: Opportunities, challenges, and research directions. International Marketing Review, 37(3), 405–424.
Katsikeas, C., Leonidou, L., & Zeriti, A. (2020b). Revisiting international marketing strategy in a digital era: Opportunities, challenges, and research directions. International Marketing Review, 37(3), 405–424.
Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49, 51–70.
Li, L., & Zhang, J. (2021). Research and analysis of an enterprise E-commerce marketing system under the big data environment. Journal of Organizational and End User Computing (JOEUC), 33(6), 1–19.
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability, 13(3), 1224.
Miraftabzadeh, S. M., Colombo, C. G., Longo, M., & Foiadelli, F. (2023). K-means and alternative clustering methods in modern power systems. IEEE Access.
Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725.
Otto, A. S., Szymanski, D. M., & Varadarajan, R. (2020). Customer satisfaction and firm performance: insights from over a quarter century of empirical research. Journal of the Academy of Marketing Science, 48(3), 543–564.
Sima, V., Gheorghe, I. G., Subić, J., & Nancu, D. (2020). Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12(10), 4035.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727.
Tabianan, K., Velu, S., & Ravi, V. (2022). K-means clustering approach for intelligent customer segmentation using customer purchase behavior data. Sustainability, 14(12), 7243.
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.
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