Precision Marketing Methods for E-commerce Based on Clustering Algorithms

NONE 20260505070820968 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 1109-9526 2224-2899 Precision Marketing Methods for E-commerce Based on Clustering Algorithms Pengfei Bai Zhengzhou Shengda University Zhengzhou 451191 CHINA With the e-commerce industry shifting from scale expansion to refined operation, traditional undifferentiated marketing strategies face challenges such as high costs and low conversion rates, making precision marketing a key direction for enterprises to enhance competitiveness. This study focuses on solving the problem of effective user segmentation and personalized marketing adaptation in e-commerce, taking clustering algorithms as the core technical support. First, it systematically combs the core theories of clustering algorithms and key theories of e-commerce precision marketing. Then, it designs a complete e-commerce precision marketing model based on clustering algorithms: it clarifies the sources of user data and conducts preprocessing steps such as data cleaning, integration, transformation, and reduction; it optimizes the K-means algorithm by determining the optimal number of clusters through the elbow method and improving initial cluster center selection via K-means++ algorithm. 05 05 2026 05 05 2026 443 33 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23207.2026.23.33 https://wseas.com/journals/bae/2026/a665107-3639.pdf Chaudhry M., Shafi I., Mahnoor M, . A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry, Vol.15, No.9, 2023, pp. 1679, 1-44. DOI: 10.3390/sym15091679 Ke Jia. Discovering e-commerce user groups from online comments: An emotional correlation analysis-based clustering method. Computers and Electrical Engineering, No.113, 2024, pp. 109035-109042. DOI: 10.1016/j.compeleceng.2023.109035 Saluja, Anjali, . Precision marketing strategy for E-Commerce by using big data technology. Journal of Informatics Education and Research, Vol.3, No.2, 2023, pp. 2554-2560. DOI:10.52783/jier.v3i2.433 Abtahi, A., . Exploring consumer preferences: The significance of personalization in ecommerce. Malaysian E Commerce Journal, Vol.8, No.1, 2023, pp. 01-07. DOI: 10.26480/mecj.01.2024.01.07 Rofi’i, Yulianto Umar. Analysis of e-commerce purchase patterns using big data: An integrative approach to understanding consumer behavior. International Journal Software Engineering and Computer Science, Vol.3, No.3, 2023, pp. 352-364. DOI: 10.35870/ijsecs.v3i3.1840. Kumar, Sumit, . Customer segmentation in ecommerce: K-means vs hierarchical clustering. TELKOMNIKA (Telecommunication Computing Electronics and Control), Vol.23, No.1, 2025, pp. 119-128. DOI: 10.12928/telkomnika.v23i1.26384 Wenz, Viola, Arno Kesper, and Gabriele Taentzer. Clustering heterogeneous data values for data quality analysis. ACM Journal of Data and Information Quality, Vol.15, No.3, 2025, pp. 1-33. DOI: 10.1145/3603710 Karrar, Abdelrahman Elsharif. The effect of using data pre-processing by imputations in handling missing values. Indonesian Journal of Electrical Engineering and Informatics, Vol.10, No.2, 2022, pp. 375-384. DOI: 10.52549/ijeei.v10i2.3730 Wang Xinyi, Liao Xiaoling. Research on accurate marketing of e-commerce based on k-means algorithm. Procedia Computer Science, Vol.261, 2025, pp. 1208-1214. DOI: 10.1016/j.procs.2025.04.706 Hu Chun, Wu Chuanjian, Huang Zongjie. Research on precision marketing of e-commerce enterprise based on cluster analysis in the big data environment. Procedia Computer Science, Vol.247, 2024, pp. 403-411. DOI: 10.34028/iajit/22/5/5.