The Precise Operational Practices Enabled by User Behavior Analysis Supported by Clustering Algorithms in the Rapid Digital Transformation of Enterprises
Main Article Content
Abstract
This study proposes a clustering-based user behavior analysis framework to support precise operational practices in rapidly transforming digital enterprises. By integrating K-means and DBSCAN algorithms, the framework identifies behavioral patterns from multidimensional user activity data, including transaction behavior, service utilization, engagement frequency, and interaction characteristics. A Precision Operation Framework (POF) is developed to transform clustering outcomes into actionable operational strategies through data acquisition, behavioral analysis, insight extraction, and decision-support modules. Experimental results based on 86,000 user records demonstrate that the proposed approach improves operational precision by 27.5%, increases targeted marketing effectiveness by 22.1%, and reduces user churn risk by 19.3% compared with traditional rule-based segmentation methods. The framework also exhibits strong robustness, interpretability, and adaptability under evolving behavioral conditions. The proposed methodology is particularly applicable to communication-intensive digital environments supported by wireless communication infrastructures and antenna-enabled mobile access networks, where continuous user interaction data can be leveraged to enhance real-time decision-making and service optimization. This study provides an effective engineering solution for intelligent enterprise operation, user-centric management, and data-driven digital transformation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Y. Ni, H. Gu, C. Ou, et al., “The impact of digital finance on the digital transformation of cultural industry enterprises,” Finance Research Letters, vol. 85, Part A, 2025, doi: 10.1016/j.frl.2025.107849.
K. Szymczyk and Ágnes Csiszárik-Kocsir, “Digital Transformation of Micro-Enterprises in the Light of the Covid-19 Pandemic,” International Conference on Information Systems Development, 2024, doi: 10.62036/isd.2024.42.
A. Ilina L, A. Pavlov A, and S. Pavlova K, “Agro-Industrial Enterprises in the Realities of the Digital Transformation,” Lecture Notes in Civil Engineering, pp. 32-42, 2024, doi: 10.1007/978-3-031-67372-6_5.
H. Zhong and M. Jing, “Digital soft decoration display design microspecialty based on double layer clustering user behavior analysis algorithm,” INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, vol. 19, no. 3, pp. 1551-1564, 2025, doi: 10.1177/18724981251314270.
M. Gangwar, “Review of: “Application of Data Mining Combined with K-means Clustering Al-gorithm in Enterprises’ Risk Audit”,” 2024, doi: 10.32388/rx5ezk.
A. Rana D S U, “Application of Data Mining Combined with K-means Clustering Algorithm in Enterprises’ Risk Audit,” Qeios, 2024, doi: 10.32388/g9g0s3.
S. Yang and Z. Kan, “Fed-BNGC: A Federated Clustering Algorithm to Solve the Problem of User Heterogeneity,” SAE International, 2025, doi: 10.4271/2025-99-0110.
X. Cao, “Research on the Application of Clustering Algorithm in the Analysis of College English Achievement,” Advances in Intelligent Systems Research, pp. 393-401, 2024, doi: 10.2991/978-94-6463-417-4_36.
L. Yu, “The Application of K-means Clustering Algorithm in the Evaluation of E-Commerce Websites,” Journal of Electrical Systems, vol. 20, no. 6s, pp. 759-769, 2024, doi: 10.52783/jes.2738.
S. Sun, “Fuzzy Clustering Algorithm for Trend Prediction of The Digital Currency Market,” Salud, Ciencia y Tecnología – Serie de Conferencias, vol. 3, pp. 1094, 2024, doi: 10.56294/sctconf20241094.
W. Cai, F. Yang, B. Yao, et al., “An adaptive k-means clustering algorithm based on grid and domain centroid weights for digital twins in the context of digital transformation,” Journal of Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01180-z.
X. Chen, C. Chen, and L. Yang, “Research and Application of Clustering Algorithm in the Behavior Analysis of Key Electric Power Customers,” 2024 Global Conference on Communications and Information Technologies (GCCIT), pp. 1-5, 2024, doi: 10.1109/gccit63234.2024.10862169.