Precise Marketing Strategy Optimization of E-Commerce Platform Based on KNN Clustering

Author:

Yang Benfang1ORCID,Li Jiye2

Affiliation:

1. School of Economics and Management, Sichuan Technology and Business University, Chengdu, Sichuan 611700, China

2. School of Computer Engineering, Chengdu Technological University, Chengdu, Sichuan 611700, China

Abstract

With the development of computer technology and the arrival of the era of artificial intelligence, the analysis of user demand bias is of great significance to the operation optimization of e-commerce platforms. Combined with CS domain signaling data, IP packet data of PS domain, and customer CRM data provided by operators, this research studies each dimension index of operator user portrait, after that the operator user portrait platform is divided into some individual subunits, and then the corresponding data mining technology is carried out to study the implementation scheme of each subunit. The system can process and mine multidimensional data of operators’ users and form user portraits on the basis of user data aggregation. Finally, based on the operator user portrait platform studied in this paper, the operator user data are analyzed from both the user’s mobile phone use behavior and user consumption behavior. Furthermore, the application value of this research in the precision marketing and personalized service of operators is illustrated.

Publisher

Hindawi Limited

Subject

General Mathematics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. User Behavior Analysis and Prediction Algorithm Design of E-commerce Platform Based on Big Data Analysis;2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC);2024-01-10

2. Pattern Detection in e-Commerce Using Clustering Techniques to Explainable Products Recommendation;Lecture Notes in Networks and Systems;2024

3. How market pressures and organizational readiness drive digital marketing adoption strategies' evolution in small and medium enterprises;Technological Forecasting and Social Change;2023-08

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