Title: A precision marketing method for e-commerce considering the hidden behavioural characteristics of user online shopping

Authors: Zheng Xu; Hengzhi Nie; Wanqing Chen

Addresses: Department of Economics and Trade, Henan Polytechnic Institute, Nanyang, 473001, Hennan, China; Department of Business Administrator, Zhongnan University of Economics and Law, Wuhan, 430073, China ' The Academy of Digital China (FuJian), Fuzhou, 350003, China ' Zhengzhou Fengyang Foreign Language School, Zhengzhou, 450000, Hennan, China

Abstract: In order to improve user order rate and achieve high marketing satisfaction, this article considers the hidden behaviour characteristics of user online shopping and designs an e-commerce precision marketing method. Firstly, use web crawler technology to collect and preprocess hidden behaviour data of e-commerce platform users during online shopping. Then, calculate the level of interest of e-commerce platform users in different labelled products during the online shopping process, and use natural language processing algorithms to identify the hidden behaviour characteristics of e-commerce platform users during online shopping. Based on the K-means clustering algorithm, perform fuzzy clustering on the hidden behaviour characteristics of online shopping. Finally, the Pearson similarity algorithm is used to calculate the similarity between feature data and target product data, and to construct an e-commerce platform's online shopping product push matrix. Based on the ranking results of product push, precise e-commerce marketing is achieved. The experimental results show that using the proposed method, user satisfaction with product marketing recommendations remains above 85%, and user order rates remain above 90%. E-commerce marketing has high accuracy and good marketing effects.

Keywords: e-commerce; electronic commerce; invisible behaviour; precision marketing; behavioural characteristics; fuzzy clustering.

DOI: 10.1504/IJBIDM.2025.145388

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.3/4, pp.346 - 361

Received: 07 Dec 2023
Accepted: 02 Aug 2024
Published online: 31 Mar 2025 *

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