Title: A precision marketing method for digital product big data based on user generated content

Authors: Jing Liu; Yiwen Ruan; Jia Lin

Addresses: College of Business and Management, Ningbo University of Finance & Economics, Ningbo, Zhejiang, China ' Business School, Yunnan University of Finance and Economics, Kunming, Yunnan, China ' Department of Business, Guangxi Technological College of Machinery and Electricity, Nan'ning, China

Abstract: In order to improve the marketing accuracy and user satisfaction of digital product big data, a precision marketing method based on user generated content for digital product big data is proposed. Firstly, vectorise the user generated evaluation text, digital product category text and image information of digital product descriptions. Secondly, convolutional fusion is performed on the text comprehensive features and image features of digital products. Finally, construct a digital product user interest model based on the level of user interest. Tag weights are used to construct a precise marketing function for digital product big data. The experimental results show that compared with existing marketing methods, this paper's method can improve the marketing accuracy of digital product big data, while also enhancing user satisfaction.

Keywords: user generated content; digital products; big data precision marketing; user interest model.

DOI: 10.1504/IJPD.2025.144847

International Journal of Product Development, 2025 Vol.29 No.1, pp.70 - 83

Received: 26 Jan 2024
Accepted: 03 Oct 2024
Published online: 05 Mar 2025 *

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