Research on User Behavior Analysis and Growth Strategies in Digital Exhibitions Based on Pattern Recognition
Authors: 
Liuru Zhu, Qiao Sui, Zhenlin Wu, Li Sui, Huinan Liu, Yuanlong XuAuthors Info & Claims
Liuru Zhu, Qiao Sui, Zhenlin Wu, Li Sui, Huinan Liu, Yuanlong XuAuthors Info & ClaimsPages 88 - 94
Abstract
This paper explores the analysis of user behavior and the development of growth strategies in digital exhibitions using pattern recognition technology. With the rapid advancement of digitalization, particularly in response to global events like the COVID-19 pandemic, digital exhibitions have become increasingly prevalent. By applying pattern recognition, this study identifies and classifies user behavior patterns, enabling companies to better understand user needs and optimize their marketing strategies. The research highlights the significant influence of perceived value on user engagement, decision-making processes, and overall satisfaction. Through a comprehensive case study of the Canton Fair's Digital Exhibitions, the paper demonstrates how companies can leverage behavioral insights to enhance user experience, increase conversion rates, and implement effective personalized marketing strategies. The findings underscore the potential of pattern recognition technology in achieving sustainable user growth and offer actionable recommendations for its application in digital exhibitions.
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October 2024
128 pages
ISBN:9798400717789
DOI:10.1145/3704391
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Published: 18 January 2025
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ITCC 2024
ITCC 2024: 2024 6th International Conference on Information Technology and Computer Communications
October 25 - 27, 2024
Singapore, Singapore
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