Research on the positioning method of online community users from the perspective of precision marketing

Xiaogang Zhao (), Hao Zhang (), Hai Shen () and Yadong Zhou ()
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Xiaogang Zhao: Xi’an International Studies University
Hao Zhang: Xi’an International Studies University
Hai Shen: Xi’an International Studies University
Yadong Zhou: Xi’an Jiaotong University

Electronic Commerce Research, 2023, vol. 23, issue 2, No 23, 1296 pages

Abstract: Abstract In precision marketing for online communities, the existing text-based methods of user positioning cannot position new users rapidly, and they have low positioning efficiency when there is a large number of users. This research proposes a systematic method for the positioning of online community users. In this method, text mining and clustering algorithms are combined to cluster users, and then the user clusters are effectively matched with users' basic attributes through a multinomial logistic regression model. By this means, efficient positioning under the circumstances of a rapid increase in new users and a large number of users can be achieved. Calculation results from a real world example show that this method can effectively solve the problems found in traditional user positioning methods and provides a productive new approach to community user positioning. The study also offers suggestions for user classification management from the perspective of precision marketing.

Keywords: Precision marketing; Online community; User positioning; Combination algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10660-021-09512-w

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