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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaogang Zhao & Hao Zhang & Hai Shen & Yadong Zhou, 2023. "Research on the positioning method of online community users from the perspective of precision marketing," Electronic Commerce Research, Springer, vol. 23(2), pages 1271-1296, June.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:2:d:10.1007_s10660-021-09512-w
    DOI: 10.1007/s10660-021-09512-w
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    References listed on IDEAS

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    1. Mei-hui Chen & Kune-muh Tsai & Yi-An Ke, 2019. "Enhancing Consumers' Stickiness to Online Brand Communities as an Innovative Relationship Marketing Strategy," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 15(3), pages 16-34, July.
    2. Sai Vijay Tata & Sanjeev Prashar & Chandan Parsad, 2021. "Typology of Online Reviewers Based on Their Motives for Writing Online Reviews," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 19(2), pages 74-88, April.
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    Cited by:

    1. Xia, Liangjie & Li, Kang & Wang, Jun & Xia, Yi & Qin, Juanjuan, 2024. "Carbon emission reduction and precision marketing decisions of a platform supply chain," International Journal of Production Economics, Elsevier, vol. 268(C).

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