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.
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The authors would like to thank the Editor-in-Chief, the Associate Editor, and the three anonymous referees for their helpful comments and constructive guidance. The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (61572397 and 71402138), the Foundation of the Ministry of Education of China (17YJC630016 and 19YJC630014), the Foundation of Education Department of Shaanxi Provincial Government of China (18JK0647) and the Foundation of Xi'an International Studies University (SSZD2019015).
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Zhao, X., Zhang, H., Shen, H. et al. Research on the positioning method of online community users from the perspective of precision marketing. Electron Commer Res (2021). https://doi.org/10.1007/s10660-021-09512-w
- Precision marketing
- Online community
- User positioning
- Combination algorithm