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Customer Segmentation and Management of Online Shops Based on RFM Model

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Application of Intelligent Systems in Multi-modal Information Analytics (MMIA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1233))

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Abstract

Based on the RFM model, this study adopts k-means clustering method to cluster users with similar purchasing behaviors. Then according to the differences in the buying behavior of each group of customers, customers are divided into different types. Then, this study innovatively adopts a special objective customer value weight calculation method to obtain the customer value calculation weight that matches the specific enterprise. Finally, the customer value of each group was calculated, and corresponding customer relationship management recommendations were given.

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Acknowledgements

This work was supported National Social Science Fund: Research on Repetitive Purchase User Identification Based on Light Review and RFM Dual Perspectives (No. 20162s0079); Independent Innovation Project: Emotional Quantitative Research on Light Reviews of Online Users (No. 2017VI016).

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Authors and Affiliations

  1. School of management, Wuhan University of Technology, Wuhan, China

    Yulu He & Yanxia Cheng

Authors
  1. Yulu He
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  2. Yanxia Cheng
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Corresponding author

Correspondence to Yanxia Cheng .

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Editors and Affiliations

  1. Department of Decision and Information Sciences, and Center for Data Science and Big Data Analytics, Oakland University, School of Business Administration, Rochester, MI, USA

    Vijayan Sugumaran

  2. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  3. Department of Informatics, University of Leicester, Leicester, UK

    Huiyu Zhou

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He, Y., Cheng, Y. (2021). Customer Segmentation and Management of Online Shops Based on RFM Model. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51431-0_6

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