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Research on E-commerce Customer Churn Based on RFM Model and Naive Bayes Algorithm

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13338))

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Abstract

In recent years, with the rapid development of e-commerce, more and more people are engaged in the e-commerce industry. In order to stand out from numerous e-commerce enterprises and retain customers, it is necessary to achieve accurate marketing, so as to segment the market, locate customer groups, and attract and retain customers through formulating marketing strategies. In this paper, RFM model and Naive Bayes algorithm are used to analyze customer churn. The three indicators of RFM model are relatively independent and have good representativeness for customer classification, and have been widely used in customer classification in various fields. Naive Bayes algorithm can calculate the probability of loss more easily, so the combination of the two can be used to identify which kind of customers are more likely to lose. Thus help enterprises to implement different marketing strategies for different customer groups, in order to save the cost of enterprises, improve the efficiency of enterprises.

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Authors and Affiliations

  1. Hunan University of Finance and Economics, Changsha, 410000, China

    Yuan Tang, Yupeng Li & Guang Sun

Authors
  1. Yuan Tang
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  2. Yupeng Li
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  3. Guang Sun
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Corresponding author

Correspondence to Guang Sun .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Tang, Y., Li, Y., Sun, G. (2022). Research on E-commerce Customer Churn Based on RFM Model and Naive Bayes Algorithm. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_30

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  • DOI: https://doi.org/10.1007/978-3-031-06794-5_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06793-8

  • Online ISBN: 978-3-031-06794-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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