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Integrating OWA and data mining for analyzing customers churn in E-commerce

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Abstract

Customers are of great importance to E-commerce in intense competition. It is known that twenty percent customers produce eighty percent profiles. Thus, how to find these customers is very critical. Customer lifetime value (CLV) is presented to evaluate customers in terms of recency, frequency and monetary (RFM) variables. A novel model is proposed to analyze customers purchase data and RFM variables based on ordered weighting averaging (OWA) and K-Means cluster algorithm. OWA is employed to determine the weights of RFM variables in evaluating customer lifetime value or loyalty. K-Means algorithm is used to cluster customers according to RFM values. Churn customers could be found out by comparing RFM values of every cluster group with average RFM. Questionnaire is conducted to investigate which reasons cause customers dissatisfaction. Rank these reasons to help E-commerce improve services. The experimental results have demonstrated that the model is effective and reasonable.

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Author information

Authors and Affiliations

  1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Jie Cao & Xiaobing Yu

  2. Information Center of Jiangsu Province, Nanjing, 210013, China

    Zhifei Zhang

Authors
  1. Jie Cao
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  2. Xiaobing Yu
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  3. Zhifei Zhang
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Corresponding author

Correspondence to Jie Cao.

Additional information

This research was supported by the Natural Science Foundation under Grant Nos. 71273139, 60804047 and the Social Science Foundation of Chinese Ministry of Education under Grant No. 12YJC630271.

This paper was recommended for publication by Editor WANG Shouyang.

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Cao, J., Yu, X. & Zhang, Z. Integrating OWA and data mining for analyzing customers churn in E-commerce. J Syst Sci Complex 28, 381–392 (2015). https://doi.org/10.1007/s11424-015-3268-0

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  • DOI: https://doi.org/10.1007/s11424-015-3268-0

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