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Ranking Potential Customers Based on Group-Ensemble

Author

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  • Zhi-Zhuo Zhang

    (South China University of Technology, China)

  • Qiong Chen

    (South China University of Technology, China)

  • Shang-Fu Ke

    (South China University of Technology, China)

  • Yi-Jun Wu

    (South China University of Technology, China)

  • Fei Qi

    (South China University of Technology, China)

  • Ying-Peng Zhang

    (South China University of Technology, China)

Abstract

Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as a ranking potential customers problem. This article proposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In our model, Bagging, RankBoost and Expending Regression Tree are applied to solve crucial data mining problems like data imbalance, missing value and time-variant distribution. The article verifies the model with data provided by PAKDD Competition 2007 and shows that Group-Ensemble can make selling strategy much more efficient.

Suggested Citation

  • Zhi-Zhuo Zhang & Qiong Chen & Shang-Fu Ke & Yi-Jun Wu & Fei Qi & Ying-Peng Zhang, 2008. "Ranking Potential Customers Based on Group-Ensemble," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 4(2), pages 79-89, April.
    • Handle: RePEc:igg:jdwm00:v:4:y:2008:i:2:p:79-89
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