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Customer choice prediction based on transfer learning

Author

Listed:
  • Bing Zhu

    (Business school of Sichuan University, Chengdu, China)

  • Changzheng He

    (Business school of Sichuan University, Chengdu, China)

  • Xiaoyi Jiang

    (University of Muenster, Muenster, Germany)

Abstract

Choice behaviour prediction is valuable for developing suitable customer segmentation and finding target customers in marketing management. Constructing good choice models for choice behaviour prediction usually requires a sufficient amount of customer data. However, there is only a small amount of data in many marketing applications due to resource constraints. In this paper, we focus on choice behaviour prediction with a small sample size by introducing the idea of transfer learning and present a method that is applicable to choice prediction. The new model called transfer bagging extracts information from similar customers from different areas to improve the performance of the choice model for customers of interest. We illustrate an application of the new model for customer mode choice analysis in the long-distance communication market and compare it with other benchmark methods without information transfer. The results show that the new model can provide significant improvements in choice prediction.

Suggested Citation

  • Bing Zhu & Changzheng He & Xiaoyi Jiang, 2015. "Customer choice prediction based on transfer learning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(6), pages 1044-1051, June.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:6:p:1044-1051
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    Citations

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    Cited by:

    1. Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
    2. Zanette, Maria Carolina & Scaraboto, Daiane, 2019. "“To Spanx or not to Spanx”: How objects that carry contradictory institutional logics trigger identity conflict for consumers," Journal of Business Research, Elsevier, vol. 105(C), pages 443-453.

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