Customer choice prediction based on transfer learning

Customer choice prediction based on transfer learning

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Article ID: iaor201525821
Volume: 66
Issue: 6
Start Page Number: 1044
End Page Number: 1051
Publication Date: Jun 2015
Journal: Journal of the Operational Research Society
Authors: , ,
Keywords: retailing, learning
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.

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