Customer choice prediction based on transfer 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.

Keywords:

• forecasting
• choice prediction
• small sample
• transfer learning
• customer segmentation

Acknowledgements

This work is supported by the MOE (Ministry of Education in China) Youth Project of Humanities and Social Sciences (Grant No. 13YJC630249), Scientific Research Starting Foundation for Young Teachers of Sichuan University (2012SCU11013) and Specialised Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China (20120181120074)