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Combination classification method for customer relationship management

Zhe Zhang (School of Management, Fudan University, Shanghai, China)
Yue Dai (School of Management, Fudan University, Shanghai, China)

Asia Pacific Journal of Marketing and Logistics

ISSN: 1355-5855

Article publication date: 31 July 2019

Issue publication date: 23 June 2020

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Abstract

Purpose

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.

Design/methodology/approach

In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.

Findings

The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.

Originality/value

The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

Keywords

Acknowledgements

The authors sincerely thank seminar participants at Center for Data-Driven Managerial Decision Making. This work is supported by the National Natural Science Foundation of China (Grant Nos 71672038 and 71222104) and The China Scholarship Council (Grant No. 201706105007).

Citation

Zhang, Z. and Dai, Y. (2020), "Combination classification method for customer relationship management", Asia Pacific Journal of Marketing and Logistics, Vol. 32 No. 5, pp. 1004-1022. https://doi.org/10.1108/APJML-03-2019-0125

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited