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

Publication date: 24 July 2019

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

Citation

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

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
If you would like to contact us about accessing this content, click the button and fill out the form.