Combination classification method for customer relationship management

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.