A comparative study of hybrid machine learning techniques for customer lifetime value prediction

Abstract

Purpose -Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly-used hybrid models by classification þ classification and clustering þ classification hybrid approaches, respectively, in terms of customer value prediction. Design/methodology/approach -To construct a hybrid model, multiple techniques are usually combined in a two-stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre-process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k-means and self-organizing maps for the clustering techniques to construct six different hybrid models. Findings -The experimental results over a real case dataset show that the classification þ classification hybrid approach performs the best. In particular, combining two-stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/ 0.43 percent). Originality/value -The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.

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