TY - JOUR
T1 - A comparative study of hybrid machine learning techniques for customer lifetime value prediction
AU - Tsai, Chih Fong
AU - hu, Ya Han
AU - Hung, Chia Sheng
AU - Hsu, Yu Feng
PY - 2013/3/22
Y1 - 2013/3/22
N2 - 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 commonlyused hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction. To construct a hybrid model, multiple techniques are usually combined in a twostage manner, in which the first stage is based on either clustering or classification techniques, which can be used to preprocess 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 kmeans and selforganizing maps for the clustering techniques to construct six different hybrid models. The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining twostage 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). 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.
AB - 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 commonlyused hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction. To construct a hybrid model, multiple techniques are usually combined in a twostage manner, in which the first stage is based on either clustering or classification techniques, which can be used to preprocess 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 kmeans and selforganizing maps for the clustering techniques to construct six different hybrid models. The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining twostage 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). 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.
KW - Customer information
KW - Customer lifetime value
KW - Data mining
KW - Database marketing
KW - Hybrid models
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=84877826503&partnerID=8YFLogxK
U2 - 10.1108/03684921311323626
DO - 10.1108/03684921311323626
M3 - 期刊論文
AN - SCOPUS:84877826503
SN - 0368-492X
VL - 42
SP - 357
EP - 370
JO - Kybernetes
JF - Kybernetes
IS - 3
ER -