A hybrid customer prediction system based on multiple forward stepwise logistic regression mode
In today's world, customer purchasing behavior prediction is one of the most important aspects of customer attraction. Good prediction can help to develop marketing strategies more accurately and to spend resources more effectively. When designing a customer prediction system (CPS) two issues are key, namely, feature selection and the prediction method to be used. Furthermore, it seems necessary to design CPSs with both high computational speed and good prediction abilities. The purpose of this paper is to develop such a system by using a hybrid approach. The resulting system is a hybrid CPS (HCPS) and is based on Multiple Forward Stepwise Logistic Regression (MFSLR) model. The MFSLR model combines a forward stepwise regression (FSR) technique that rapidly selects an optimal subset of features with multiple logistic regression (MLR) technique. In practice, the new MFSLR model provides very good prediction results. Since customer identification is one of the principal concerns in the insurance industry, an insurance company dataset has been used. The obtained results show that the FSR selects around 55% of the initially available features, in this way considerably reducing computational costs. In addition, the results show that the MLR method leads to more accurate prediction than some other methods we tried, namely, feedforward neural networks, radial basis networks and regression trees.
feature selection forward stepwise regression multiple logistic regression neural networks customer relationship management