Hybrid model of customer response modeling through combination of neural networks and data pre-processing

Due to the increasing data volume, it is difficult for direct marketing decision makers to find target customers. Computational intelligence models that computerize human analysis have recognized a testing device for customer response modeling and target customers identification. Widespread research has resulted in numerous direct marketing applications using computational intelligence models; customer response modeling is an important activity of direct marketing. This paper proposes a hybrid model for customer response modeling. The Proposed model evolves neural network primary weights by genetic algorithms and optimizes it by back-propagation. We also utilize data pre-processing methods such as data reduction for improving precision of the model. We test ability of the proposed method by applying it to standard data set. Results show that the proposed approach is capable to deal with the customer response modeling and it also yields superior prediction accuracy.