Customer list segmentation using the combined response model

Response models such as RFM (Recency, Frequency, Monetary), Logistic Regression, and Neural Networks estimate a single response model in direct marketing for segmenting and targeting customers. However, if there is considerable customer heterogeneity in the database, the models can be potentially misleading. To reflect this heterogeneity, researchers have introduced ways to combine two or more methods. Suggesting the capability of the combined model using the low correlation coefficient between them, the previous research on the combined response model did not provide answers for two important questions: (1) What are the response models that have a low correlation coefficient between them when combined? (2) Does the low correlation coefficient ensure improved performance? In this paper, we propose RFM as a method that has a low correlation coefficient when combined with Logistic Regression or Neural Networks. Our case study also concludes that the low correlation coefficient does not always ensure improved performance.