A case-based customer classification approach for direct marketing

Case-based reasoning (CBR) shows signi®cant promise for improving the effectiveness of complex and unstructured decision making. CBR is both a paradigm for computer-based problem-solvers and a model of human cognition. However the design of appropriate case retrieval mechanisms is still challenging. This paper presents a genetic algorithm (GA)-based approach to enhance the case-matching process. A prototype GA±CBR system used to predict customer purchasing behavior is developed and tested with real cases provided by one worldwide insurance direct marketing company, Taiwan branch. The results demonstrate better prediction accuracy over the results from the regression-based CBR system. Also an optimization mechanism is integrated into the classi®cation system to reveal those customers most likely and most unlikely customers to purchase insurance.