یک روش طبقه بندی مشتریان بر اساس حروف برای بازاریابی مستقیم

Case-based reasoning (CBR) shows significant 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 classification system to reveal those customers most likely and most unlikely customers to purchase insurance.

Case-based reasoning (CBR) shows significant promise for improving the effectiveness of complex and unstructured decision making. It is a problem-solving technique that is similar to the decision making process used in many real world applications. CBR is both a paradigm for computer-based problem-solvers and a model of human cognition. The reasoning mechanism in the CBR system is based on the synergy of various case features. Therefore this method differs from a rule-based system because of its inductive nature. That is CBR systems reason using analogy concepts rather than the pure decision tree (or IF-THEN rules) usually adopted in rule-based systems. Basically the CBR core steps are (1) retrieving past cases that resemble the current problem; (2) adapting past solutions to the current situation; (3) applying these adapted solutions and evaluating the results; and (4) updating the case base. Basically CBR systems make inferences using analogy to obtain similar experiences for solving problems. Similarity measurements between pairs of features play a central role in CBR (Kolodner, 1992). However the design of an appropriate case-matching process in the retrieval step is still challenging. Some CBR systems represent cases using features and employ a similarity function to measure the similarities between new and prior cases (Shin & Han, 1999). Several approaches have been presented to improve the case retrieval effectiveness. These include the parallel approach (Kolodner, 1988), goal-oriented model (Seifert, 1988), decision trees induction approach (Quinlan, 1986 and Utgoff, 1989), domain semantics approach (Pazzani and Silverstein, 1991), instance-based learning algorithms (Aha, 1992), fuzzy logic method (Jeng & Liang, 1995), etc. These methods have been demonstrated effective in retrieval processes. However, most of these research works focused on the similarity function aspect rather than synergizing the matching results from individual case features. In essence when developing a CBR system, determining useful case features that are able to differentiate one case from others must be resolved first. Furthermore the weighting values used to determine the relevance of each selected feature has to be assigned before proceeding with the case matching process. Rather than being precisely or optimally constructed, the weighting values are usually determined using subjective judgment or a trial and errors basis. To provide an alternative solution this article presents a genetic algorithm (GA)-based approach to automatically construct the weights by learning the historical data. A prototype CBR system used to predict which customers are most likely to buy life insurance products is developed. The data provided by one worldwide insurance direct marketing subsidiary in Taiwan was used for constructing this model. The results show that the GA-based design of CBR system generates more accurate and consistent decisions than the regression-based CBR system.

Defining appropriate feature weighting values is a crucial issue for effective case retrieval. This paper proposed the GA-based approach to determine the fittest weighting values for improving the case identification accuracy. Compared to the regression model, the proposed method has better learning and testing performance. In this study the proposed GA-based CBR system is employed to classify potential customers in insurance direct marketing. The results show significant promise for mining customer purchasing insights that are complex, unstructured, and mixed with qualitative and quantitative information. By using the GA's rapid search strengths this system is able to determine the optimum customer characteristics that reflect the customer features that are most likely and unlikely to buy the insurance products. This system has not only demonstrated its better performance for prediction but also the ability to understand a model. While traditional approaches may provide many similar capabilities, other types of business data can be intensively investigated and tested to assure the GA–CBR strength in modeling classification problems. Because the similarity functions may influence the case association process, future research may work on different combinations of similarity functions between case features to examine their retrieval effectiveness.