近年來智慧型計算(Intelligent Computing)和顧客回應模式(Customer Response Model)由於能夠協助企業挖掘出顧客隱性知識並預測具回應傾向的顧客因此逐漸受到重視。本研究希望能夠利用在智慧型計算中頗受重視的資料探勘技術，將交易資料所挖掘所得的購買行為型樣藉以建立顧客回應模式，協助企業更正確地預測出行銷推廣的目標顧客以及後續之行銷策略。研究中以國內A 壽險公司為例，蒐集該公司188464 筆顧客交易資料，透過決策樹歸納演算法加以探勘，模式包括943 項具代表性的顧客回應之預測性規則，並經過驗證達到80.7%的預測準確率。此外，研究中將所得到的規則加以歸納，進一步探究屬性與熱賣壽險類別之間的關係以供擬定行銷策略之參考。With the increased use of intelligent computing, target marketing that utilizes data mining in databases to seek for the potential customers as well as to derive marketing strategies is gradually capturing attentions of management. The Customer Response Model (CResM) with the capability of potential customers prediction is a useful tool that can help in targeting customers who are most likely to reply marketing promotion. In this paper, a data mining based CResM is proposed to help elicit potential customers who is likely to response for marketing promotions. The size of the transaction database collected from a leading insurance company in Taiwan was 188464. The mining mechanism used was the induction-based algorithm. An 80.7% test accuracy was obtained, indicating that the mining mechanism used was adequate. The final results obtained were 943 decision rules. Knowledge interpretation as well as managerial implications with respect to the application case were also provided in this research also.