運用資料探勘技術於高價值顧客之探索研究-以台灣某醫藥生技公司為例

隨著產業市場環境國際化與自由化，國內企業面臨的競爭也越來越激烈，在如此激烈的環境中企業要如何獲取最大利益是相當重要的。企業為因應國際化的趨勢，提高產品品質及降低生產成本，已是最基本的要求，若要能夠脫穎而出勢必需要透過客戶關係管理找出主要客戶在哪裡。因此，建置一套能準確的篩選出主要目標客戶選擇模型的確有其必要性。在不同的產業中客戶篩選的標準都不同，不外乎是透過訂貨量來做區分，但在藥品市場中考量的因素更多，所以透過資料探勘技術，從所有因素當中找出主要的決定因素，將可提升客戶分級的準確性。因此，本研究將利用資料探勘技術中的決策樹（Decision Tree）、支持向量機（Support Vector Machines, SVM）、關聯法則與群集分析現有資料，找出分級規則，並提出整合性之解釋。

關鍵字

資料探勘；決策樹；支援向量機；關聯法則；群集分析；客戶關係管理

並列摘要

With the globalization and liberalization of the industrial market environment, the competition faced by domestic companies is also becoming more and more intense. It is very important for companies to obtain maximum benefits in such a fierce environment. In order to meet the trend of internationalization, enterprises must improve product quality and reduce production costs. This is the most basic requirement. To be able to stand out from the crowd, it is necessary to find out where the major customers are through customer relationship management. Therefore, it is indeed necessary to establish a set of accurate selection of the main target customer selection model. In different industries, the screening criteria for customers are different. It is nothing more than distinguishing through order quantity. However, there are more factors considered in the pharmaceutical market. Therefore, through data mining technology, the main determinants are determined from all factors , and thay will improve the accuracy of customer ratings. Therefore, this study uses the decision tree, Support Vector Machines （SVM）, association rules and cluster analysis to analyze the existing data, find the classification rules, and propose integrated interpretation.