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研究生(外文):Hsiao-Hsien Chen
論文名稱(外文):A study on mining the contribution and loyalty of customers using the distribution of principal component scores
指導教授(外文):Mei-Pin Shi
外文關鍵詞:degree of contributiondegree of loyaltyprincipal component analysis
  • 被引用被引用:6
  • 點閱點閱:258
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  • 下載下載:56
  • 收藏至我的研究室書目清單書目收藏:1
With the deregulation of foreign investment and the implementation of financial holding codes, trends of globalization、expanding and multiple products have become the norm of the stock industry. However, under the course of “the big one getting more bigger and the small one getting even more smaller”, how can a small traditional broker survive with only a single product? Perhaps, by analyzing the precious resource, customer’s basic attributes and transaction records, to obtain a better customer relationship is the only choice for the company to remain a competition advantage.
Traditionally, the marketing strategy is to increase market share but ignoring the main income source, the transaction commission. Although short-term operating investors may increase the revenue to the company, but they also enjoy a higher discount rate of the fees charged. To a single product broker, the single source of income is the transaction charge from buying and selling stocks. Thus, the short-term investor did not profit single-product brokers, as it may seem.
Therefore, we move our focus from market share to customer’s retaining rate. We start from the aspect of better customer relationship, classify the customers by the profitability at institutional viewpoint, and then discover the customer group with high contribution and loyalty. This information should be useful for managers in managing investment portfolio strategies for a better customer profitability and retention rate, and in alerting the potential lost of customers with higher degree of contribution before it happened. That is, using our model, the brokers may impose appropriate strategies in order to retain “better customers” in time.
In our research, we extract historical transaction data from a broker’s database, after cleansing and transformation, we classify customers into four groups: lost without profitability, lost but profitable, retained without profitability and retained with profitability. Next, we calculate the first principal component scores of all entries; identify the distribution of the scores of each group and develop the discrimination function for classifying customers into a group. Finally we validated our model by a simulation study.
摘要 i
Abstract iii
誌謝 v
目次 vi
表目錄 vii
圖目錄 viii
第一章 序論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第二章 理論基礎 3
第一節 知識發掘 3
第二節 資料探勘 5
第三節 主成份分析 8
第四節 RFM分析模型 12
第三章 研究方法 15
第一節 研究流程 15
第二節 顧客屬性 17
第三節 分析潛在顧客群 19
第四章 實驗設計 22
第一節 實驗資料 22
第二節 軟體環境 25
第五章 實驗結果 30
第六章 結論 34
參考文獻 35
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