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題名:捐款人流失預測之決策系統
作者:呂理邦
作者(外文):Li-Pang Lu
校院名稱:國立東華大學
系所名稱:企業管理學系
指導教授:許芳銘
學位類別:博士
出版日期:2012
主題關鍵詞:捐款者流失二元羅吉斯回歸法倒傳遞類神經網路法決策樹支援向量機器簡單貝式法donor defectionbinary logistic regressionback-propagation neural networkdecision treesupport vector machinenaïve bayes
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以往關於捐款者流失的研究多關注在捐款者流失的原因或是如何讓捐款者變為忠誠,但是如何在事前就預測出容易流失的捐款者仍需要進一步探究。因此,為了填補此一研究上的空缺,本研究使用一個二階段的方法並選出18個在捐款者忠誠度、社會人口與捐款者和機構間之關係長度的領域變數,且這些變數是從資料庫中獲得而非從問卷獲得。在探索階段,本研究使用了二元羅吉斯回歸法來找出有預測力的變數;在預測階段,本研究比較了幾種常用的分類技術,包括二元羅吉斯回歸法、倒傳遞類神經網路法、支援向量機、決策樹以及簡單貝式法,來建構捐款者流失的決策支援系統。此外,本研究設計了以型一誤差、型二誤差與經濟成本為考量的成本衡量指標,發現其更合適來評估捐款者流失預測模型。最後,實驗結果顯示決策樹在以權重最近捐款分數、是否為定期定額捐款以及捐款者與機構間之關係長度為預測變數時有最好的效能。
In previous study, most of existing researches about donor defection have focused on why the donors defect or how to keep them loyal, but what remain explored is that how to predict the likely defecting donors in advance. Hence, in order to help fill this gap on the donor defection, this study utilized a two stages process of dealing with 18 variables within three topics: customer loyalty, socio-demographic information and the duration of donor relationship when data is obtained from a database instead of a survey. At the exploration stage, this study used binary logistic regression to find out the predictive variables; at the prediction stage, we compared several famous classifier techniques including binary logistic regression, back-propagation neural network, support vector machine, decision tree and naïve bayes to construct a donor defection decision support system. In addition, this study designed a misclassification cost measurement by taking type I errors, type II errors and economic cost into account, which is more suitable to evaluate the donor defection prediction model. Finally, results show that decision tree has best performance with three predictors: weighted recency, regular donation and duration of donor relationship.
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