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研究生(外文):Chih-Chien Weng
論文名稱(外文):Applying data mining technique in customer relationship management–taking a pediatric dental clinic as an example
指導教授(外文):Hsin-Hung Wu
外文關鍵詞:Customer Relationship ManagementData MiningLRFM modelpediatric dentalSelf-Organizing MapK-MeansCART
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本研究透過LRFM (Length, Recency, Frequency, and Monetary)模型針對某家兒童牙醫診所的病患資料進行資料轉換與分析,再透過自組織映射圖網路(Self-Organizing Maps)與K-Means兩階段分群法對病患進行分群,並對此分群後的12群病患做敘述性統計分析,了解各分群病患分別的特徵與消費行為,找出高價值病患與潛在重要病患,並藉由研究結果給予該診所醫師適合的行銷建議;另外以F為目標產出變數,性別、年齡、L、R與郵遞區號為投入變數,建立分類與回歸樹(Classification and Regression Tree, CART)決策樹預測模型,透過決策樹歸納出12條判斷規則,找出能為診所帶來獲利的重要顧客群以及流失顧客群的行為特徵,以提供該診所醫師在未來針對這兩類類群顧客擬訂適合的行銷策略與資源分配。

In recent years, the domestic living standards have been improved, and the national health insurance has become maturely such that the residents in Taiwan have paid much attention to enhance the quality requirements of the medical service. Thus, the purpose of this research is to improve patients’ relationship by increasing the quality of medical care and patient satisfaction. This research uses data mining technique to transform the raw data into the useful information, identify the important patients, design marketing strategies to satisfy a wide variety of patients, and then reduce the cost to improve the performance.
This study uses LRFM (Length, Recency, Frequency, and Monetary) model to analyze patients’ database of a pediatric dental clinic by deploying a two-stage clustering method, including SOM (Self-Organizing Map) and K-Means methods. The descriptive analyses and patients’ characteristics and behaviors of the twelve clusters are summarized. Moreover, frequency is chosen to be the target output variable, while gender, age, length, recency, and postal code are the input variables to establish the decision tree forecasting model of classification and regression tree (CART). Twelve rules generated by CART are depicted to identify the high value patients as well as the loss patients for this dental clinic. The findings can also provide this dental clinic to make applicable marketing strategies and allocate resource more effectively for these two patient groups.


摘要 I
圖目錄 VI
表目錄 VII

第一章 緒論 1
第一節 研究動機與背景 1
第二節 研究目的 3
第三節 研究流程 3
第二章 文獻探討 5
第一節 顧客關係管理 5
第二節 顧客價值 6
第三節 顧客忠誠度 7
第四節 資料探勘 8
一、資料探勘的定義 8
二、資料探勘的流程 9
三、資料探勘的功能 9
四、RFM模型 10
五、自組織映射圖網路與K-Means演算法 12
第五節 決策樹 14
第三章 研究方法 17
第一節 研究架構 17
第二節 資料來源 17
第三節 分群法資料前置處理 17
第四節 分群法資料分析 18
第五節 決策樹資料前置處理 18
第六節 決策樹資料分析 20
第四章 研究結果 21
第一節 分群結果與分析 21
一、 敘述性統計分析 21
二、 LRFM資料分析 22
三、 SOM與K-Means分群分析 22
第二節 決策樹結果與分析 25
一、CART預測模型建置 25
二、預測準確率分析 26
三、重要性變數分析 28
四、決策樹規則分析 30
第五章 結論與建議 36
第一節 分群法結論與建議 36
第二節 決策樹分析結論與建議 36
第三節 分群法與決策樹分析整合建議 37
參考文獻 39

圖1.1 研究流程圖 4
圖4.1 SOM顧客分群圖 22
圖4.2 CART投入變數重要性 29
圖4.3 CART決策樹分類結果 31

表3.1 實際資料與五等記分轉換表 19
表4.1 年齡與性別統計分析 21
表4.2 看診次數與性別統計分析 21
表4.3 LRF敘述統計 22
表4.4 SOM與K-Means顧客分群結果 23
表4.5 CART決策樹訓練組與測試組比例配置結果 26
表4.6 CART決策樹模型預測準確率 27
表4.7 CART決策樹訓練組與測試組預測準確率結果 27
表4.8 CART投入變數重要性 29
表4.9 CART決策樹分類規則與結果 33

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