「生物經濟」將接替資通訊科技產業成為新的未來台灣經濟產業命脈。台灣生技產業面對產業目標5000億元的挑戰，如何利用電子資訊和生物經濟雙引擎動能，應用於實際銷售狀況和有效率地把手邊的資料轉化成有用的資訊，找尋出最佳行銷活動依據，合理化的進行資源分配，深耕客戶關係，以建構台灣發展生技產業的競爭優勢，有助於台灣生技產業經濟的永續成長，為目前生技業界重要的議題。
本研究應用資料探勘技術於實際交易資料庫，並參考CRISP-DM為基礎流程，規劃建立適合生技業顧客價值的分析與預測的一套標準作業程序Standard Operation Procedure(SOP)。過程中彙整客戶交易資料後，運用RFM(Recency Frequency Monetary)模型三項指標，作為客戶價值分類的基準，將醫療院所分成四種類型客戶。應用資料探勘工具的決策樹分析，挖掘出不同客戶群的銷售數據規則。最後，將資料探勘的結論交由專家訪談評估適合的解決方案，歸納不同價值的客戶群對應的行銷支援與策略規劃。研究結果，除提供生技業瞭解醫療院所的交易特性，有助於產品行銷外，亦可提供其他Business to Business (B2B)的業者，規劃銷售管理及維繫客戶關係管理的參考。

關鍵字：資料探勘、客戶價值、決策樹、生物科技業

“Bio-economy” will replace the information communication technology (ICT) industry as a core sector of Taiwan’s economy in the future. In the face of the NT$500-billion target of Taiwan’s biotechnology industry, using the kinetic energy of the double engines of electronic information and bio-economy in actual sales to seek the best marketing activities, reasonable resource distribution, and deeper customer relationships to construct national competitiveness for biotechnology industry development can benefit the industry’s sustainable growth. This is one of the most important issues in the industry today.
     This study aims at the application of data mining technology to the actual transaction database. With process planning based on CRISP-DM, it is able to build a set of standard operating procedures (SOP) for the analysis and prediction of customer value in the biotech industry. After collecting customer transaction data, first, it takes the RFM (Recency. Frequency, and Monetary) classification model as three indicators for the benchmarks of customer value, dividing customers of medical institutes into four types. Next, through decision tree analysis of data mining tools, it can dig out different customer sales data rules. Finally, expert interviews based on the data mining results can evaluate suitable solutions, concluding marketing support and strategic planning according to different values of customer groups. The study results allow the biotech industry to understand the trading characteristics of medical institutes, and this also contributes to product marketing. As for other Business to Business (B2B) operators, the results serve as effective references for the planning of sales management as well as maintenance of customer relationship.

主目錄	VI
表目錄	VIII
圖目錄	IX
第一章	緒論	1
第一節	研究背景與動機	1
第二節	研究目的	3
第三節	研究流程	4
第二章	台灣生物技術產業概況	7
第一節	台灣生物技術產業	7
第二節	台灣生物技術產業的特徵	12
第三節	生技產業的銷售管理	18
第四節	非侵入性胎兒染色體基因檢測	19
第五節	NIFTY基因檢測台灣市場分析	22
第三章	文獻探討	26
第一節	生物科技定義	26
第二節	客戶價值定義	27
第三節	客戶價值衡量	28
第四節	資料探勘	30
第五節	決策樹分析	35
第六節	類神經分析	39
第七節	應用分析	41
第四章	研究流程與方法	43
第一節	研究流程	43
第二節	設計模型與比較	46
第五章	實證研究流程	48
第一節	資料收集與選取	48
第二節	資料處裡與客戶價值	49
第三節	決策樹分析	54
第四節	分析與評估	64
第五節	專家訪談	74
第六章	研究結論與建議	81
第一節	研究結論	81
第二節	管理意涵	83
第三節	研究限制	84
第四節	未來研究建議	85
參考文獻	86

表目錄
=========================
表2-1 2013~2014 年我國生技產業經營現況	11
表2-2 生技產業特性整理	13
表2-3 台灣應用生技產業之領域別及其產品	20
表3-1 各種客戶價值的衡量	29
表3-2 資料探勘的定義	31
表3-3 各種決策樹的比較	38
表5-1 研究變數欄位	48
表5-2 RFM分級與價值轉換參數說明	50
表5-3 客戶價值與命名	51
表5-4 IBM SPSS Modeler RFM 分析	54
表5-5 顧客價值五等分法決策樹模型分析說明	59
表5-6 IBM SPSS Modeler-RFM分法決策樹模型分析說明	61
表5-7 J48決策樹萃取規則結果	65
表5-8 人類專家分類方法	72
表5-9 專家背景介紹	75
表5-10 專家訪談內容彙整	76
 
圖目錄
=========================
圖1-1 未來五年生物經濟產業發展目標	2
圖1-2 研究流程圖	6
圖2-1 台灣物技術產業範疇	9
圖2-2 生技產業供應鏈的結構	19
圖3-1 CRISP- DM	35
圖3-2 決策樹的結構	36
圖4-1 本研究流程	45
圖5-1 IBM SPSS Modeler RFM 分析模型	53
圖5-2 IBM SPSS Modeler RFM 樣本結果	53
圖5-3 顧客價值五等分法資料匯入WEKA	56
圖5-4 IBM SPSS Modeler-RFM分法資料匯入WEKA	57
圖5-5 顧客價值五等分法採用J48決策樹分析	58
圖5-6 IBM SPSS Modeler-RFM分法採用J48決策樹分析	59
圖5-7 XLMINER決策樹分析	63
圖5-8 J48決策樹示意圖	65
圖5-9 類神經分析法	69
圖5-10 類神經分析法隱藏層	70
圖5-11 採用類神經分析法	71
圖5-12 人類專家分類方法採用J48決策樹分析	74

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