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論文中文名稱:應用基因演算法於RFM模型權重最佳化之研究 [以論文名稱查詢館藏系統]
論文英文名稱:Applying Genetic Algorithms on RFM weight Optimization [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與運籌管理研究所
畢業學年度:101
出版年度:102
中文姓名:陳秀慧
英文姓名:Hsiu-Hui Chen
研究生學號:100938007
學位類別:碩士
語文別:中文
口試日期:2013-07-03
論文頁數:72
指導教授中文名:翁頌舜
指導教授英文名:1. 甘豐榮,以基因演算法為基礎之k-均數群集技術應用於心臟疾病診斷分析,碩士論文,義守大學資訊管理研究所,2012。 2. 李中彥,崔灝東,姚慶邦,張詠傑,「以基因演算方法建構企業信用評等之財務指數
口試委員中文名:吳瑞堯;蕭瑞祥
口試委員英文名:Rei-Yao Wu;Ruey-shiang Shaw
中文關鍵詞:顧客關係管理RFM模型基因演算法
英文關鍵詞:Customer Relationship Management (CRM)RFM modelGenetic Algorithms (GA)
論文中文摘要:近年來,企業積極發展目標顧客,使用資料探勘(Data Mining)的分析技術研究顧客行為(Customer Behavior),精確地鎖定潛在顧客(Potential Customer),為企業帶來更大效益,故顧客關係管理(Customer Relationship Management, CRM)更加不能忽視,而顧客價值分析是顧客關係管理的基礎,過去文獻中分析顧客價值,RFM模型是最常被提及與運用的方法,藉由RFM模型的三個衡量指標,可以簡單而清楚的看出顧客消費行為的輪廓。學者許雅涵等人(2011) 以RFM模型為基礎,考量石油在產品、定價策略上的特性改良為RFQ模型。本研究以加油站為例,以RFQ模型為基礎,提出以基因演算法演化RFQ三項指標之最佳權重,修正為GA-WRFQ模型,並配合K-means方法進行群集分析,輔助加油站將顧客依消費行為進行群集分析,做為加油站業者後續策略擬定的參考,如此一來,加油站業者可以針對目標顧客進行有效的行銷策略,並提升加油站競爭優勢。
論文英文摘要:In recent years, organizations seek to find target customers, and use analytical techniques in Data Ming domain to understand customer behavior while focus on potential customer and bring more revenue to organization. So Customer Relationship Management (CRM) becomes more important. Customer value analysis is the cornerstone of CRM. In the past studies, RFM is the method mentioned and used in customer value analysis most popularly. By using the three indicators of RFM, one can clearly understand how the customer behaves in a simple way. Considering the product and pricing strategy character in oil industry, Shiu, et al. (2011) enhanced RFM model and proposed RFQ model. In this research, we propose a GA-WRFQ model, which is based on RFQ model, and using Genetic Algorithm (GA) to evolve weights of the three indicators in RFQ model. Based on K-means clustering algorithm, this mechanism can cluster customers by their consuming behavior. We also apply this mechanism to a real oil station, help them cluster customers and develop strategies. In this way, oil station manager may develop strategies that focus on target customers and make every penny count.
論文目次:中文摘要 i
英文摘要 ii
致 謝 iii
目 錄 iv
表 目 錄 vii
圖 目 錄 ix
第一章 緒論 1
1.1 背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 6
2.1 RFM模型 6
2.1.1 RFM模型之定義 6
2.1.2 RFM分群方法 7
2.1.3 RFM模型之優勢 8
2.1.4 RFM模型之應用 9
2.1.5 RFM模型指標之定義 11
2.1.6 RFM模型為基礎並結合其他之變數 12
2.1.7 RFM指標權重之建構 15
2.1.8 RFQ模型 16
2.2 資料探勘 17
2.2.1 群集分析 17
2.2.2 Dunn Index 20
2.3 顧客終身價值 20
2.3.1 計算顧客終身價值 21
2.4 基因演算法 22
2.4.1 演算機制 23
2.4.2 使用基因演算法演化權重之應用 28
2.4.3 屬性權重之影響 29
第三章 研究方法 32
3.1 研究架構 32
3.2 資料前處理 33
3.2.1 修正RFQ之模型 33
3.2.2 計算RFQ值 34
3.3 GA-WRFQ 模型評估 36
3.4 基因演算法環境參數設置 36
3.4.1 編碼與解碼 36
3.4.2 適應函數 38
3.4.3 選擇、交配與突變 38
3.4.4 終止條件 39
3.5 分群數之選擇 40
3.6 實驗參數設定 40
第四章 實驗結果 43
4.1 環境設定 43
4.2 研究對象與範圍 43
4.3 整體交易資料之探討 44
4.4 基因演算法參數之比較 46
4.4.1 實驗(一)終止條件之設定 46
4.4.2 實驗(二) 族群大小之比較 48
4.4.3 實驗(三) 突變率、交配率之比較 48
4.5 屬性權重之差異 50
4.6 分群數之差異 51
4.7 分群結果探討 53
4.7.1 適應函數 53
4.7.2 RFQ演化之結果 54
4.7.3 計算各群之顧客終身價值 55
4.7.4 各群型態之分析 55
4.7.5 目標顧客群之行銷策略 62
第五章 結論 64
5.1 研究結論與貢獻 64
5.2 未來方向與建議 65
參考文獻 67
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