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研究生:羅巧芳
研究生(外文):Chiao-Fang Lo
論文名稱:應用資料探勘於戶外活動用品專賣店之顧客忠誠與價值分析
論文名稱(外文):Applying Data Mining to An Outfitter’s Customer Loyalty and Value Analysis
指導教授:吳信宏吳信宏引用關係
指導教授(外文):Hsin-Hung Wu
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:行銷與流通管理研究所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:70
中文關鍵詞:資料探勘RFM模型顧客分群Two-StepK-MeansSOM
外文關鍵詞:data miningRFM modelcustomer clusterTwo-stepK-meansSOM
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國人逐漸重視休閒活動,愈來愈多人利用假日從事戶外活動。為了提升活動品質與維護安全,從事戶外活動者對於戶外活動用品亦愈來愈重視,讓戶外活動用品業者擁有無限商機。依據80/20法則,業者若能發現對企業具有高度價值的重要顧客,並找出潛在的重要顧客,而適當地分配有限資源,制定有效行銷策略提供各區隔顧客所需要及想要的產品,可建立企業的競爭優勢。
企業為了瞭解不同消費族群的消費行為,以利制定適當的行銷策略來滿足其需求,因而建立會員制度,欲透過對會員資料的詳盡分析來掌握主要顧客的消費情況並預測一般消費者的消費行為,企業所建立的會員資料經常隨著會員數目與購買紀錄的增加日益龐大,若從此資料庫進行挖掘與分析,將會發現許多有助於管理者制定決策的資訊。
本研究二次運用RFM模型針對某家戶外活動用品專賣店的會員資料進行分群與分析,發現重要顧客有八位及潛在重要顧客有十三位;利用RFM模型並以Two-Step、K-Means與SOM三種分群方法對顧客進行分群,發現可將顧客分為六群。然而透過分群品質評估方法加以比較,Two-Step群聚方法與K-Means分群方法之分群品質不分軒輊,但皆較SOM分群方法為佳。欲以Two-Step群聚方法或K-Means分群方法之分群結果為制定策略依據則視決策者的需要決定。透過敘述性統計分析,提供業者一些管理方針且針對重要與潛在重要顧客以及Two-Step群聚方法與K-Means分群方法而得之六集群顧客給予行銷策略上的建議。
More and more citizens in Taiwan are actively doing outdoor activities during their leisure time. The safety and quality of outdoor activities have become an important issue for participants. Thus, there exists business potential opportunities for outdoor goods industry. According to the 80/20 principle, if a company can identify the high value and potential important customers and then distribute the resources effectively and efficiently, customized marketing strategies can be made to fulfill different customers’ needs and the competitive advantage can be gained as well.
By establishing loyalty programs, a company can figure out different customer behaviors by collecting customer data to design and then apply different marketing strategies to fulfill different customers’ needs. As the transaction records have become much larger as time goes by, it might be of interest to use data mining techniques to provide useful decisional information for management.
In this study, the RFM model was applied twice and the results show that there are eight important customers and thirteen potential customers. In addition, the cluster methods, including Two-Step, K-Means, and SOM, are used based on RFM model to analyze customer value. In addition, two-step, K-means, and SOM methods are used based on RFM model to analyze customer value. The results show that there are six clusters for each method. Besides, two types of cluster quality assessment methods are used in order to identify which cluster method performs best. It reveals that both two-step and K-means methods perform equally well but outperform SOM method. The selection of two-step or K-means method depends on the needs of the decision maker. Some analyses based on descriptive statistics which lead to managerial insights are discussed for serving both important and potential customers. Finally, some marketing strategies of customers from each cluster based upon the Two-Step and K-Means methods are provided.
目 錄

目 錄 I
圖 目 錄 III
表 目 錄 IV
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
第二章 文獻探討 3
2.1 資料探勘 3
2.1.1資料探勘的定義 3
2.1.2資料探勘技術的功能 5
2.1.3分群技術 6
2.1.4資料探勘的應用 9
2.2 顧客關係管理 10
2.3 顧客價值 11
2.3.1顧客價值定義 11
2.3.2顧客價值模型 12
2.3.3 RFM模型 13
2.3.4顧客分群 14
第三章 研究方法 18
3.1 研究架構 18
3.2 資料來源與前置處理 19
3.3 資料分析 21
第四章 研究結果 23
4.1 敘述性統計分析 23
4.2 RFM資料分析之九宮格構面圖 25
4.3 RFM資料分析之分群結果與比較 31
4.3.1 Two-Step群聚模式分群結果 32
4.3.2 K-Means模式分群結果 39
4.3.3 SOM模式分群結果 47
4.3.4 Two-Step、K-Means與SOM分群比較 55
4.4 RFM之散佈圖與Two-Step群聚方法及K-Means分群方法的結果比較 59
第五章 結論與建議 62
5.1 研究結論與行銷策略意涵 62
5.2 研究限制與未來研究建議 64
參考文獻 66


圖 目 錄


圖2- 1知識發掘過程圖 4
圖2- 2 SOM類神經元二維網路架構圖 8
圖2- 3 CRM七步驟與五要素 11
圖2- 4顧客類別 14
圖2- 5顧客忠誠之類別 16
圖2- 6區隔忠誠 16
圖3- 1本研究之研究架構圖 19
圖4- 1 R-F散佈圖 27
圖4- 2 R-M散佈圖 28
圖4- 3 F-M散佈圖 28
圖4- 4扣除重要顧客的R-F散佈圖 30
圖4- 5扣除重要顧客的R-M散佈圖 30
圖4- 6扣除重要顧客的F-M散佈圖 31

表 目 錄

表3- 1本研究輸入因素之各欄位項說明 21
表4- 1會員總金額敘述性統計 23
表4- 2性別對總消費金額的敘述性統計 23
表4- 3會員年齡的次數分配 24
表4- 4星座對總消費金額的敘述性統計 25
表4- 5雙子座與天蠍座之總消費金額t檢定 25
表4- 6居住地區對總消費金額的敘述性統計 25
表4- 7 RFM敘述性統計 26
表4- 8重要顧客的資料庫資料明細 29
表4- 9 Two-Step分群結果 33
表4- 10 Two-Step群組一與群組二最近一次消費得分之次數分配表 34
表4- 11 Two-Step群組一與群組二消費次數之次數分配表 34
表4- 12 Two-Step群組一與群組二平均客單價之次數分配表 35
表4- 13 Two-Step群組三與群組四最近一次消費得分之次數分配表 36
表4- 14 Two-Step群組三與群組四消費次數之次數分配表 36
表4- 15 Two-Step群組三與群組四平均客單價之次數分配 37
表4- 16 Two-Step群組五與群組六最近一次消費得分之次數分配表 38
表4- 17 Two-Step群組五與群組六消費次數之次數分配表 38
表4- 18 Two-Step群組五與群組六平均客單價之次數分配表 39
表4- 19 K-Means分群結果 40
表4- 20 K-Means群組一與群組二最近一次消費得分之次數分配表 41
表4- 21 K-Means群組一與群組二消費次數之次數分配表 42
表4- 22 K-Means群組一與群組二平均客單價之次數分配表 42
表4- 23 K-Means群組三與群組四最近一次消費得分之次數分配表 43
表4- 24 K-Means群組三與群組四消費次數之次數分配表 44
表4- 25 K-Means群組三與群組四平均客單價之次數分配表 44
表4- 26 K-Means群組五與群組六最近一次消費得分之次數分配表 45
表4- 27K-Means群組五與群組六消費次數之次數分配表 46
表4- 28 K-Means群組五與群組六平均客單價之次數分配表 46
表4- 29 SOM分群結果 48
表4- 30 SOM群組一與群組二最近一次消費得分之次數分配表 49
表4- 31 SOM群組一與群組二消費次數之次數分配表 49
表4- 32 SOM群組一與群組二平均客單價之次數分配表 50
表4- 33 SOM群組三與群組四最近一次消費得分之次數分配表 51
表4- 34 SOM群組三與群組四消費次數之次數分配表 51
表4- 35 SOM群組三與群組四平均客單價之次數分配表 52
表4- 36 SOM群組五與群組六最近一次消費得分之次數分配表 53
表4- 37 SOM群組五與群組六消費次數之次數分配表 53
表4- 38 SOM群組五與群組六平均客單價之次數分配表 54
表4- 39方法一之分群品質評估表 55
表4- 40方法二之分群品質評估表 57
表4- 41方法二資料轉換總樣本標準差 58
表4- 42資料庫編碼27與82之顧客資料庫資料 60
一、中文部份
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二、英文部分
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三、網路資源
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