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系統識別號 U0002-0107201415560200
中文論文名稱 資料探勘應用於戶外用品實體與虛擬通路推薦機制之研究
英文論文名稱 Data Mining Implement on a Recommendation Mechanism for the Physical and Virtual Channel of Outdoor Appliance
校院名稱 淡江大學
系所名稱(中) 管理科學學系碩士班
系所名稱(英) Master’s Program, Department of Management Sciences
學年度 102
學期 2
出版年 103
研究生中文姓名 蔡逸珊
研究生英文姓名 Yi-Shan Tsai
學號 601620148
學位類別 碩士
語文別 中文
口試日期 2014-06-21
論文頁數 123頁
口試委員 指導教授-廖述賢
委員-陳盈如
委員-倪衍森
中文關鍵字 推薦機制  資料探勘  商業智慧  虛實整合  商店設計 
英文關鍵字 Recommendation Mechanism  Data Mining  Business Intelligence  Online to Offline  Store Layout 
學科別分類
中文摘要 現代人因忙碌的生活過於緊繃,以至於紛紛開始注重養身及崇尚健康,認同悠活及樂活的人也越來越多,間接活絡了產業的發展,使戶外用品業者擁有無限商機。但也因此導致競爭過於激烈,品牌及產品的同質性過高,顧客在選購上就得多需花一點心力,而企業主也須制定適當的行銷策略來滿足不同之顧客群。因此企業該如何在實體通路店內運用貨架和櫃的佈置引導顧客購物動線,並且又精準的推薦適當的品牌和產品組合給目標顧客群,以及如何運用合適的促銷活動來吸引不同通路之顧客群,即成了重要課題。
本研究採用問卷調查法,透過資料探勘的方式以分類樹、集群分析與關聯法則,藉由POS系統結合市場調查資料,利用未分群之顧客輪廓,進行商品之間的互補性分析,並且進而設計出實體通路假設性的商店設計。接著再利用集群分析後的顧客輪廓進行深入探討,以了解顧客於不同通路時會購買的品牌和產品以及偏好的促銷活動;根據推薦機制導出何種品牌及產品組合適合何種促銷手法並且可在哪個通路推廣,以提供企業在行銷策略上能做為參考依據。
而研究發現不同集群之顧客,對於不同的品牌及產品組合有著不同的偏好度,再者對於喜愛的促銷活動及通路也有所不同。因此企業可針對不同集群之顧客進行不同的行銷策略,以提升顧客滿意度和企業營運績效此雙贏局面。
英文摘要 Nowadays, people become much busier and start to consider that health is the most important thing in life, so it indirectly increases the scale market of outdoor appliance. However, customers possibly spend more time to select the homogeneous products; so many enterprises start to revise the channel strategy. In regard to this, how to set a nice store Layout in physical channel, improve the right image for brand, and increase competitive advantage are the core issues that worth discussing.
This study uses questionnaire survey, and through the ways of data mining: CART, cluster analysis and association rule, to discover how to use the complementarity of the merchandise to design the physical channel, and find out that what product/brand would be the customers buy in different channels and the preference of promotion. According to the above method can tell that which brand and product combination are suit to which promotion and in which channel. Company can use it as the reference of marketing strategy.
In the conclusion, the different groups of customers have the different preferences in varied brands and product combination. Company can use different marketing strategies to different groups, it can increase the satisfaction of customers and the business performance.
論文目次 目錄

謝辭 II
中文摘要 III
英文摘要 IV
目錄 V
表目錄 IX
圖目錄 XI
第一章 緒論 1
1.1研究背景與動機 1
1.2研究問題與目的 3
1.3研究方法與流程 4
第二章 文獻回顧 6
2.1通路 6
2.1.1通路之結構 8
2.1.2實體通路與虛擬通路之整合 10
2.2推薦機制 14
2.2.1推薦機制的定義 14
2.2.2推薦機制的技術 15
2.2.3推薦機制的種類 17
2.3資料探勘 18
2.3.1資料探勘的定義 19
2.3.2資料探勘的流程 21
2.3.3資料探勘的功能 21
2.3.4資料探勘的應用 23
2.4商業智慧 25
2.4.1商業智慧定義 25
2.4.2商業智慧的架構 27
2.5商店設計 28
2.5.1商店設計的定義 28
2.5.2商店設計的種類及應用 29
第三章 個案公司- G公司 34
3.1 G公司簡介 34
3.2營業內容 35
3.3個案問題探討 35
第四章 研究方法 37
4.1研究設計 37
4.2系統架構與資料庫設計 38
4.2.1系統架構與流程 38
4.2.2資料庫的建立與設計 39
4.3問卷設計與發放 45
4.3.1問卷設計 45
4.3.2抽樣方法 46
4.3.3問卷發放 46
4.4關聯法則和集群分析 47
4.4.1關聯法則 47
4.4.2集群分析 51
4.5分類迴歸樹 52
4.6資料分析軟體 SPSS Modeler 54
第五章 資料探勘與實證分析 56
5.1回收樣本結構描述 56
5.2 K-means集群分析之探勘 58
5.2.1分群後的顧客輪廓 60
5.3顧客輪廓與品牌之產品與推薦機制 63
5.4實體通路商店設計之推薦機制 66
5.4.1實體通路商店設計之推薦機制分析 66
5.5通路、品牌、產品與促銷活動之推薦機制 79
5.5.1通路區隔和品牌與促銷活動之推薦機制分析 81
5.5.2通路區隔和產品與促銷活動之推薦機制分析 87
5.5.3產品組合和品牌與通路搭配促銷活動之推薦機制分析 93
第六章 結論與建議 99
6.1研究結論 99
6.1.1實體通路商店設計之推薦機制結論 99
6.1.2通路區隔和品牌與促銷活動之推薦機制結論 103
6.1.3通路區隔和產品與促銷活動之推薦機制結論 105
6.1.4產品組合和品牌與通路搭配促銷活動之推薦機制結論 107
6.2管理意涵 109
6.2.1學術管理意涵 109
6.2.2實務管理意涵 109
6.3研究限制及後續研究之建議 109
6.3.1後續研究及建議 109
6.3.2研究限制 110
參考文獻 111
附錄一 正式問卷 120


表目錄
表2-1通路之定義 7
表2-2通路功能 9
表2-3實體商店與網路購物功能之比較 12
表2-4實體通路與虛擬通路的優點與缺點 13
表2-5通路之成本與利益之比較 13
表2-6各學者對推薦機制之定義 15
表2-7各學者對資料探勘之定義 20
表2-8資料探勘流程 21
表2-9資料探勘所應用的領域 24
表2-10各學者對商業智慧之定義 26
表3-1個案公司大事記 35
表3-2 POS系統和市場調查擁有之題項比較 36
表4-1實體、關聯與屬性的概述 40
表4-2資料庫中的交易記錄 50
表5-1問卷回收統計表 56
表5-2基本資料統計表 57
表5-3 K-means 分群結果 62
表5-4商店設計之分類樹統整說明 70
表5-5品類之互補關聯表 72
表5-6 A、B區品類之互補關聯表 72
表5-7自創品牌搭配代理及經銷品牌互補關聯表 75
表5-8混合搭售互補關聯表 77
表5-9樂活目標開發群之通路和品牌與促銷活動之關聯規則 82
表5-10小資男女消費群之通路區隔和品牌與促銷活動之關聯規則 84
表5-11巔峰潛在開發群之通路區隔和品牌與促銷活動之關聯規則 86
表5-12樂活目標開發群之通路區隔和產品與促銷活動之關聯規則 88
表5-13小資男女消費群之通路區隔和產品與促銷活動之關聯規則 90
表5-14巔峰潛在開發群之通路區隔和產品與促銷活動之關聯規則 92
表5-15樂活目標開發群之產品組合和品牌與通路搭配促銷活動之關聯規則 94
表5-16小資男女消費群之產品組合和品牌與通路搭配促銷活動之關聯規則 96
表5-17巔峰潛在開發群之產品組合和品牌與通路搭配促銷活動之關聯規則 98
表6-1品牌與商品之推薦表 101
表6-2各集群之通路區隔和品牌與促銷活動之推薦表 103
表6-3各集群之通路區隔和產品與促銷活動之推薦表 105
表6-4各集群之產品組合和品牌與通路搭配促銷活動之推薦表 107

圖目錄
圖1-1研究流程圖 5
圖2-1通路的階層圖 9
圖2-2資訊管理的金字塔 19
圖2-3 IBM 商業智慧之架構 28
圖2-4 商店構成要素之相互關係 30
圖4-1研究設計圖 37
圖4-2系統架構圖 38
圖4-3建立關聯式資料庫步驟 39
圖4-4概念性資料庫設計 41
圖4-5邏輯性資料庫 43
圖4-6資料庫轉換圖 44
圖4-7實體性資料庫關聯圖 44
圖4-8問卷架構圖 45
圖4-9 Apriori演算法產生之候選項目集合與高頻項目集合之流程 50
圖5-1資料節點串流圖 58
圖5-2 K-means集群分佈圖 59
圖5-3各集群之品牌偏好關聯圖 63
圖5-4各集群之產品偏好關聯圖 64
圖5-5光譜圖 64
圖5-6推薦機制之關聯分析 65
圖5-7各集群通路偏好之關聯圖 65
圖5-8實體通路商店設計推薦機制之關聯分析 66
圖5-9商店平面圖 68
圖5-10市場調查之分類樹結果 69
圖5-11品類蛛網圖及品類光譜 73
圖5-12實體通路之假設性商店平面圖 74
圖5-13品牌蛛網圖及品牌光譜 76
圖5-14混合搭售 78
圖5-15通路和品牌與促銷活動推薦機制之關聯分析 79
圖5-16通路和產品與促銷活動推薦機制之關聯分析 80
圖5-17產品組合和品牌與通路搭配促銷活動推薦機制之關聯分析 80
圖5-18樂活目標開發群之品牌偏好關聯圖 81
圖5-19樂活目標開發群之品牌偏好光譜圖 81
圖5-20樂活目標開發群之蛛網圖 82
圖5-21小資男女消費群之品牌偏好關聯圖 83
圖5-22小資男女消費群之品牌偏好光譜圖 83
圖5-23小資男女消費群之蛛網圖 84
圖5-24巔峰潛在開發群之品牌偏好關聯圖 85
圖5-25巔峰潛在開發群之品牌偏好光譜圖 85
圖5-26巔峰潛在開發群之蛛網圖 86
圖5-27樂活目標開發群之產品偏好關聯圖 87
圖5-28樂活目標開發群之產品偏好光譜圖 87
圖5-29樂活目標開發群之蛛網圖 88
圖5-30小資男女消費群之產品偏好關聯圖 89
圖5-31小資男女消費群之產品偏好光譜圖 89
圖5-32小資男女消費群之蛛網圖 90
圖5-33巔峰潛在開發群之產品偏好關聯圖 91
圖5-34巔峰潛在開發群之產品偏好光譜圖 91
圖5-35巔峰潛在開發群之蛛網圖 92
圖5-36樂活目標開發群之產品組合和品牌關聯圖 93
圖5-37樂活目標開發群之通路及產品品牌組合關聯圖 94
圖5-38小資男女消費群之產品組合和品牌關聯圖 95
圖5-39小資男女消費群之通路及產品品牌組合關聯圖 96
圖5-40巔峰潛在開發群之產品組合和品牌關聯圖 97
圖5-41巔峰潛在開發群之通路及產品品牌組合關聯圖 98
圖6-1實體通路之假設性商店設計 100
圖6-2搭售推薦 102
圖6-3各集群之通路區隔和品牌與促銷活動之行銷知識地圖 104
圖6-4各集群之通路區隔和產品與促銷活動之行銷知識地圖 106
圖6-5各集群之產品組合和品牌與通路搭配促銷活動之行銷知識地圖 108
參考文獻 參考文獻
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