過去二十年來，企業皆以利用多通路的策略來行銷，多通路的策略可增加企業的範圍，並擴大與消費者的接觸，達成消費者的多種需求，選擇他們喜好的通路，讓消費者使用企業所安排好的通路組合；亦可增加產品進入市場的機會增加廠商的範圍；其目標是將通路做資源分配，以滿足消費者和利潤極大化。

　　隨著多通路的分配越來越普遍，消費者不斷地面臨購買與產品選擇，且偏好不同的通路。本研究以資訊3C產品為例，採用資料採礦（data mining）分析之相關應用技術，以集群分析與關聯法則探討消費者在購買過程中，哪些因素會造成消費者對通路與產品有所區隔，而不同區隔的消費者，偏好在何種通路型態下購買的產品項目以及品牌名稱。 

　　研究結果發現，不同集群的消費者在各自以通路及產品做為選擇因素的區隔前提下，所重視的通路屬性與產品屬性確實各不相同，並具有不同的消費行為，且購買某些特定產品時，會有所偏好的通路型態，以及產品組合、品牌之間的關聯性，均有其顯著的差異。製造商與通路商可藉此了解不同集群與行銷組合方式，使業者能在前期的行銷策略上，推出更吸引顧客的方案及服務。

In the last two decades, the firm all use the multichannel strategy makes a product available to the market through two or more channels of distribution. Multichannel strategies allow firms to reach customers in multiple ways, increasing the firms' reach. In addition, multiple channels allow customers to reach businesses by using their preferred channel, and using a mix of channel formats, as a multichannel marketer's objectives are to distribute resources across the channel mix to satisfy customers and maximize profits. 

　As multichannel distribution becomes increasingly prevalent, customers face an expanding array of purchase and communication options. This study take 3C product for example, used data mining technology of the Cluster Analysis and Association Rule to look for the reason that customer approach channel and product segmentation, and the different segment’s customer prefer what kind of product, brand in the differ by channel type. Understand the different clusters and marketing mix, release the plan and service to attract customers in the early marketing strategy. The results showed that different clusters of customers placed importance on different channel, product quality and consumer behavior under valued by the segment. Also had preference channel type on purchase of specific products and  the correlation between product portfolio and brand has obvious differences.

目錄					I
表目錄					IV
圖目錄					V
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 研究流程			3

第二章 文獻探討				5
　　2.1 通　　路			5
　　　　2.1.1 通路之定義		5
　　　　2.1.2 通路之功能		7
　　　　2.1.3 通路之結構		8
　　　　2.1.4 實體與虛擬通路之特性	10
　　　　2.1.5 多通路之特性		12
　　2.2 區隔理論			14
　　　　2.2.1 市場區隔之定義		15
　　　　2.2.2 市場區隔之變數		17
　　　　2.2.3 通路區隔			18
　　　　2.2.4 產品區隔			20
　　2.3 資料採礦			22
　　　　2.3.1 資料採礦之定義		23
　　　　2.3.2 資料採礦之功能		25
　　　　2.3.3 資料採礦之流程		27
　　　　2.3.4 資料採礦之應用		28
　　2.4 小結				29

第三章 研究方法				30
　　3.1 研究設計	30
　　3.2 系統架構圖與資料庫設計	31
　　　　3.2.1 系統架構與流程	31
　　　　3.2.2 資料庫的建立與設計	33
　　3.3 問卷設計與發放	39
　　　　3.3.1 問卷設計	39
　　　　3.3.2 抽樣方法	40
　　　　3.3.3 問卷發放	41
　　　　3.3.4 信度與效度分析	41
　　3.4 關聯法則與集群分析	42
　　　　3.4.1 關聯法則	42
　　　　3.4.2 Apriori演算法	44
　　　　3.4.3 集群分析	46
　　3.5 資料分析軟體 SPSS Clementine	48

第四章 資料採礦與實證分析	51
　　4.1 回收樣本結構描述	51
　　4.2 市場區隔與消費者輪廓之探勘	53
　　4.3 通路區隔之探勘	56
　　4.4 產品區隔之探勘	60
　　4.5 通路與產品組合之探勘	63
　　4.6 通路與產品輔以考量品牌之探勘	66

第五章 結論與建議	70
　　5.1 管理意涵	70
　　　　5.1.1 單一產品多通路行銷	73
　　　　5.1.2 新產品上市與多通路服務	75
　　　　5.1.3 通路與產品組合之搭售	76
　　　　5.1.4 品牌與製造商聯盟	78
　　5.2 結論	80
　　5.3 研究限制	82
　　5.4 後續研究之建議	83

參考文獻	84
　　一、中文資料	84
　　二、英文資料	85
　　三、網路資料	96

附錄	97
　　前測問卷	97
　　正式問卷	104
 
表目錄
　　表2.1 通路之定義	6
　　表2.2 市場區隔之定義	16
　　表2.3 資料採礦之定義	24
　　表2.4 資料採礦之功能	26
　　表2.5 資料採礦之流程	27
　　表3.1 實體、關聯與屬性的概述	33
　　表3.2 實體-屬性一覽表	35
　　表3.3 資料探勘軟體之使用頻率調查	49
　　表4.1 問卷回收統計表	51
　　表4.2 基本資料統計表	51
　　表4.3 Two-Step分群結果	55
　　表4.4 集群一之通路區隔關聯法則	57
　　表4.5 集群二之通路區隔關聯法則	59
　　表4.6 集群一之產品區隔關聯法則	60
　　表4.7 集群二之產品區隔關聯法則	62
　　表4.8 集群一之通路與產品組合關聯法則	63
　　表4.9 集群二之通路與產品組合關聯法則	64
　　表4.10 集群一之品牌與製造商聯盟關聯法則	67
　　表4.11 集群二之品牌與製造商聯盟關聯法則	68
　　表5.1 集群與關聯之整合分析表	71

圖目錄
　　圖1.1 研究流程圖	4
　　圖2.1 通路階層圖	8
　　圖3.1 研究設計圖	30
　　圖3.2 系統架構圖	32
　　圖3.3 概念性資料庫設計 E-R圖	34
　　圖3.4 邏輯性資料庫	37
　　圖3.5 資料庫轉換圖	38
　　圖3.6 實體資料庫關聯圖	39
　　圖3.7 問卷架構圖	40
　　圖3.8 Apriori演算法之架構圖	44
　　圖3.9 Apriori演算法產生之後選項目集合與高頻項目集合	46
　　圖3.10 集群分析方法架構圖	48
　　圖4.1資料節點串流圖	53
　　圖4.2 Two-Step集群分佈圖	54
　　圖4.3 集群一蛛網圖(調整後)	58
　　圖4.4 集群二蛛網圖(調整後)	59
　　圖4.5 集群一蛛網圖(調整後)	61
　　圖4.6 集群二蛛網圖(調整後)	62
　　圖4.7 集群一蛛網圖(調整後)	64
　　圖4.8 集群二蛛網圖(調整後)	65
　　圖4.9 集群一蛛網圖(調整後)	67
　　圖4.10 集群二蛛網圖(調整後)	69
　　圖5.1 集群分析之行銷地圖	72
　　圖5.2 通路區隔之行銷地圖	74
　　圖5.3 產品區隔之行銷地圖	75
　　圖5.4 通路與產品組合之行銷地圖	77
　　圖5.5 品牌與製造商聯盟之行銷地圖	79

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三、網路資料
AMA, American Marketing Association
http://www.marketingpower.com/content24159.php
Kdnuggets網站（2009）最常使用的資料探勘技術
http://www.kdnuggets.com/polls/2009/data-mining-tools-used.htm