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研究生:陳映君
研究生(外文):Ying-Chun Chen
論文名稱:資料探勘應用於童裝市場之價值顧客分析-以某童裝品牌為例
論文名稱(外文):Data Mining Applied in Analyzing Characteristics of Value Customers in Children Apparel Industry: Evidence for One Children Apparel Brand
指導教授:翁振益翁振益引用關係
指導教授(外文):Jehn-Yih Wong
學位類別:碩士
校院名稱:銘傳大學
系所名稱:企業管理學系碩士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:60
中文關鍵詞:購物籃分析決策樹童裝業資料探勘
外文關鍵詞:Basket AnalysisDecision TreesData MiningChildren Apparel Industry
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由於臺灣少子化的現象越趨明顯,導致童裝市場處於競爭激烈的環境下,童裝業者若能掌握「誰是目標顧客」,與顧客可能購買的「童裝商品」之資訊,深入了解顧客於不同的價值區隔之屬性,和顧客偏好的童裝商品,以擬定合宜的行銷策略,進而維持或增加市場佔有率。
本研究主要以國內某童裝品牌為研究範圍,透過交易資料與問卷調查資料之結合,收集顧客二年內曾經購買童裝之人口變數、購買行為和商品類型資料。以資料探勘技術作為本文的研究方法:首先利用RFMNA(最近購買日期、總購買次數、總購買金額、小孩個數及最小小孩年齡)模型衡量顧客價值高低;接著以資料探勘技術的決策樹進行高低價值顧客之屬性分析,且以購物籃分析進行童裝商品偏好分析,以探勘不同屬性的顧客其最適搭配銷售的童裝商品,便於童裝業者促銷合宜童裝商品給顧客,提高購買率。
研究結果得知依「顧客價值」所區隔之二個不同顧客群,依據小女孩個數、小男孩個數等屬性,可以了解高低價值顧客之屬性。此外,藉由整體顧客曾經購買童裝商品類型之購物籃分析結果推論出,先購買褲子、洋裝、配件、裙子或鞋子,下一個商品會選擇購買上衣的機率最高;先購買洋裝、配件、裙子或鞋子,下一個商品會選擇購買褲子及上衣的機率最高;先購買洋裝或裙子,下一個會選擇購買上衣及配件的機率最高;先購買裙子,下一個會選擇購買上衣及鞋子的機率最高,整體而言,發現顧客似乎較偏好給小孩穿著基本的樣式。
本研究之「顧客面」與「商品面」結果,可協助童裝業者了解不同價值區隔之顧客屬性及商品偏好,具體提供童裝業者可針對不同之顧客,規劃合宜的商品搭配及行銷策略。
The competition among children apparel industry became more and fiercer because of the declining birthrate in Taiwan. In this situation, agencies must focus on the target customers and know the information well about product items that attract customers. Agencies can make appropriate marketing policies and strategies by understanding the characteristics of different segments among valued customers and the preference of valued customers for children apparel. By these strategies, agencies are able to maintain or increase marketing share as well.
The subject of this study was domestic children apparel brand. This study combined two years of transaction data and questionnaires investigation data to collect demographic variables, purchase behaviors and product information. The analyzing method of research was Data Mining Techniques. First, the valued customers were defined by the variables of RFMNA(R is the recent times of purchase, F is the frequency of purchase, M is the money spent of purchase, N is the number of child and A is the age of youngest children). Second, the Decision Tree of Data Mining Techniques was used to analyze the valued customers’ characteristics. Third, this study applied Basket Analysis to product preference. It offered the information for the executive of children apparel industry to make marketing strategies to raise the buying rate.
The study segmented two segmentations by using customer value. It discovered that the characteristics, such as the number of little girl, the number of little boy, were appropriated segmentation bases of targeting the value customers. This study got some information from the Basket Analysis of children apparel products. The preferred children apparel product results showed that those who had purchased pants, dresses, accessories, skirts or shoes would next purchased blouses with high probability; those who had purchased dresses, accessories, skirts or shoes would next purchased pants and blouses with high probability; those who had purchased dresses or skirts would next purchased blouses and accessories with high probability; and those who had purchased skirts would next purchased blouses and shoes with high probability. According to the whole Association Rule, the study found that customers seem to prefer a basic style of clothing for children.
This study helped children apparel agency to understand the characteristics of different segments among valued customers and the preference for children apparel by the results both in “customer concepts” and “products concepts”. Results in this study also provided children apparel agency for appropriate products collocation and marketing policies for different valued customers.
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 1
1.2 研究動機與目的 1
1.3 研究流程 3
第二章 文獻探討 4
2.1 童裝市場分析 4
2.2 顧客價值 7
2.2.1 顧客價值定義 7
2.2.2 顧客價值衡量指標 7
2.3 市場區隔 8
2.3.1 市場區隔定義 8
2.3.2 市場區隔變數 9
2.3.3 市場區隔方法 10
2.4 資料探勘 11
2.4.1 資料探勘的定義 11
2.4.2 資料探勘的功能 14
2.4.3 資料探勘的技術 15
2.5 相關研究探討 16
2.6 小結 17
第三章 研究方法 18
3.1 童裝公司探勘流程 18
3.2 問卷設計與抽樣方法 19
3.3 資料分析方法 22
3.4 顧客價值模式區隔準則定義 24
3.5 顧客屬性分析 26
3.6 顧客購買之童裝商品探勘層級 27
3.7 輔助軟體 28
第四章 研究結果與分析 29
4.1 顧客基本資料分析 29
4.2 顧客價值區隔之屬性分析 32
4.3 童裝商品偏好分析 38
4.3.1 整體樣本之童裝商品偏好分析 38
4.3.2 高低顧客價值區隔之童裝商品偏好分析 40
4.3.3 童裝商品偏好之小結 42
第五章 結論與建議 44
5.1 結論 44
5.2 建議 45
5.3 研究限制與後續研究方向 46
參考文獻 47
圖 目 錄

圖1-1 研究之概念 2
圖1-2 研究流程圖 3
圖2-1 目標行銷之三步驟 9
圖2-2 資料庫知識發現過程 12
圖3-1 探勘內容架構圖 19
圖4-1 顧客區隔屬性之決策樹 34
表 目 錄

表2-1 童裝銷售通路之區分 4
表2-2 童裝產品風格屬性之區分 6
表2-3 顧客價值之定義 7
表2-4 顧客價值衡量指標表 7
表2-5 市場區隔之定義 8
表2-6 區隔效果衡量指標 9
表2-7 市場區隔變數表 10
表2-8 市場區隔方法 11
表2-9 資料探勘之定義 12
表2-10 資料探勘技術適用功能表 15
表3-1 問卷調查資料庫之欄位說明表 20
表3-2 顧客區隔屬性分析之採用變數 21
表3-3 購買童裝商品組合分析之採用變數 22
表3-4 顧客價值區隔定義 24
表3-5 顧客價值區隔準則表 25
表3-6 顧客價值分類表 26
表3-7 顧客屬性決策樹分析之變數選取表 27
表3-8 購物籃分析探勘之童裝商品類型 28
表3-9 輔助軟體說明表 28
表4-1 顧客基本資料之敘述性統計表 29
表4-2 顧客參與各類活動程度統計表 31
表4-3 顧客參與活動資訊來源之統計表 31
表4-4 各滿意程度問項衡量之平均數及標準差 32
表4-5 學歷及職業變數合併前後之次數表 33
表4-6 顧客區隔屬性之決策樹判斷結果表 33
表4-7 顧客區隔屬性之決策樹規則表 35
表4-8 顧客區隔屬性之規則歸納表 37
表4-9 整體樣本之童裝商品之二項物購物籃分析表 39
表4-10 整體樣本之童裝商品之三項物購物籃分析表 39
表4-11 低價值顧客樣本之童裝商品之二項物購物籃分析表 40
表4-12 低價值顧客樣本之童裝商品之三項物購物籃分析表 41
表4-13 高價值顧客樣本之童裝商品之二項物購物籃分析表 41
表4-14 高價值顧客樣本之童裝商品之三項物購物籃分析表 42
表4-15 整體樣本及顧客價值之童裝商品偏好分析整理 43
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