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系統識別號 U0002-3006202118454800
中文論文名稱 資料探勘應用於線上食品產業顧客關係管理之研究
英文論文名稱 Applying Data Mining Methods for Customer Relationship Management in Online Food Industry
校院名稱 淡江大學
系所名稱(中) 企業管理學系碩士班
系所名稱(英) Department of Business Administration
學年度 109
學期 2
出版年 110
研究生中文姓名 高立勤
研究生英文姓名 Li-Chin Kao
學號 608610449
學位類別 碩士
語文別 中文
口試日期 2021-06-03
論文頁數 67頁
口試委員 指導教授-李月華
委員-張瑋倫
委員-吳坤山
委員-李月華
中文關鍵字 顧客關係管理  電商4P  集群分析  RFM  決策樹  Apriori 
英文關鍵字 CRM  E-commerce 4P  Cluster Analysis  RFM  CART Decision Tree  Apriori 
學科別分類
中文摘要 電腦運算功能日益增強,企業過去至今也累積龐大之數據資料,資料探勘技術隨之蓬勃發展。企業逐漸意識到透過資料探勘方式對於決策的價值,本研究取用某線上食品零售業者交易數據進行資料探勘,以提出顧客關係管理方案。
本研究根據電商4P之概念建構顧客關係管理模式,以RFM指標對線上食品零售公司的顧客進行兩階段K-means分群,形成4種具有顯著差異的顧客群體,再以決策樹CART以及Apriori法對顧客群進行資料探勘。
根據研究結果,集群分析將顧客區分為「鮮肉型顧客」、「沉睡巨人型顧客」、「忠誠型顧客」、「流失型顧客」四群,進一步透過決策樹CART與Apriori法掌握各群顧客特徵與產品購買關聯性,以期作為日後企業對顧客群廣告投放、行銷預測及服務策略擬定之參考依據。
英文摘要 Computer computing functions are increasing day by day. The companies has also accumulated huge amounts of data in the past. This has led to the popularization of data mining technology. Many companies are gradually realizing the value of data mining for decisions. This research used the transaction data of the online food retailer for data mining to propose a customer relationship management plan.
This research is based on concept of e-commerce 4P to construct a customer relationship management model. Using RFM indicators for two-stage K-means clustering on customers of online food retail company. There were 4 types of customer groups with significant differences formed. Then use the decision tree CART and the Apriori method to mine the data of the customer groups.
According to the research results, cluster analysis divides customers into 4 groups: fresh meat customers, sleeping giant customers, loyalty customers and churning customers. Further, through the decision tree CART and the Apriori method, grasp customers characteristics and product relevance. It’s expected to be used as a reference for advertising, marketing prediction and service strategy in the future.
論文目次 目錄
目錄 I
圖目錄 II
表目錄 III
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 3
第二章 文獻探討 5
第一節 電子商務 5
第二節 顧客關係管理與RFM模型 9
第三節 目標客群與集群分析 12
第四節 決策樹分析與Apriori法 14
第三章 研究方法 17
第一節 分析流程 17
第二節 資料來源與變數提取 18
第三節 分析方法 21
第四節 使用軟體介紹 27
第四章 實證分析 29
第一節 資料整理 29
第二節 顧客分群 34
第三節 預測模型 44
第五章 結論與建議 51
第一節 研究發現 51
第二節 研究結論 52
第三節 研究限制與建議 54
參考文獻 55

圖目錄
圖1-1研究流程圖 4
圖3-1分析流程圖 18
圖4-1年齡遺漏值分布(填補前) 32
圖4-2年齡遺漏值分布(填補後) 33
圖4-3階層式集群分析樹狀圖 36
圖4-4輪廓係數 36
圖4-5四群之資料分布 37
圖4-6四群顧客之年齡分布 42
圖4-7決策樹結果 45
圖4-8決策樹混淆矩陣 47

表目錄
表2-1電子商務模式 6
表3-1資料檔案筆數 19
表3-2會員資料檔案變數說明 19
表3-3訂單主檔變數說明 20
表3-4訂購明細檔變數說明 20
表3-5三種決策樹演算法整理 25
表3-6混淆矩陣表 26
表4-1居住地區重新編碼 30
表4-2消費總額合併前(取10筆) 31
表4-3消費總額合併後(取10筆) 31
表4-4遺漏值統計-年齡 32
表4-5顧客採購清單(取10筆) 33
表4-6類別型基本資料統計 34
表4-7數值型資本資料統計 34
表4-8調整後RFM指標定義 35
表4-9會員RFM資料 35
表4-10四群之RFM 37
表4-11RFM指標之ANOVA結果 38
表4-12RFM指標之事後檢定結果 38
表4-13會員等級之卡方檢定結果 40
表4-14四群顧客類型之會員等級分布狀況 40
表4-15年齡之ANOVA結果 41
表4-16年齡之事後檢定結果 41
表4-17性別之卡方檢定結果 43
表4-18四群顧客之性別分布狀況 43
表4-19名單來源之卡方檢定結果 44
表4-20四群顧客類型之名單來源分布狀況 44
表4-21決策樹之模型指標 47
表4-22鮮肉顧客群之單項商品支持度 48
表4-23鮮肉顧客群之商品關聯規則 48
表4-24沉睡巨人顧客群之單項商品支持度 49
表4-25忠誠型顧客群之單項商品支持度 49
表4-26忠誠型顧客群之商品關聯規則 49
表4-27流失型顧客群之單項商品支持度 50
表4-28流失型顧客群之商品關聯規則 50
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