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研究生:李明蒓
研究生(外文):Ming-Chun Lee
論文名稱:應用資料探勘技術於顧客關係管理之個案研究
論文名稱(外文):A Case Study of Application of Data Mining to Achieve Customer Relationship Management
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:2011
畢業學年度:99
語文別:中文
論文頁數:62
中文關鍵詞:顧客關係管理RFM模型資料探勘自組織映射圖網路K-Means分群法
外文關鍵詞:Customer Relationship ManagementRFM modelData MiningSelf-Organizing MapK-Means Clustering
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企業必須以顧客為中心,建構完善之顧客關係管理資訊系統,方能有效地管理、規劃消費者的通路,增加消費者購買意願,進而提升顧客滿意度。本研究運用RFM (Recency, Frequency, and Monetary)模型,藉由資料探勘之功能,挖掘不同顧客群間產品購買的歷史訊息、消費記錄等資料,來判斷顧客的需求與偏好,進而提供符合顧客之商品與服務,有效的幫助業界人士了解哪些群體之顧客特質與長期顧客關係有關,明確的辨別出企業最有價值之顧客群,提供企業進行資源配置與行銷活動擬定的參考依據,加強企業與消費者間的強度關係。

本研究運用RFM 模型針對某專業美髮產品業者之9,620筆顧客交易紀錄進行資料轉換與分析,再透過自組織映射圖網路與K-Means分群法的技術將顧客分為11群,並在RFM三個變數的構面下對此11群顧客做敘述性統計分析,找出高貢獻度顧客群與潛在顧客群,並透過研究結果提出本季的消費者可區分為四種類型,分別為忠誠顧客群、潛在顧客群、新進顧客群與挽留顧客群,並提出適當的促銷活動與產品服務,提供給決策者瞭解不同消費族群的消費行為,最終的目的是建立良好的顧客關係管理,培養顧客忠誠度增加顧客再購意願,促進企業的可持續發展,發展成為一種新的競爭優勢。

關鍵字:顧客關係管理、RFM模型、資料探勘、自組織映射圖網路、K-Means分群法


Corporations must focus on customers to construct good customer relationship management systems so that they can effectively manage and plan consumers’ channels and increase consumers’ purchasing intention to enhance the degree of customer satisfaction. This research uses RFM (Recency, Frequency, and Monetary) model. By using data mining techniques with purchasing history and transaction records among different customer groups, this RFM model can identify customers’ needs and preferences and further provide suitable products and services for them. It can also be effectively helpful for corporations to understand what characteristics of customers are related to long-term customer’s relationships, clearly distinguish the most valuable groups of customers, provide references for the business to allocate resources and make plans for marketing events to reinforce the relationship between corporations and customers.

This research employs RFM model to analyze the data transformed from 9,620 raw customer transaction records of a professional beauty product business. This study uses self-organizing map and K-means clustering techniques to segment customers into eleven different groups. The descriptive analyses to these eleven groups are depicted based on three RFM variables to identify high value customers and potential customers. From the research results, the entire customers can be divided into four groups, namely loyal customers, latent customers, new customers and retained customers. The findings can provide insights for managers to understand the behaviors of different groups of customers such that the strategies of proper promotion events and product services can be made. The objective is to build up good customer relationship management, increase customer loyalty and their repeat purchase intention, and promote competition among corporations to be a new competitive advantage.
Keywords: Customer Relationship Management, RFM model, Data Mining, Self-Organizing Map, K-Means Clustering


誌謝 I
中文摘要 II
Abstract III
圖目錄 VI
表目錄 VII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究範圍與對象 3
第四節 研究流程 3
第二章 文獻探討 5
第一節 個案產業發展近況 5
第二節 顧客關係管理 6
第三節 顧客價值 11
第四節 市場區隔 19
第五節 資料探勘 24
第六節 分群技術 30
第七節 RFM模型 33
第三章 研究方法 37
第一節 研究架構 37
第二節 資料來源 38
第三節 資料前置處理 38
第四節 分群方法 39
第四章 研究結果 40
第一節 敘述性統計分析 40
第二節 RFM資料分析之分群結果 40
第三節 SOM與K-Means顧客分群之結果分析 46
第四節 各群體顧客最常購買之產品分析 48
第五章 結論 50
第一節 顧客價值區隔 50
第二節 後續研究建議 52
參考文獻 54

圖目錄
圖1.1 研究流程圖 4
圖2.1 顧客關係管理之步驟 9
圖2.2 目標市場區隔、選擇及定位之步驟 22
圖2.3 資料庫知識發現過程 28
圖2.4 SOM進行資料聚類分析示意圖 31
圖2.5 K-Means流程步驟 32
圖3.1研究架構 37
圖4.1透過SOM將顧客分成11群 41

表目錄
表2.1 國內外學者對顧客關係管理之相關應用 10
表2.2 顧客價值的分類 15
表2.3 顧客價值之相關應用 18
表2.4 國內外學者對市場區隔定義一覽表 20
表2.5資料探勘方法說明 25
表2.6 傳統作業系統與資料探勘系統的比較 28
表2.7 RFM 顧客指標定義與評估顧客 34
表2.8 RFM 顧客分類 36
表4.1 描述性統計分析 40
表4.2 第一群敘述統計分析 41
表4.3 第二群敘述統計分析 42
表4.4 第三群敘述統計分析 42
表4.5 第四群敘述統計分析 43
表4.6 第五群敘述統計分析 43
表4.7 第六群敘述統計分析 43
表4.8 第七群敘述統計分析 44
表4.9 第八群敘述統計分析 44
表4.10 第九群敘述統計分析 45
表4.11 第十群敘述統計分析 45
表4.12 第十一群敘述統計分析 46
表4.13 SOM與K-Means顧客分群結果 47
表4.14 各群體顧客最常購買之產品 48
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