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研究生: 郭瀚揚
Kuo, Han-Yang
論文名稱: 資料探勘應用之研究:零售業的RFM分析架構
A study of data mining application: RFM analytical framework of a retailer
指導教授: 周世玉
Chou, Shih-Yu
學位類別: 碩士
Master
系所名稱: 全球經營與策略研究所
Graduate Institute of Global Business and Strategy
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 53
中文關鍵詞: RFM資料採礦集群分析判別分析決策樹分析
英文關鍵詞: RFM, Data mining, Cluster analysis, Discriminant analysis, Decision tree analysis
DOI URL: http://doi.org/10.6345/NTNU201900931
論文種類: 學術論文
相關次數: 點閱:267下載:67
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  • 在資料庫行銷領域中,RFM模型一直是一個很重要的角色,他能提供一個簡單的框架去量化顧客。隨著時代的演進,RFM模型結合資料採礦能使企業對於顧客的分析更透徹,不論是用於對顧客進行分群或是分析顧客價值。本研究使用公開平台的交易資料進行分析,以真實零售商之交易資料分析該企業的顧客,嘗試以RFM模型結合資料採礦的方法,對客戶進行分群,最後建立預測模型並驗證其預測力,同時本研究也著重在資料前處理的描寫。本研究以二階段集群分析結合RFM指標將顧客分成四群,並且將分群後的結果作為目標變數,以決策樹分析與判別分析建立預測模型,最後發現判別分析之預測率較好,但決策樹擁有較易解釋的規則。

    In the field of database marketing, the Recency, Frequency, Monetary model has always played an important role, it provides a simple framework to quantify customers. With the evolution of the technology, the RFM model combined with data mining enables companies to analyze customers more thoroughly, whether it is used to segment customers or analyze customer value. This study uses the transaction data of the open data platform, and analyzes the customers of the retailer's transaction data. It attempts to combine the data mining method with the RFM model, and then builds the predicting model and verifies its predictability. This study also focuses on the process of data pre-processing. In this study, the two-phase cluster analysis combined with the RFM index divides the customers into four groups, and the results of the grouping are used as the target variables. The prediction model is established by decision tree analysis and discriminant analysis. Finally, the prediction rate of the discriminant analysis is better, but the decision tree is easier to explain.

    摘要 I ABSTRACT II 目錄 III 圖目錄 IV 表目錄 V 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 3 第三節 論文結構 4 第二章 文獻回顧 5 第一節 RFM模型 5 第二節 資料採礦 8 第三節 集群分析 11 第四節 決策樹分析 12 第五節 判別分析 13 第三章 研究方法 14 第一節 研究設計 14 第二節 資料來源及變數說明 16 第三節 分析方法 18 第四章 實證分析 26 第一節 資料前處理 26 第三節 集群分析 36 第四節 建立預測模型 40 第五章 結論與建議 48 第一節 研究發現 48 第二節 研究結論 49 第三節 研究限制與建議 49 參考文獻 51

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