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研究生: 劉彥辰
Liu, Yen-Chen
論文名稱: 應用資料探勘技術探討顧客保留模型
The Application of Data Mining Techniques in Customer Retention Model
指導教授: 施人英
Shih, Jen-Ying
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: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 81
中文關鍵詞: 資料探勘顧客關係管理顧客保留決策樹類神經網路羅吉斯迴歸
英文關鍵詞: data mining, customer relationship management, customer retention, decision tree, artificial neural network, logistic regression
DOI URL: https://doi.org/10.6345/NTNU202205449
論文種類: 學術論文
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  • 直效行銷近年來在物流發展與法規的保障等因素下已經有明顯的成長,各大通路業者拜資料庫系統健全之賜,透過來往交易中得到豐富的顧客資料與交易紀錄,如性別、年齡、購買金額、購買時間、購買商品類別等,因此如何妥善利用這些資料來提升對顧客的了解,並使用適當的資料探勘技術,提供給顧客個人化的行銷與服務,讓通路業者能在競爭中獲得先機,將成為未來廠商重要的課題。
    本研究利用資料探勘中集群、分類、關聯分析技術於顧客關係管理,達到顧客保留之目的。將透過一個年度消費者交易資料及商品資料,轉換出176筆攸關變數,先將消費者依照購買行為做出分群,再運用決策樹、類神經網路與羅吉斯迴歸作為分類分析的工具,尋找出下一個年度第一期型錄寄發名單中的消費者是否購買商品,並評估分類之準確度,再比較未經分類直接採用各分類分析的準確度。透過關聯分析中將顧客分為男女兩組,探討顧客在商品購買中的關聯性,結合分析結果,找出目標消費客群,並期望能將此資料探勘之成果研擬出針對不同顧客的行銷策略,達到顧客保留之目的。
    透過本研究之分析,通路廠商能夠更有效對顧客進行區隔,根據顧客之消費特性及基本資料所產生的變數列出分類規則,能夠在型錄寄發對象的選擇有更客觀的參考依據,而研究成果期待能為通路廠商將有限的行銷資源做出最有效率的發揮。

    In recent years, direct-marketing has grown significantly based on the evolvement of logistics and the rule of legal protection. Most of stores developed database system to collect customer profiles and transaction records. The most important issue in these stores is how to use the information to enhance realization of customer transactions properly. We can use data-mining technology to analyze these data and provide customization service to customers.
    This research used data-mining (clustering, classification and association) in customer relationship management to reach the target customer retention. The customer profiles and one-year transaction records were used to generate 176 variables. The first step is to conduct cluster analysis based on purchasing behavior of consumers then classification analysis is used in each group of customers to find out who will buy through catalogue in the next year. The second step is to compare the precision of the model without using cluster analysis. Finally, we use association to explore customers’ purchase association among items. We integrate the results of analysis to find target customers and develop marketing strategies for customer retention.
    Through this research, companies can sell products effectively. According to the classification rules, companies can choose the right customers to send catalogues objectively. The expectation of this research is to let the sellers maximize their efficiency under the condition of limited marketing resources through data mining.

    摘要 I ABSTRACT II 表目錄 VI 圖目錄 VII 致謝 IX 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 研究方法與流程 4 1.4 論文結構 6 第二章 文獻探討 7 2.1 顧客關係管理 7 2.1.1 顧客關係管理之定義和發展 7 2.1.2 顧客關係管理架構 9 2.2資料探勘 11 2.2.1資料探勘之定義與發展 11 2.2.2資料探勘之步驟 13 2.2.3 資料探勘過程CRISP-DM 14 2.3 資料探勘應用於顧客關係管理 16 2.4 顧客保留 19 2.4.1 顧客保留的定義 19 2.4.2 顧客保留的重要性 20 2.5 變數的選取 21 2.6 型錄 22 2.6.1 沿革 22 2.6.2 型錄的定義 23 第三章 研究方法 25 3.1研究架構 25 3.2 研究變數之選取和定義 27 3.2.1 變數的分類 27 3.2.2 顧客行為分析指標 27 3.2.3商品類別影響購買 31 3.3 採用資料探勘之方法 32 3.3.1決策樹(Decision Tree) 32 3.3.2類神經網路(Artificial Neural Network) 35 3.3.3羅吉斯迴歸(Logistic Regression) 40 3.3.4 集群分析(Cluster Analysis) 41 3.3.5 購物籃分析(Market Basket Analysis) 46 3.4.1錯差矩陣(Confusion Matrix) 48 3.4.2累積增益圖(Cumulative Lift Chart) 49 第四章 資料分析與結果 50 第一節 研究流程 50 4.1 變數說明 51 4.2 資料整合與資料預處理 52 4.3 集群分析 53 4.4決策樹分析 56 4.6 集群分析後各集群之分類分析結果 59 4.7模型比較 65 4.8 關聯分析 66 第五章 結論與建議 71 5.1結論 71 5.2 建議 72 參考文獻 74

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