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研究生:李章偉
研究生(外文):Chang-wei Lee
論文名稱:資料庫行銷之顧客價值分析:以3C流通業為例
論文名稱(外文):Customer Valuation in Database Marketing : An Empirical Study of 3C Distributor
指導教授:任立中任立中引用關係
指導教授(外文):Li-chung Jen
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:國際企業學研究所
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:2001
畢業學年度:89
語文別:中文
論文頁數:133
中文關鍵詞:資料庫行銷顧客價值分析產品關聯性分析貝氏層級模型RFM模型
外文關鍵詞:Database MarketingCustomer Valuation AnalysisProduct Combination AnalysisHierarchical Bayesian ModelRFM Model
相關次數:
  • 被引用被引用:121
  • 點閱點閱:2314
  • 評分評分:
  • 下載下載:413
  • 收藏至我的研究室書目清單書目收藏:15
國內、外有關資料庫行銷的實證研究對象多以直銷商為主,本研究嘗試以3C流通業者為研究對象,藉由資料庫中個別顧客的購買歷史記錄進行平均購買期間分析、顧客活躍性分析、顧客價值分析、靜態產品關聯性分析,以及動態產品關聯性分析。
在推估顧客的平均購買期間時,「貝氏層級估計法(HB)」與「最大概似估計法(MLE)」或「加權最大概似估計法(WMLE)」的最大差別在於:貝氏層級估計法不僅考慮了可觀察到之個別顧客購買率的不同,更考慮了無法觀察到的顧客異質性;因而當個別顧客因交易記錄過少,可能導致估計上有所偏誤時,貝氏層級模型可以將所有的顧客購買記錄併入估計,作為個別顧客在推估平均購買期間時的修正。
在分別由顧客「活躍性指標」與「購買次數」所得出的顧客活躍性分析,以及顧客「平均購買金額」與「購買次數」的顧客價值分析,我們可以針對不同群體的不同購買行為,來擬定符合其所需的行銷策略。
不論是「產品相關性矩陣」或「因素結構矩陣」的靜態產品關聯性分析,皆可作為「商品陳列與賣場動線」以及「商品聯合促銷與交叉銷售」之規劃與設計的決策依據。
而「產品平均購買期間矩陣」的動態產品關聯性分析,則可作為「向上銷售」或「持續銷售」之產品組合建議的決策基礎。
The purposes of this research are twofold. One is aimed to conduct customer purchase behavior analysis, including Inter-purchase Time Analysis, Customer Active Analysis, and Customer Value Analysis. Another is to conduct product assortment analysis, including Static Product Combination Analysis and Dynamic Product Combination Analysis.
As estimating the inter-purchase time of individual customer, the main difference between Hierarchical Bayesian Model and Maximum Likelihood Estimation or Weighted Maximum Likelihood Estimation is that HB considers not only the different purchase rate of individual customer but also the different regression coefficient of individual customer. Thus HB model has the better ability to forecast future purchase behavior of individual customer.
From Customer Active Analysis and Customer Value Analysis, we can make various marketing strategies corresponding to the different purchase behaviors of different groups.
Conducting Static Product Combination Analysis, both Product Correlation Matrix and Factor Structure Matrix may be the decision basis of product exhibition or traffic design in retailing stores and product bundling or cross-selling.
Finally, the Product Inter-purchase Time Matrix could be the decision basis of up-selling and continuity-selling.
中文摘要 ………………………………………………………… Ⅰ
英文摘要 ………………………………………………………… Ⅱ
謝 辭 ………………………………………………………… Ⅲ
第一章 緒論……………………………………………………… 01
第一節 研究背景與動機…………………………………… 01
第二節 研究目的…………………………………………… 04
第三節 研究範圍…………………………………………… 05
第四節 研究流程…………………………………………… 06
第五節 研究架構…………………………………………… 07
第二章 文獻探討………………………………………………… 08
第一節 顧客關係管理……………………………………… 11
第二節 資料庫行銷………………………………………… 19
第三節 直接行銷…………………………………………… 22
第四節 關係行銷…………………………………………… 28
第三章 研究方法………………………………………………… 34
第一節 RFM模型…………………………………………… 34
第二節 一般化伽瑪模型…………………………………… 38
第三節 貝氏層級模型……………………………………… 42
第四節 因素分析…………………………………………… 49
第四章 實證分析………………………………………………… 54
第一節 資料簡介…………………………………………… 54
第二節 顧客購買行為分析………………………………… 56
第三節 產品關聯性分析…………………………………… 82
第五章 結論與建議……………………………………………… 96
第一節 研究結論與管理意涵……………………………… 96
第二節 研究限制…………………………………………… 101
第三節 後續研究建議……………………………………… 102
參考文獻 ………………………………………………………… 103
附錄 ……………………………………………………………… 109
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