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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 任立中(Li-Chung Jen) | |
dc.contributor.author | Wan-Ling Chen | en |
dc.contributor.author | 陳宛伶 | zh_TW |
dc.date.accessioned | 2021-05-20T20:47:12Z | - |
dc.date.available | 2008-07-11 | |
dc.date.available | 2021-05-20T20:47:12Z | - |
dc.date.copyright | 2008-07-11 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-07-07 | |
dc.identifier.citation | 一、中文部分
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(1988), ”Impact Of Sales Promotions On When, What, and How Much To Buy,” Journal of Marketing Research, 25 (4), 342-355 19.Horne, D.A.; Horne, D.R.(2002), “Database marketing: When does good practice become an invasion of privacy?” American Marketing Association. Conference Proceedings; 13, 480-486 20.Hughes, Arthur M. (2000),“Strategic Database Marketing-The Master plan for Starting and Managing a Profitable, Customer-Based Marketing Program.” McGraw-Hill Professional Publishing; 2nd Ed. 21.Jackson, R. and Wang, P. (1994), Strategic Database Marketing , NTC Business Book. 11, Lincolnwood 22.Kalakota, R. (1999), e-Business: Roadmap for Success , Addison-Wesley Longman, USA 23.Kim, B. D. & Kim, S.O. (2001), “A new recommender system to combine content-based and collaborative filtering systems”, Journal of Database Marketing, 8(3), 244-252 24.Lenk, P. (2001), Bayesian Inference and Markov Chain Monte Carlo, Bayesian Applications and Methods in Marketing Conference and Tutorial 25.Linden, G.; Smith, B. and York J. (2003), “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, IEEE Internet Computing, Jan/Feb, 76-80 26.Montgomery, A. L.; Li, S.; Srinivasan, K.; Liechty, J.C. (2004), “Modeling Online Browsing and Path Analysis Using Clickstream Data”, Marketing Science, 23(4), 579-595 27.Morris, T. (1994), “Customer Relationship Management”, CMA magazine,68(7), 22-25 28.Newell, F. (1997), The New Rules of Marketing: how to use one –on –one relationship marketing to be the leader in your industry, New York: McGraw-Hill, Inc. 29.Parvatiyar, A. and Sheth, J.N. (2001), “Conceptual Framework of Customer Relationship Management,” in Customer Relationship Management—Emerging Concepts, Tools and Applications, eds. New Delhi, India: Tata/McGraw-Hill, 3–25. 30.Payne, A. and Frow, P. 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(2001), “A Framework for Customer Relationship Management”, California Management Review, 43(4), 89-105 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9883 | - |
dc.description.abstract | 本研究是以資料庫行銷的技術來剖析每一位顧客的真實消費狀況,利用顧客過去的交易紀錄,來推測其未來的購買行為,同時導入顧客推薦系統的概念,將顧客視為有不同偏好的獨立個體,分別推薦顧客不同的產品,以期能為企業贏得最大的利潤。
全文通篇將資料庫內所有的顧客劃分為兩群組:第一群顧客是交易紀錄超過一筆,顯示為曾經重複購買者,被視為「舊顧客」;第二群顧客是僅有一筆紀錄,則視其為「新顧客」。從舊顧客的資料中個別挑選出最近期的一次紀錄,作為樣本內估計顧客購買偏好,及分析顧客購買行為的依據;剩餘的所有資料則分別用以預測舊顧客與新顧客的購買機率。一但了解顧客的購買偏好與發生交易的機率後,便可以對顧客進行最直接的產品推薦。 首先由顧客最初的購買行為之需求端切入,將顧客的購買偏好依照相異程度的不同,導入統計方法來分析,並且估計顧客購買偏好,來預測顧客下一次的購買;若預測顧客應該會購買,而資料顯示顧客確實有購買紀錄時,則謂之「擊中」。而後便比較此三種不同的新產品推薦模式,以累積擊中率來判斷其模式的優劣,分別包含平均機率法推薦模式、總合邏吉斯推薦模式,以及在行銷理論模型中,充分展現預測效果最佳的層級貝氏邏吉斯推薦模式。 最終實證後所得出的結論顯示,層級貝氏邏吉斯推薦模式的表現的確最好,但是仍有些許外在因素干擾其預測結果,倘若加以改善則可擁有更好的預測能力。本研究逐一針對各推薦模式的實證結果給予正反向評判,期望能由顧客關係管理中精準的資料庫行銷技術當作指引,一方面提供顧客客製化的推薦服務等,另一方面則帶領企業獲得顧客的終身價值,協助企業帶來更大的商機,創造買賣雙贏的局面。 | zh_TW |
dc.description.abstract | It’s getting obvious that customer relationship management could be viewed as a lethal weapon. To give the customers exactly what they want in affordable price can easily enhance the customer satisfaction. And this study is based on database marketing techniques, which is the key point of CRM, to analyze each customer’s purchasing behavior. By examining the transaction records, we’ll predict each customer’s next purchasing behavior, and apply the concept of customer recommendation systems to customize their recommended products. We believe that with one strong and precise recommendation system, we could encourage cross-buying, develop customer loyalty, and finally improve the customer retention, which would lead to great profits.
In this research, we hold two purposes, one is to find out the online books buying preference, the other is to compare the different kinds of recommendation systems. We separate all customers into two groups by their repetitive purchasing in turn representing“Old Customers” and “New Customers”. Later we try three types of statistical models, “the Common Average Method”, “the Aggregate Logit Recommendation System”, and the “Hierarchical Bayesian Logit Recommendation System”, and see which one of them can perform the best in the accumulated hit ratio for predicting customers purchasing possibilities. The study result shows several buying habits on different cluster of customers, for example, people with only high school educational backgrounds prefer buying “Business and Finance” to “Lifestyle” types of books, they would like to buy books that are thicker, and so on. In addition to the purchasing behavior, we also found that Hierarchical Bayesian Logit Recommendation System do the best prediction, just like the Hierarchical Bayes theory proposed, no matter for old customers or new customers. It’s is quite evident that more and more customers now they share heterogeneity needs,. In that, to best serve all individual’s need, the company better keep on customizing the recommendation system. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T20:47:12Z (GMT). No. of bitstreams: 1 ntu-97-R95724049-1.pdf: 2417961 bytes, checksum: 1e428d58598127b5ae688e9e6916653a (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 謝詞 ……………………………………………………… i
中文摘要…………………………………………………... ii 英文摘要…………………………………………………... iii 目錄 ……………………………………………………… v 圖次 ……………………………………………………… vii 表次 ……………………………………………………… viii 第一章 緒論………………………………………………… 1 第一節 研究背景與動機…………………………… 3 第二節 研究目的…………………………………… 6 第三節 研究範圍…………………………………… 7 第四節 論文架構…………………………………… 7 第五節 研究流程…………………………………… 8 第二章 文獻探討…………………………………………… 9 第一節 顧客關係管理……………………………… 9 第二節 顧客推薦系統……………………………… 20 第三節 隨機品牌選擇模式………………………… 24 第三章 研究方法…………………………………………… 29 第一節 研究架構…………………………………… 29 第二節 研究設計…………………………………… 30 第三節 邏吉斯產品選擇模型……………………… 39 第四節 層級貝氏統計模型………………………… 42 第五節 層級貝氏邏吉斯模型……………………… 49 第四章 實證分析…………………………………………… 57 第一節 樣本描述…………………………………… 57 第二節 無任何資訊下之推薦系統………………… 64 第三節 總合邏吉斯模型分析……………………… 67 第四節 建立線上推薦系統………………………… 76 第五章 結論與建議………………………………………… 89 第一節 研究發現…………………………………… 89 第二節 策略意涵…………………………………… 93 第三節 研究限制與未來建議……………………… 95 參考文獻 ……………………………………………………… 97 | |
dc.language.iso | zh-TW | |
dc.title | 線上新產品推薦系統-以亞馬遜網路書店為例 | zh_TW |
dc.title | Internet Recommendation System-Take Amazon.com as an Example | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳厚銘(Ho-Min Chen),謝明慧(Ming-Huei Hsieh) | |
dc.subject.keyword | 資料庫行銷,顧客推薦系統,層級貝氏模式,亞馬遜網路書店, | zh_TW |
dc.subject.keyword | Cusomer Relationship Management,Database Marketing,Internet Recommendation systems,Hierarchical Bayes Model,Amazon.com, | en |
dc.relation.page | 99 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2008-07-07 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
顯示於系所單位: | 國際企業學系 |
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