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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 郭瑞祥(Ruey-Shan Guo) | |
dc.contributor.author | Yi-Fen Chen | en |
dc.contributor.author | 陳薏棻 | zh_TW |
dc.date.accessioned | 2021-06-13T06:04:55Z | - |
dc.date.available | 2006-07-03 | |
dc.date.copyright | 2006-07-03 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-06-16 | |
dc.identifier.citation | 中文部分:
1.林玉青,“應用雙變量層級貝氏模型於顧客價值與行為穩定性之分析”,國立台灣大學國際企業研究所碩士論文,2003 2.陳宏毅,“顧客價值分析之隨機模型建立及實證”,國立台灣大學商學研究所碩士論文,2003 3.張容華,“運用層級貝氏理論於顧客價值隨機模型之參數估計”,國立台灣大學商學研究所碩士論文,2004 4.劉子維,“運用層級貝氏理論建立顧客個人價值估計之統計模型與實証分析—以工業電腦產品為例”,國立台灣大學商學研究所碩士論文,2003 5.呂玉敏, “應用雙變量層級貝式模型於顧客價值分析—以購物網站為例” ,國立台灣大學商學研究所碩士論文,2005 英文部分: 6.Agrawal, R., and Srikant, R., “Fast algorithms for mining association rules,” Proceedings of the 20th VLDB Conference, Santiago, Chile. (1994) 7.Ainslie, A., and Rossi, P. E., “Similarities in Choice Behavior across Product Categories,” Marketing Science, 17, 2, (1998), 91-106. 8.Allenby, Greg M., Robert P. Leone, and Lichung Jen, , 'A Dynamic Model of Purchase Timing with Application to Direct Marketing,' Journal of American Statistics Association, Vol. 94, No. 446, (1999), 365-374, 9.Bucchter, O., and Wirth, R., “Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis,” Research and Development in Knowledge Discovery and Data Mining. Second Pacific-Asia Conference, PAKDD-98, Melbourne, Australia. Springer, Berlin, (1998), 36-47. 10.Chintagunta, P.K., Haldar, S., “Investigating Purchase Timing Behavior in Two Related Product Categories,” Journal of Marketing Research, 35 (February), (1998), 43-53. 11.Green, W. H., Econometrics Analysis, 3rd ed., New Jersey: Prentice-Hall, (1997). 12.Harlam, B.A., Lodish, L.M., “Modeling Consumers’ Choices of Multiple Items,” Journal of Marketing Research, 32 (November), (1995), 404-418. 13.Hilderman, R.J., Carter, C.L., Hamilton, H.J., Cercone, N., “Mining Market Basket Data Using Share Measures and Characterized Itemsets,” Research and Development in Knowledge Discovery and Data Mining. Second Pacific-Asia Conference, PAKDD-98, Melbourne, Australia. Springer, Berlin, (1998), 159-173. 14.Jen, L., Chou, C. H., and Allenby, G. M.. “A Bayesian approach to modeling purchase frequency,” Marketing Letters, 14, 1, (2003), 5-20. 15.Lenk, P. J. and A. G. Rao, “New Models from Old: Forecasting Product Adoption By Hierarchical Bayes Procedures,” Marketing Science, 9(1), (1999), 42-53. 16.Mild, A, and Thomas Reutterer, “An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data,” Journal of Retailing and Consumer Services, 10, 3, (2003), 123-133 17.Ma, Y. and P.B. Seetharaman, “Multivariate Hazard Models for Multicategory Purchase Timing Behavior,” Rice University, (2005). 18.Manchanda, P., A. Ansari, S. Gupta, “The “shopping basket”: A model for multicategory purchase incidence decisions,” Marketing Science, 18, (1999), 95–114. 19.Mulhern, F.J., Leone, R.P., “Implicit price bundling of retail products: A multi-product approach to maximizing store profitability,” Journal of Marketing, 55 (October), (1991), 63-76. 20.Peppers, D., Rogers, M, “The One to One Future – Building Relationships One Customer at One Time,” Currency Doubleday, New York, NY, (1993). 21.Rossi, P. E., McCulloch, R. E., and Allenby, G. M. “The value of purchase history data in target marketing,” Marketing Research, 31, (1996), 289-303. 22.Seetharaman, P.B. “The Additive Risk Model for Purchase Timing,” Marketing Science, 23, 2, (2004), 234-242. 23.Seetharaman, P.B. and P.K. Chintagunta “The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications,” Journal of Business and Economic Statistics,.21, 3, (2003), 368-382. 24.Walters, R.G., “Assessing the impact of retail promotions on product substitution, complementary purchase, and inter-store sales displacement,” Journal of Marketing, 55 (April), (1991), 17-28. 25.Wu, C. C., and Chen, H. L. “Counting your customers:Compounding customer’s in-store decisions, interpurchase time and repurchasing behavior,” European Journal of Operational Research, 127, (2000), 109-119. 26.Wyner, G. A., “Customer Valuation: Linking Behavior to Economics,” Marketing Research, 8, 2, (1996), 36-38. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34363 | - |
dc.description.abstract | 近年來,由於消費者需求日漸分歧,大眾媒體式微,利用資料庫進行一對一行銷已成為必然的趨勢,其中,顧客之購買期間變數對於虛擬通路之企業維持顧客關係相當重要。過去學術界上已經有不少以顧客購買期間為主要變數之研究,然而,跨商品類別之議題卻較少被提及;跨商品類別之顧客行為模型多半著墨在顧客購買時點(Purchase Incidence)或是品牌選擇之議題。不同之商品類別各有其購買週期,且在多商品類別的環境下,不同商品類別之顧客購買期間並不互相獨立,因此,本研究擬利用層級貝式模型可估計出顧客異質性參數之優點,建構一跨商品類別之顧客購買期間行為預測模型。
本研究根據Allenby et al.(1999)所提出之顧客購買期間模型為基礎,以Generalized Gamma分配來配適顧客購買期間變數,並利用Multiplicative Model將商品類別變數引入基礎購買期間模型當中,以建立跨商品類別之顧客購買期間預測模型。最後,利用危險率函數的概念,本研究計算出每位顧客之購買機率,並進行排序作為預測之用。 本研究利用國內一型錄購物公司之顧客交易資料進行實證研究,並將跨商品類別模型與過去之層級貝式購買期間基礎模型進行預測命中力之比較。此外,本研究亦將顧客交易資料依照購買商品類別區隔開,各自進行基礎模型之參數估計並比較預測命中力。本研究根據此實證資料可得到以下結論: 1.驗證過去以Generalized Gamma分配配適顧客購買期間的適當性 2.於此資料當中,本研究所提出之跨商品類別購買期間模型之預測命中率優於過去之購買期間基礎模型。 3.跨商品類別顧客購買期間模型可以估計出不同商品類別的購買轉換乘數,進而瞭解不同商品類別轉換對購買期間的影響程度。 | zh_TW |
dc.description.abstract | Because of the recent diversity of consumers’ demand and the less popularity of mass media, one-to-one database marketing has been utilized by companies to increase their competitive capability. To maintain better customer relationship, companies such as on-line stores must understand the customers’ behavior in terms of inter-purchase time. There have been many research literatures addressing the issue of inter-purchase time; however, few of them consider the impact of multi-category of products on inter-purchase time which may very under different products. Therefore, the goal of this paper is to build a one-to-one multi-category inter-purchase time model by using the hierarchical Bayesian model.
The hierarchical Bayesian model proposed by Allenby et al.(1999) has been extended based on the generalized gamma distribution and multiplicative model formulations. With the use of Hazard rate function, the model is then used to derive the purchase probability of each individual customer. To validate the proposed model, field data from a local catalog company are collected. Prediction hit rates by different models are compared. The conclusions are as follows: 1. Generalized Gamma distribution is a good and flexible distribution to model customer inter-purchase time. 2. The multi-category inter-purchase time model has better prediction hit rate than a basic model. 3. By using the multiplicative model, our multi-category model can estimate the behavior of product transitions between two consecutive purchases. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T06:04:55Z (GMT). No. of bitstreams: 1 ntu-95-R93741002-1.pdf: 658988 bytes, checksum: b202897d611eaad71c30b10054668e58 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 第一章 緒論 1
1.1 研究動機 1 1.2 研究目的 3 1.3 論文架構 4 第二章 文獻探討 6 2.1 跨商品類別之分析模型 6 2.1.1 探索性方法 6 2.1.2 解釋型模型 7 2.1.3 跨商品類別購買期間模型 7 2.2 層級貝式模型於行銷領域上之應用 8 2.2.1 貝氏統計模式 9 2.2.2 層級貝式模型 10 2.2.3 馬可夫鏈蒙地卡羅法 (MCMC) 11 2.2.4 跨商品類別之層級貝式模型 11 2.2.5 購買期間之層級貝式模型 12 2.3 小結 13 第三章 層級貝式模型建立 15 3.1. 顧客購買期間基礎模型 15 3.1.1. 基本符號定義 15 3.1.2. 模型分配假設 16 3.1.3. 模型後驗分配之推導 17 3.1.4. 估計模式參數 17 3.2. 跨商品類別顧客購買期間模型 20 3.2.1. 基本符號定義 20 3.2.2. 模型分配假設 20 3.2.3. 跨商品類別模型特性 22 3.2.4. 後驗機率推導 23 3.2.5. 純先驗分配參數設定 25 第四章 實證研究 28 4.1 樣本描述 28 4.1.1 基本資料描述與分析 29 4.2 模型參數估計 33 4.2.1 情境設定 33 4.2.2 參數估計結果 34 4.3 樣本預測結果 36 4.3.1 預測方法說明 36 4.3.2 預測結果比較 38 4.4 商品類別轉換參數 50 4.5 小結 52 第五章 結論與建議 54 5.1. 研究結論與發現 54 5.2. 研究貢獻 55 5.3. 管理上之建議與應用 56 5.3.1. 建構完整資料庫 56 5.3.2. 依據顧客行為進行顧客分群 57 5.4. 研究限制與未來研究方向 58 5.4.1. 研究限制 58 5.4.2. 未來研究方向 58 參考文獻 60 | |
dc.language.iso | zh-TW | |
dc.title | 應用層級貝式理論於跨商品類別之顧客購買期間預測模型 | zh_TW |
dc.title | Applying Hierarchical Bayesian Theory to the Multi-Category Inter-purchase Time Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 蔣明晃(David Chiang) | |
dc.contributor.oralexamcommittee | 任立中(Li-chung Jen),黃俊堯(Chun-yao Huang) | |
dc.subject.keyword | 跨商品類別,層級貝式模型,購買期間,型錄購物, | zh_TW |
dc.subject.keyword | multi-category,hierarchical Bayesian model,inter-purchase time,catalog shopping, | en |
dc.relation.page | 62 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-06-16 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 商學研究所 | zh_TW |
顯示於系所單位: | 商學研究所 |
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