請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30471完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 任立中 | |
| dc.contributor.author | Wei-Lin Wang | en |
| dc.contributor.author | 汪偉霖 | zh_TW |
| dc.date.accessioned | 2021-06-13T02:04:41Z | - |
| dc.date.available | 2010-07-05 | |
| dc.date.copyright | 2010-07-05 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-03 | |
| dc.identifier.citation | Allenby, G. M., Leone, R. P., and Jen, L. (1999), “A Dynamic Model of Purchase Timing With Application to Direct Marketing,” Journal of the American Statistical Association, 94(446), pp. 365-374.
Anderson, J. C. and Narus, J. A. (1998), “Business Marketing: Understand What Customer’s Value,” Harvard Business Review, 76(6), pp. 53-65. Ansari, A., Essegaier, S., and Kohli, R. (2000), “Internet Recommendation Systems,” Journal of Marketing Research, 37(3), pp. 363-375. Blattberg, R., Getz, G. and Thomas, J. S. (2001), “Customer Equity: Building and Managing Relationship as Valuable Assets,” Harvard Business School Press, Boston. Bernardo, J. M. and Smith, A. F. M. (1994), “Bayesian Theory,” John Wiley & Sons Ltd, Chichester. Bolton, R. N. (1998), “A Dynamic Model of the Duration of the Customer’s Relationship with a Continuous Service Provider: The Role of Satisfaction,” Marketing Science, 17(1), pp. 46-65. Bower, M. and Garda, R. A. (1985), “The Role of Marketing in Management,” McKinsey Quarterly, (3), pp. 34-46. Bughin, J. and Copeland, T. E. (1997), “The Virtuous Cycle of Shareholder Value Creation,” McKinsey Quarterly, (2), pp. 156-167. Buzzell, R. D. and Gale, B. T. (1987), “The PIMS Principle,” The Free Press, New York. Carlin, B. P. and Chib, S. (1995), “Bayesian Model Choice via Markov Chain Monte Carlo Methods,” Journal of Royal Statistical Society: B, 57(3), pp. 473-484. Christopher, M. (1996), “From Brand Values to Customer Value,” Journal of Marketing Practice, 2(1), pp. 55-66. Cleland, A. S. and Bruno, A. V. (1997), “Building Customer and Shareholder Value,” Strategy and Leadership, 25(3), pp. 22-28. Connor, T. (1999), “Customer-led and Market-oriented: A Matter of Balance,” Strategic Management Journal, 20(12), pp. 1157-1163. Craven, D. W. (1998), “Examining the Impact of Market-based Strategy Paradigms on Marketing Strategy,” Journal of Strategic Marketing, 6(3), pp. 197-208. Dekimpe, M. and Hanssens, D. M. (2004), “Persistence Modeling for Assessing Marketing Strategy Performance,” in Assessing Marketing Strategy Performance, Lehmann, D. and Moorman, C. (eds.), Marketing Science Institute, Cambridge. Desarbo, W. S., Jedidi, K., and Sinha, I. (2001), “Customer value analysis in a heterogeneous market,” Strategic Management Journal, 22, pp. 845-857. Doyle, P. (2000), “How Shareholder Value Analysis Redefines Marketing,” Market Leader, 8(spring), pp. 16-25. Fader, P. S., Hardie, B. G. S. and Lee, K. L. (2005), “RFM and CLV: Using Iso-CLV Curves for Customer Base Analysis,” Journal of Marketing Research, 42(4), pp. 415-430. Flint, D. J., Woodruff, R. B., and Gardial, S. F. (1997), “Customer Value Change in Industrial Marketing Relationships: A Call for New Strategies and Research,” Industrial Marketing Management, 26, pp.163-175. Flint, D. J., Woodruff, R. B., and Gardial, S. F. (2002), “Exploring the Phenomenon of Customers’ Desired Value Change in a Business-to-Business Context,” Journal of Marketing, 66(4), pp.102-117. Friedman, J. H. (1991), “Multivariate Adaptive Regression Splines,” The Annals of Statistics, 19(1), pp. 1-67. Friedman, J. H. (2006), “Recent Advances in Predictive (Machine) Learning,” Journal of Classification, 23(2), pp. 175-197. Gardial, S. F., Flint, D. J., and Woodruff, R. B. (1996), “Trigger Events: Exploring the Relationships between Critical Events and Consumers’ Evaluations, Standards, Emotions, Values and Behavior,” Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 9, pp.35-51. Gassenheimer, J. B., Houston, F. S. and Davis, J. C. (1998), “The Role of Economic Value, Social Balue, and Perceptions of Fairness in Interorganizational Relationship Retention Decisions,” Journal of the Academy of Marketing Science, 26(4), pp. 322-337. Geman, S. and Geman, D. (1984), “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, pp. 721-741. Gensch, D. H. (1984), “Targeting the Switchable Industrial Customer,” Marketing Science, 3(1), pp. 41-54. Gronroos, C. (1997), “Value-driven Relational Marketing: from Products to Resources and Competencies,” Journal of Marketing Management, 13(5), pp. 407-419. Gummesson, E. (1999), “Total Relationship Marketing,” Butterworth-Heinemann, Oxford. Gupta, S., Lehmann, D. R., and Stuart, J. A. (2004), “Valuing Customers,” Journal of Marketing Research, 17(1), pp. 9-24. Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., and Sriram, S. (2006), “Modeling Customer Lifetime Value,” Journal of Service Research, 9(12), pp. 139-155. Gutman, J. (1982), “A Means-End Chain Model Based on Consumer Categorization Processes,” Journal of Marketing, 46(2), pp. 60-72. Hansotia, B. and Wang, P. (1997), “Analytical Challenges in Customer Acquisition,” Journal of Direct Marketing, 11(2), pp. 7-19. Hastie, T. J. and Tibshirani, R. J. (1990), “Generalized Additive Models,” Chapman and Hall, New York. Hastings, W. K. (1970), “Monte Carlo Sampling Methods Using Markov Chains and Their Application,” Biometrika, 57, pp. 97-109. Heskett, J.L., Jones, T. O., Loveman, G. W., Sasser, W. E. Jr, and Schlesinger, L. A. (1994), “Putting the Service-Profit Chain to Work,” Harvard Business Review, 72(2), pp. 164-170. Ho, T., Park, Y, and Zhou, Y. (2006), “Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value,” Marketing Science, 25(3), pp. 260-277. Holbrook, M. S. (1996), “Customer Value — A Framework for Analysis and Research,” Advance in Consumer Research, 23, pp. 138-142. Huber, F., Herrmann, A., and Morgan, R.E. (2001),” Gaining Competitive Advantage through Customer Value Oriented Management,” The Journal of Consumer Marketing, 18(1), pp. 41-53. Jen, L., Chou, C. and Allenby, G. M. (2003), “A Bayesian Approach to Modeling Purchase Frequency,” Marketing Letters, 14(1), pp. 5-20. Jen, L., Chou, C. and Allenby, G. M. (2006), “A Bayesian Approach to Customer Scoring in Direct Marketing,” Working Paper, National Taiwan University. Johnson, M. D., and Selnes, F. (2004), “Customer Portfolio Management: Toward a Dynamic Theory of Exchange Relationships,” Journal of Marketing, 68, pp. 1-17. Kahle, L. R. (ed.) (1983), “Social Values and Social Change: Adaptation to Life in America,” Pradger Publisher, New York. Kalbfleisch, J. D. and Prentice, R. L. (2002), “The Statistical Analysis of Failure Time Data, 2th ed.,” John Wiley & Sons, Inc., Hoboken. Khalifa, A. S. (2004), “Customer Value: A Review of Recent Literature and An Integrative Configuration,” Management Decision, 42(5), pp.645-666. Knott, A., Hayes, A., and Neslin, S. (2002), “Next-Product-to-Buy Models for Cross-Selling Applications,” Journal of Interactive Marketing, 16(3), pp. 59-75. Kumar, V., Lemon, K. N., and Parasuraman, A. (2006a), “Managing Customers for Value: An Overview and Research Agenda,” Journal of Service Research, 9(2), pp. 87-94. Kumar, V., Shah, D., and Venkatesan, R (2006b), “Managing Retailer Profitability ― One Customer at a Time!,” Journal of Retailing, 82(4), pp.277-294. Lee, P. M. (1997), “Bayesian Statistics, 2th ed.,” Arnold, London. Lenk, P., and DeSarbo, W. S. (2000), “Bayesian inference for finite mixtures of generalized linear models with random effects,” Psychometrika, 65(1), pp. 93-119. Lemon, K. N., Pust, R. T., and Zeithaml, V. A. (2001), “What Drives Customer Equity,” Marketing Management, 10(1), pp.20-25. Leonard, T., and Hsu, J. S. J. (1999), “Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers,” Cambridge University Press, Cambridge. Levitt, T. (1969), “The Marketing Mode: Pathways to Corporate Growth,” McGraw-Hill, New York. Levitt, T. (1980), “Marketing Success through Differentiation – of Anything,” Harvard Business Review, 58(1), pp.83-91. Levitt, T. (1981), “Marketing Intangible Products and Product Intangibles,” Harvard Business Review, 59(3), pp. 94-102. Lewis, B. R. (2005), “Blackwell Encyclopedic Dictionary of Marketing,” Blackwell Publishing, Oxford. Li, S., Sun, B., and Wilcox, R. (2005), “Cross-Selling Sequentially Ordered Products: An Application to Consumer Banking Services,” Journal of Marketing Research, 42(2), pp. 233-239. Lovelock, C. (1995), “Competing on Service: Technology and Teamwork in Supplementary Services,” Planning Review, 23(4), pp. 32-39. McLachlan, G and Peel, D. (2000), “Finite Mixture Models,” John Wiley & Sons Inc., New York. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953), “Equations of State Calculations by Fast Computing Machines,” Journal of Chemical Physics, 21, pp. 1087-1092. Mitchell, A. (1983), “The Nine American Lifestyles,” Macmillan Publishing, New York. Normann, R. and Ramirez, R. (1993), “From Value Chain to Calue Constellation: Designing interactive strategy,” Harvard Business Review, 71(4), pp. 65-77. Payne, A., and Holt, S. (2001), “Diagnosing Customer Value: Integrating the Value Process and Relathinship Marketing,” British Journal of Management, 12, pp.159-182. Parasuraman, A., Berry, L. L., and Zeithaml, V. A. (1985), “Refinement and Reassessment of the SERVQUAL Scale,” Journal of Retailing, 67(4), pp. 420-450. Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1985), “A Conceptual Model of Service Quality and Its Implications for Future Research,” Journal of Marketing, 49(4), pp. 41-50. Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1988), “SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Quality,” Journal of Retailing, 64(1), pp.12-37. Perrien, J., Paradis, S., and Banting, P. M. (1995), “Dissolution of a Relationship: The Salesforce Perception,” Industrial Marketing Management, 24(4), pp. 317-327. Pfeifer, P. and Carraway, R. (2000), “Modeling Customer Relationships as Markov Chains,” Journal of Interactive Marketing, 14(2), pp. 43-55. Porter, M. E. (1985), “Competitive Advantage: Creating and Sustaining Superior Performance,” The Free Press, New York. Ravald, A. and Gronroos, C. (1996), “The Value Concept and Relationship Marketing,” European Journal of Marketing, 30(2), pp. 19-30. Reichheld, F. F. (1996), “The loyalty Effect: the Hidden Force behind Growth, Profits, and Lasting Value,” Harvard Business School Press, Boston. Reinartz, W. J. and Kumar, V. (2000), “On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing,” Journal of Marketing, 64(4), pp. 17-35. Reinartz, W. J. and Kumar, V. (2002), “The Mismanagement of Customer Loyalty,” Harvard Business Review, 80(7), pp. 86-94. Reinartz, W. J. and Kumar, V. (2003), “The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration,” Journal of Marketing, 67(1), pp. 77-99. Rossi, P. E. and Allenby, G. M. (2003), “Bayesian Statistics and Marketing,” Marketing Science, 22(3), pp. 304-328. Rossi, P. E., Allenby, G. M., and McCulloch, R. (2005), “Bayesian Statistics and Marketing,” John Wiley & Sons, Ltd., Chichester. Rust, R. T., Ambler, T., Carpenter, G., Kumar, V., and Srivastava, R. (2004), “Measuring Marketing Productivity: Current Knowledge and Future Directions,” Journal of Marketing, 68(4), pp. 1454-1462. Schmittlein, D. C., Morrison, D. G., Colombo, R. (1987), “Counting Your Customers: Who They Are and What Will They Do Nest?” Management Science, 33(1), pp. 1-24. Slater, S. F. and Narver, J. C. (1994), “Market Orientation, Customer Value, and Superior Performance,” Business Horizons, 37(2), pp. 22-28. Smith, J. B., and Colgate, M. (2007), “Customer Value Creation: A Practical Framework,” Journal of Marketing Theory and Practice, 15(1) (winter 2007), pp. 7-23. Sorensen, D. and Gianola, D. (2002), “Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics,” Springer-Verlag New York, Inc, New York. Stigler, S. M. (1986), “Laplace’s 1774 Memoir on Inverse Probability,” Statistical Science, 1(3), pp. 359-363. Stone, B. (1995), “Successful Direct Marketing Methods, 5th ed.,” NTC Business Books, Lincolnwood. Titterington, D. M., Smith, A. F. M., and Makov, U. E.(1985), “Statistical Analysis of Finite Mixture Distributions,” John Wiley & Sons Ltd., New York. Thomas, J. (2001), “A Methodology for linking Customer Acquisition to Customer Retention,” Journal of Marketing Research, 38(2), pp. 262-268. Thomas, J., Blattberg, R., and Fox, E. (2004), “Recaptruing Lost Customers,” Journal of Marketing Research, 41(1), pp. 31-45. Vandermerwe, S. (1993), “From Tin Soldiers to Russian Dolls: Creating Added Value through Service,” Butterworth-Heinemann, Oxford. Venkatesan, R. and Kumar, V. (2004), “A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy,” Journal of Marketing, 68(4), pp. 106-125. Verhoef, P., Franses, P., and Hoekstra J. (2001), “The Impact of Satisfaction and Payment Equity on Cross-Buying: A Dynamic Model for a Multi-service Provider,” Journal of Retailing, 77(3), pp. 359-378. Villanueva, J., Yoo, S., and Hanssens, D. M. (2007), “The Impact of Marketing-Induced vs. Word-of-Mouth Customer Acquisition on Customer Equity,” Journal of Marketing Research, Forthcoming. Wilson, D. T. and Jantrania, S. A. (1994), “Understanding the Value of a Relationship,” Asia-Australia Marketing Journal, 2(1), pp. 55-66. Woodall, T. (2003), “Conceptualising “Value for the Customer”: An Attributional, Structural and Dispositional Analysis,” Academy of Marketing Science Review, 12, pp. 1-42. Woodruff, R. B. and Gardial, S. F.(1996), “Know Your Customer: New Approaches to Understanding Customer Value and Satisfaction,” Blackwell Publishing, Cambridge. Woodruff, R. B. (1997), “Customer Value: the Next Source of Competitive Advantage,” Journal of the Academy of Marketing Science, 25(2), pp. 139-153. Yoo, S. and Hanssens, D. M., (2005), “Modeling the Sales and Customer Equity Effects on the Marketing Mix,” Working Paper, University of California, Los Angeles, Anderson School of Management. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30471 | - |
| dc.description.abstract | 顧客價值為企業制定行銷策略的重要依據。從廠商的角度出發，顧客終生價值衡量個別顧客為廠商創造的利潤之折現值。然而受限於資料取得不易，顧客終身價值模型通常忽略異常事件對顧客終生價值所造成的干擾，導致估計值與實際值的偏離。
本研究以事後評估的觀點，設定受到異常事件干擾之顧客為實驗組，運用個體層次層級貝氏模型，以事件前資料估計模型參數，模擬在未受事件影響之下實驗組顧客的顧客終生價值。其次假設另一群未受事件干擾的顧客為控制組，使用同樣方法估計其終生價值，再以控制組的估計偏誤調整實驗組之模擬值。最後比較實驗組顧客之調整後模擬值與其受干擾之事後實際值，進而估算出異常事件對該群顧客價值之干擾效果。 實證研究以台灣某線上文具供應商為例，該公司之顧客資料庫為離職員工所盜竊，該名員工亦在離職後對資料庫所紀錄之顧客進行行銷活動。針對此異常事件，本研究根據事件前已列入資料庫之顧客，挑選其中在事件前後皆有兩筆以上交易資料者為實驗組樣本，並以相同標準挑選事件後才列入資料庫之顧客為控制組樣本。研究發現此異常事件造成實驗組顧客終身價值之減少，以累積銷售而言，廠商在事件後第228天之損失金額為新台幣3,377,181元。 本研究在兩組樣本皆發現，儘管全體顧客在事件後仍持續購買行為，然而部份顧客於事件後延長其購買期間，另一部份顧客則縮短其購買期間。另就群體行為來看，延長購買期間的顧客在實驗組所佔比重較高。針對此發現，本研究運用個體層次層級貝氏混合模型，討論實驗組中個別顧客是否因事件影響而造成購買期間之結構性改變。然而研究過程中面臨模型認定不足之問題，故本研究並未對前述命題做出結論，唯待未來研究對模型進行改良並對此一命題提出解釋。 | zh_TW |
| dc.description.abstract | Customer value is a key reference for businesses to formulate marketing strategies. From the suppliers’ perspectives, customer lifetime value is a managerial tool which is used to calculate the present value of the profit contributed by each customer. However, customer lifetime value models usually ignore the impact of abnormal events due the lack of data, and therefore overestimate/underestimate the true customer lifetime value.
Since the occurrence of an abnormal event is identified, this study regards customers who have been affected by the event as the experimental group. Besides, this research uses individual-level hierarchical Bayesian models to simulate the true customer lifetime value of customers belong to the experimental group based on data previous to the occurrence of the event. Furthermore, another group of customers who have not been influenced by the event are regarded as the control group which is used to adjust the simulated customer lifetime value of the experimental group. Finally, by comparing the simulated value after adjustment and the realized value, the disturbance of the abnormal event to customers from the experimental group could be estimated. In empirical study, this research studies an online stationary vender in Taiwan. The customer database of this company was stolen and leveraged to run a new business by an ex-employee of the company. To study this abnormal event, this research select customers who had been recorded in the database before the event occurred with at least two transactions both ex ante and ex post as the experimental group. In addition, other customers who was recorded in the database after the occurrence of the event and have at least two transactions are chosen as the control group, After the analysis, this research finds that the abnormal event does decrease the customer lifetime value of customers from the experimental group, and the loss value in terms of cumulative sales at the 228 day after the event occurred is NT$ 3,377,181. Another finding in this study is that although all customers keep purchasing after the occurrence of the event, some of them lengthen their inter-purchase time, while others shorten their inter-purchase time. This phenomenon is detected in both groups, but comparing to the control group, more customers from the experimental group lengthen their inter-purchase time. In order to identify whether each customer belonging to the experimental group has a structural change in his or her purchase behavior in terms of inter-purchase time after the event occurred, individual-level hierarchical Bayesian mixture models are introduced. Unfortunately, these models face the question of under-identified. Thus, future researches are needed to improve the method and to study the tapped but unsolved issue. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T02:04:41Z (GMT). No. of bitstreams: 1 ntu-96-R94724014-1.pdf: 1267080 bytes, checksum: 05c5a205dd6bf1eec6242e3bbd49fbf2 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 謝辭 i
中文摘要 iii Abstract v Table of Contents vii List of Tables xi List of Figures xiii Chapter 1 Introduction 1 1.1 Research Motives 1 1.2 Research Objectives 2 1.3 Framework 2 Chapter 2 Literature Review 3 2.1 Customer Value 3 2.1.1 Consumer Values and Consumer Value 3 2.1.2 The Augmented Product Concept 4 2.1.3 Customer Satisfaction and Service Quality 4 2.1.4 The Value Chain 5 2.1.5 Creating and Delivering Superior Customer Value 5 2.1.6 Customer-perceived Value 6 2.1.7 Customer Value and Shareholder Value 6 2.1.8 Relationship Value 7 2.1.9 Changes in Customer Value 7 2.2 Customer’s Value to the Firm and CLV Modeling 9 2.2.1 Fundamentals of CLV Modeling 10 2.2.2 RFM Models 10 2.2.3 Probability Models 11 2.2.4 Econometric Models 12 2.2.5 Persistence Models 16 2.2.6 Diffusion/Growth Models 17 2.2.7 Computer Science Models 18 2.2.8 Changes in Customer Lifetime Value 18 2.3 Hierarchical Bayesian Models 19 2.3.1 Bayesian Theorem 19 2.3.2 Hierarchical Bayesian 20 2.3.3 Finite Mixture Modeling 21 2.3.4 Markov Chain Monte Carlo 21 Chapter 3 Research Method 27 3.1 Hierarchical Bayesian Model 33 3.1.1 Model 33 3.1.2 Estimation Algorithm 34 3.2 Hierarchical Bayesian Regression Model 38 3.2.1 Model 38 3.2.2 Estimation Algorithm 39 3.3 Hierarchical Bayesian Mixture Model 44 3.3.1 Model 44 3.3.2 Estimation Algorithm 45 3.4 Hierarchical Bayesian Regression Mixture Model 51 3.4.1 Model 51 3.4.2 Estimation Algorithm 51 3.5 Average Price Paid Per Order by Each Customer 56 Chapter 4 Empirical Research 59 4.1 Sample Description 61 4.1.1 Purchase Frequency 64 4.1.2 Customers’ Average Inter-purchase Time 65 4.1.3 Average Price Paid Per Order by each Customer 66 4.1.4 Subgroup Classification 67 4.2 Estimation Results 70 4.2.1 Inter-purchase Time 70 4.2.2 Average Price Paid Per Order by Each Customer 76 4.2.3 The Impact of the Abnormal Event to the Firm 78 4.2.4 Structural Changes in Inter-purchase Time 81 Chapter 5 Conclusion and Suggestion 85 5.1 Conclusion 85 5.2 Managerial Implications 87 5.3 Limitations and Future Research 88 Reference 91 Appendix 101 Appendix 1. MCMC Process 101 Appendix 2. GAUSS Language for GIGR model 120 Appendix 3. GAUSS Language for GIGMR model 125 | |
| dc.language.iso | en | |
| dc.subject | 異常事件 | zh_TW |
| dc.subject | 層級貝式模型 | zh_TW |
| dc.subject | 顧客價值 | zh_TW |
| dc.subject | 層級貝式混合模型 | zh_TW |
| dc.subject | 購買期間 | zh_TW |
| dc.subject | 顧客終生價值 | zh_TW |
| dc.subject | hierarchical Bayesian mixture model | en |
| dc.subject | customer value | en |
| dc.subject | abnormal event | en |
| dc.subject | hierarchical Bayesian model | en |
| dc.subject | inter-purchase time | en |
| dc.subject | customer lifetime value | en |
| dc.title | 異常事件對顧客價值之干擾效果-個體層次層級貝氏模型之運用 | zh_TW |
| dc.title | The Disturbance of Abnormal Events to Customer Value: An Application of Individual-level Hierarchical Bayesian Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周建亨,陳靜怡 | |
| dc.subject.keyword | 顧客價值,顧客終生價值,異常事件,層級貝式模型,購買期間,層級貝式混合模型, | zh_TW |
| dc.subject.keyword | customer value,customer lifetime value,abnormal event,hierarchical Bayesian model,inter-purchase time,hierarchical Bayesian mixture model, | en |
| dc.relation.page | 133 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-03 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-96-1.pdf 未授權公開取用 | 1.24 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。