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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 任立中 | |
dc.contributor.author | ChungTing Hsueh | en |
dc.contributor.author | 薛仲廷 | zh_TW |
dc.date.accessioned | 2021-06-17T03:10:20Z | - |
dc.date.available | 2020-07-26 | |
dc.date.copyright | 2018-07-26 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-07-18 | |
dc.identifier.citation | Allenby, G. M., Arora, N., and Ginter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, pages 152–162.
Allenby, G. M. and Lenk, P. J. (1994). Modeling household purchase behavior with logis- tic normal regression. Journal of the American Statistical Association, 89(428):1218– 1231. Allenby, G. M. and Rossi, P. E. (1998). Marketing models of consumer heterogeneity. Journal of econometrics, 89(1):57–78 Bob Stone,1995, Successful Direct Marketing Methods, Lincol- nwood IL:NTC Business Books. Chen, I.J. and Popovich, K. (2003). Understanding customer relationship management (crm) people, process and technology. Business process management journal,9(5):672-688. Chib, S. and Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The american statistician, 49(4):327–335. Day G.S(1986). Analysis fo Strategic Marketing Decision Frederick Newell (1997) The New Rules of Marketing,The McGrew-Hill Inc. Gilks, W.R., Richardson,S., and Spiegelhalter, D. (1995). Markov chain Carlo in practice. CRC press. Hughes, AM,(1994). “Strategic Database Marketing Chicago,IL:Probus Publishing Company” Kalakota R.M. (2001), “e-Business: Roadmap for Success”, 2nd Ed Addison Wesley. Kotler, P. (2000). Marketing Management, International Edition, Prentice Hall. Kumar,V.(2010). Customer relationship management. Wiley Online Library. Mulhern, F. J. (1999). Customer profitability analysis: measurement, concentration, and research directions. Journal of Interactive Marketing, 13, 25-40. Newell, Frederick,(1997) .The New Rules of Marketing, The McGrew-Hill Inc. Roberts, C. B.(1996).” The Impact of Information Technology on the Management of System Design,” Technology in Society, 18(3), pp.333-355. Shaw, R., & M. Stone .(1990), Database Marketing: Strategy and Implementation, John Wiley & Sons Inc.. Stone, B. (1994), Successful Direct Marketing Methods, NTC Business Books, McGraw-Hill Education, New York, NY. Wyner, G. (1996). Customer valuation: Linking behavior and economics. Marketing Research, 8(2), 36-38. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52, 2-22. 王雍智 (2012). 客戶價值分析與產品推薦系統-以鎖匣交易為例. 國立暨南大學國際企業學研究所碩士論文。 任立中,陳靜怡(2015),行銷研究-發展有效行銷策略之基石,前程文化事業有限公司,11月初版。 李偉章 (2001). 資料庫行銷之顧客價值分析:以3C流通業為例. 國立臺灣大學國際企業學研究所碩士論文。 林慧晶 (1997). 資料庫行銷之客戶價值分析與行銷策略應用. 國立臺灣大學國際企業學研究所碩士論文。 徐茂鍊 (2006). “顧客關係管理”,全華科技圖書。 劉傑滔 (2016). 以RFM分析和CAI分析建置顧客推薦系統—以超級市場為例 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69190 | - |
dc.description.abstract | 購買間隔時間是行銷領域中常常使用的變數之一,企業可以透過購買間隔時間的變化來衡量顧客長時間的消費趨勢,也就是判斷顧客的活躍性,而過往針對顧客活躍性的分析最常使用的就是CAI指標,但其中計算加權時的權數給予在高購買頻率時會顯得不合理且不適當,因此本文的研究引入了指數移動平均(EMA)這個方法當作新的加權模型,並與原始CAI的結果作比對,確實發現了高購買頻率下原始CAI的不靈敏,而EMA CAI在此狀況下則是的表現較佳的。
傾聽與關心這些高價值顧客的意見是企業差異化策略的關鍵之一,使用本文所推薦的EMA CAI便能更清楚觀察到這些原本購買次數多、忠誠度高的消費族群近期的活躍度是否明顯下降,才能讓企業的顧客流失率下降並維持較高的收益。 | zh_TW |
dc.description.abstract | Inter-Purchase time is one of the variables commonly used in the marketing field. Enterprises can measure the long-term consumption trend of customers through the change of purchase interval. That is, judge the customer's activity, and we usually use CAI to evaluate a customer is active or inactive. Though the weight given when calculating CAI is unreasonable and inappropriate at high purchase frequency. Therefore, the research in this paper introduces the exponential moving average (EMA) method as a new weighted model. Compare these two CAIs, we observe the original CAI is insensitive at high purchase frequencies, while the EMA CAI performed better under this condition.
Listening and caring for these high-value customers is one of the keys to differentiation strategy. Using the EMA CAI recommended in this paper, it is more clear that the recent activity of these consumer groups with many purchases and high loyalty is obvious. Enterprises have a decline in the company's customer churn rate and maintain high returns. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T03:10:20Z (GMT). No. of bitstreams: 1 ntu-107-R05H41011-1.pdf: 1899675 bytes, checksum: 338a8a78dc3c6226275387bbba5a6677 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 目錄
第一章 緒論 ....................................................................................................... 1 第一節 研究背景 ........................................................................................... 1 第二節 研究動機與目的 ................................................................................ 3 第三節 研究流程 ........................................................................................... 5 第二章 文獻回顧 ............................................................................................... 6 第一節 資料庫行銷 ....................................................................................... 6 第二節 顧客價值 ........................................................................................... 7 第三節 層級貝氏模型 (Hierarchical Bayesian Model) .................................. 8 第三章 研究方法 ............................................................................................... 9 第一節 顧客活躍性指標 (Customer Activity Index, CAI) ............................. 9 第二節 指數移動平均 (Exponential Moving Average, EMA) ..................... 11 第三節 層級貝氏模型.................................................................................. 13 第四節 研究架構 ......................................................................................... 16 第四章 實證分析 ............................................................................................. 18 第一節 資料與實驗設計 .............................................................................. 18 第二節 參數估計結果.................................................................................. 19 第三節 研究結果與解釋 .............................................................................. 23 第五章 結論與後續建議.................................................................................. 28 參考文獻 ................................................................................................................ 29 | |
dc.language.iso | zh-TW | |
dc.title | 利用不同加權法評估顧客活躍性指標 | zh_TW |
dc.title | Using Different Weighted Method to Evaluate Customer
Activity Index | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳靜怡,王鴻龍 | |
dc.subject.keyword | 層級貝氏模型,購買間隔時間,顧客活躍度,加權模型, | zh_TW |
dc.subject.keyword | Hierarchical Bayesian Model,Inter-Purchase Time,Customer Activity,Weighted Model, | en |
dc.relation.page | 31 | |
dc.identifier.doi | 10.6342/NTU201801641 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-07-19 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
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