| 研究生: |
葉廸珺 Yeh, Ti-Chen |
|---|---|
| 論文名稱: |
以Bootstrap來建立一個穩定的忠誠客戶預測模型 Establishing A Stable and Loyal Customers Prediction Model with Bootstrap |
| 指導教授: |
徐立群
Shu, Lih-Chyun |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 會計學系 Department of Accountancy |
| 論文出版年: | 2017 |
| 畢業學年度: | 105 |
| 語文別: | 中文 |
| 論文頁數: | 39 |
| 中文關鍵詞: | Bootstrap 、活性機率 、再購次數 、再購金額 |
| 外文關鍵詞: | Bootstrap, Activity rate, repurchase times, repurchase amount |
| 相關次數: | 點閱:83 下載:6 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
在這網路科技日新月異的時代中,網路購物也成了許多人心中不可或缺的交易平台,從過去的研究中也已經透過活性機率來建立出能夠預測總體及個體的消費行為(包含消費者的再購率以及購買金額)都獲得良好的預測結果,然而在相同資料但不同時間點常造成其預測準確率的不穩定,這個原因可歸咎於總體參數的數值非最佳化,導致管理者在決策上的困擾,基於此,我們延伸過去的研究,透過改良所計算出來的活性機率值,以及透過Bootstrap的方式以常態值來模擬實際值,計算出一個穩定且精準的預測結果,以供企業使用。透過台灣某電子公司所提供的資料,從2013年1月1日至2016年3月31日為止,共39個月,分析訓練期間220名顧客在未來一期的交易情形,驗證結果趨於穩定且準確率為最佳且對於管理決策上亦有幫助。
In this changing time of network technology, online shopping has become an indispensable trading platform for many people. Previous studies also found that activity rate can be used to forecast both general and the individual consumer behavior (including consumers’ re-purchases rate and amount). However, applying the same data into different time points often cause unstable forecast results. This can be attributed by general parameters’ non-optimization, which leads to managers’ trouble in decision-making. Based on this, our study focus on an improved activity rate calculation, as well as a normal value simulation by Bootstrap method, to achieve a stable and accurate forecast that could be used by enterprises.
參考文獻
Chang, S. C, and Tang, Y. C, and Chu, J. K, 2014. “Revisiting Online Repeat Purchase Behavior: Deterministic Versus Stochastic Behavioral Modeling,” Journal of Data Analysis. (9:5), pp.23-46.
David, L. B, 1977. “Probing privacy,” Gonzaga Law Review (12:4), pp.587-619.
Efron, B. and Tibshirani, R.J. 1994. “An Introduction to the Bootstrap”
Fader, P. S, and Hardie, B. G. S, and Lee, K. L, 2005. “Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model,” Marketing Science (24:2), pp. 275-284.
Huang, 2009. “利用 BG/NBD 模型預測顧客未來行為,以提升流失管理效率--以藥品產業為例,” 國立交通大學經營管理研究所碩士論文
Hsiao, B, Shu, L, and Hsieh, S. J.2015a. “Predicting and Testing Probability of Continuous Purchasing of Online Customers,” 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)
Hsiao, B, Shu, L, and Hsieh, S. J, Chen, P. Y., 2015b. “Trial Plan With Capitation Payment of the National Healthcare Insurance in Taiwan: Establishing a Loyal Patient Selection Model,” PACIS 2015 Proceedings. 245.
Pearson, J. W, Olver, S, and Porter, M. A. 2015. “Numerical Methods for the Computation of the Confluent and Gauss Hypergeometric Function,” Numerical Algorithms (74:3), pp. 821–866.
Schmittlein, D. C, Morrison, D. G, and Cowmbo, R.1987. “Counting your customers who are they and what will they do next,” Management science (33:1), pp. 1-24.
Schmittlein, D. C, and Peterson, R. A.1994. “Customer base analysis: An industrial purchase process application,” Management science (13:1), pp. 41-67.
Simar, L, and Wilson, P. W.1998. “Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models,” Management Science (44:1), pp.49-61.
Wang, 2000. “The Relationships among Consumer Attributes, Users' Satisfactions on Web and WWW Purchase Intention,” JBA (48), pp.121-137.