學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 結合類神經網路與BG/BB模型預測線上顧客回購
Integrating Artificial Neural Network and BG/BB Model to Predict Online Customer Repurchasing
作者 周平
Chou, Ping
貢獻者 梁定澎<br>周彥君
Liang, Ting-Peng<br>Chou, Yen-Chun
周平
Chou, Ping
關鍵詞 電子商務
顧客回購
人工智慧
類神經網路
BG/BB模型
e-Commerce
Repurchase
Artificial Intelligence
Neural Network
BG/BB Model
日期 2019
上傳時間 5-Sep-2019 15:43:41 (UTC+8)
摘要 長久以來,顧客流失一直被視為最重要的預測問題之一,例如:顧客未來回購的可能性,或是進一步找出確切的回購時機等。過去曾有許多不同的領域都在探討回購的預測,而這些領域也發展出個別的預測模型,然而,有關於不同領域的模型的比較與探討仍然相當匱乏。本研究整合了行銷領域常使用的BG/BB(Beta Geometric/Beta Bernoulli)機率模型,以及電腦科學及人工智慧領域常使用的類神經網路,提出兩種模型的混合模型。本研究結果顯示,使用當季交易資料與BG/BB參數的混合模型,相較兩個個別模型,在預測下一季回購的平均精準度(average precision),可分別提升6.5%及8.1%。此混合模型在預測精準度的改善,表示以統計為基礎的行銷模型與類神經網路具有資訊的互補性。此外,本研究發現資料分群可以找出預測較為精準的顧客群,而使用Recency相較於用K-Means進行分群,其預測表現並沒有差別,但有更低的計算成本及更高的解釋性。而在混合模型與長短期記憶模型的比較中,本研究發現混合模型有較複雜時間序列模型更好的預測效果,其建模的成本也更低。上述研究成果,在實務面,可透過較低的成本,幫助企業提升預測精準度,進而提升預測及行銷的投資報酬率,而在學術面,回購預測在行銷領域及資料探勘領域有各自的發展,而本研究是首篇進行跨領域模型探討,並探討整合兩種領域模型之混合模型的過程與其績效評估的研究。
Customer churn has long been recognized as one of the most important predictive issues. Through customer churn prediction, companies can know the likelihood of a customer repurchasing in the future, as well as the exact timing of the repurchase. In the past, there have been many different areas exploring the repurchase predictions, and these areas have developed individual prediction models. However, the lack of discussions and comparisons of models in different areas motives the research. This study proposes a hybrid model that integrates the BG/BB (Beta Geometric/Beta Bernoulli) probability model frequently used in the field of marketing, and the neural network commonly used in computer science and artificial intelligence. The hybrid model using transaction data of current season and the BG/BB parameters has improved the prediction performance (average precision) by 6.5% and 8.1% compared to the two individual models respectively. The improvement indicates that the statistical marketing model and the neural network can complement to each other. We further perform data clustering and identify the set of customers with better predictability. Especially, we find that using Recency instead of K-Means as the clustering indicator has lower computational costs and more interpretability while the prediction performance is similar. We also compare the hybrid model and the LSTM model. The finding indicates that the hybrid model has better prediction performance than complex time series model, and the cost of modeling is even lower. The study has both practical and academic contributions. First, the proposed hybrid model can help companies improve forecasting accuracy with relatively low cost, thereby improving the return on investment of marketing. In addition, while the development of repurchase forecasting in marketing and data mining has been very vigorous, this study is the first to integrate models across areas and presents the process of building the hybrid model and further evaluate its performance.
參考文獻 Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of interactive marketing, 12(1), 17-30.
Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19(2), 133-151.
Burez, J., & Van den Poel, D. (2007). CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277-288.
Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636.
Dai, Y. S., Peilan, S., & Sun, H. X. (2010). Research for E-commerce customer churns Based on Pareto/NBDModel. Sci Technol Eng, 1671-1815.
Fader, P. S., & Hardie, B. G. (2009). Probability models for customer-base analysis. Journal of interactive marketing, 23(1), 61-69.
Fader, P., Hardie, B., & Berger, P. D. (2004). Customer-base analysis with discrete-time transaction data.
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586-594.
Glady, N., Baesens, B., & Croux, C. (2009). Modeling churn using customer lifetime value. European Journal of Operational Research, 197(1), 402-411.
Glorot, X., Bordes, A., & Bengio, Y. (2011, June). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315-323).
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N & Sriram, S. (2006). Modeling customer lifetime value. Journal of service research, 9(2), 139-155.
Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902-2917.
Haykin, S. (1994). Neural networks (Vol. 2). New York: Prentice hall.
Hopmann, J., & Thede, A. (2005). Applicability of customer churn forecasts in a non-contractual setting. In Innovations in classification, data science, and information systems (pp. 330-337). Springer, Berlin, Heidelberg.
Huang, G. B. (2003). Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks, 14(2), 274-281.
Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2), 181-188.
IBM, 2013. The Four V’s of Big Data. (accessed 19.02.05).
Jain, D., & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of interactive marketing, 16(2), 34.
Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or overvalued customers: capturing total customer engagement value. Journal of service research, 13(3), 297-310.
Lee, Y., Oh, S. H., & Kim, M. W. (1991, July). The effect of initial weights on premature saturation in back-propagation learning. In IJCNN-91-Seattle international joint conference on neural networks (Vol. 1, pp. 765-770). IEEE.
Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml (Vol. 30, No. 1, p. 3).
Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of marketing research, 38(1), 131-142.
Mulhern, F. J. (1999). Customer profitability analysis: Measurement, concentration, and research directions. Journal of interactive marketing, 13(1), 25-40.
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of marketing research, 43(2), 204-211.
Neyshabur, B., Wu, Y., Salakhutdinov, R. R., & Srebro, N. (2016). Path-normalized optimization of recurrent neural networks with relu activations. In Advances in Neural Information Processing Systems (pp. 3477-3485).
Nguyen, D., & Widrow, B. (1990, June). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In 1990 IJCNN International Joint Conference on Neural Networks (pp. 21-26). IEEE.
Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.
Olle, G. D. O., & Cai, S. (2014). A hybrid churn prediction model in mobile telecommunication industry. International Journal of e-Education, e-Business, e-Management and e-Learning, 4(1), 55.
Olsen, S. O. (2002). Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty. Journal of the academy of marketing science, 30(3), 240-249.
Rosset, S., Neumann, E., Eick, U., Vatnik, N., & Idan, Y. (2002, July). Customer lifetime value modeling and its use for customer retention planning. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 332-340). ACM.
Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science, 13(1), 41-67.
Seurat Company, 2002. Precision Marketing. (accessed 19.02.06).
Sharma, A., Panigrahi, D., & Kumar, P. (2013). A neural network based approach for predicting customer churn in cellular network services. arXiv preprint arXiv:1309.3945.
Sun, Y., Wang, X., & Tang, X. (2013). Hybrid deep learning for face verification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1489-1496).
Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.
Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European journal of operational research, 166(2), 557-575.
Van den Poel, D., & Piasta, Z. (1998, June). Purchase prediction in database marketing with the ProbRough system. In International Conference on Rough Sets and Current Trends in Computing (pp. 593-600). Springer, Berlin, Heidelberg.
Wanas, N., Auda, G., Kamel, M. S., & Karray, F. A. K. F. (1998, May). On the optimal number of hidden nodes in a neural network. In IEEE Canadian Conference on Electrical and Computer Engineering (Vol. 2, pp. 918-921).
Xia, G. E., & Jin, W. D. (2008). Model of customer churn prediction on support vector machine. Systems Engineering-Theory & Practice, 28(1), 71-77.
Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 5445-5449.
Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853.
Yu, X., Guo, S., Guo, J., & Huang, X. (2011). An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 38(3), 1425-1430.
Zhang, Y., Pang, L., Shi, L., & Wang, B. (2014). Large scale purchase prediction with historical user actions on B2C online retail platform. arXiv preprint arXiv:1408.6515.
Zhang, Y., Qi, J., Shu, H., & Cao, J. (2007, October). A hybrid KNN-LR classifier and its application in customer churn prediction. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 3265-3269). IEEE.
描述 碩士
國立政治大學
資訊管理學系
106356007
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106356007
資料類型 thesis
dc.contributor.advisor 梁定澎<br>周彥君zh_TW
dc.contributor.advisor Liang, Ting-Peng<br>Chou, Yen-Chunen_US
dc.contributor.author (Authors) 周平zh_TW
dc.contributor.author (Authors) Chou, Pingen_US
dc.creator (作者) 周平zh_TW
dc.creator (作者) Chou, Pingen_US
dc.date (日期) 2019en_US
dc.date.accessioned 5-Sep-2019 15:43:41 (UTC+8)-
dc.date.available 5-Sep-2019 15:43:41 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2019 15:43:41 (UTC+8)-
dc.identifier (Other Identifiers) G0106356007en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125524-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 106356007zh_TW
dc.description.abstract (摘要) 長久以來,顧客流失一直被視為最重要的預測問題之一,例如:顧客未來回購的可能性,或是進一步找出確切的回購時機等。過去曾有許多不同的領域都在探討回購的預測,而這些領域也發展出個別的預測模型,然而,有關於不同領域的模型的比較與探討仍然相當匱乏。本研究整合了行銷領域常使用的BG/BB(Beta Geometric/Beta Bernoulli)機率模型,以及電腦科學及人工智慧領域常使用的類神經網路,提出兩種模型的混合模型。本研究結果顯示,使用當季交易資料與BG/BB參數的混合模型,相較兩個個別模型,在預測下一季回購的平均精準度(average precision),可分別提升6.5%及8.1%。此混合模型在預測精準度的改善,表示以統計為基礎的行銷模型與類神經網路具有資訊的互補性。此外,本研究發現資料分群可以找出預測較為精準的顧客群,而使用Recency相較於用K-Means進行分群,其預測表現並沒有差別,但有更低的計算成本及更高的解釋性。而在混合模型與長短期記憶模型的比較中,本研究發現混合模型有較複雜時間序列模型更好的預測效果,其建模的成本也更低。上述研究成果,在實務面,可透過較低的成本,幫助企業提升預測精準度,進而提升預測及行銷的投資報酬率,而在學術面,回購預測在行銷領域及資料探勘領域有各自的發展,而本研究是首篇進行跨領域模型探討,並探討整合兩種領域模型之混合模型的過程與其績效評估的研究。zh_TW
dc.description.abstract (摘要) Customer churn has long been recognized as one of the most important predictive issues. Through customer churn prediction, companies can know the likelihood of a customer repurchasing in the future, as well as the exact timing of the repurchase. In the past, there have been many different areas exploring the repurchase predictions, and these areas have developed individual prediction models. However, the lack of discussions and comparisons of models in different areas motives the research. This study proposes a hybrid model that integrates the BG/BB (Beta Geometric/Beta Bernoulli) probability model frequently used in the field of marketing, and the neural network commonly used in computer science and artificial intelligence. The hybrid model using transaction data of current season and the BG/BB parameters has improved the prediction performance (average precision) by 6.5% and 8.1% compared to the two individual models respectively. The improvement indicates that the statistical marketing model and the neural network can complement to each other. We further perform data clustering and identify the set of customers with better predictability. Especially, we find that using Recency instead of K-Means as the clustering indicator has lower computational costs and more interpretability while the prediction performance is similar. We also compare the hybrid model and the LSTM model. The finding indicates that the hybrid model has better prediction performance than complex time series model, and the cost of modeling is even lower. The study has both practical and academic contributions. First, the proposed hybrid model can help companies improve forecasting accuracy with relatively low cost, thereby improving the return on investment of marketing. In addition, while the development of repurchase forecasting in marketing and data mining has been very vigorous, this study is the first to integrate models across areas and presents the process of building the hybrid model and further evaluate its performance.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 1
第三節 研究問題 3
第四節 研究流程 4
第二章 文獻回顧與探討 6
第一節 顧客回購預測 6
一、顧客終身價值 6
二、顧客流失 7
三、顧客回購 9
第二節 交易情境與機率模型 10
第三節 BG/BB模型 12
第四節 類神經網路 15
一、網路運作機制 15
二、網路結構 18
三、激發函數 19
四、學習優化器 20
第三章 研究方法 23
第一節 研究流程與模型 23
第二節 模型參數 24
第三節 分析工具 27
第四章 研究分析 28
第一節 研究資料 28
第二節 資料整理 28
第三節 敘述統計 31
第四節 衡量指標 37
第五節 分析結果 39
一、BG/BB模型 39
二、類神經網路模型 43
三、混合模型 44
第六節 模型比較 46
第七節 額外分析 51
一、分群優化 51
二、穩健性檢驗 54
第五章 結論 57
第一節 研究發現 57
一、特徵工程 57
二、模型表現 57
三、模型優化 58
第二節 研究貢獻 58
第三節 研究限制 59
第四節 研究建議 60
第六章 參考文獻 61
zh_TW
dc.format.extent 1322267 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356007en_US
dc.subject (關鍵詞) 電子商務zh_TW
dc.subject (關鍵詞) 顧客回購zh_TW
dc.subject (關鍵詞) 人工智慧zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) BG/BB模型zh_TW
dc.subject (關鍵詞) e-Commerceen_US
dc.subject (關鍵詞) Repurchaseen_US
dc.subject (關鍵詞) Artificial Intelligenceen_US
dc.subject (關鍵詞) Neural Networken_US
dc.subject (關鍵詞) BG/BB Modelen_US
dc.title (題名) 結合類神經網路與BG/BB模型預測線上顧客回購zh_TW
dc.title (題名) Integrating Artificial Neural Network and BG/BB Model to Predict Online Customer Repurchasingen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of interactive marketing, 12(1), 17-30.
Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19(2), 133-151.
Burez, J., & Van den Poel, D. (2007). CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277-288.
Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636.
Dai, Y. S., Peilan, S., & Sun, H. X. (2010). Research for E-commerce customer churns Based on Pareto/NBDModel. Sci Technol Eng, 1671-1815.
Fader, P. S., & Hardie, B. G. (2009). Probability models for customer-base analysis. Journal of interactive marketing, 23(1), 61-69.
Fader, P., Hardie, B., & Berger, P. D. (2004). Customer-base analysis with discrete-time transaction data.
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586-594.
Glady, N., Baesens, B., & Croux, C. (2009). Modeling churn using customer lifetime value. European Journal of Operational Research, 197(1), 402-411.
Glorot, X., Bordes, A., & Bengio, Y. (2011, June). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315-323).
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N & Sriram, S. (2006). Modeling customer lifetime value. Journal of service research, 9(2), 139-155.
Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902-2917.
Haykin, S. (1994). Neural networks (Vol. 2). New York: Prentice hall.
Hopmann, J., & Thede, A. (2005). Applicability of customer churn forecasts in a non-contractual setting. In Innovations in classification, data science, and information systems (pp. 330-337). Springer, Berlin, Heidelberg.
Huang, G. B. (2003). Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks, 14(2), 274-281.
Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2), 181-188.
IBM, 2013. The Four V’s of Big Data. (accessed 19.02.05).
Jain, D., & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of interactive marketing, 16(2), 34.
Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or overvalued customers: capturing total customer engagement value. Journal of service research, 13(3), 297-310.
Lee, Y., Oh, S. H., & Kim, M. W. (1991, July). The effect of initial weights on premature saturation in back-propagation learning. In IJCNN-91-Seattle international joint conference on neural networks (Vol. 1, pp. 765-770). IEEE.
Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml (Vol. 30, No. 1, p. 3).
Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of marketing research, 38(1), 131-142.
Mulhern, F. J. (1999). Customer profitability analysis: Measurement, concentration, and research directions. Journal of interactive marketing, 13(1), 25-40.
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of marketing research, 43(2), 204-211.
Neyshabur, B., Wu, Y., Salakhutdinov, R. R., & Srebro, N. (2016). Path-normalized optimization of recurrent neural networks with relu activations. In Advances in Neural Information Processing Systems (pp. 3477-3485).
Nguyen, D., & Widrow, B. (1990, June). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In 1990 IJCNN International Joint Conference on Neural Networks (pp. 21-26). IEEE.
Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.
Olle, G. D. O., & Cai, S. (2014). A hybrid churn prediction model in mobile telecommunication industry. International Journal of e-Education, e-Business, e-Management and e-Learning, 4(1), 55.
Olsen, S. O. (2002). Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty. Journal of the academy of marketing science, 30(3), 240-249.
Rosset, S., Neumann, E., Eick, U., Vatnik, N., & Idan, Y. (2002, July). Customer lifetime value modeling and its use for customer retention planning. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 332-340). ACM.
Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis: An industrial purchase process application. Marketing Science, 13(1), 41-67.
Seurat Company, 2002. Precision Marketing. (accessed 19.02.06).
Sharma, A., Panigrahi, D., & Kumar, P. (2013). A neural network based approach for predicting customer churn in cellular network services. arXiv preprint arXiv:1309.3945.
Sun, Y., Wang, X., & Tang, X. (2013). Hybrid deep learning for face verification. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1489-1496).
Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.
Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European journal of operational research, 166(2), 557-575.
Van den Poel, D., & Piasta, Z. (1998, June). Purchase prediction in database marketing with the ProbRough system. In International Conference on Rough Sets and Current Trends in Computing (pp. 593-600). Springer, Berlin, Heidelberg.
Wanas, N., Auda, G., Kamel, M. S., & Karray, F. A. K. F. (1998, May). On the optimal number of hidden nodes in a neural network. In IEEE Canadian Conference on Electrical and Computer Engineering (Vol. 2, pp. 918-921).
Xia, G. E., & Jin, W. D. (2008). Model of customer churn prediction on support vector machine. Systems Engineering-Theory & Practice, 28(1), 71-77.
Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 5445-5449.
Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853.
Yu, X., Guo, S., Guo, J., & Huang, X. (2011). An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications, 38(3), 1425-1430.
Zhang, Y., Pang, L., Shi, L., & Wang, B. (2014). Large scale purchase prediction with historical user actions on B2C online retail platform. arXiv preprint arXiv:1408.6515.
Zhang, Y., Qi, J., Shu, H., & Cao, J. (2007, October). A hybrid KNN-LR classifier and its application in customer churn prediction. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on (pp. 3265-3269). IEEE.
zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU201900757en_US