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題名 零售顧客回購預測模型分析
Analysis of Retail Customer Retention Model作者 涂逸凡
Tu, I-Fan貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
涂逸凡
Tu, I-Fan關鍵詞 零售業
機器學習
Retailing
RFM
Regularity
Pareto/NBD
Pareto/GGG
Machine learning日期 2018 上傳時間 29-Aug-2018 15:48:29 (UTC+8) 摘要 RFM模型(Recency, Frequency, Monetary)已長期被廣泛使用於行銷領域，對消費者行為模式具有良好的預測能力和分群的能力，本研究主要探討以超商零售業銷售資料預測顧客行為的模型與方法，並以Recency、Frequency指標之經典模型Pareto/NBD(Schmittlein, Morrison, & Colombo, 1987)為基礎進行延伸，加入ITTs(Inter Transaction Times)指標之Pareto/GGG模型(Platzer & Reutterer, 2016)進行分析，以馬可夫鏈蒙地卡羅法進行參數模擬，對以ITTs估計出Regularity指標k進行分析，並使用機器學習之演算法以估計出之參數為特徵值，對會員到店天數之預測做監督式學習，優化預測結果，對行銷策略提供更好的方向。
With outstanding prediction and segmentation performance, the RFM(Recency, Frequency, Monetary) model has been widely used in various business area. Based on the classic Pareto/NBD(Schmittlein et al., 1987) model, the Pareto/GGG model(Platzer & Reutterer, 2016) proposes a new concept ITTs(Inter Transaction Times) including a new parameter k which describes the regularity of purchase behaviors. With 120 thousands transaction record of a leading convenience store in Taiwan, this research analyzes the predictive performance of the Pareto/GGG model. Additionally, using parameter estimated from Markov chain Monte Carlo as input features, we conduct supervised learning on customer purchase frequencies to improve forecast accuracy of customer shopping behaviors.參考文獻 Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Paper presented at the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. Coussement, K., & Van den Poel, D. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36(3), 6127-6134. Fader, P., Hardie, B., & Berger, P. D. (2004). Customer-base analysis with discrete-time transaction data. Fader, P. S., Hardie, B. G., & Lee, K. L. (2005a). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing science, 24(2), 275-284. Fader, P. S., Hardie, B. G., & Lee, K. L. (2005b). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. 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. Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264. Hughes, A. M. (1994). Strategic database marketing: the masterplan for starting and managing a profitable. Customer-based Marketing Program, Irwin Professional. Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63. King, S. F. (2007). Citizens as customers: Exploring the future of CRM in UK local government. Government Information Quarterly, 24(1), 47-63. Kumar, V., Venkatesan, R., & Reinartz, W. (2006). Knowing what to sell, when, and to whom. Harvard business review, 84(3), 131-137. Liu, D.-R., & Shih, Y.-Y. (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software, 77(2), 181-191. McCarty, J. A., & Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research, 60(6), 656-662. Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. The journal of marketing, 20-38. Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing science, 35(5), 779-799. Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who-are they and what will they do next? Management science, 33(1), 1-24. Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. Thomas, J. S. (2001). A methodology for linking customer acquisition to customer retention. Journal of Marketing Research, 38(2), 262-268. Wheat, R. D., & Morrison, D. G. (1990). Estimating purchase regularity with two interpurchase times. Journal of Marketing Research, 87-93. Yeh, I.-C., Yang, K.-J., & Ting, T.-M. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3), 5866-5871. 描述 碩士
國立政治大學
資訊管理學系
105356012資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356012 資料類型 thesis dc.contributor.advisor 莊皓鈞<br>周彥君 zh_TW dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chun en_US dc.contributor.author (Authors) 涂逸凡 zh_TW dc.contributor.author (Authors) Tu, I-Fan en_US dc.creator (作者) 涂逸凡 zh_TW dc.creator (作者) Tu, I-Fan en_US dc.date (日期) 2018 en_US dc.date.accessioned 29-Aug-2018 15:48:29 (UTC+8) - dc.date.available 29-Aug-2018 15:48:29 (UTC+8) - dc.date.issued (上傳時間) 29-Aug-2018 15:48:29 (UTC+8) - dc.identifier (Other Identifiers) G0105356012 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119719 - 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) 105356012 zh_TW dc.description.abstract (摘要) RFM模型(Recency, Frequency, Monetary)已長期被廣泛使用於行銷領域，對消費者行為模式具有良好的預測能力和分群的能力，本研究主要探討以超商零售業銷售資料預測顧客行為的模型與方法，並以Recency、Frequency指標之經典模型Pareto/NBD(Schmittlein, Morrison, & Colombo, 1987)為基礎進行延伸，加入ITTs(Inter Transaction Times)指標之Pareto/GGG模型(Platzer & Reutterer, 2016)進行分析，以馬可夫鏈蒙地卡羅法進行參數模擬，對以ITTs估計出Regularity指標k進行分析，並使用機器學習之演算法以估計出之參數為特徵值，對會員到店天數之預測做監督式學習，優化預測結果，對行銷策略提供更好的方向。 zh_TW dc.description.abstract (摘要) With outstanding prediction and segmentation performance, the RFM(Recency, Frequency, Monetary) model has been widely used in various business area. Based on the classic Pareto/NBD(Schmittlein et al., 1987) model, the Pareto/GGG model(Platzer & Reutterer, 2016) proposes a new concept ITTs(Inter Transaction Times) including a new parameter k which describes the regularity of purchase behaviors. With 120 thousands transaction record of a leading convenience store in Taiwan, this research analyzes the predictive performance of the Pareto/GGG model. Additionally, using parameter estimated from Markov chain Monte Carlo as input features, we conduct supervised learning on customer purchase frequencies to improve forecast accuracy of customer shopping behaviors. en_US dc.description.tableofcontents 第一章 緒論 5 第二章 文獻探討 8 第一節 顧客終身價值(Customer Lifetime Value, CLV) 8 第二節 RFM模型(Recency、Frequency、Monetary) 9 第三章 模型介紹 12 第一節 Pareto/NBD模型 12 第二節 Pareto/GGG模型 13 第四章 Pareto/GGG模型分析結果 16 第一節 資料敘述 16 第二節 規律性分析 18 第三節 季節分析 26 第五章 以機器學習演算法改善預測 29 第一節 模型介紹 29 第二節 機器學習演算法結果 30 第三節 混合式預測 33 第六章 結論 39 參考文獻 41 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356012 en_US dc.subject (關鍵詞) 零售業 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Retailing en_US dc.subject (關鍵詞) RFM en_US dc.subject (關鍵詞) Regularity en_US dc.subject (關鍵詞) Pareto/NBD en_US dc.subject (關鍵詞) Pareto/GGG en_US dc.subject (關鍵詞) Machine learning en_US dc.title (題名) 零售顧客回購預測模型分析 zh_TW dc.title (題名) Analysis of Retail Customer Retention Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Paper presented at the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. Coussement, K., & Van den Poel, D. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36(3), 6127-6134. Fader, P., Hardie, B., & Berger, P. D. (2004). Customer-base analysis with discrete-time transaction data. Fader, P. S., Hardie, B. G., & Lee, K. L. (2005a). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing science, 24(2), 275-284. Fader, P. S., Hardie, B. G., & Lee, K. L. (2005b). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. 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. Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264. Hughes, A. M. (1994). Strategic database marketing: the masterplan for starting and managing a profitable. Customer-based Marketing Program, Irwin Professional. Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63. King, S. F. (2007). Citizens as customers: Exploring the future of CRM in UK local government. Government Information Quarterly, 24(1), 47-63. Kumar, V., Venkatesan, R., & Reinartz, W. (2006). Knowing what to sell, when, and to whom. Harvard business review, 84(3), 131-137. Liu, D.-R., & Shih, Y.-Y. (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software, 77(2), 181-191. McCarty, J. A., & Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research, 60(6), 656-662. Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. The journal of marketing, 20-38. Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing science, 35(5), 779-799. Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who-are they and what will they do next? Management science, 33(1), 1-24. Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. Thomas, J. S. (2001). A methodology for linking customer acquisition to customer retention. Journal of Marketing Research, 38(2), 262-268. Wheat, R. D., & Morrison, D. G. (1990). Estimating purchase regularity with two interpurchase times. Journal of Marketing Research, 87-93. Yeh, I.-C., Yang, K.-J., & Ting, T.-M. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3), 5866-5871. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.019.2018.A05 -
