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Title | 零售顧客回購預測模型分析 Analysis of Retail Customer Retention Model |
Creator | 涂逸凡 Tu, I-Fan |
Contributor | 莊皓鈞<br>周彥君 Chuang, Hao-Chun<br>Chou, Yen-Chun 涂逸凡 Tu, I-Fan |
Key Words | 零售業 機器學習 Retailing RFM Regularity Pareto/NBD Pareto/GGG Machine learning |
Date | 2018 |
Date Issued | 29-Aug-2018 15:48:29 (UTC+8) |
Summary | 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. |
Description | 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 |
Type | 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 | - |