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Title: | 零售顧客回購預測模型分析 Analysis of Retail Customer Retention Model |
Authors: | 涂逸凡 Tu, I-Fan |
Contributors: | 莊皓鈞 周彥君 Chuang, Hao-Chun Chou, Yen-Chun 涂逸凡 Tu, I-Fan |
Keywords: | 零售業 機器學習 Retailing RFM Regularity Pareto/NBD Pareto/GGG Machine learning |
Date: | 2018 |
Issue Date: | 2018-08-29 15:48:29 (UTC+8) |
Abstract: | 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. |
Reference: | 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 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105356012 |
Data Type: | thesis |
DOI: | 10.6814/THE.NCCU.MIS.019.2018.A05 |
Appears in Collections: | [資訊管理學系] 學位論文
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