| 參考文獻 |
Abirami, M., & Pattabiraman, V. (2016). Data mining approach for intelligent customer behavior analysis for a retail store. Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC–16’),
Alderson, W. (2006). The analytical framework for marketing. In A twenty-first century guide to Aldersonian marketing thought (pp. 61-73). Springer.
Alizadeh Zoeram, A., & Karimi Mazidi, A. R. (2018). New approach for customer clustering by integrating the LRFM model and fuzzy inference system. Iranian Journal of Management Studies, 11(2), 351-378.
Alvandi, M., Fazli, S., Abdoli, F. S. J. I. R. J. o. A., & Sciences, B. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. 3(11), 2294-2302.
Au, W.-H., & Chan, K. C. (2003). Mining fuzzy association rules in a bank-account database. IEEE Transactions on Fuzzy Systems, 11(2), 238-248.
Bailey, J. (2018). Alternative clustering analysis: A review. Data Clustering, 535-550.
Bhide, A. (1996). The Questions Every Entrepreneur Must Answer. Harvard Business Review.
Borle, S., Singh, S. S., & Jain, D. C. (2008). Customer lifetime value measurement. Management science, 54(1), 100-112.
Carter, N. M., Stearns, T. M., Reynolds, P. D., & Miller, B. A. (1994). New venture strategies: Theory development with an empirical base. Strategic Management Journal, 15(1), 21-41.
Chan, C.-C. H. (2005). Online auction customer segmentation using a neural network model. International Journal of Applied Science and Engineering, 3(1), 101-109.
Chang, H., & Tsay, S. (2004). Integrating of SOM and K-mean in data mining clustering: An empirical study of CRM and profitability evaluation. International Journal of Applied Science and Engineering.
Chao, S.-H., Chen, M.-K., & Wu, H.-H. (2021). An LRFM model to analyze outpatient loyalty from a medical center in taiwan. SAGE Open, 11(3), 21582440211031899.
Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19(3), 197-208.
Chen, S., & Huang, M. (2011). Constructing credit auditing and control & management model with data mining technique. Expert systems with applications, 38(5), 5359-5365.
Chen, Y.-L., Kuo, M.-H., Wu, S.-Y., & Tang, K. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications, 8(5), 241-251.
Chen, Y.-S., Cheng, C.-H., Lai, C.-J., Hsu, C.-Y., & Syu, H.-J. (2012). Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment. Computers in biology and medicine, 42(2), 213-221.
Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.
Chu, Y., Yang, H.-K., & Peng, W.-C. (2019). Predicting online user purchase behavior based on browsing history. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW),
Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert systems with applications, 34(1), 313-327.
Daly, J. L. (2002). Pricing for profitability: Activity-based pricing for competitive advantage. John Wiley & Sons.
Davidovici-Nora, M. (2013). Innovation in business models in the video game industry: Free-To-Play or the gaming experience as a service. The computer games journal, 2(3), 22-51.
de Carvalho Santos, G. V. (2020). Feature importance analysis for User Lifetime Value prediction in games using Machine Learning: an exploratory approach. DATA MINING
Dhini, A., Budiani, L. R., & Laoh, E. (2020). Segmenting and Targeting the Potential Markets of a Muslim Fashion Company. 2020 International Conference on ICT for Smart Society (ICISS),
Fader, P. S., & Hardie, B. G. (1996). Modeling consumer choice among SKUs. Journal of marketing Research, 33(4), 442-452.
Fader, P. S., & Hardie, B. G. (2009). Probability models for customer-base analysis. Journal of interactive marketing, 23(1), 61-69.
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.
Fernandez, G. (2010). Data mining using SAS applications. CRC press.
Fu, X., Chen, X., Shi, Y.-T., Bose, I., & Cai, S. J. D. S. S. (2017). User segmentation for retention management in online social games. 101, 51-68.
Ghousi, R. (2015). Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures. Journal of Industrial and Systems Engineering, 8(3), 59-76.
Glady, N., Baesens, B., & Croux, C. (2009a). Modeling churn using customer lifetime value. European Journal of Operational Research, 197(1), 402-411.
Glady, N., Baesens, B., & Croux, C. (2009b). A modified Pareto/NBD approach for predicting customer lifetime value. Expert systems with applications, 36(2), 2062-2071.
Gramling, K., Orschell, J., & Chernoff, J. (2021). How E-Commerce Fits into Retail′s Post- Pandemic Future. Harvard Business Review.
Ha, S. H., & Park, S. C. (1998). Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert systems with applications, 15(1), 1-31.
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.
Hamka, F., Bouwman, H., De Reuver, M., Kroesen, M. J. T., & Informatics. (2014). Mobile customer segmentation based on smartphone measurement. 31(2), 220-227.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Helgesen, Ø. (2006). Are loyal customers profitable? Customer satisfaction, customer (action) loyalty and customer profitability at the individual level. Journal of Marketing Management, 22(3-4), 245-266.
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.
Hosseini, Z. Z., & Mohammadzadeh, M. (2016). Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services. Iranian journal of pharmaceutical research: IJPR, 15(1), 355.
Hu, Y.-H., & Yeh, T.-W. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, 61, 76-88.
Huang, B., Kechadi, M. T., & Buckley, B. (2012). Customer churn prediction in telecommunications. Expert systems with applications, 39(1), 1414-1425.
Huang, S.-C., Chang, E.-C., & Wu, H.-H. J. E. S. w. A. (2009). A case study of applying data mining techniques in an outfitter’s customer value analysis. 36(3), 5909-5915.
Hughes, A. M. (2005). Strategic database marketing. McGraw-Hill Pub. Co.
Jo-Ting, W., Shih-Yen, L., & Hsin-Hung, W. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), 4199-4206.
Jonker, J.-J., Piersma, N., & Van den Poel, D. (2004). Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert systems with applications, 27(2), 159-168.
Kagan, S., & Bekkerman, R. (2018). Predicting purchase behavior of website audiences. International Journal of Electronic Commerce, 22(4), 510-539.
Kahreh, M. S., Tive, M., Babania, A., & Hesan, M. (2014). Analyzing the applications of customer lifetime value (CLV) based on benefit segmentation for the banking sector. Procedia-Social and Behavioral Sciences, 109, 590-594.
Kandeil, D. A., Saad, A. A., & Youssef, S. M. (2014). A two-phase clustering analysis for B2B customer segmentation. 2014 International Conference on Intelligent Networking and Collaborative Systems,
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the academy of marketing science, 29(4), 374-390.
Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
Kirui, C., Hong, L., Cheruiyot, W., & Kirui, H. (2013). Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining. International Journal of Computer Science Issues (IJCSI), 10(2 Part 1), 165.
Kotler, P., & Keller, K. L. (2016). A framework for marketing management. Pearson Boston, MA.
Kumar, V., Dalla Pozza, I., Petersen, J. A., & Shah, D. (2009). Reversing the logic: The path to profitability through relationship marketing. Journal of interactive marketing, 23(2), 147-156.
Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of marketing, 80(6), 36-68.
Kumar, V., & Reinartz, W. J. (2006). Customer relationship management: A databased approach. Wiley Hoboken.
Lee, J., Jung, O., Lee, Y., Kim, O., & Park, C. (2021). A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1472-1491.
Lewis, P., & Thomas, H. (1990). The linkage between strategy, strategic groups, and performance in the UK retail grocery industry. Strategic Management Journal, 11(5), 385-397.
Li, D.-C., Dai, W.-L., & Tseng, W.-T. J. E. S. w. A. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. 38(6), 7186-7191.
Lin, C. (2013). Application of fuzzy Delphi method (FDM) and fuzzy analytic hierarchy process (FAHP) to criteria weights for fashion design scheme evaluation. International Journal of Clothing Science and Technology.
Liu, D.-R., Lai, C.-H., & Lee, W.-J. (2009). A hybrid of sequential rules and collaborative filtering for product recommendation. Information Sciences, 179(20), 3505-3519.
Luo, X., Jiang, C., Wang, W., Xu, Y., Wang, J.-H., & Zhao, W. (2019). User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Future Generation Computer Systems, 93, 1023-1035.
Madeira, S., & Sousa, J. M. (2002). Comparison of target selection methods in direct marketing. European symposium on intelligent technologies, hybrid systems and their implementation on smart adaptive systems,
Michael, C. (2022). EY Future Consumer Index: Growing economic uncertainty and rising costs dent post-pandemic hopes.
Miglautsch, J. R. (2000). Thoughts on RFM scoring. Journal of Database Marketing & Customer Strategy Management, 8(1), 67-72.
Mzoughia, M. B., & Limam, M. (2015). An improved customer lifetime value model based on Markov chain. Applied Stochastic Models in Business and Industry, 31(4), 528-535.
O′Connor, G. C., & O′Keefe, B. (1997). Viewing the Web as a marketplace: the case of small companies. Decision Support Systems, 21(3), 171-183.
Parvaneh, A., Abbasimehr, H., & Tarokh, M. J. J. G. J. o. T. (2012). Data mining application in retailer segmentation based on LRFM variables: case study. 1.
Payne, A., & Holt, S. (2001). Diagnosing customer value: integrating the value process and relationship marketing. British Journal of management, 12(2), 159-182.
Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence & Planning.
Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779-799.
Reutterer, T. (2015). Models for Customer Valuation. Management Review, 43(4), 89-105.
Rust, R. T., Kumar, V., & Venkatesan, R. (2011). Will the frog change into a prince? Predicting future customer profitability. International Journal of Research in Marketing, 28(4), 281-294.
Rygielski, C., Wang, J.-C., & Yen, D. C. (2002). Data mining techniques for customer relationship management. Technology in society, 24(4), 483-502.
Saltan, M., Terzi, S., & Küçüksille, E. U. (2011). Backcalculation of pavement layer moduli and Poisson’s ratio using data mining. Expert systems with applications, 38(3), 2600-2608.
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.
Schneider, G. (2016). Electronic commerce. Cengage Learning.
Sharma, S. (2021). Customer Lifetime Value Modelling. IEEE access.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-means clustering algorithm. IEEE access, 8, 80716-80727.
Spring, P., Leeflang, P. S., & Wansbeek, T. (1999). The combination strategy to optimal target selection and offer segmentation in direct mail. Journal of Market-Focused Management, 4(3), 187-203.
Swarts, K. M., Lehman, K., & Lewis, G. K. (2016). The use of social customer relationship management by building contractors: evidence from Tasmania. Construction Management and Economics, 34(4-5), 302-316.
Swenson, E. R., Bastian, N. D., & Nembhard, H. B. (2016). Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients. Expert systems with applications, 60, 118-129.
Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018). Customer segmentation and strategy development based on user behavior analysis, RFM model and data mining techniques: a case study. 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE),
Tomkovick, C., & Miller, C. (2000). Perspective—riding the wind: managing new product development in an age of change. Journal of Product Innovation Management: AN INTERNATIONAL PUBLICATION OF THE PRODUCT DEVELOPMENT & MANAGEMENT ASSOCIATION, 17(6), 413-423.
Vanderveld, A., Pandey, A., Han, A., & Parekh, R. (2016). An engagement-based customer lifetime value system for e-commerce. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining,
Wübben, M., & Wangenheim, F. v. (2008). Instant customer base analysis: Managerial heuristics often “get it right”. Journal of marketing, 72(3), 82-93.
Weber, B. G., & Mateas, M. (2009). A data mining approach to strategy prediction. 2009 IEEE Symposium on Computational Intelligence and Games,
Webster Jr, F. E. (1992). The changing role of marketing in the corporation. Journal of marketing, 56(4), 1-17.
Wei, J. T., Lin, S.-Y., Yang, Y.-Z., & Wu, H.-H. (2019). The application of data mining and RFM model in market segmentation of a veterinary hospital. Journal of Statistics and Management Systems, 22(6), 1049-1065.
Wheaton, P. (2000). The Life cycle view of customers. US Banker, June, 110, 77-78.
Xiao, S., Wei, C.-P., & Dong, M. (2016). Crowd intelligence: Analyzing online product reviews for preference measurement. Information & Management, 53(2), 169-182.
Yao, Z., Sarlin, P., Eklund, T., & Back, B. (2014). Combining visual customer segmentation and response modeling. Neural Computing and Applications, 25(1), 123-134.
Yoseph, F., & Heikkila, M. (2018). Segmenting retail customers with an enhanced RFM and a hybrid regression/clustering method. 2018 International Conference on Machine Learning and Data Engineering (iCMLDE),
Zhao, G., Qian, X., & Xie, X. (2016). User-service rating prediction by exploring social users′ rating behaviors. IEEE transactions on multimedia, 18(3), 496-506.
Zhao, S., Wu, R., Tao, J., Qu, M., Zhao, M., Fan, C., & Zhao, H. (2022). perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games. ACM Transactions on Information Systems (TOIS).
Zhao, Y., Luo, F., Chen, M., Wang, Y., Xia, J., Zhou, F., Wang, Y., Chen, Y., & Chen, W. (2018). Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE transactions on visualization and computer graphics, 25(1), 12-21. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2024-9-1以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit