Elucidating strategic patterns from target customers using multi-stage RFM analysis

References

• Aeron, H., Kumar, A., & Moorthy, J. (2012). Data mining framework for customer lifetime value-based segmentation. Journal of Database Marketing & Customer Strategy Management, 19(1), 17–30. https://doi.org/10.1057/dbm.2012.1
   Google Scholar
• Ahn, Y., Kim, D., & Lee, D.-J. (2019). Customer attrition analysis in the securities industry: A large-scale field study in Korea. International Journal of Bank Marketing, 38(3), 561–577. https://doi.org/10.1108/IJBM-04-2019-0151
   Web of Science ®Google Scholar
• Albadvi, A., & Shahbazi, M. (2010). Integrating rating-based collaborative filtering with customer lifetime value: New product recommendation technique. Intelligent Data Analysis, 14(1), 143–155. https://doi.org/10.3233/IDA-2010-0412
   Web of Science ®Google Scholar
• Asllani, A., & Halstead, D. (2011). Using RFM data to optimize direct marketing campaigns: A linear programming approach. Academy of Marketing Studies Journal, 15(2), 59–76.
   Google Scholar
• Asllani, A., & Halstead, D. (2015). A multi-objective optimization approach using the RFM model in direct marketing. Academy of Marketing Studies Journal, 19(3), 49.
   Google Scholar
• Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., & Dedene, G. (2002). Bayesian neural network learning for repeat purchase modeling in direct marketing. European Journal of Operational Research, 138(1), 191–211. https://doi.org/10.1016/S0377-2217(01)00129-1
   Web of Science ®Google Scholar
• Berrueta, L. A., Alonso-Salces, R. M., & Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography, 1158(1), 196–214. https://doi.org/10.1016/j.chroma.2007.05.024
   Web of Science ®Google Scholar
• Bhaskar, T., Subramanian, G., Bal, D., Moorthy, A., Saha, A., & Rajagopalan, S. (2009). An optimization model for personalized promotions in multiplexes. Journal of Promotion Management, 15(1–2), 229–246. https://doi.org/10.1080/10496490902837296
   Google Scholar
• Bishop, C. M. (1996). Neural networks for pattern recognition. Oxford University Press.
   Google Scholar
• Blattberg, R. C., Kim, B. D., & Neslin, S. A. (2008). Why database marketing? In J. Eliashberg (Ed.), Database marketing (pp. 13–46). Springer.
   Google Scholar
• Bolton, R. N., Lemon, K. N., & Verhoef, P. C. (2004). The theoretical underpinnings of customer asset management: A framework and propositions for future research. Journal of the Academy of Marketing Science, 32(3), 271–292. https://doi.org/10.1177/0092070304263341
   Web of Science ®Google Scholar
• Borle, S., Singh, S. S., & Jain, D. C. (2008). Customer lifetime value measurement. Management Science, 54(1), 100–112. https://doi.org/10.1287/mnsc.1070.0746
   Web of Science ®Google Scholar
• Bose, I., & Chen, X. (2009). Quantitative models for direct marketing: A review from systems perspective. European Journal of Operational Research, 195(1), 1–16. https://doi.org/10.1016/j.ejor.2008.04.006
   Web of Science ®Google Scholar
• Brock, J. K. U. (2021). The emergence and implications of technologized marketing. Journal of Global Scholars of Marketing Science, 31(3), 372–391. https://doi.org/10.1080/21639159.2021.1921607
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.ejor.2003.12.010
   Web of Science ®Google Scholar
• Bulysheva, L., & Bulyshev, A. (2012). Segmentation modeling algorithm: A novel algorithm in data mining. Information Technology and Management, 13(4), 263–271.
   Web of Science ®Google Scholar
• Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28(4), 773–781. https://doi.org/10.1016/j.eswa.2004.12.033
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1057/dbm.2012.17
   Google Scholar
• Chen, K., Hu, Y. H., & Hsieh, Y. C. (2015). Predicting customer churn from valuable B2B customers in the logistics industry: A case study. Information Systems and E-Business Management, 13(3), 475–494. https://doi.org/10.1007/s10257-014-0264-1
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.elerap.2009.03.002
   Web of Science ®Google Scholar
• Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., & Du, H. N. (2006). Approaches for automated detection and classification of masses in mammograms. Pattern Recognition, 39(4), 646–668. https://doi.org/10.1016/j.patcog.2005.07.006
   Web of Science ®Google Scholar
• Chiang, W. Y. (2012a). To establish online shoppers’ markets and rules for dynamic CRM systems: An empirical case study in Taiwan. Internet Research, 22(5), 613–625. https://doi.org/10.1108/10662241211271572
   Web of Science ®Google Scholar
• Chiang, W. Y. (2012b). Applying a new model of customer value on international air passengers’ market in Taiwan. International Journal of Tourism Research, 14(2), 116–123. https://doi.org/10.1002/jtr.840
   Web of Science ®Google Scholar
• Cho, Y. S., Park, H. W., & Ryu, K. H. (2015). Clustering of item category with weight and bayesian probability for recommender system. Advanced Science Letters, 21(3), 391–395. https://doi.org/10.1166/asl.2015.5815
   Google Scholar
• Collins, K. (2001). Analytical CRM: Driving profitable customer relationships. Strategic Planning.
   Google Scholar
• Correia, A., Zins, A. H., & Silva, F. (2015). Why do tourists persist in visiting the same destination? Tourism Economics, 21(1), 205–221. https://doi.org/10.5367/te.2014.0443
   Web of Science ®Google Scholar
• Coussement, K., Van den Bossche, F. A., & De Bock, K. W. (2014). Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751–2758. https://doi.org/10.1016/j.jbusres.2012.09.024
   Web of Science ®Google Scholar
• Davenport, T. H., Harris, J. G., & Kohli, A. K. (2001). How do they know their customers so well? MIT Sloan Management Review, 42(2), 63. Retrieved January 12, 2022, from https://sloanreview.mit.edu/article/how-do-they-know-their-customers-so-well/
   Web of Science ®Google Scholar
• Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
   Web of Science ®Google Scholar
• De Cannière, M. H., De Pelsmacker, P., & Geuens, M. (2009). Relationship quality and the theory of planned behavior models of behavioral intentions and purchase behavior. Journal of Business Research, 62(1), 82–92. https://doi.org/10.1016/j.jbusres.2008.01.001
   Web of Science ®Google Scholar
• Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 1–30.
   Web of Science ®Google Scholar
• Dhandayudam, P., & Krishnamurthi, I. (2013). Customer behavior analysis using rough set approach. Journal of Theoretical and Applied Electronic Commerce Research, 8(2), 21–33. https://doi.org/10.4067/S0718-18762013000200003
   Google Scholar
• Djurisic, V., Kascelan, L., Rogic, S., & Melovic, B. (2020). Bank CRM optimization using predictive classification based on the support vector machine method. Applied Artificial Intelligence, 34(12), 941–955. https://doi.org/10.1080/08839514.2020.1790248
   Web of Science ®Google Scholar
• Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18(April), 153–160. https://doi.org/10.1016/j.tmp.2016.03.001
   Google Scholar
• Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415–430. https://doi.org/10.1509/jmkr.2005.42.4.415
   Web of Science ®Google Scholar
• Fader, P. S., & Hardie, B. G. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23(1), 61–69. https://doi.org/10.1016/j.intmar.2008.11.003
   Web of Science ®Google Scholar
• Faraway, J. J. (2016). Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC.
   Google Scholar
• Filzmoser, P., Hron, K., & Templ, M. (2018). Discriminant analysis. In P. Diggle, U. Gather, & S. Zeger (Eds.), Applied compositional data analysis (pp. 163–179). Springer.
   Google Scholar
• Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701. https://doi.org/10.1080/01621459.1937.10503522
   Google Scholar
• Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1), 86–92. https://doi.org/10.1214/aoms/1177731944
   Google Scholar
• Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/aos/1176347963
   Web of Science ®Google Scholar
• Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y., & Harb, A. (2020). Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences, 10(7), 2581. https://doi.org/10.3390/app10072581
   Google Scholar
• Güçdemir, H., & Selim, H. (2015). Integrating multi-criteria decision making and clustering for business customer segmentation. Industrial Management & Data Systems, 115(6), 1022–1040. https://doi.org/10.1108/IMDS-01-2015-0027
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/S0957-4174(98)00008-6
   Web of Science ®Google Scholar
• Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.
   Google Scholar
• Han, J., & Kamber, M. (2001). Anthony. KH Tung., spatial clustering methods in data mining: A survey, geographic data mining and knowledge discovery (H. Miller & J. Han, Eds., pp. 1–29).
   Google Scholar
• Hastie, T., Friedman, J., & Tibshirani, R. (2001). Additive models, trees, and related methods. In P. Bühlmann, P. Diggle, U. Gather, & S. Zeger (Eds.), The elements of statistical learning (pp. 257–298). Springer.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer-Verlag.
   Google Scholar
• Heldt, R., Silveira, C. S., & Luce, F. B. (2021). Predicting customer value per product: From RFM to RFM/P. Journal of Business Research, 127, 444–453. https://doi.org/10.1016/j.jbusres.2019.05.001
   Web of Science ®Google Scholar
• Hosseini, M., & Shabani, M. (2015). New approach to customer segmentation based on changes in customer value. Journal of Marketing Analytics, 3(3), 110–121. https://doi.org/10.1057/jma.2015.10
   Google Scholar
• Hsieh, N. C. (2004). An integrated data mining and behavior scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623–633. https://doi.org/10.1016/j.eswa.2004.06.007
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.knosys.2014.02.009
   Web of Science ®Google Scholar
• Hunt, S. D. (2018). The philosophy of science foundations of marketing research: For scientific realism and the inductive realist models of theory status and generation. Journal of Global Scholars of Marketing Science, 28(1), 1–32. https://doi.org/10.1080/21639159.2017.1410776
   Web of Science ®Google Scholar
• Johnson, R. A., & Wichern, D. W. (1988). Multivariate statistics, a practical approach.
   Google Scholar
• Johnson, J. W., Peck, J., & Schweidel, D. A. (2014). Can purchase behavior predict relationship perceptions and willingness to donate? Psychology & Marketing, 31(8), 647–659. https://doi.org/10.1002/mar.20725
   Web of Science ®Google Scholar
• Ke, D., Zhang, H., Yu, N., & Tu, Y. (2021). Who will stay with the brand after posting non-5/5 rating of purchase? an empirical study of online consumer repurchase behavior. Information Systems and E-Business Management, 19(2), 405–437.
   Web of Science ®Google Scholar
• Khobzi, H., Akhondzadeh-Noughabi, E., & Minaei-Bidgoli, B. (2014). A new application of RFM clustering for guild segmentation to mine the pattern of using banks’ e-payment services. Journal of Global Scholars of Marketing, 27(3), 178–190. https://doi.org/10.1080/08911762.2013.878428
   Google Scholar
• Kim, E. Y., & Jackson, V. P. (2009). The effect of E-SERVQUAL on e-loyalty for apparel online shopping. Journal of Global Academy of Marketing, 19(4), 57–65. https://doi.org/10.1080/12297119.2009.9707305
   Google Scholar
• Kim, G., Chae, B. K., & Olson, D. L. (2013). A support vector machine (SVM) approach to imbalanced datasets of customer responses: Comparison with other customer response models. Service Business, 7(1), 167–182. https://doi.org/10.1007/s11628-012-0147-9
   Web of Science ®Google Scholar
• Kim, Y. J. (2004). Effective marketing module to the optimization of consumer information in mid-small e-commerce shopping mall. Journal of Global Scholars of Marketing Science, 14, 125–144.
   Google Scholar
• Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine Learning, 30(2–3), 271–274.
   Google Scholar
• Kumar, V., Venkatesan, R., Bohling, T., & Beckmann, D. (2008). The power of CLV: Managing customer lifetime value at IBM. Marketing Science, 27(4), 585–599. https://doi.org/10.1287/mksc.l070.0319
   Web of Science ®Google Scholar
• Kumar, V., & Vannan, M. (2021). It takes two to tango: Statistical modeling and machine learning. Journal of Global Scholars of Marketing Science, 31(3), 296–317. https://doi.org/10.1080/21639159.2020.1808838
   Web of Science ®Google Scholar
• Latinovic, Z., & Chatterjee, S. C. (2022). Achieving the promise of AI and ML in delivering economic and relational customer value in B2B. Journal of Business Research, 144, 966–974. https://doi.org/10.1016/j.jbusres.2022.01.052
   Web of Science ®Google Scholar
• Leal-Rodriguez, A. L. (2020). Generating customer value through the boosting of relationships and organisational innovativeness. Knowledge Management Research & Practice, 18(3), 336–347.
   Google Scholar
• Ledolter, J. (2013). Multinomial logistic regression. Data Mining and Business Analytics with R, 132–149. https://doi.org/10.1002/9781118596289
   Google Scholar
• Lee, B. H. (2008). Transfer of marketing knowledge within multinational corporations and its impact on performance: Moderating effects of absorptive capacity, socialization, and local knowledge. Journal of Global Scholars of Marketing Science, 18(4), 277–306. https://doi.org/10.1080/12297119.2008.9730182
   Google Scholar
• Leeflang, P. S., Spring, P. N., Van Doorn, J., & Wansbeek, T. (2013). Identifying the direct mail-prone consumer. Journal of Global Scholars of Marketing Science, 23(2), 175–195. https://doi.org/10.1080/21639159.2012.760923
   Google Scholar
• Li, C., Xu, Y., & Li, H. (2005). An empirical study of dynamic customer relationship management. Journal of Retailing and Consumer Services, 12(6), 431–441. https://doi.org/10.1016/j.jretconser.2005.01.003
   Google Scholar
• Li, S. T., Shue, L. Y., & Lee, S. F. (2008). Business intelligence approach to supporting strategy making of ISP service management. Expert Systems with Applications, 35(3), 739–754. https://doi.org/10.1016/j.eswa.2007.07.049
   Web of Science ®Google Scholar
• Liao, Y. W., Wang, Y. S., & Yeh, C. H. (2014). Exploring the relationship between intentional and behavioral loyalty in the context of e-tailing. Internet Research, 24(5), 668–686. https://doi.org/10.1108/IntR-08-2013-0181
   Web of Science ®Google Scholar
• Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? on AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56. https://doi.org/10.1016/j.intmar.2020.04.002
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.ins.2009.06.004
   Web of Science ®Google Scholar
• Liu, D. R., & Shih, Y. Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management, 42(3), 387–400. https://doi.org/10.1016/j.im.2004.01.008
   Web of Science ®Google Scholar
• Liu, F., Zhao, S., & Li, Y. (2017). How many, how often, and how new? a multivariate profiling of mobile app users. Journal of Retailing and Consumer Services, 38, 71–80. https://doi.org/10.1016/j.jretconser.2017.05.008
   Web of Science ®Google Scholar
• Lönnstedt, I., & Speed, T. (2002). Replicated microarray data. Statistica Sinica, 12(1), 31–46. http://www.jstor.org/stable/24307034
   Web of Science ®Google Scholar
• Lumsden, S. A., Beldona, S., & Morison, A. M. (2008). Customer value in an all-inclusive travel vacation club: An application of the RFM framework. Journal of Hospitality & Leisure Marketing, 16(3), 270–285. https://doi.org/10.1080/10507050801946858
   Google Scholar
• Malthouse, E. C. (2002). Performance‐based variable selection for scoring models. Journal of Interactive Marketing, 16(4), 37–50. https://doi.org/10.1002/dir.10043
   Google Scholar
• Marcus, C. (1998). A practical yet meaningful approach to customer segmentation. Journal of consumer marketing, 15(5), 494–504. https://doi.org/10.1108/07363769810235974
   Google Scholar
• Martins, M. C. M., & Cardoso, M. G. (2012). Cross-Validation of segments of credit card holders. Journal of Retailing and Consumer Services, 19(6), 629–636. https://doi.org/10.1016/j.jretconser.2012.08.004
   Google Scholar
• 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. https://doi.org/10.1016/j.jbusres.2006.06.015
   Web of Science ®Google Scholar
• McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (Vol. 37). CRC Press.
   Google Scholar
• Mefford, R. N. (2011). The economic value of a sustainable supply chain. Business and Society Review, 116(1), 109–143. https://doi.org/10.1111/j.1467-8594.2011.00379.x
   Google Scholar
• Miguéis, V. L., Van den Poel, D., Camanho, A. S., & Falcao E Cunha, J. (2012). Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Systems with Applications, 39(12), 11250–11256. https://doi.org/10.1016/j.eswa.2012.03.073
   Web of Science ®Google Scholar
• Morales, P. A., True, S., & Tudor, R. K. (2020). Insights, challenges and recommendations for research on sustainability in marketing. Journal of Global Scholars of Marketing Science, 30(4), 394–406. https://doi.org/10.1080/21639159.2020.1803757
   Web of Science ®Google Scholar
• Morganosky, M. A. (1997). Retail involvement in direct marketing. Journal of Retailing and Consumer Services, 4(3), 145–151. https://doi.org/10.1016/S0969-6989(96)00021-5
   Google Scholar
• Nelder, J. A., & Baker, R. J. (1972). Generalized linear models. Encyclopedia of Statistical Sciences.
   Google Scholar
• Nemenyi, P. B. (1963). Distribution-free multiple comparisons [ Ph.D. thesis]. Princeton University.
   Google Scholar
• Olson, D. L., Cao, Q., Gu, C., & Lee, D. (2009). Comparison of customer response models. Service Business, 3(2), 117–130. https://doi.org/10.1007/s11628-009-0064-8
   Web of Science ®Google Scholar
• Olson, D. L., & Chae, B. K. (2012). Direct marketing decision support through predictive customer response modeling. Decision Support Systems, 54(1), 443–451. https://doi.org/10.1016/j.dss.2012.06.005
   Web of Science ®Google Scholar
• Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. Operations Research Perspectives, 6, 100127. https://doi.org/10.1016/j.orp.2019.100127
   Web of Science ®Google Scholar
• Oyedele, A. O., Ajayi, A. O., & Oyedele, L. O. (2021). Machine learning predictions for lost time injuries in power transmission and distribution projects. Machine Learning with Applications, 6, 100158. https://doi.org/10.1016/j.mlwa.2021.100158
   Google Scholar
• Pino-Mejías, R., Cubiles de la-Vega, M. D., Anaya-Romero, M., Pascual-Acosta, A., Jordán-López, A., & Bellinfante-Crocci, N. (2010). Predicting the potential habitat of oaks with data mining models and the R system. Environmental Modeling & Software, 25(7), 826–836. https://doi.org/10.1016/j.envsoft.2010.01.004
   Web of Science ®Google Scholar
• Pohlert, T. (2015). The pairwise multiple comparison of mean ranks package (PMCMR).
   Google Scholar
• Rahim, M. A., Mushafiq, M., Khan, S., & Arain, Z. A. (2021). RFM-Based repurchase behavior for customer classification and segmentation. Journal of Retailing and Consumer Services, 61, 102566. https://doi.org/10.1016/j.jretconser.2021.102566
   Web of Science ®Google Scholar
• Rather, R. A., Tehseen, S., Itoo, M. H., & Parrey, S. H. (2019). Customer brand identification, affective commitment, customer satisfaction, and brand trust as antecedents of customer behavioral intention of loyalty: An empirical study in the hospitality sector. Journal of Global Scholars of Marketing Science, 29(2), 196–217. https://doi.org/10.1080/21639159.2019.1577694
   Web of Science ®Google Scholar
• Ravasan, A. Z., & Mansouri, T. (2015). A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector. International Journal of Information Systems in the Service Sector, 7(2), 71–86. https://doi.org/10.4018/ijisss.2015040105
   Google Scholar
• Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1), 77–99. https://doi.org/10.1509/jmkg.67.1.77.18589
   Web of Science ®Google Scholar
• Ripley, B., Venables, B., Bates, D. M., Hornik, K., Gebhardt, A., Firth, D., & Ripley, M. B. (2013). Package ‘mass’. Cran R, 538, 113–120.
   Google Scholar
• 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. https://doi.org/10.1016/j.ijresmar.2011.05.003
   Web of Science ®Google Scholar
• Rust, R. T., Zeithaml, V. A., & Lemon, K. N. (2000). Driving customer equity: How customer lifetime value is reshaping customer strategy. Free Press.
   Google Scholar
• Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 34(4), 446–461. https://doi.org/10.1108/MIP-03-2015-0060
   Web of Science ®Google Scholar
• Schijns, J. M., & Schröder, G. J. (1996). Segment selection by relationship strength. Journal of Direct Marketing, 10(3), 69–79. https://doi.org/10.1002/(SICI)1522-7138(199622)10:3<69:AID-DIR6>3.0.CO;2-W
   Google Scholar
• Singh, A., & Rumantir, G. W. (2015). Two-Tiered clustering classification experiments for market segmentation of EFTPOS retailers. Australasian Journal of Information Systems, 19, S117–S132. https://doi.org/10.3127/ajis.v19i0.1184
   Google Scholar
• Sun, Y., & Ko, E. (2016). Influence of sustainable marketing activities on customer equity. Journal of Global Scholars of Marketing Science, 26(3), 270–283. https://doi.org/10.1080/21639159.2016.1174537
   Web of Science ®Google Scholar
• Swets, J. A. (1973). The relative operating characteristic in psychology. Science, 182(4116), 990–1000. https://doi.org/10.1126/science.182.4116.990
   PubMed Web of Science ®Google Scholar
• Trawiński, B., Smętek, M., Telec, Z., & Lasota, T. (2012). Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. International Journal of Applied Mathematics and Computer Science, 22(4), 867–881. https://doi.org/10.2478/v10006-012-0064-z
   Web of Science ®Google Scholar
• Van den Poel, D., & Buckinx, W. (2005). Predicting online-purchasing behaviour. European Journal of Operational Research, 166(2), 557–575.
   Web of Science ®Google Scholar
• Vapnik, V., Guyon, I., & Hastie, T. (1995). Support vector machines. Retrieved January 13, 2016, from http://www.support-vector-machines.org
   Google Scholar
• Varadarajan, R. (2020). Customer information resources advantage, marketing strategy and business performance: A market resources based view. Industrial Marketing Management, 89, 89–97. https://doi.org/10.1016/j.indmarman.2020.03.003
   Web of Science ®Google Scholar
• Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. Springer Science & Business Media.
   Google Scholar
• Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106–125. https://doi.org/10.1509/jmkg.68.4.106.42728
   Web of Science ®Google Scholar
• Venkatesan, R., Kumar, V., & Bohling, T. (2007). Optimal customer relationship management using bayesian decision theory: An application for customer selection. Journal of Marketing Research, 44(4), 579–594. https://doi.org/10.1509/jmkr.44.4.579
   Web of Science ®Google Scholar
• Viaene, S., Baesens, B., Van den Poel, D., Dedene, G., & Vanthienen, J. (2001). Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing. Intelligent Systems in Accounting, Finance and Management, 10(2), 115–126. https://doi.org/10.1002/isaf.195
   Google Scholar
• Winters, E., & Ha, S. (2012). Consumer evaluation of customer loyalty programs: The role of customization in customer loyalty program involvement. Journal of Global Scholars of Marketing Science, 22(4), 370–385. https://doi.org/10.1080/21639159.2012.720752
   Google Scholar
• Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
   Google Scholar
• Wong, J. Y., & Chung, P. H. (2007). Managing valuable Taiwanese airline passengers using knowledge discovery in database techniques. Journal of Air Transport Management, 13(6), 362–370. https://doi.org/10.1016/j.jairtraman.2007.07.001
   Web of Science ®Google Scholar
• Xu, Y., Yen, D. C., Lin, B., & Chou, D. C. (2002). Adopting customer relationship management technology. Industrial Management & Data Systems, 102(8), 442–452. https://doi.org/10.1108/02635570210445871
   Web of Science ®Google Scholar
• Zeithaml, V. A., Rust, R. T., & Lemon, K. N. (2001). The customer pyramid: Creating and serving profitable customers. California Management Review, 43(4), 118–142.
   Web of Science ®Google Scholar
• Zhang, Y., Bradlow, E. T., & Small, D. S. (2015). Predicting customer value using clumpiness: From RFM to RFMC. Marketing Science, 34(2), 195–208. https://doi.org/10.1287/mksc.2014.0873
   Web of Science ®Google Scholar
• Zineldin, M. (2006). The royalty of loyalty: CRM, quality and retention. Journal of Consumer Marketing, 23(7), 430–437. https://doi.org/10.1108/07363760610712975
   Google Scholar