Predicting customer attrition using binge trading patterns: Implications for the financial services industry

References

• 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
• Ali, Ö. G., & Arıtürk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41(17), 7889–7903. https://doi.org/10.1016/j.eswa.2014.06.018
   Web of Science ®Google Scholar
• Backiel, A., Baesens, B., & Claeskens, G. (2016). Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. Journal of the Operational Research Society, 67(9), 1135–1145. https://doi.org/10.1057/jors.2016.8
   Web of Science ®Google Scholar
• Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., & Dedene, G. (2002). Bayesian neural network learning for repeat purchase modelling 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
• Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645–1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x
   Web of Science ®Google Scholar
• Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129
   Web of Science ®Google Scholar
• Bar-Eli, M., Avugos, S., & Raab, M. (2006). Twenty years of ‘hot hand’ research: Review and critique. Psychology of Sport and Exercise, 7(6), 525–553. https://doi.org/10.1016/j.psychsport.2006.03.001
   Web of Science ®Google Scholar
• Barabási, A.-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207–211. https://doi.org/10.1038/nature03459
   PubMed Web of Science ®Google Scholar
• Barber, B. M., & Odean, T. (2013). The behavior of individual investors. In G. M. Contantinides, M. Harris, and R. M. Stulz (Eds.), Handbook of the economics of finance (Vol. 2, pp. 1533–1570). Elsevier.
   Google Scholar
• Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1), 17–30. https://doi.org/10.1002/(SICI)1520-6653(199824)12:1<17::AID-DIR3>3.0.CO;2-K
   Google Scholar
• Bhat, S. A., & Darzi, M. A. (2016). Customer relationship management: An approach to competitive advantage in the banking sector by exploring the mediational role of loyalty. International Journal of Bank Marketing, 34(3), 388–410. https://doi.org/10.1108/IJBM-11-2014-0160
   Web of Science ®Google Scholar
• Bolton, R. N. (1998). A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45–65. https://doi.org/10.1287/mksc.17.1.45
   Web of Science ®Google Scholar
• Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378–394. https://doi.org/10.1287/mksc.14.4.378
   Web of Science ®Google Scholar
• Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., & Barabási, A.-L. (2008). Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 41(22), 224015. https://doi.org/10.1088/1751-8113/41/22/224015
   Web of Science ®Google Scholar
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.
   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
• Clemes, M. D., Gan, C., & Zhang, D. (2010). Customer switching behaviour in the Chinese retail banking industry. International Journal of Bank Marketing, 28(7), 519–546. https://doi.org/10.1108/02652321011085185
   Google Scholar
• Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629–1636. https://doi.org/10.1016/j.jbusres.2012.12.008
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.eswa.2006.09.038
   Web of Science ®Google Scholar
• Cui, G., Wong, M. L., & Lui, H.-K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597–612. https://doi.org/10.1287/mnsc.1060.0514
   Web of Science ®Google Scholar
• De Bock, K. W., & Van den Poel, D. (2012). Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Systems with Applications, 39(8), 6816–6826. Vohttps://doi.org/10.1016/j.eswa.2012.01.014
   Web of Science ®Google Scholar
• De Caigny, A., Coussement, K., De Bock, K. W., & Lessmann, S. (2020). Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4), 1563–1578. Vohttps://doi.org/10.1016/j.ijforecast.2019.03.029
   Web of Science ®Google Scholar
• Devenow, A., & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40(3–5), 603–615. https://doi.org/10.1016/0014-2921(95)00073-9
   Web of Science ®Google Scholar
• Engle, R. F., & Russell, J. R. (1998). Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica, 66(5), 1127–1162. https://doi.org/10.2307/2999632
   Web of Science ®Google Scholar
• Evans, M., Nancarrow, C., Tapp, A., & Stone, M. (2002). Future marketers: Future curriculum: Future shock? Journal of Marketing Management, 18(5–6), 579–596. https://doi.org/10.1362/0267257022683677
   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
• Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Garland, R. (2002). Estimating customer defection in personal retail banking. International Journal of Bank Marketing, 20(7), 317–324. https://doi.org/10.1108/02652320210451214
   Google Scholar
• Garson, G. D. (1991). Interpreting neural-network connection weights. AI Expert, 6(4), 46–51.
   Google Scholar
• Goh, A. T. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143–151. https://doi.org/10.1016/0954-1810(94)00011-S
   Google Scholar
• Gonçalves, B., & Ramasco, J. J. (2008). Human dynamics revealed through web analytics. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 78(2 Pt 2), 026123. https://doi.org/10.1103/PhysRevE.78.026123
   PubMed Web of Science ®Google Scholar
• González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779–782. https://doi.org/10.1038/nature06958
   PubMed Web of Science ®Google Scholar
• Gourio, F., & Rudanko, L. (2014). Customer capital. The Review of Economic Studies, 81(3), 1102–1136. https://doi.org/10.1093/restud/rdu007
   Web of Science ®Google Scholar
• Guido, G., Prete, M. I., Miraglia, S., & De Mare, I. (2011). Targeting direct marketing campaigns by neural networks. Journal of Marketing Management, 27(9–10), 992–1006. https://doi.org/10.1080/0267257X.2010.543018
   Google Scholar
• Guo, L., Zhang, M., Sun, L., & Wang, Z. (2006). Churn analysis model of securities business based on the decision tree. In 2006 6th World Congress on Intelligent Control and Automation (Vol. 2, pp. 6048–6051).
   Google Scholar
• Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139–155. https://doi.org/10.1177/1094670506293810
   Web of Science ®Google Scholar
• Haykin, S. (1994). Neural networks. Prentice Hall.
   Google Scholar
• Heaton, J. (2008). Introduction to neural networks with Java. Heaton Research Inc.
   Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.
   Google Scholar
• Kabadayi, S. (2016). Customers’ dissatisfaction with banking channels and their intention to leave banks: The moderating effect of trust and trusting beliefs. Journal of Financial Services Marketing, 21(3), 194–208. Vohttps://doi.org/10.1057/s41264-016-0005-2
   Web of Science ®Google Scholar
• Kim, D., Park, K., Lee, D. J., & Ahn, Y. (2020). Predicting mobile trading system discontinuance: The role of attention. Electronic Commerce Research and Applications, 44, 101008. https://doi.org/10.1016/j.elerap.2020.101008
   Web of Science ®Google Scholar
• Kim, T., Kim, D., & Ahn, Y. (2022). Instant customer base analysis in the financial services sector. Expert Systems with Applications, 202, 117326. https://doi.org/10.1016/j.eswa.2022.117326
   Web of Science ®Google Scholar
• Kullback, S. (1997). Information theory and statistics. Courier Corporation.
   Google Scholar
• Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694
   Google Scholar
• Larivière, B., & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29(2), 472–484.
   Web of Science ®Google Scholar
• Lemmens, A., & Croux, C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2), 276–286. https://doi.org/10.1509/jmkr.43.2.276
   Web of Science ®Google Scholar
• Lima, E., Mues, C., & Baesens, B. (2009). Domain knowledge integration in data mining using decision tables: Case studies in churn prediction. Journal of the Operational Research Society, 60(8), 1096–1106. https://doi.org/10.1057/jors.2008.161
   Web of Science ®Google Scholar
• Mai, L.-W., & Ness, M. (1997). Consumers’ perceptions of mail-order speciality foods. Journal of Marketing Management, 13(7), 705–724. https://doi.org/10.1080/0267257X.1997.9964506
   Google Scholar
• Malthouse, E. C. (2001). Assessing the performance of direct marketing scoring models. Journal of Interactive Marketing, 15(1), 49–62. https://doi.org/10.1002/1520-6653(200124)15:1<49::AID-DIR1003>3.0.CO;2-F
   Google Scholar
• Malthouse, E. C., & Blattberg, R. C. (2005). Can we predict customer lifetime value? Journal of Interactive Marketing, 19(1), 2–16. https://doi.org/10.1002/dir.20027
   Google Scholar
• Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204–211. https://doi.org/10.1509/jmkr.43.2.204
   Web of Science ®Google Scholar
• Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273–15285. https://doi.org/10.1016/j.eswa.2011.06.028
   Web of Science ®Google Scholar
• Oertzen, A. S., & Odekerken-Schröder, G. (2019). Achieving continued usage in online banking: A post-adoption study. International Journal of Bank Marketing, 37(6), 1394–1418. https://doi.org/10.1108/IJBM-09-2018-0239
   Web of Science ®Google Scholar
• Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function networks. Neural Computation, 3(2), 246–257. https://doi.org/10.1162/neco.1991.3.2.246
   PubMed Web of Science ®Google Scholar
• Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779–799. https://doi.org/10.1287/mksc.2015.0963
   Web of Science ®Google Scholar
• Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17–35. https://doi.org/10.1509/jmkg.64.4.17.18077
   Web of Science ®Google Scholar
• Routh, P., Roy, A., & Meyer, J. (2021). Estimating customer churn under competing risks. Journal of the Operational Research Society, 72(5), 1138–1155. https://doi.org/10.1080/01605682.2020.1776166
   Web of Science ®Google Scholar
• Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109–127. https://doi.org/10.1509/jmkg.68.1.109.24030
   Web of Science ®Google Scholar
• Schweidel, D. A., & Moe, W. W. (2016). Binge watching and advertising. Journal of Marketing, 80(5), 1–19. https://doi.org/10.1509/jm.15.0258
   Web of Science ®Google Scholar
• Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
   Google Scholar
• Smith, K. A., Willis, R. J., & Brooks, M. (2000). An analysis of customer retention and insurance claim patterns using data mining: A case study. Journal of the Operational Research Society, 51(5), 532–541. https://doi.org/10.1057/palgrave.jors.2600941
   Web of Science ®Google Scholar
• Tang, L., Thomas, L., Fletcher, M., Pan, J., & Marshall, A. (2014). Assessing the impact of derived behavior information on customer attrition in the financial service industry. European Journal of Operational Research, 236(2), 624–633. https://doi.org/10.1016/j.ejor.2014.01.004
   Web of Science ®Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
   Web of Science ®Google Scholar
• Van den Poel, D., & Larivière, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157(1), 196–217. https://doi.org/10.1016/S0377-2217(03)00069-9
   Web of Science ®Google Scholar
• Vyas, V., & Raitani, S. (2014). Drivers of customers’ switching behaviour in Indian banking industry. International Journal of Bank Marketing, 32(4), 321–342. https://doi.org/10.1108/IJBM-04-2013-0033
   Google Scholar
• Wübben, M., & Wangenheim, F. V. (2008). Instant customer base analysis: Managerial heuristics often “get it right”. Journal of Marketing, 72(3), 82–93. https://doi.org/10.1509/jmkg.72.3.082
   Web of Science ®Google Scholar
• Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36(3), 5445–5449. https://doi.org/10.1016/j.eswa.2008.06.121
   Web of Science ®Google Scholar
• Zhang, Y., Bradlow, E. T., & Small, D. S. (2013). New measures of clumpiness for incidence data. Journal of Applied Statistics, 40(11), 2533–2548. https://doi.org/10.1080/02664763.2013.818627
   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