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Special Issue on Data Science for Better Productivity

Estimating customer churn under competing risks

ORCID Icon, ORCID Icon & ORCID Icon
Pages 1138-1155 | Received 29 Jan 2019, Accepted 25 May 2020, Published online: 06 Aug 2020

References

  • Ascarza, E., Iyengar, R., & Schleicher, M. (2016). The perils of proactive churn prevention using plan recommendations: Evidence from a field experiment. Journal of Marketing Research, 53(1), 46–60. https://doi.org/10.1509/jmr.13.0483
  • Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., Provost, F., & Schrift, R. (2018). In pursuit of enhanced customer retention management: Review, key issues, and future directions. Customer Needs and Solutions, 5(1-2), 65–81. https://doi.org/10.1007/s40547-017-0080-0
  • Ascarza, E., Netzer, O., & Hardie, B. G. (2018). Some customers would rather leave without saying goodbye. Marketing Science, 37(1), 54–77. https://doi.org/10.1287/mksc.2017.1057
  • Bodapati, A., & Gupta, S. (2004). A direct approach to predicting discretized response in target marketing. Journal of Marketing Research, 41(1), 73–85. https://doi.org/10.1509/jmkr.41.1.73.25081
  • 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
  • Braun, M., & Schweidel, D. A. (2011). Modeling customer lifetimes with multiple causes of churn. Marketing Science, 30(5), 881–902. https://doi.org/10.1287/mksc.1110.0665
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Cleveland, W. S. (1993). Visualizing data. visualizing data. visualizing data. Hobart Press.
  • Cox, D. R. (1992). Regression models and life-tables. In Breakthroughs in statistics (pp. 527–541). Springer.
  • Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association, 97(457), 77–87. https://doi.org/10.1198/016214502753479248
  • Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. https://doi.org/10.1080/01621459.1999.10474144
  • Gerds, T. A., & Schumacher, M. (2006). Consistent estimation of the expected brier score in general survival models with right-censored event times. Biometrical Journal. Biometrische Zeitschrift, 48(6), 1029–1040. https://doi.org/10.1002/bimj.200610301
  • Gönül, F. F., Kim, B.-D., & Shi, M. (2000). Mailing smarter to catalog customers. Journal of Interactive Marketing, 14(2), 2–16. https://doi.org/10.1002/(SICI)1520-6653(200021)14:2<2::AID-DIR1>3.0.CO;2-N
  • Gooley, T. A., Leisenring, W., Crowley, J., & Storer, B. E. (1999). Estimation of failure probabilities in the presence of competing risks: New representations of old estimators. Statistics in Medicine, 18(6), 695–706. https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O
  • Graf, E., Schmoor, C., Sauerbrei, W., & Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18(17-18), 2529–2545. https://doi.org/10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5
  • Greenwell, B. M. (2017). pdp: An r package for constructing partial dependence plots. The R Journal, 9(1), 421–436. https://doi.org/10.32614/RJ-2017-016
  • Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7–18. https://doi.org/10.1509/jmkr.41.1.7.25084
  • Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69(4), 210–218. https://doi.org/10.1509/jmkg.2005.69.4.210
  • Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. Springer.
  • Hoel, D. G. (1972). A representation of mortality data by competing risks. Biometrics, 28(2), 475–488. https://doi.org/10.2307/2556161
  • Ishwaran, H., Gerds, T. A., Kogalur, U. B., Moore, R. D., Gange, S. J., & Lau, B. M. (2014). Random survival forests for competing risks. Biostatistics (Oxford, England), 15(4), 757–773. https://doi.org/10.1093/biostatistics/kxu010
  • Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics, 2(3), 841–860. https://doi.org/10.1214/08-AOAS169
  • Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J., & Lauer, M. S. (2010). High-dimensional variable selection for survival data. Journal of the American Statistical Association, 105(489), 205–217. https://doi.org/10.1198/jasa.2009.tm08622
  • Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.
  • Kamakura, W. A., Wedel, M., De Rosa, F., & Mazzon, J. A. (2003). Cross-selling through database marketing: A mixed data factor analyzer for data augmentation and prediction. International Journal of Research in Marketing, 20(1), 45–65. https://doi.org/10.1016/S0167-8116(02)00121-0
  • Knott, A., Hayes, A., & Neslin, S. A. (2002). Next-product-to-buy models for cross-selling applications. Journal of Interactive Marketing, 16(3), 59–75. https://doi.org/10.1002/dir.10038
  • Kumar, V., Leszkiewicz, A., & Herbst, A. (2018). Are you back for good or still shopping around? investigating customers’ repeat churn behavior. Journal of Marketing Research, 55(2), 208–225. https://doi.org/10.1509/jmr.16.0623
  • LeBlanc, M., & Crowley, J. (1995). A review of tree-based prognostic models. In Recent advances in clinical trial design and analysis (pp. 113–124). Springer.
  • 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
  • Levinthal, D. A., & Fichman, M. (1988). Dynamics of interorganizational attachments: Auditor-client relationships. Administrative Science Quarterly, 33(3), 345–369. https://doi.org/10.2307/2392713
  • Lewis, M. (2004). The influence of loyalty programs and short-term promotions on customer retention. Journal of Marketing Research, 41(3), 281–292. https://doi.org/10.1509/jmkr.41.3.281.35986
  • Menard, S. (2002). Applied logistic regression analysis (Vol. 106). Sage.
  • Mogensen, U. B., Ishwaran, H., & Gerds, T. A. (2012). Evaluating random forests for survival analysis using prediction error curves. Journal of Statistical Software, 50(11), 1–23. https://doi.org/10.18637/jss.v050.i11
  • 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
  • Neslin, S. A., Taylor, G. A., Grantham, K. D., & McNeil, K. R. (2013). Overcoming the “recency trap” in customer relationship management. Journal of the Academy of Marketing Science, 41(3), 320–337. https://doi.org/10.1007/s11747-012-0312-7
  • Prentice, R. L., & Gloeckler, L. A. (1978). Regression analysis of grouped survival data with application to breast cancer data. Biometrics, 34(1), 57–67. https://doi.org/10.2307/2529588
  • Reinartz, W., & 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
  • Reinartz, W., Thomas, J. S., & Kumar, V. (2005). Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing, 69(1), 63–79. https://doi.org/10.1509/jmkg.69.1.63.55511
  • Routh, P. (2018). A framework for estimating customer worth under competing risks. (Unpublished doctoral dissertation), Bowling Green State University.
  • 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
  • Wedel, M., & Kannan, P. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413

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