Abstract
Customer churn is a major concern for large companies (notably telcos), even in a big data world. Customer retention campaigns are routinely used to prevent churn, but targeting the right customers on the basis of their historical profile is a difficult task. Companies usually have recourse to two data-driven approaches: churn prediction and uplift modeling. In churn prediction, customers are selected on the basis of their propensity to churn in a near future. In uplift modeling, only customers reacting positively to the campaign are considered. Though uplift is better suited to maximize the efficiency of the retention campaign because of its causal aspect, it suffers from several estimation issues. To improve the uplift accuracy, this paper proposes to leverage historical data about the reachability of customers during a campaign. We suggest several strategies to incorporate reach information in uplift models, and we show that most of them outperform the classical churn and uplift models. This is a promising perspective for churn prevention in the telecommunication sector, where uplift modeling has failed so far to provide a significant advantage over non-causal approaches.
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Notes
- 1.
Note that ML stands for maximum likelihood of churn.
References
Athey, S., Imbens, G.: Recursive partitioning for heterogeneous causal effects. Proc. Nat. Acad. Sci. 113(27), 7353–7360 (2016). https://doi.org/10.1073/PNAS.1510489113, https://www.pnas.org/content/113/27/7353
Bose, I., Chen, X.: Quantitative models for direct marketing: a review from systems perspective. Eur. J. Oper. Res. 195(1), 1–16 (2009)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Devriendt, F., Berrevoets, J., Verbeke, W.: Why you should stop predicting customer churn and start using uplift models. Inf. Sci. 584, 497–515 (2019)
Farris, P.W., Bendle, N., Pfeifer, P.E., Reibstein, D.: Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Pearson Education (2010)
Fernández, C., Provost, F.: Causal Classification: Treatment Effect vs. Outcome Prediction. NYU Stern School of Business (2019)
Guelman, L., Guillén, M., Pérez-Marín, A.M.: Uplift random forests. Cybern. Syst. 46(3–4), 230–248 (2015). https://doi.org/10.1080/01969722.2015.1012892
Guido, G., Prete, M.I., Miraglia, S., De Mare, I.: Targeting direct marketing campaigns by neural networks. J. Mark. Manag. 27(9–10), 992–1006 (2011)
Gutierrez, P., Gérardy, J.Y.: Causal inference and uplift modelling: a review of the literature. In: Hardgrove, C., Dorard, L., Thompson, K., Douetteau, F. (eds.) Proceedings of the 3rd International Conference on Predictive Applications and APIs. Proceedings of Machine Learning Research, Microsoft NERD, Boston, USA, vol. 67, pp. 1–13. PMLR (January 2016). http://proceedings.mlr.press/v67/gutierrez17a.html
Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Computer assisted customer churn management: state-of-the-art and future trends. Comput. Oper. Res. 34(10), 2902–2917 (2007)
Hansotia, B.J., Rukstales, B.: Direct marketing for multichannel retailers: issues, challenges and solutions. J. Database Mark. Customer Strategy Manage. 9(3), 259–266 (2002)
Idris, A., Khan, A.: Ensemble based efficient churn prediction model for telecom. In: 2014 12th International Conference on Frontiers of Information Technology (FIT), pp. 238–244 (2014). https://doi.org/10.1109/fit.2014.52
Jaskowski, M., Jaroszewicz, S.: Uplift modeling for clinical trial data. In: ICML Workshop on Clinical Data Analysis (2012)
Kayaalp, F.: Review of customer churn analysis studies in telecommunications industry. Karaelmas Fen ve Mühendislik Dergisi 7(2), 696–705 (2017). https://doi.org/10.7212/zkufbd.v7i2.875
Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., Abbasi, U.: Improved churn prediction in telecommunication industry using data mining techniques. Appl. Soft Comput. 24, 994–1012 (2014). https://doi.org/10.1016/j.asoc.2014.08.041
Künzel, S.R., Sekhon, J.S., Bickel, P.J., Yu, B.: Metalearners for estimating heterogeneous treatment effects using machine learning. Proc. Natl. Acad. Sci. U.S.A. 116(10), 4156–4165 (2019). https://doi.org/10.1073/pnas.1804597116
Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009). https://doi.org/10.1109/tsmcb.2008.2007853
Mitrović, S., Baesens, B., Lemahieu, W., De Weerdt, J.: On the operational efficiency of different feature types for telco Churn prediction. Eur. J. Oper. Res. 267(3), 1141–1155 (2018). https://doi.org/10.1016/j.ejor.2017.12.015
Olle, G.D.O., Cai, S.: A hybrid churn prediction model in mobile telecommunication industry. Int. J. e-Educ. e-Bus. e-Manage. e-Learn. 4(1), 55 (2014)
Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., Vanthienen, J.: Social network analytics for churn prediction in telco: Model building, evaluation and network architecture. Exp. Syst. Appl. 85, 204–220 (2017). https://doi.org/10.1016/j.eswa.2017.05.028
Óskarsdóttir, M., Van Calster, T., Baesens, B., Lemahieu, W., Vanthienen, J.: Time series for early churn detection: using similarity based classification for dynamic networks. Exp. Syst. Appl. 106, 55–65 (2018). https://doi.org/10.1016/j.eswa.2018.04.003
Pearl, J.: Causality: Models, Reasoning, and Inference, vol. 6. Cambridge University Press (2009)
Umayaparvathi, V., Iyakutti, K.: Attribute selection and customer churn prediction in telecom industry. In: 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 84–90. IEEE (2016)
Vafeiadis, T., Diamantaras, K.I., Sarigiannidis, G., Chatzisavvas, K.C.: A comparison of machine learning techniques for customer churn prediction. Simul. Model. Pract. Theor. 55, 1–9 (2015). https://doi.org/10.1016/j.simpat.2015.03.003
Verbeke, W., Martens, D., Baesens, B.: Social network analysis for customer churn prediction. Appl. Soft Comput. 14, 431–446 (2014). https://doi.org/10.1016/j.asoc.2013.09.017
Verhelst, T., Caelen, O., Dewitte, J.C., Bontempi, G.: Does causal reasoning help preventing churn? (2021, under submission)
Winer, R.S.: A framework for customer relationship management. Calif. Manage. Rev. 43(4), 89–105 (2001)
Zaniewicz, L., Jaroszewicz, S.: Support vector machines for uplift modeling. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 131–138. IEEE (2013)
Zhu, B., Baesens, B., vanden Broucke, S.K.L.M.: An empirical comparison of techniques for the class imbalance problem in churn prediction. Inf. Sci. 408, 84–99 (2017). https://doi.org/10.1016/j.ins.2017.04.015
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Verhelst, T., Shrestha, J., Mercier, D., Dewitte, JC., Bontempi, G. (2021). Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_4
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