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Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention

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Discovery Science (DS 2021)


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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    Note that ML stands for maximum likelihood of churn.


  1. Athey, S., Imbens, G.: Recursive partitioning for heterogeneous causal effects. Proc. Nat. Acad. Sci. 113(27), 7353–7360 (2016).,

  2. Bose, I., Chen, X.: Quantitative models for direct marketing: a review from systems perspective. Eur. J. Oper. Res. 195(1), 1–16 (2009)

    Article  MathSciNet  Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  4. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  5. Devriendt, F., Berrevoets, J., Verbeke, W.: Why you should stop predicting customer churn and start using uplift models. Inf. Sci. 584, 497–515 (2019)

    MathSciNet  Google Scholar 

  6. Farris, P.W., Bendle, N., Pfeifer, P.E., Reibstein, D.: Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Pearson Education (2010)

    Google Scholar 

  7. Fernández, C., Provost, F.: Causal Classification: Treatment Effect vs. Outcome Prediction. NYU Stern School of Business (2019)

    Google Scholar 

  8. Guelman, L., Guillén, M., Pérez-Marín, A.M.: Uplift random forests. Cybern. Syst. 46(3–4), 230–248 (2015).

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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).

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. 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).

  14. Jaskowski, M., Jaroszewicz, S.: Uplift modeling for clinical trial data. In: ICML Workshop on Clinical Data Analysis (2012)

    Google Scholar 

  15. Kayaalp, F.: Review of customer churn analysis studies in telecommunications industry. Karaelmas Fen ve Mühendislik Dergisi 7(2), 696–705 (2017).

    Article  Google Scholar 

  16. 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).

    Article  Google Scholar 

  17. 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).

    Article  Google Scholar 

  18. 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).

    Article  Google Scholar 

  19. 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).

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. 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).

    Article  Google Scholar 

  22. 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).

    Article  Google Scholar 

  23. Pearl, J.: Causality: Models, Reasoning, and Inference, vol. 6. Cambridge University Press (2009)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. 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).

    Article  Google Scholar 

  26. Verbeke, W., Martens, D., Baesens, B.: Social network analysis for customer churn prediction. Appl. Soft Comput. 14, 431–446 (2014).

    Article  Google Scholar 

  27. Verhelst, T., Caelen, O., Dewitte, J.C., Bontempi, G.: Does causal reasoning help preventing churn? (2021, under submission)

    Google Scholar 

  28. Winer, R.S.: A framework for customer relationship management. Calif. Manage. Rev. 43(4), 89–105 (2001)

    Article  Google Scholar 

  29. 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)

    Google Scholar 

  30. 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).

    Article  Google Scholar 

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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.

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