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

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 12986)

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.

Keywords

  • Causal inference
  • Churn prediction
  • Uplift modeling

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  • DOI: 10.1007/978-3-030-88942-5_4
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Notes

  1. 1.

    Note that ML stands for maximum likelihood of churn.

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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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  • DOI: https://doi.org/10.1007/978-3-030-88942-5_4

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