Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference
Telecommunication companies are evolving in a highly competitive market where attracting new customers is much more expensive than retaining existing ones [3] Retention campaigns can be used to prevent customer churn, but their effectiveness depends on the availability of accurate prediction models. Churn prediction is notoriously a difficult problem because of the large amount of data, non linearity, imbalance and low separability between the classes of churners and non churners. In this paper, we discuss a real case of churn prediction based on Orange Belgium customer data. In the first part of the paper we focus on the design of an accurate prediction model. The large class imbalance between the two classes is handled with the EasyEnsemble algorithm [4] using a random forest classifier. The dataset contains 73 variables and about 7.6 million entries, covering a 5 months time window in 2018. The classification model is trained on the first 4 months of data and evaluated on the last month. We also assess the impact of different data preprocessing techniques including feature selection and engineering. Results show that feature selection can be used to reduce computation time and memory requirements, though engineering variables does not necessarily improve performance. In the second part of the paper we explore the application of data driven causal inference, which allows to infer causal relationships between variables purely from observational data. More specifically, we applied 5 different causal inference methods, namely PC [6] Grow shrink (GS) [5] Incremental Association Markov Blanket (IAMB) [7] Minimum interaction maximum relevance (mIMR) [2] and D2C [1] PC infers the set of causal graphs faithful to the dataset, GS and IAMB infer the Markov blanket of the churn variable, and mIMR and D2C return the direct causes of churn. Two implementations of the mIMR algorithm are used: one based on histograms to estimate mutual information, and another assuming Gaussian variables, thus allowing a closed form formula for the computation of the mutual information. The results of these algorithms (summa
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