Deep Learning as a Vector Embedding Model for Customer Churn

Tjeng Wawan Cenggoro · Raditya Ayu Wirastari · Edy Rudianto · Mochamad Ilham Mohadi · Dinne Ratj · Bens Pardamean

37 citations0/0 files storedProcedia Computer Science

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Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

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References

2013Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining 2010Applying Data Mining to Customer Churn Prediction in an Internet Service Provider 2017A Novel Approach for Churn Prediction Using Deep Learning 2018Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning 2017Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry 2018A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games

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