Volume 9, Number 13, November 2019

Prediction and Causality analysis of churn using deep learning

  Authors

Muzaffar Shah, Darshan Adiga, Shabir Bhat and Viveka Vyeth, Datoin Bangalore, India

  Abstract

In almost every type of business a retention stage is very important in the customer life cycle because according to market theory, it is always expensive to attract new customers than retaining existing ones. Thus, a churn prediction system that can predict accurately ahead of time, whether a customer will churn in the foreseeable future and also help the enterprises with the possible reasons which may cause a customer to churn is an extremely powerful tool for any marketing team. In this paper, we propose an approach to predict customer churn for nonsubscription based business settings. We suggest a set of generic features that can be extracted from sales and payment data of almost all non-subscription based businesses and can be used in predicting customer churn. We have used the neural network-based Multilayer perceptron for prediction purposes. The proposed method achieves an F1-Score of 80% and a recall of 85%, comparable to the accuracy of churn prediction for subscription-based business settings. We also propose a system for causality analysis of churn, which will predict a set of causes which may have led to the customer churn and helps to derive customer retention strategies.

  Keywords

churn Analysis, Causality Analysis, Machine Learning, Business Analytics , Deep Neural Network

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