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

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.

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