Predicting Customer Churn on OTT Platforms

Abstract

No industry can thrive without customers and with customers comes the chances ofcustomer churn. Since customer churn have direct-impact on the revenue, all theindustries are focusing in understanding the factors influencing churn and aredeveloping methods to predict the customer churn effectively. Today, never as before,customers have wide variety of options to choose between any service or product. Inaddition, nowadays customers enjoy multiple subscriptions of service providers acrosssectors. In this study we aim to identify: i) Factors influencing customer churn on OTTplatform, and ii) Predict customer churn on OTT platform. The data for this study iscollected from 317 respondents, using questionnaire method, who have multiple OTTplatform subscription. The questionnaire data contains 19 items which includesdemographic features, usage of OTG platform, and user contentment factors about OTTservice. We have identified factors influencing customer churn in Over-The-Top (OTT)platform by combining Recursive Feature Elimination (RFE), Linear Regression, andRidge Regression feature ranking methods. We have used Hierarchical LogisticRegression, to understand impact of two newly introduced factors namely 'MultipleSubscription' and 'Switching Frequency' on the overall performance of the customerchurn prediction. Finally, customer churn prediction is done using Decision Tree,Random Forest, AdaBoost, and Gradient boosting techniques. We found that randomforest method gives better prediction results.