Original scientific paper
https://doi.org/10.31341/jios.46.2.10
Predicting Customer Churn on OTT Platforms: Customers with Subscription of Multiple Service Providers
Manish Mohan
; Symbiosis Centre for Information Technology, Pune, India
Anil Jadhav
; Symbiosis Centre for Information Technology, Pune, India
Abstract
No industry can thrive without customers and with customers comes the chances of customer churn. Since customer churn have direct-impact on the revenue, all the industries are focusing in understanding the factors influencing churn and are developing methods to predict the customer churn effectively. Today, never as before, customers have wide variety of options to choose between any service or product. In addition, nowadays customers enjoy multiple subscriptions of service providers across sectors. In this study we aim to identify: i) Factors influencing customer churn on OTT platform, and ii) Predict customer churn on OTT platform. The data for this study is collected from 317 respondents, using questionnaire method, who have multiple OTT platform subscription. The questionnaire data contains 19 items which includes demographic features, usage of OTG platform, and user contentment factors about OTT service. We have identified factors influencing customer churn in Over-The-Top (OTT) platform by combining Recursive Feature Elimination (RFE), Linear Regression, and Ridge Regression feature ranking methods. We have used Hierarchical Logistic Regression, to understand impact of two newly introduced factors namely 'Multiple Subscription' and 'Switching Frequency' on the overall performance of the customer churn prediction. Finally, customer churn prediction is done using Decision Tree, Random Forest, AdaBoost, and Gradient boosting techniques. We found that random forest method gives better prediction results.
Keywords
Customer Churn Prediction; Over-The-Top (OTT); Multiple Subscription; Machine Learning Classifiers; Decision Tree
Hrčak ID:
291221
URI
Publication date:
22.12.2022.
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