Churn Prediction in Over-The-Top (OTT) for Customer Retention Using Machine Learning Algorithms

G. Shabana, M. Jyothi, K. Ali Mahaboob Basha, D. Mounika Reddy, M. Charan Tej

2025

Abstract

In the view of content providers like Over-The- Top (OTT), the ability to predict the amount of churn is a key part of the organization. With these predictions, the company can make better strategies in order to reduce the churn rate. This paper presents a comprehensive study on Churn Prediction in Over-The-Top (OTT) using various Machine Learning Algorithms. These include Decision Tree using both Entropy and Gini as parameters, Random Forest, XG Boost, Gradient Boost algorithms. In which the class imbalance is found and treated using Synthetic Minority Over- sampling Technique (SMOTE) and re-performed the machine learning algorithms, in which the accuracy all algorithms are greater than 74% and better F1-Score, these findings can be useful to the companies with real time data and to find the reasons behind customer attrition and increase their customer life value and customer satisfaction.

Paper Citation

in Harvard Style

Shabana G., Jyothi M., Basha K., Reddy D. and Tej M. (2025). Churn Prediction in Over-The-Top (OTT) for Customer Retention Using Machine Learning Algorithms. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 224-228. DOI: 10.5220/0013910700004919

in Bibtex Style

@conference{icrdicct`2525,
author={G. Shabana and M. Jyothi and K. Basha and D. Reddy and M. Tej},
title={Churn Prediction in Over-The-Top (OTT) for Customer Retention Using Machine Learning Algorithms},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={224-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013910700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Churn Prediction in Over-The-Top (OTT) for Customer Retention Using Machine Learning Algorithms
SN - 978-989-758-777-1
AU - Shabana G.
AU - Jyothi M.
AU - Basha K.
AU - Reddy D.
AU - Tej M.
PY - 2025
SP - 224
EP - 228
DO - 10.5220/0013910700004919
PB - SciTePress