What does this research mean for the field?

Combining machine learning-based behavioral analysis with targeted, individualized intervention strategies effectively predicts and reduces customer churn in OTT platforms. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to explore how machine learning techniques can predict and mitigate customer churn in OTT platforms by analyzing user behavior.

June 1, 2026Open Access

Using Machine Learning to Predict and Reduce Customer Churn in OTT Platforms: A Practical Approach with Behavioural Data

Key Points

The aim is to explore how machine learning techniques can predict and mitigate customer churn in OTT platforms by analyzing user behavior.
Utilized machine learning models to analyze customer behavior data, including viewing patterns and interaction history.
Employed real-time data monitoring and continual model updates to enhance prediction accuracy.
Developed intervention strategies like targeted content recommendations and promotional offers based on predictive insights.
Machine learning models accurately identified at-risk users, leading to enhanced customer retention strategies.
Implemented targeted recommendations resulted in a significant reduction in churn rates.
Predictive analytics improved user satisfaction and contributed to sustainable profitability for OTT service providers.

Abstract

The digital streaming market has become highly competitive with the introduction of Over-the-Top (OTT) platforms such as Netflix, Amazon Prime Video, and Disney+. Customer retention is a major challenge for the digital streaming industry. In this paper, the authors explore how to use Machine Learning (ML) techniques to predict and reduce customer churn from customer behaviour. ML models can accurately identify at-risk users by analysing their behaviour including viewing patterns, time spent and interaction history. But predicting is not enough. There needs to be timely and individual intervention strategies. This paper outlines a practical approach that combines real-time data monitoring, continual model updates, and actionable insights, like targeted content recommendations and promotional offers. This study also highlights the potential of predictive analytics and adaptive retention measures to boost user satisfaction, curb churn, and drive sustainable profitability for OTT service providers.

Using Machine Learning to Predict and Reduce Customer Churn in OTT Platforms: A Practical Approach with Behavioural Data

Key Points

Abstract

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