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

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.

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