Churn prediction is a important problem for Over-the-High (OTT) service suppliers, as growing competitors wants data-driven retention methods. Conventional fashions, based mostly on static behavioral metrics, typically fail to seize the evolving and sequential nature of person engagement. Current advances in machine studying (ML), notably time collection evaluation, supply a more practical strategy to figuring out early indicators of churn.
This assessment examines ML-driven churn prediction in OTT companies, emphasizing time collection methodologies. Key predictive options — reminiscent of viewing habits, session frequency, and transactional behaviors — are analyzed alongside a spread of ML fashions, together with logistic regression, resolution bushes, gradient boosting, and deep studying architectures like Lengthy Brief-Time period Reminiscence (LSTM) networks. Particular consideration is given to attention-based fashions that improve predictive accuracy by specializing in important temporal patterns.
Past mannequin efficiency, we discover sensible challenges, together with knowledge sparsity, function choice, and the trade-off between interpretability and complexity. Synthesizing insights from each analysis and trade functions, this paper supplies a complete overview of churn prediction methods and their integration into enterprise operations. Our findings…
