Netflix tests millions of thumbnail combinations daily using multi-armed bandit algorithms — a technique smarter than traditional A/B testing. In this episode, Lucas and Luna break down how contextual bandits balance exploration and exploitation to serve the most clickable image for each user, without wasting traffic on losing variants. They walk through the epsilon-greedy approach, why Netflix abandoned pure A/B splits, and how this same logic applies to ad creatives, recommendation carousels, and any high-stakes personalisation problem. Real example: how a single show's artwork variant drove a 20% lift in plays. If you work with user-facing models or optimise for engagement metrics, this episode gives you a practical framework you can borrow tomorrow.

#Netflix #BanditAlgorithms #MultiArmedBandit #ContextualBandit #ThumbnailPersonalization #A/BTesting #ExplorationVsExploitation #EpsilonGreedy #RecommendationSystems #ConversionOptimization #DataScience #MachineLearning #Personalization #Technology #FexingoBusiness #BusinessPodcast #DataDriven #UserEngagement

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