Episode 93 of The Data Science Podcast with Fexingo dives into Thompson Sampling, a Bayesian approach to online experimentation that balances exploration and exploitation better than traditional A/B testing. Lucas and Luna walk through a concrete example from a real e-commerce site that ran a 50-variant landing page test — and how Thompson Sampling found the winner in half the time with 30% less traffic wasted. They also discuss Thompson Sampling's role in multi-armed bandit problems, how it handles changing user behavior, and why it's becoming a go-to technique for data scientists in marketing and product optimization. Plus: a quick note on how listener support keeps the podcast ad-free.

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