A/B testing splits live users into groups that each see a different variant - of an algorithm, layout, artwork, or flow - and measures which moves a target metric like watch time, conversion, or retention. It replaces opinion with evidence and is the engine of data-driven product development at streaming services.
Streaming experimentation is famously deep: recommendations, artwork, onboarding, and even encoding choices are tuned through controlled experiments. Doing it well requires a real experimentation platform - proper randomization, sample-size and significance discipline, guardrail metrics, and awareness of long-term effects - so that short-term wins do not mask harm to retention or QoE.
