If you run A/B testing on real revenue flows, variance isn't a stats problem. It's a cash problem.
Every extra week you wait for confidence is a week you keep a worse checkout, a weaker onboarding, or a lower-priced plan. That slows Decision making, and it quietly taxes your growth strategy.
The CUPED method is one of the few techniques that can shorten that wait without changing the product or buying more traffic. It does it by using pre-period data (what users did before the experiment) to cancel out "who they are" noise. Think noise-canceling headphones for experimentation.
Why variance is expensive (and why CUPED pays for itself)
Most teams underestimate how often tests fail for boring reasons. Not because the idea was wrong, but because the metric was noisy.