Let’s get one thing out of the way: conversion rate is useful—but it’s far from the full story. Too many teams laser-focus on that single number, thinking it’s the north star for optimization. But real growth? It lives in the deeper layers of A/B testing metrics.
To build reliable experiments, you need more than a single percentage point to guide your decisions. Here are the core A/B testing metrics every optimizer should include in their dashboard:
Let’s talk statistical power—the silent hero behind every meaningful test result.
In A/B testing, statistical power is the probability that your test will detect a real effect, assuming one exists. In other words, it helps you avoid false negatives (thinking your variant didn’t work when it actually did).
Low power = unreliable conclusions
Tests may end too early, showing false “no effect” results. You risk wasting time, traffic, and momentum
Think of statistical power as your truth detector. If you’re not measuring it, your test might be lying to you.
Conversion rate doesn’t always tell you how engaged users are with your content. That’s where engagement uplift comes in—helping you understand what people actually do before or after clicking.
These signals reveal the story between entry and exit, vital for diagnosing friction and uncovering opportunities.
Beyond engagement and conversions, some lesser-tracked metrics provide serious context to your A/B testing metrics strategy. Think of these as your bonus insight boosters:
These aren't just “nice to have”—they provide the why behind the what.
You can have the fanciest dashboard on the planet, but if your team doesn’t care about the right A/B testing metrics, it won’t matter. That’s where culture kicks in.
To build a culture that prioritizes engagement, uplift, and long-term value over vanity wins:
When everyone on your team speaks the same data language, experimentation becomes less risky and more rewarding.
What is statistical power in A/B testing?
Statistical power measures how likely your test is to detect a real difference between variants if one actually exists. High power increases test reliability, while low power risks false negatives and misleading results.
What’s better: conversion rate or engagement?
Neither is better on its own. Conversion rate tells you if users act, while engagement shows how they interact. Together, they reveal the full picture of user behavior and test effectiveness.
Can scroll depth improve test quality?
Yes. Scroll depth helps identify how far users engage with your content. If users aren’t reaching your CTA, it’s not a copy issue—it’s a visibility problem. It’s essential for diagnosing content layout and user attention.
