Is your A/B test result real or random? Check statistical significance for email subject lines, landing pages, and ad creatives — with revenue impact projection to see what winning tests are actually worth.
Recipients or visitors
conversion rate: 10.00%
Recipients or visitors
conversion rate: 11.50%
How sure do you need to be?
Changes labels and context tips
What this means: Your test reached 98.8% confidence — above your 95% threshold. Variant B's conversion rate of 11.50% is higher than Control A's 10.00%, with a +15.0% lift. You can confidently roll out Variant B.
Divide your audience into two equal groups at random. In Klaviyo, this happens automatically when you set up an A/B test on a campaign. The randomization ensures any difference you see is caused by the change, not by audience differences.
Change one variable at a time — subject line, design, offer, send time. Testing multiple changes simultaneously makes it impossible to know which one caused the difference. Keep it clean: one test, one variable.
Track the outcome that aligns with your goal: open rate for subject lines, click rate for design changes, conversion rate for offers. Make sure your tracking is set up before you send.
This is the step most people skip. A 15% lift sounds great, but on 200 recipients it could easily be noise. Use this calculator to check statistical significance before declaring a winner.
A 20% lift on 200 recipients means nothing. Random variation can easily produce that. You need enough data before making decisions — this calculator tells you exactly how much.
Picking a "winner" that isn't actually better means rolling out a change that does nothing — or worse, hurts performance. Let the math decide, not your gut.
A statistically significant 10% lift in email click rate across 50,000 subscribers compounds every single send. Small wins, applied consistently, add up to serious revenue. Measure your campaign ROAS to see the full picture.
If your list is small or the change is minor, the test may never reach significance. Focus A/B tests on high-impact elements: subject lines, offers, send times, hero images.
The classic A/B test and the highest impact per effort. Test length, personalization, urgency, emojis vs no emojis. Even a 2-3% improvement in open rate compounds across every send.
Morning vs evening, weekday vs weekend, Tuesday vs Thursday. Your audience has patterns — find them. Test with at least 10,000 recipients per variant for reliable results.
Percentage off vs dollar off vs free shipping — which converts better for your brand? The answer varies by audience and price point. Know your margins before testing discount offers so you can set profitable thresholds.
Single CTA vs multiple CTAs, long-form vs short-form, image-heavy vs text-focused. Design changes affect click rate more than open rate — measure accordingly.
Personalized product recommendations vs generic bestsellers, dynamic content blocks vs static. Personalization often wins, but test to prove it with your audience.
Let our team help you implement data-driven strategies that drive real results.