Stop guessing and start scaling. Just because Variation B has more clicks doesn’t mean it’s the winner. Use this tool to calculate Statistical Significance and know when to cut the loser.
✨ AI Split-Test Audit: Once you have a winner, click the purple button to generate a prompt for ChatGPT to analyze the psychological reasons why one design beat the other.
A/B Split Test Calculator
Determine Statistical Significance & Performance Winners
Frequently Asked Questions
What is an A/B Split Test?
An A/B test is a method of comparing two versions of an ad (Variation A and Variation B) to see which one performs better. By changing only one element at a time (like the headline or button color), you can identify exactly what drives your audience to click.
What is Statistical Significance?
In marketing, statistical significance is a way of proving that your test result isn’t just a lucky coincidence. A “95% Confidence Level” means that if you ran the same test 100 times, the same variation would win 95 times. This is the gold standard for media buyers.
How long should I run my split test?
You should run your test until you hit two milestones:
1. You have reached at least 95% Confidence Level.
2. You have run the test for at least 7 full days to account for weekend vs. weekday behavior variations.
What should I test first?
For web banners, the elements with the highest impact on CTR are usually:
1. The Headline: The specific benefit you are promising.
2. The Image: The visual focus of the ad.
3. The CTA: The color and text of your button.