Website A/B Testing Tool
Create, configure, and implement A/B tests to optimize your website conversion rates
Configure Your A/B Test
A/B test configuration preview will appear here
Implementation Code
Data-Driven Decisions
Make informed decisions based on real user data rather than assumptions or guesswork about what will improve your conversion rates :cite[1]:cite[5].
Easy Implementation
Our tool generates ready-to-use code that can be easily integrated into your website with minimal technical knowledge required :cite[1]:cite[6].
Advanced Targeting
Segment your audience and target specific user groups with tailored experiences to maximize the impact of your tests :cite[3]:cite[5].
Statistical Significance
Ensure your test results are reliable with built-in statistical significance calculations to avoid false positives :cite[2]:cite[3].
Recommended A/B Testing Products
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Enterprise Testing Platform
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Frequently Asked Questions
A/B testing (also known as split testing) is a method of comparing two versions of a webpage or app against each other to determine which one performs better. It involves showing two variants (A and B) to similar visitors simultaneously and measuring the difference in performance through statistical analysis :cite[5].
A/B testing allows you to make data-driven decisions about changes to your website rather than relying on assumptions. It helps optimize conversion rates, reduce bounce rates, improve user engagement, and ultimately increase revenue. Companies that regularly conduct A/B tests typically see significant improvements in their key performance metrics :cite[5]:cite[8].
Most A/B tests should run for at least 1-2 weeks to account for weekly variations in traffic and behavior. However, the exact duration depends on your traffic volume, the magnitude of the difference between variations, and your desired confidence level. It's important to run the test until you reach statistical significance rather than stopping based on a predetermined time frame :cite[2]:cite[3].
Statistical significance is a measure of whether the difference between your test variations is likely due to actual changes you made or just random chance. A result is typically considered statistically significant if it has a 95% or higher confidence level, meaning there's less than a 5% probability that the observed difference occurred by chance :cite[2]:cite[3].
Yes, many A/B testing tools including ours support mobile app testing. The implementation process may vary slightly from website testing, but the core principles remain the same. You can test different layouts, features, or content in your mobile app to optimize user experience and conversion rates :cite[3]:cite[9].
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