With experiments, you can A/B test multiple visual element changes on your site or application to validate or reject a hypothesis (e.g., "changing banner design will increase conversions by 10%"). Experiments allow you to apply statistical analysis methods to ensure that the primary metric change you notice is not caused by a change or error.

Use cases

• Trial length optimization. Compare the performance of different trial lengths to determine which leads to higher conversion rates
• Landing page optimization. Experiment with different value propositions to identify which variants drive more sign-ups or demo requests
• Product page optimization. Test different arrangements of elements on product pages to see which layout leads to higher engagement and conversion rates.
• CTA button optimization. Test variations in CTA buttons to identify which versions lead to more completed purchases
• Checkout flow optimization. Compare a multi-step checkout process with a simplified, single-page checkout to determine which leads to lower cart abandonment rates

Where to start

• Server-side vs. client-side A/B testing & personalization

Client-side experiment

• Creating client-side experiments
• Using the visual editor

Server-side experiment

• Creating server-side experiments
• Using the code editor

Experiment analytics

• Analyzing the data

Statistics engine

• How the statistics engine work