'A/B Testing', also known as split testing, is a method in web analytics where two versions of a webpage are compared to determine which one performs better in terms of a specific metric, such as conversion rate, click-through rate, or time on page.

This testing involves showing the two variants (A and B) to different segments of website visitors at the same time and then analyzing which version achieves better performance. A/B testing is essential for making data-driven decisions in website design, content, and functionality, leading to improved user experiences and higher conversion rates.

Through A/B testing, organizations can systematically optimize their websites by identifying elements that resonate better with their target audience. This iterative approach to website improvement ensures that changes are based on empirical evidence and user behavior, resulting in continuous enhancement and better online performance.