A/B testing compares two versions of a webpage against each other to determine which produces better results on a predefined metric. Half of visitors see version A (the control), half see version B (the variant), and performance is measured on a specific business outcome like conversion rate, click-through rate, or revenue. The goal is to replace assumptions with evidence about what works.
In practice, A/B testing follows a structured process: identify a single variable to test (headline, CTA copy, button placement, form length, image, layout), create the variant, split traffic randomly between control and variant, run the test for a sufficient duration to reach statistical significance, and analyze results. Common test subjects in a website context include landing page headlines, product page layouts, checkout process steps, pricing presentation, and form field quantity. The method works because it isolates the impact of one specific change while holding everything else constant. Tools like HubSpot, Optimizely, and VWO manage traffic splitting, variant serving, and statistical analysis. A/B testing transforms decision-making from "we think this will work" to "we know this works, and the data shows a statistically significant difference."