A/B Testing

In an A/B test, your traffic is randomly split between two variants: variant A (the original) and variant B (the change). You measure which variant performs better on a defined target metric — conversion rate, add-to-cart rate or average order value, for example. Only when the difference is statistically significant does the result count as reliable.

For Shopify merchants, A/B testing is how you replace gut feeling with data. Typical test areas are product detail pages, the cart, shipping-cost communication, product images and calls to action. What matters most is a clean hypothesis before the test: what are you changing, why should it work, and how will you measure success?

In practice, most tests fail for lack of traffic. As a rule of thumb you need several thousand sessions and enough conversions per variant — otherwise the test runs for months or produces random results. Low-traffic stores are better served by solid UX standards and qualitative research than by testing every pixel.