A/B testing (or split testing) compares two versions of a page, flow, or campaign, shown to different segments of users, to see which one performs better against a defined goal. It’s a core method for making data-driven product and growth decisions.

Examples

• A team tests two versions of a landing page headline to see which drives more wallet connects.

• A mint flow is A/B tested with and without a countdown timer to measure impact on conversion rate.

• An incentive campaign compares two reward structures to see which drives higher retention.

FAQs

What’s needed to run a valid A/B test?

A clear goal metric, random and comparable audience segments, and enough traffic to reach statistical significance.

How long should an A/B test run?

Long enough to reach a meaningful sample size and account for normal day-to-day or weekly variation in traffic.

Can A/B testing be used for onchain flows?

Yes. Teams can test different contract interactions, fee structures, or UI flows and measure the impact on onchain conversion rate.