Guides Tools
Wiki Platforms & Suppliers Success Stories Case Studies
Marketing & traffic

A/B testing

Comparing two versions of a page or ad against real traffic to see which one performs better.

Provided to you by
AliDropship
15 years in business · 1.5M+ stores launched · $1.5B+ earned by store owners
On this page
Definition

A/B Testing — Comparing two versions of a page, ad, or email against each other to see which one gets a better result.

What Is A/B Testing?

You show version A to half your visitors and version B to the other half, then compare a specific metric — like conversion rate — to see which version wins. Only one thing should change between the two versions, or you won’t know what actually caused the difference.

How A/B Testing Works

  • Pick one variable to test — headline, image, button color, or price.
  • Split traffic evenly and randomly between version A and version B.
  • Run the test long enough to collect a statistically meaningful sample.
  • Keep the version that performs better and retire the other.

A/B Testing vs. Multivariate Testing

A/B TestingMultivariate Testing
Variables changedOne at a timeSeveral at once
Traffic neededLowerMuch higher
Best forMost storesHigh-traffic stores only

Why A/B Testing Matters for Dropshippers

Testing a $29.99 price against a $32.00 price on the same product for two weeks, then keeping whichever price produced more total revenue — not just more orders — is a simple example. Because traffic and budgets are often limited, A/B testing helps you make pricing, ad, and page decisions based on real customer behavior instead of guesswork.

FAQ

How long should an A/B test run?

It depends on your traffic, but most stores need at least one to two weeks and a few hundred conversions per version before the result is reliable.

What should I test first?

Start with high-impact elements like your product price, main image, or headline — these tend to move conversion rate the most.

Can I test more than one thing at once?

You can, but that becomes multivariate testing, which needs much more traffic to get a reliable answer than testing one variable at a time.