A/B-testing
A/B testing is a method of optimizing advertising campaigns in affiliate marketing where two or more versions of a single element within a funnel are compared while all other conditions remain unchanged.
The goal is to determine which version delivers the best results based on key performance metrics.
In affiliate marketing, A/B testing is a core performance-marketing tool that enables data-driven decision-making rather than relying on assumptions. The purpose of split testing is to identify changes that positively impact CTR, CR, CPA, ROI, or overall revenue.
Key characteristics of A/B testing
- only one element is tested at a time
- all other campaign parameters remain unchanged
- decisions are based on statistically significant data
- used during both testing and scaling phases
- helps identify and strengthen profitable funnels
What can be tested with A/B testing
- ad creatives (images, headlines, copy)
- landing pages and pre-landers
- offers and GEOs
- targeting and audience settings
- ad formats
In practice, A/B testing allows marketers to quickly eliminate underperforming variants and focus budget on the most profitable solutions. Systematic testing is how arbitrage marketers discover scalable funnels and reduce conversion costs.
What is A/B testing in simple terms?
A/B testing means running several versions of a campaign and seeing which one performs better—then allocating budget only to the version that delivers stronger results.
A/B Testing (Split Testing) — Frequently Asked Questions
What’s the difference between A/B testing and multivariate testing?
A/B testing compares two variants of a single element, while multivariate testing evaluates multiple combinations at once, which requires significantly more traffic.
Is A/B testing mandatory?
Yes. Without testing, it’s impossible to scale campaigns consistently or maintain control over traffic performance.