Marketing Analytics

A/B Testing

Comparing two versions of a page, email, or ad to determine which performs better.

Definition

A/B Testing (also called split testing) is a controlled experiment where two versions of a marketing asset (A and B) are shown simultaneously to different segments of the same audience. The version that achieves the better result on a defined metric (conversion rate, CTR, revenue) is declared the winner. A/B tests should be run until statistical significance is achieved. Multivariate testing extends the concept to test multiple variables simultaneously.

Why A/B Testing Matters

A/B testing replaces opinion and intuition with data. Even seemingly obvious design or copy decisions often produce surprising results. Systematic A/B testing is the engine of continuous improvement for landing pages, emails, ads, and product experiences.

Real-World Example

Two landing page headlines are tested: Version A: "Try Free for 14 Days". Version B: "Start Your Free Trial — No Credit Card Required". Version B achieves 23% higher conversion rate over a 3-week test with 10,000 visitors.

A/B Testing FAQs

How long should I run an A/B test?

At minimum until you reach statistical significance (typically 95% confidence level) AND have completed at least one full business cycle (usually 2+ weeks to avoid day-of-week bias). Use A/B test duration calculators for precise guidance.