Summary
This chapter explains how to design and run A/B tests by isolating a single variable, randomizing users into two groups, and measuring statistical significance to determine which version performs better.

General domain of usage
Marketing optimization

**A/B testing** compares two versions of the same element, such as an ad, a landing page, or a subject line, to see which one performs better.

### How to Design a Good A/B Test

1. **Isolate one variable**: change only one thing: a headline, button color, CTA, image, or layout;
2. **Randomize your audience**: ensure groups A and B are similar so differences aren't skewed;
3. **Run the test long enough**: give it time to reach statistical significance;
4. **Review results objectively**: the goal isn't to win, it's to learn what users respond to.

Get familiar with the essential concepts of digital marketing analytics, including foundational metrics and conversion path analysis. Gain a clear understanding of how digital marketing performance is measured and how to map customer journeys from awareness to conversion, aligning with the strategic goals of modern marketing teams.

Experimentation and A/B Testing

How to Design a Good A/B Test