Summary  
This chapter explains how to implement controlled experiments (A/B tests) by randomly serving two code variations, isolating a single variable, and using statistical analysis to determine which version performs better.  

General domain of usage  
Marketing campaigns

**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.

Gain the ability to measure what matters and turn data into actionable insights. Learn how to track performance across channels, interpret key metrics, build dashboards, and use experimentation to optimize campaigns and drive smarter marketing decisions.

Experimentation and A/B Testing

How to Design a Good A/B Test