Which of the following best describes A/B testing?

A/B testing is best described as a method of comparing two versions of an ad to determine which one performs better. This process involves creating two variations (A and B) of an advertisement, with subtle differences in elements such as headlines, images, or calls to action. The goal of A/B testing is to analyze the performance of each version based on specific metrics, such as click-through rates, conversion rates, or engagement levels. By identifying which version resonates more with the target audience, marketers can make data-driven decisions to optimize their advertising strategies.

Other options provide different concepts related to advertising but do not accurately encapsulate the essence of A/B testing. Testing multiple brands at once doesn’t focus on the comparative analysis of two versions of a single ad, while measuring overall ad reach pertains more to assessing the scope of an ad's visibility rather than its effectiveness. Lastly, setting advertising budgets involves financial planning and allocation rather than experimenting with ad variations.