The practice of comparing two versions of a marketing asset to determine which one performs better with a defined audience.
A practical way to improve decisions using real audience behavior rather than assumptions.
Short and sweet:
The practice of comparing two versions of a marketing asset to determine which one performs better with a defined audience.
A practical way to improve decisions using real audience behavior rather than assumptions.
See also: Conversion Rate, Landing Page, Statistical Significance
A/B testing shows two versions of a single element, like a headline, button color, or email subject line, to separate but comparable groups of people at the same time, then measures which version performs better against a clearly defined goal like clicks or conversions. Running both versions simultaneously, rather than one after the other, controls for outside factors like seasonality or a random traffic spike that could otherwise make results misleading. Because a test needs enough traffic and enough time to reach statistical significance, small or low-traffic pages sometimes struggle to produce a genuinely reliable result within a reasonable testing window.
Netflix runs constant A/B testing on its thumbnail artwork, showing different subscribers different images for the exact same show to see which one drives more clicks.
An e-commerce team might use this same method to compare a green “Add to Cart” button against a red one, discovering something as small as color can shift conversion rates.
Political campaigns rely on this kind of comparison for fundraising emails, sending two different subject lines to small groups first before blasting the higher-performing version to the entire list.
