I used to argue with clients about button colors. Red converts better. No, green does. Actually, orange is the power color. We'd go back and forth, everyone armed with their favorite case study or gut feeling, and nothing would get resolved until someone finally said: "Why don't we just test it?"

That question changed how I approach marketing entirely. A/B testing (also called split testing) is the practice of comparing two versions of a marketing asset to determine which one performs better against a defined metric. It's the closest thing marketers have to the scientific method, and in my experience, it's responsible for more revenue growth than any creative brainstorm I've ever been part of.

What Is A/B Testing?

A/B testing is a controlled experiment where you show two variants (A and B) of a page, email, ad, or other marketing asset to similar audiences at the same time, then measure which variant produces a better outcome. The "A" variant is usually the control (current version), and the "B" variant is the challenger (your hypothesis for improvement).

Adobe's testing guide defines it as "a method of comparing two versions of a webpage or app against each other to determine which one performs better." Brafton's 2026 overview adds important nuance: the test must change only one variable at a time to produce valid results. If you change the headline and the button color simultaneously, you won't know which change drove the outcome.

The fundamental premise is simple: stop guessing, start measuring. Every marketing decision that can be tested, should be tested.

A Brief History of A/B Testing

A/B testing didn't start in Silicon Valley. Its roots go back to agricultural experiments in the 1920s, when statistician Ronald Fisher developed the principles of randomized controlled experiments at the Rothamsted Experimental Station in England. He was testing fertilizer combinations on crop fields, not landing page headlines, but the logic is identical: split your subjects, change one variable, measure the outcome.

Direct mail marketers adopted the approach in the 1960s and 1970s, testing different envelope designs, copy, and offers against each other. The digital revolution accelerated everything. Google famously ran its first A/B test in 2000, testing different numbers of search results per page. By the mid-2010s, VWO reports that A/B testing had become standard practice across digital marketing, with dedicated platforms like Optimizely, VWO, and Google Optimize making it accessible to teams of all sizes.

Today, according to Influence Flow's 2026 analysis, the A/B testing software market is projected to reach $12.5 billion by 2032, growing at a CAGR of 16.2%. That growth tells you something about how central testing has become to modern marketing.

How A/B Testing Works: The Process

Here's the step-by-step framework I use with every testing program:

1. Identify the problem. Start with data, not hunches. Where are users dropping off? Which emails have low open rates? Which landing pages have high bounce rates? Your analytics should point you toward what to test.

2. Form a hypothesis. "If we change [X], we expect [Y metric] to improve because [reason]." Good hypotheses are specific and falsifiable. Bad hypotheses are vague ("let's make it look better").

3. Create the variant. Change exactly one element. This is critical. If you change multiple things, you're running a multivariate test (different methodology, higher complexity).

4. Split the traffic. Randomly assign visitors to either the control or variant. The randomization is what makes this a valid experiment. Most testing platforms handle this automatically.

5. Run until statistical significance. This is where most teams get impatient and make mistakes. You need enough data to be confident the observed difference isn't random noise. HubSpot's A/B testing guide recommends a minimum 95% confidence level before declaring a winner.

6. Analyze and implement. If the variant wins, implement it permanently. If it doesn't, you've still learned something. Document the result either way.

What to A/B Test in Marketing