What is A/B Testing?

In every startup, there comes a moment where the team argues over a design choice. The designer wants the landing page to be blue. The marketer wants it to be red. They argue for an hour about color theory and brand identity. This argument is a waste of time. The only opinion that matters is the user’s opinion, and they vote with their clicks.

A/B Testing is a user experience research methodology causing two variants of a webpage or app to be shown to users at random to determine which performs better. It is the process of comparing version A (the control) against version B (the variant) to see which one drives more conversions.

For a founder, A/B testing is the antidote to ego. It moves decision making from “I think” to “the data shows.” It allows you to optimize your business based on reality rather than intuition.

To run a valid test, you need software that splits your web traffic. Fifty percent of visitors see the original page. Fifty percent see the new version.

The software then tracks a specific goal. This could be clicking a “Sign Up” button, purchasing a product, or entering an email address. At the end of the experiment, you look at the data. If Version A converted at 2 percent and Version B converted at 4 percent, Version B is the winner. You deploy Version B to everyone and start the next test.

This sounds simple, but it requires rigorous discipline. You must only test one variable at a time. If you change the headline, the image, and the button color all at once, you will not know which change caused the improvement. This is the difference between science and guessing.

Founders often confuse simple A/B testing with Multivariate Testing. They are similar but serve different stages of growth.

A/B testing compares two distinct versions with a single variable change. It is clean and requires less traffic to reach a statistically significant result.

Multivariate testing compares multiple variables simultaneously. You might test three different headlines combined with two different images and two different button colors. This creates dozens of combinations. To get reliable data on this, you need massive amounts of traffic. Most startups do not have enough volume for multivariate testing. Stick to A/B testing until you have hundreds of thousands of visitors.

A common mistake is testing low impact elements. Google famously tested 41 shades of blue for their links. They could do this because they had billions of users. You do not.

Startups should focus on high leverage tests.

• The Offer: Are you selling a subscription or a one time purchase?
• The Headline: Does a funny headline work better than a serious one?
• The Price: Does $49 convert better than $99?

Do not waste time testing the color of a button if your headline is confusing. Fix the big things first.

The most dangerous trap in A/B testing is false confidence. If you have 100 visitors and 5 of them convert on Version B versus 2 on Version A, the sample size is too small. The result is likely random noise.

You need statistical significance. This usually requires thousands of visits per test. If you are an early stage startup with low traffic, A/B testing is likely a waste of time. You are better off calling ten users on the phone and asking them why they didn’t buy. Qualitative data beats statistically insignificant quantitative data every time.