What is A/B testing? A step-by-step guide for 2025

The best business decisions happen because of data, and A/B testing is one of the best methods of obtaining statistics for conversion rate optimization. 

What is A/B testing? Put simply, an A/B test compares two versions of an asset to determine which one performs better. It is used by companies wanting answers to questions like: 

• What’s most likely to make people click? 
• Which version of our offer will drive more sales? 
• How can we design a website that keeps users coming back?

If, for example, you wanted to see if your emails perform better with or without images, you could perform an A/B test. You would send one version of an email with an image to half of your audience and one version without an image to the rest, and note which email garners a higher open rate, click-through rate, and conversion rate. 

A/B testing is a cornerstone of data-driven decision-making. In its simplest form, it can test headings and product descriptions, but with the right resources, it can refine entire user experiences.

A/B testing in six steps

A/B testing isn’t just about comparing two versions of a webpage, email, or app; it is a structured approach to learning what resonates with your unique audience. Whether you’re a marketer testing a button color or a developer testing a product feature, the steps are all the same.

1. Define your objective

The first step for every A/B test is establishing a simple goal. What are you trying to improve? Your objective should clearly answer this question. Some common objectives include:

• Increasing click-through rates on pop-ups, emails, or blog posts.
• Reducing cart abandonment on product or checkout pages.
• Boosting engagement on banners or interactive website elements.

2. Design your test and create a hypothesis

Once you’ve established your goal, choose the variables you’d like to test to form your hypothesis. Your variables can be anything you have the ability to change; for example:

• Visual elements, including images, colors, or layout changes.
• Text elements, like descriptions, or CTAs.
• Functional elements, such as user flow adjustments or new features.

If you’re testing the wording of a headline, for example, your variable is a test element. If you’re testing the color of a display, it is a visual element.

Remember to focus on one variable at a time to isolate its impact. If you change more than one element of your asset, you have no way of knowing which change contributed to the results of your experiment.

A useful way to approach this step is to write a hypothesis. For example:

“Changing the color of the ‘Buy Now’ button from red to green will increase clicks by 10%, because green is better associated with positivity and action.”

A focused hypothesis keeps your test aligned with your goals and makes you more likely to achieve actionable insights.

3. Split your audience based on your hypothesis

To ensure your test is statistically valid, you will typically divide your audience randomly. This is important for avoiding bias and ensuring each group represents a similar mix of users.

Depending on your hypothesis, it might also make sense to segment your audience based on known factors. For example, you can A/B test to determine whether a specific set of your audience is more likely to convert. Are returning visitors more likely to convert than first-time visitors? Do new users respond better to certain promotions than existing customers? 

The way you split your audience will have a significant impact on the insights you can collect. Often a random split will work best, but there are many hypotheses that can only be proved through specific segmenting. 

4. Run your test for a sufficient duration

One of the most common mistakes a brand can make in A/B testing is stopping the test too early. Early results can be misleading, as they’re often influenced by random fluctuations. Two important things to keep in mind are your minimum sample size and pre-determined test duration.

• Minimum sample size. Always make sure enough users have interacted with both versions for statistically reliable results. Your base conversion rate plays an important role in determining how long to test for.
• Test duration. Run the test for at least two full business cycles to account for fluctuations and atypical stretches.

Patience is key—always let the data stabilize before drawing conclusions!

5. Analyze the results

Once your test is complete, the next step is to analyze your data, focusing on your primary success metric to determine the winner. Look for:

Lift, or the percentage improvement in performance between the control and the variant.

The statistical significance of your results. Ideally, you should have at least a 95% confidence level before acting on the results of your test.

6. Iterate and expand

Ideally, A/B testing is something you perform continuously, because every new test provides insights that guide next steps, new ideas, and further iterations. Once you have the results from your initial test, you can:

• Retest your winning variation to confirm results and avoid false positives.
• Build on successes by testing additional variables. After refining a headline, test different images or CTAs. The more data you have, the better!
• Keep iterating with small, incremental changes, which often lead to significant improvements over time.

An example: testing a checkout flow

To put everything together, imagine we’re testing a checkout flow. A summarized A/B test might look something like this:

• Objective: Reduce the abandonment rate, as many users are adding items to their cart and exiting the site before completing their purchase.
• Hypothesis: Adding a progress bar to the checkout page will encourage users to complete their purchase by showing how close they are to finishing.
• Test:
  • Version A: The current checkout flow.
  • Version B: The checkout flow with a progress bar.
• Execution: Randomly split visitors to the site over four weeks, showing 50% of users Version A and the other 50% Version B.
• Results:
  • Abandonment rate for Version A: 11.38%
  •  Abandonment rate for Version B: 9.13%
• Analysis: The abandonment rate for Version B is 19.7% lower than the abandonment rate for Version A, which is in line with the original hypothesis and achieves the objective.

This test would strongly suggest that implementing a progress bar to your checkout flow leads to lower abandonment rates and more successful purchases. This is valuable data that directly impacts conversions.

Learn more about A/B testing with Kameleoon

A/B testing is a valuable resource for any business when implemented correctly. Click here to learn more about A/B testing, and determine the best ways to implement it into your business website, apps, features, or customer experience.