What is A/B Testing? 10 Things to Know

A/B testing, split testing, or bucket testing allows you to compare two similar website pages, apps, posts, ads, emails, or marketing campaigns against each other to determine which one is most effective.

In this process, you show two variants, “A” and “B,” to users randomly and simultaneously. By collecting data on how each variant performs, you can make data-driven decisions to optimize and improve your product or marketing strategy.

It’s likely that you have seen options in many of your tools or software to try an A/B test. But what should you know before you get started? Here, we have 10 A/B testing best practices to help you create better campaign tests.

1. Define Clear and Measurable Objectives

Before starting an A/B test, clearly define what you want to achieve. Set specific and measurable objectives aligned with your business goals. It could be improving click-through rates, increasing conversions, boosting sales, or any other metric that is relevant to your project.

Having a well-defined objective helps you stay focused and ensures that test results are meaningful. The design elements you test will tie directly to those objectives.

2. Think Scientifically

Formulate a hypothesis based on your understanding of the problem and desired outcome.

For example, if you think changing the color of a call-to-action button will increase conversions, your hypothesis might be: “Changing the button color to green will lead to a higher conversion rate.”

From there, you can identify the elements you want to test (e.g., webpage layout, headline, images, CTA buttons). Clearly define the variations (A and B) for each element you want to compare.

Things you might A/B test include:

• Headlines
• Calls to action
• Image or video choice
• Text elements
• Form fields
• Subject lines
• Hero image or option
• Pop-ups
• Direct offers

3. Test Significant Variations

To see substantial differences in test results, ensure that the variants you test are significantly different. Minor changes may not yield noticeable impacts on user behavior, leading to inconclusive results.

For example, changing a button from one shade of green to another might not be enough for users to discern, but if you have green and purple variants, that can be significant.

Experiment with bold and impactful variations to understand what truly resonates with your audience and drives desired outcomes. But remember, it’s essential to test just one variable (or element) at a time. This allows you to understand the impact of that specific change on the desired outcome.

4. Statistical Significance and Sample Size Are Important

Run the A/B test for a long enough duration and with a sufficient sample size to achieve statistical significance. This ensures that the observed differences in performance are not due to random chance.

Use statistical tools and methods to determine the required sample size for the test based on your desired level of confidence and effect size.

5. Randomly Assign Variants

Randomly divide your audience into two random groups for testing purposes. Half will see version “A,” and the other half will see version “B.” This random assignment helps ensure that any differences in the results are not due to biased user groups.

Understanding your target audience and how you will segment them for the test is also important. You want to test with a realistic sample.

The good news is that many tools and platforms can do much of this work for you. For example, Mailchimp and Facebook offer A/B test randomization based on your audience, so you don’t have to think about it as a manual process.

6. Create a Data Collection Method

During the test, track the performance metrics relevant to your objective for both variants. What actions or interactions happened with each option? Is there a clearly defined separation between the variants?

7. Make Sure You Have All the Tools You Need

It’s important to make sure all the tools you need to collect data are in place before you launch the A/B test. This can include analytics tools that you already have or new tools that you must set up to track and measure key performance indicators relevant to your objective.

Start with metrics rooted in the current performance of the element you want to test. This will serve as a baseline to compare the performance of variants A and B.

Ensure that you have the technical capabilities to implement and run the A/B test properly. This might involve using A/B testing software or tools.

Finally, make sure that the changes you are testing are ethical and do not harm users or violate any policies. Depending on the test, you may ask for consent or need a waiver from test participants.

8. Be Open to Change and Iteration

A/B testing is innately iterative. Use the insights from one test to inform the next. Continuously optimize and improve your product or marketing strategy through successive rounds of testing.

While you want to test quickly to make improvements, rushing tests may lead to unreliable results. Find a balance between speed and accuracy to ensure valid outcomes.

And don’t be afraid to pivot. Sometimes you can tell right away that a test is not working – not enough participation or difference between variants are common issues. It’s ok to start over.

9. Give Yourself Time to Analyze the Results

Once enough data is collected, compare the performance of variant “A” with variant “B.” Analyze the data to see which version outperforms the other and whether the difference in results is statistically significant.

Sometimes, A/B tests can yield unexpected results. Be open to these findings and be willing to make changes based on data, even if they challenge your initial assumptions.

Based on the results and statistical significance, decide whether to implement the changes from the better-performing variant or keep the current version.

Run tests again as needed. A/B testing is an iterative process. After implementing changes from one test, you can continue testing other elements to further optimize your product or marketing efforts.

10. Have a Backup Plan

What happens if it doesn’t work or your hypothesis doesn’t pan out? Create a Plan B for your A/B test in case test results are inconclusive or unexpected.

This might involve iterating and retesting with different variations or running a follow-up test.

Remember to keep your initial objective in mind as you work through variants. A/B testing should be aligned with business objectives to focus on improving key performance metrics related to overall goals.

Putting It All Together

In the end, it all comes down to finding what works best for your business, product, and audience.

Sometimes that can mean testing new strategies or content types.

Other times, it means making small updates or tweaks to your existing strategies and content types.

Either way, A/B testing is a valuable marketing, communications, and content tool that shouldn’t be ignored.