How to Conduct an A/B Test for Your Marketing Campaign

Marketers guide consumers to decisions, which means they aim for an emotional response to advertisements. Eliciting reactions in ads or through user experience is effective in bringing traffic to a website. Though they inspire feelings through promotions, marketers themselves cannot rely on their gut when coming up with a strategy.

Here are a few tips for successfully implementing an A/B test.

1. Know why you are running a test

Your test allows you to view different metrics at once, but you should have a focus or dependent variable before you even start testing. This can be the number of people who sign up for your course, or the revenue collected when people respond to the call-to-action on your website.

Make a hypothesis about the variable and how it will be affected by the other factors. How do you think this metric would perform? Your hypothesis should be informed by the goal of this website, or what is important to your team at this point in the campaign.

2. Choose an experimental variable

a/b test example | showing buttons

When you have chosen a dependent variable, it is time to choose your experimental one. For you to determine whether your chosen metric impacts the dependent variable, it should be the only one you are testing. You can run various A/B tests on the same website or campaign, but keep these runs independent of each other.

Your experimental variable can either be a change in form or in content. Color in your landing page could be your experimental variable—perhaps you can provide customers with one CTA button in gray, and another one in red. Alternatively, you can include a single line to your e-mail copy which will not appear in the control version. Small changes like these are easier to measure and can bring clearer results compared to bigger ones.

Before you run your test, ensure you already have two versions of your page or copy. You have the one being tested—the new CTA button, the e-mail with an additional line—and the original or control version.

3. Decide on a sample size

traffic allocation example. 50%

The sample size is affected by the type of marketing tool you are testing. Testing an e-mail, for example, would need you to send the control and experimental texts to two smaller groups on your list. This is to ensure statistical significance. When you determine which e-mail is more effective, you can send that to the group that received a less effective copy.

A web page A/B test is different because it does not have a limited audience. The only constraint you have on traffic is the amount of time your page is up. You would need to keep your test running long enough to get the right amount of views; taking one of the versions down prematurely would result in skewed numbers.

4. Divide the test groups equally

Your test groups must contain roughly the same amount of people. Randomizing your demographics will also make your test more reliable, especially for groups whose access to data is under your control, like e-mail subscribers.

If you use an experimentation platform for your research, you can segment your customer base according to the attributes that you think will be helpful in getting results. You can also be granular in your performance requirements.


5. Consider statistical significance

Statistical significance shows a mathematical basis for the prediction you make at the start of an A/B test. It proves whether your experimental variable has an impact on the goal or the result you want to have. A high statistical significance gives marketers a rational justification for the strategies they are adopting.


Conducting an A/B test will help you get you more detailed results of why your campaign does or does not work. Like in any experiment, it is necessary to set it up with well-defined parameters.

For more free tips and tricks for advertisers and small business owners, visit our site today. We provide simple and proven strategies for digital marketing to help you launch successful campaigns.

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James LePage
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