A/B testing allows one to effectively learn about an audience’s behavior and use these insights for campaign optimization. Essentially, this method confirms which media converts more users, what is an ideal target audience, and minimizes the costs for the rest of your campaign.

This article provides a short explanation of what A/B testing actually is, how it is different from multivariate testing, how these methods can be done in native campaigns and drive more traffic or conversions.

What is A/B testing?

A/B testing is an optimization method that involves testing different versions of a certain component of the native campaign, such as, a visual, headline, creative, editorial, landing page, or even the promoted product itself. This approach assumes that variations are identical except for a few important details that can affect a user’s behavior. For example, you may want to test different color schemes on a landing page. Next, these variations are randomly chosen to be shown to users, and their overall performance is measured and compared.

Multivariate testing, on the other hand, implies that you test multiple components of the marketing funnel in different variations. For example, you may test four headlines, three images, and two pre-landers at once in different combinations. However, when adopting this approach you should be wary about their mutual fit. Advertisements should be relevant and give readers a sneak peek into the editorials and landing pages they direct traffic to.

How it is used in native campaigns

A/B tests can be set up anywhere across the conversion funnel. At the first stage of the funnel, you may test different titles or images or the particular ad creatives. For example, you can compare the same visual with different copies, several images with one copy, or completely different ad units. These media can be simply uploaded within one campaign. As a rule of thumb, we recommend testing three to five different ad creatives during the launch of your campaign and adding a few new ones regularly as you keep it in rotation.

Going down the sales funnel, you can test different versions of pre-landers, landing pages, or even the promoted products. To perform these A/B tests, you have to create a URL that randomly redirects to different web pages. Typically, tracking tools allow setting these links and analyzing outcomes you get from tests.

Note that all variations of pre-landers, landing pages, and products should be relevant to ad creatives, aligned with each other, and compliant. You can’t use celebrity endorsement or violate our content policy in any of the tested variations. You should also inform the account manager or support team before launching the campaign with varying links so that your actions don’t appear suspicious.

Even though they provide valuable insights, A/B tests require additional spending. Also, slightly different variations are likely to produce similar results, so you don’t learn anything. Instead, we recommend creating contrasting options and rather compare different approaches targeted at different audience segments. For example, you can test storytelling and interactive landing pages, or test all campaign elements in two different languages.

Measure and compare results

Typically, you should compare the number of conversions or revenue generated by each variation of the funnel and choose the most efficient option. For traffic campaigns, bounce rate or other engagement metrics can be used as key performance indicators. Note that statistical calculations have to be made to find out if the difference in performance is not a coincidence.

In some cases, however, you might want to dive deeper into the analysis of user behavior. For example, click maps on tested landing pages can point out what elements drag more attention. To keep track of a user’s KPIs and preserve consistency (i.e. always show a yellow button if it was shown initially), cookies are stored on the visitors’ devices.

Final thought

A/B testing is one of the most efficient ways to set your campaign for success. It helps you find out which components of the marketing funnel or even their combinations generate higher engagement among your audience.

In A/B tests you change only one thing at a time, whereas in multivariate testing you can test multiple components within your marketing funnel. In native advertising, you can split-test different ad creatives, pre-landers, landing pages, and products. After you put the tested variants in rotation, compare their performance, and use the derived insights to achieve the set goals.