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All you should know about A/B tests: the types and tips

Affiliate marketing is a process of trial and error on the path to success, so even experienced advertisers go through a testing step to get to a high CTR. A/B testing helps you achieve the best results, so if your goal is to increase profits, then this article is for you!

It's time to talk about A/B tests, so let's dive in!

Why are A/B tests needed?

You may have probably heard another name for this stage – split testing. These are not just marketing buzzwords, but a real opportunity to get to know your audience better, but why?

Imagine that you are launching a campaign. You have prepared an attractive creative and are already getting ready to receive a lot of clicks and conversions. But the reality turns out to be completely different and you only lose your investment. It may turn out that what you think is an attractive creative turns out to be unconvincing to users. Therefore, before driving traffic, you need to conduct a test period comparing different materials to determine effectiveness. This is part of the normal process even for experienced affiliates, so we recommend that you spend plenty of time on this step.

Pros of A/B tests:
– Your ads are watched by people, and split tests will help make your ads attractive to people because you can learn about the interests of your audience before driving traffic.
– Understanding your audience stimulates conversion growth, as you can successfully motivate users to take targeted actions.
– You can optimize your budget by investing only in those strategies that help you convert users into customers.

How long does A/B testing take?

The duration of testing may vary for many reasons that need to be identified. For example: demographics, hours that convert, features of the offer, type of testing, and even your marketing skills.

To better understand the duration of split tests, you need to understand the main goal: A/B testing is necessary to determine the cost of conversion. The main task is to minimize the cost of conversion and maximize the quantity.

What types of tests?

A/B tests can be divided into 3 types:

1. Comparison of visual effects: testing different elements such as colors, images, and fonts. The goal is to make your advertising the most eye-catching for users.

2. Testing the content: comparing texts and headings. The goal is to motivate users to read your offer and click to learn more.

3. Landing Pages: experiment with different landing page options to determine effective design and structure. The goal is to motivate users to take a targeted action (registration, giving contacts, making a purchase, etc.).

Are there other types of tests?

Yes, split tests can be carried out not only for your creatives, these are only the most popular types. You can also compare traffic from different ad networks, or desktop and mobile traffic. Or you may compare a campaign with and without a pre-landing page.

Recommendations for split tests

The A/B testing phase requires you to be patient because rushing can ruin all the results. It is a big mistake to conduct all types of testing at the same time. The point is that you need to understand the main problem of low conversions, and if you test several elements at the same time, then your results are unreliable. Take the time to test your creatives step by step to find the problem and fix it for good results. Simply put, it's normal to run multiple split tests on a single campaign.

Don't rush to complete testing. Make sure you get enough traffic for the test results to be reliable. A couple of days may not make sense in the case of A/B testing since different days of the week may show different results. The day of the week and time of day can be important indicators in optimizing your ad.

If you find a successful formula, then do not forget that it may be losing in another GEO or with another similar offer. Also, user interest may change over time. This means that A/B tests are necessary for each of your campaigns.

Our team wishes you effective A/B tests and great performance.

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