Split testing, also known as A/B testing or multivariate testing, is a method used in pay-per-click (PPC) advertising to determine the most effective version of an advertisement or website element by comparing two or more variations. This technique allows marketers to make data-driven decisions about which version of an ad or website element will perform best with a target audience.
How Split Testing Works
Split testing works by randomly dividing a target audience into two or more groups and showing each group a different version of the ad or website element being tested. The performance of each variation is then measured and compared to determine which version is more effective.
For example, a marketer might want to test the effectiveness of two different versions of a landing page for a PPC campaign. One version of the landing page might have a strong call to action, while the other version might have a more subtle call to action. The marketer would then split the target audience into two groups and show each group one of the two versions of the landing page. The marketer could then measure the performance of each version by looking at metrics such as conversion rate and cost per conversion.
Split testing can be used to test a wide range of elements, including ad copy, images, call to action buttons, and more. It is a valuable tool for PPC marketers because it allows them to make informed decisions about which elements are most effective at driving conversions.
Comparison of Split Testing Methods
|Compares two versions of an ad or website element
|Simple to set up and requires less traffic
|Can only test one element at a time
|Compares multiple versions of an ad or website element at the same time
|Can test multiple elements at once
|Requires more traffic and is more complex to set up
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Benefits of Split Testing
There are several benefits to using split testing in PPC campaigns:
- Improved campaign performance: By testing different versions of an ad or website element, marketers can identify the most effective version and optimise their campaigns accordingly. This can lead to improved campaign performance and a higher return on investment (ROI).
- Increased conversions: Split testing can help marketers identify the elements that are most effective at driving conversions. By focusing on these elements, marketers can increase the number of conversions their campaigns generate.
- Better targeting: Split testing can help marketers understand the preferences and behaviours of their target audience. This can inform targeting decisions and help marketers create more effective campaigns.
- Data-driven decision making: Split testing allows marketers to make informed decisions based on data rather than guesswork. This can help marketers optimise their campaigns and achieve better results.
Tips for Effective Split Testing
To get the most out of split testing, it’s important to follow best practices:
- Test one element at a time: To accurately determine the impact of a specific element, it’s important to test only one element at a time. Testing multiple elements at once can make it difficult to determine which element had the greatest impact on performance.
- Test on a large enough sample size: To get reliable results from split testing, it’s important to test on a large enough sample size. The larger the sample size, the more accurate the results will be.
- Use statistical significance: Statistical significance is a measure of how likely it is that the results of a test are due to a specific factor rather than random chance. It’s important to use statistical significance to ensure that the results of a split test are reliable.
- Use a control group: A control group is a group of people who are not shown the ad or website element being tested. Using a control group can help marketers accurately measure the impact of the element being tested.
Examples of Split Testing
Here are a few examples of how split testing can be used in PPC campaigns:
- Ad copy: A marketer might want to test two different versions of ad copy to see which version performs better. For example, one version of the ad might have a strong call to action, while the other version might be more informative. By comparing the performance of each version, the marketer can determine which ad copy is more effective at driving clicks and conversions.
- Call to action buttons: A marketer might want to test different versions of a call to action button to see which version performs better. For example, one version of the button might say “Sign Up Now,” while the other might say “Get Started.” By comparing the performance of each version, the marketer can determine which button is more effective at driving conversions.
- Landing pages: A marketer might want to test two different versions of a landing page to see which performs better. For example, one version of the landing page might have a more detailed product description, while the other version might have a more streamlined design. By comparing the performance of each version, the marketer can determine which landing page is more effective at driving conversions.
Frequently Asked Questions
How long should a split test run for?
The length of a split test will depend on the size of the sample and the amount of traffic the website or ad receives. In general, the larger the sample size and the more traffic the website or ad receives, the shorter the split test can be. It’s important to run the split test for long enough to get reliable results, but not so long that it becomes too costly or time-consuming.
What is a good conversion rate for a split test?
The conversion rate of a split test will depend on the specific goals of the campaign and the target audience. In general, a conversion rate of 2-5% is considered good for most campaigns. However, it’s important to keep in mind that conversion rates can vary widely depending on the specific industry, product, and target audience.
What is the difference between A/B testing and multivariate testing?
A/B testing is a type of split test that compares two versions of an ad or website element. Multivariate testing is a type of split test that compares multiple versions of an ad or website element at the same time. A/B testing is generally easier to set up and requires less traffic, but it can only test one element at a time. Multivariate testing can test multiple elements at once, but it requires more traffic and is more complex to set up.