Split testing, also known as A/B testing, is a critical component of Conversion Rate Optimization (CRO) for e-commerce. It is a method used by businesses to compare two versions of a webpage, email, or other customer touchpoint to determine which one performs better. It is a data-driven way to make decisions about the design and content of your online store, and can significantly improve your conversion rates.
Split testing is based on the scientific method. You start with a hypothesis about what might improve your conversion rate, such as changing the color of your 'Add to Cart' button, or rewording your product descriptions. You then create two versions of the same page - one with the change (the 'treatment'), and one without (the 'control'). You then randomly assign your website visitors to see one version or the other, and measure the conversion rate for each.
Split testing is a key tool in your CRO toolkit because it allows you to make data-driven decisions about your website design and content. Instead of guessing what might work, or relying on your gut instinct, you can use split testing to gather data about what actually works.
Split testing can help you avoid costly mistakes. If you make a change to your website without testing it first, you might find that your conversion rate actually decreases. With split testing, you can test a change on a small portion of your traffic before rolling it out to everyone.
Every split test starts with a hypothesis. This is a statement about what you believe will improve your conversion rate. For example, you might hypothesize that changing the color of your 'Add to Cart' button from blue to green will increase your conversion rate.
Your hypothesis should be specific and measurable. Instead of saying 'Changing the color of the 'Add to Cart' button will improve our conversion rate', say 'Changing the color of the 'Add to Cart' button from blue to green will increase our conversion rate by 5%'. This gives you a clear goal to aim for, and a clear way to measure your success.
Once you have your hypothesis, the next step is to create your control and your treatment. The control is the current version of your webpage, email, or other customer touchpoint. The treatment is the same, but with the change you are testing.
It's important to only test one change at a time. If you change multiple things at once, you won't know which change caused any difference in your conversion rate. This is known as 'isolating your variables', and it's a key principle of scientific testing.
Conducting a split test involves several steps. First, you need to decide what you are going to test. This could be anything from the color of a button, to the wording of a headline, to the layout of a page. Once you have decided what to test, you need to create your control and your treatment.
Next, you need to decide how you are going to split your traffic. The simplest way to do this is to randomly assign each visitor to your site to see either the control or the treatment. This ensures that your test results are not biased by any external factors, such as the time of day or the type of device the visitor is using.
Choosing what to test can be one of the most challenging parts of split testing. It's important to choose something that you believe will have a significant impact on your conversion rate. If you choose something trivial, your test may not yield meaningful results.
Some common things to test include the color and size of your 'Add to Cart' button, the wording of your product descriptions, the layout of your product pages, and the images you use. You can also test more complex things, such as the flow of your checkout process, or the structure of your navigation menu.
Once you have decided what to test, the next step is to create your control and your treatment. The control is the current version of your webpage, email, or other customer touchpoint. The treatment is the same, but with the change you are testing.
It's important to only test one change at a time. If you change multiple things at once, you won't know which change caused any difference in your conversion rate. This is known as 'isolating your variables', and it's a key principle of scientific testing.
Once you have run your split test for a sufficient amount of time, it's time to analyze the results. The key metric you are interested in is the conversion rate for each version of your webpage, email, or other customer touchpoint.
If the conversion rate for the treatment is significantly higher than the conversion rate for the control, then your hypothesis was correct, and you should consider implementing the change. If the conversion rate for the treatment is lower, then your hypothesis was incorrect, and you should not implement the change.
When interpreting the results of a split test, it's important to consider the concept of statistical significance. This is a measure of whether the difference in conversion rates between the control and the treatment is likely to be due to chance, or whether it is likely to be a real effect.
If your results are statistically significant, this means that it is unlikely that the difference in conversion rates is due to chance. If your results are not statistically significant, this means that the difference could easily be due to chance, and you should not make any decisions based on these results.
Another important concept in split testing is the confidence interval. This is a range of values that is likely to contain the true conversion rate for each version of your webpage, email, or other customer touchpoint.
The wider the confidence interval, the less certain you can be about the true conversion rate. If the confidence intervals for the control and the treatment overlap, this means that there is a chance that the true conversion rate for the treatment is actually lower than the true conversion rate for the control, even if the observed conversion rate for the treatment was higher.
While split testing is a powerful tool for CRO, there are several common pitfalls that you need to be aware of. These include not running your test for long enough, not testing a large enough sample size, and not isolating your variables.
Another common pitfall is 'p-hacking', which is the practice of stopping a test as soon as the results reach statistical significance. This can lead to false positives, as the results may have reached significance due to chance, and may not be repeatable.
One of the most common mistakes in split testing is not running your test for long enough. If you stop your test too soon, you may not have collected enough data to reach a reliable conclusion.
As a rule of thumb, you should aim to run your test for at least two weeks, and until you have collected data from at least 1,000 visitors. This will give you enough data to detect even small differences in conversion rates.
Another common mistake is not testing a large enough sample size. The smaller your sample size, the less reliable your results will be.
As a rule of thumb, you should aim to test at least 1,000 visitors for each version of your webpage, email, or other customer touchpoint. This will give you enough data to detect even small differences in conversion rates, and to reach a reliable conclusion.
While split testing can be complex, there are several best practices that can help you get the most out of your tests. These include always starting with a hypothesis, isolating your variables, running your test for long enough, and testing a large enough sample size.
Another best practice is to always follow up a successful test with a validation test. This is a second test that uses the same control and treatment, but with a new sample of visitors. This can help you confirm that your results were not due to chance, and that the change you tested will actually improve your conversion rate when implemented.
Every split test should start with a hypothesis. This is a statement about what you believe will improve your conversion rate. Your hypothesis should be specific and measurable, and it should be based on data or research, not just a gut feeling.
Starting with a hypothesis not only gives you a clear goal to aim for, but it also helps you design your test. If you know what you are trying to achieve, you can design your test to measure that specific outcome.
When conducting a split test, it's important to isolate your variables. This means only testing one change at a time.
If you test multiple changes at once, you won't know which change caused any difference in your conversion rate. By isolating your variables, you can be sure that any difference in conversion rate is due to the change you tested, and not some other factor.
Split testing is a powerful tool for Conversion Rate Optimization (CRO) for e-commerce. It allows you to make data-driven decisions about your website design and content, and can significantly improve your conversion rates.
While split testing can be complex, following the best practices outlined in this guide can help you avoid common pitfalls and get the most out of your tests. Always start with a hypothesis, isolate your variables, run your test for long enough, and test a large enough sample size. And always follow up a successful test with a validation test to confirm your results.