How to A/B test your checkout without fooling yourself

The problem of A/B testing your checkout without fooling yourself

A/B testing your checkout without fooling yourself is a critical process for online store owners and operators who want to stop leaking revenue at the checkout. This process involves creating multiple versions of your checkout, directing a portion of your traffic to each version, and measuring which version performs better. However, many online store owners and operators make critical mistakes when A/B testing their checkout, which can lead to incorrect conclusions and a loss of revenue.

According to a study by Moz, A/B testing can increase conversions by up to 25 percent. However, to achieve this increase, online store owners and operators must ensure that their A/B testing is valid and reliable. This requires a thorough understanding of statistical significance, sample size, and test duration.

Understanding the method of A/B testing your checkout

To A/B test your checkout without fooling yourself, you must first understand the method of A/B testing. This involves creating a control version and a treatment version of your checkout. The control version is the original version of your checkout, while the treatment version is the modified version. You then direct a portion of your traffic to each version and measure which version performs better.

A critical aspect of A/B testing is statistical significance. This refers to the likelihood that the results of your test are due to chance. To determine statistical significance, you can use a statistical significance calculator or consult with a statistician. A commonly used threshold for statistical significance is 95 percent, which means that there is only a 5 percent chance that the results of your test are due to chance.

Another critical aspect of A/B testing is sample size. This refers to the number of visitors that are directed to each version of your checkout. A larger sample size increases the accuracy of your test results. However, a larger sample size also requires more traffic and a longer test duration. As a general rule, you should aim for a sample size of at least 1,000 visitors per version.

Calculating sample size and test duration

To calculate sample size and test duration, you can use a sample size calculator or consult with a statistician. A commonly used formula for calculating sample size is the following: sample size = (Z^2 * p * (1-p)) / E^2, where Z is the Z-score corresponding to the desired level of statistical significance, p is the conversion rate of the control version, and E is the minimum detectable effect.

For example, let’s say you want to A/B test a new checkout design and you expect a conversion rate of 2 percent for the control version. You want to detect a minimum increase in conversion rate of 10 percent, which is 0.2 percent. Using a Z-score of 1.96, which corresponds to a statistical significance level of 95 percent, and a conversion rate of 2 percent, you can calculate the required sample size as follows: sample size = (1.96^2 * 0.02 * (1-0.02)) / 0.002^2 = 9,604 visitors per version.

A worked example of A/B testing your checkout

Let’s say you own an online store that sells clothing and accessories. You want to A/B test a new checkout design that includes a progress bar and a secure payment badge. You expect a conversion rate of 2 percent for the control version and you want to detect a minimum increase in conversion rate of 10 percent.

Using the formula above, you calculate the required sample size as 9,604 visitors per version. You then direct 9,604 visitors to the control version and 9,604 visitors to the treatment version. After a test duration of two weeks, you measure the conversion rate for each version. The results are as follows: control version: 2.1 percent conversion rate, treatment version: 2.3 percent conversion rate.

Using a statistical significance calculator, you determine that the results are statistically significant at a level of 95 percent. This means that you can conclude that the new checkout design increases the conversion rate by 10 percent.

How to apply A/B testing to your checkout

To apply A/B testing to your checkout, you should follow these steps: identify a hypothesis, create a control and treatment version, direct a portion of your traffic to each version, measure the results, and draw conclusions. You should also ensure that your A/B testing is valid and reliable by using a large enough sample size and a long enough test duration.

It’s also important to avoid common mistakes such as testing too many variables at once, not controlling for external factors, and not using a reliable A/B testing tool. You should also use a free store scanner to identify areas of your checkout where you can improve the user experience and increase conversions.

• Identify a hypothesis: What do you want to test and why?
• Create a control and treatment version: What changes will you make to the treatment version?
• Direct a portion of your traffic to each version: How will you split your traffic?
• Measure the results: What metrics will you use to measure the performance of each version?
• Draw conclusions: What do the results mean and what actions will you take?

Common mistakes to avoid

When A/B testing your checkout, there are several common mistakes to avoid. These include testing too many variables at once, not controlling for external factors, and not using a reliable A/B testing tool. You should also avoid making changes to your checkout during the test, as this can affect the validity of the results.

According to a study by Google Search Central, A/B testing can be affected by external factors such as seasonality and user behavior. To control for these factors, you should use a reliable A/B testing tool that can account for external factors and provide accurate results.

In conclusion, A/B testing your checkout without fooling yourself requires a thorough understanding of statistical significance, sample size, and test duration. By following the steps outlined above and avoiding common mistakes, you can ensure that your A/B testing is valid and reliable and that you can make data-driven decisions to improve the user experience and increase conversions.