A/B testing is a way to measure the performance of a page by comparing it to another. To conduct the test, you create two versions of a web page that differ in only one aspect, such as a headline, button, or image. You then distribute traffic evenly between the two pages and see which one hits the target more often.
Step 1: Make a Hypothesis
Running an A/B test allows you to check whether a website change affects its performance. For instance, you might hypothesize: "If a video review is added to the home page, the conversion rate will increase by 10%."
In this step, determine what will be changed, what key metric will be measured, and what will be done once the results are in.
Step 2: Determine the Sample Size For the Experiment
During A/B testing, website visitors are randomly divided into two equal groups - control and experimental. The first group will see the original page and the second one the modified page.
To ensure that the results of A/B testing are statistically significant, i.e. not due to chance, you need to calculate in advance how many people should visit your pages.
To do this, use a calculator such as https://abtestguide.com/calc
Step 3: Get the Pages Ready And Run the Experiment
Duplicate the page and modify the copy according to your hypothesis. You can use tools like Google Optimize (avail. until 30th of September), Adobe Target, or Mixpanel to run the experiment.
Tip: Before the A/B test, consider running an A/A test to see if the tool randomizes users well enough.
Step 4: Analyze the Results
When the test is complete, check the statistical significance of the results. You can use a calculator to do this. If the results in the tested variants are different and consistent with your hypothesis, the tested modification can be scaled to all users.
Tip: A/B testing is suitable for websites with good traffic. During the experiment, don't look at the progress of the experiment and stop it only when the required number of users is reached.
Step 1: Make a Hypothesis
Running an A/B test allows you to check whether a website change affects its performance. For instance, you might hypothesize: "If a video review is added to the home page, the conversion rate will increase by 10%."
In this step, determine what will be changed, what key metric will be measured, and what will be done once the results are in.
Step 2: Determine the Sample Size For the Experiment
During A/B testing, website visitors are randomly divided into two equal groups - control and experimental. The first group will see the original page and the second one the modified page.
To ensure that the results of A/B testing are statistically significant, i.e. not due to chance, you need to calculate in advance how many people should visit your pages.
To do this, use a calculator such as https://abtestguide.com/calc
Step 3: Get the Pages Ready And Run the Experiment
Duplicate the page and modify the copy according to your hypothesis. You can use tools like Google Optimize (avail. until 30th of September), Adobe Target, or Mixpanel to run the experiment.
Tip: Before the A/B test, consider running an A/A test to see if the tool randomizes users well enough.
Step 4: Analyze the Results
When the test is complete, check the statistical significance of the results. You can use a calculator to do this. If the results in the tested variants are different and consistent with your hypothesis, the tested modification can be scaled to all users.
Tip: A/B testing is suitable for websites with good traffic. During the experiment, don't look at the progress of the experiment and stop it only when the required number of users is reached.
