How do you create multiple versions of the same element you are testing? How do you split traffic randomly? How do you know if your results are statistically significant?

In this article, we will look behind the scenes at how you can use CCK to do A/B testing on your funnel pages and automate the process so that you don’t have to maintain a dozen Excel spreadsheets. What’s more important, we will point you to the actions you can take after running a successful test.

With landing page A/B testing you have one URL and two or more versions of the page. When you send traffic to that URL, visitors will be randomly sent to one of your variations. Standard landing page A/B testing tools remember which page the reader landed on and will keep showing that page to the user. For statistical validity split tests need to set a cookie on each visitor to ensure the visitor sees the same variation each time they go to the tested page. This is how CCK's advanced funnel testing tool works.

CCK's advanced landing page A/B test tool, enables you to create A/B tests and track the number of metrics to evaluate how your experiment is performing. It keeps a record of the number of people who viewed each variation and number of the people who took the intended action.

For example, it might inform you that each of your landing page variations was viewed 180 times, with the top-performing one generating a 70% conversion rate and the lowest-performing one generating only a 30% conversion rate.

From a high-level view, you can probably tell if your test results are significant or not. If the difference between the tests is very small, it may be that the variable you tested just doesn’t influence the behaviour of your viewers.

However, it’s important to test your results for statistical significance from a mathematical perspective. CCK’s advanced built-in landing page calculator will automatically tell you when the A/B test becomes statistically significant and whether you should continue the test or stop it.

You may conduct an A/B test but not find statistically significant results. This doesn’t mean that your A/B test has failed. Figure out a new iteration on your next test. For example, consider testing the same variable again with different variations, and see if that makes a difference. If not, that variable may have little bearing on your conversion rates, but there may be another element on your page that you can adjust to increase leads. Effective A/B testing focuses on continuous improvement. Remember that as long as you keep working towards improvement, you’re going in the right direction. Statistically insignificant tests aren’t failures; they’re just additional learnings that will save you time in the future.

Call-to-action split testing works pretty much the same way as landing page split testing. You create two variations of your CTA, place them on the same page and they should be displayed to visitors randomly. The goal here is to determine which call-to-action attracts the most clicks.

However, remember, it is important to look for results further down in the sales funnel. So it will be most useful to know the number of conversions each of your CTA versions drove. This result is influenced by the landing page and how well it is aligned with the call-to-action.

Remember that you should be running only one A/B test at a time, so don’t try to optimize both the call-to-action and the landing page simultaneously. Make changes to one variable at a time so that you understand which element triggered the results you are seeing.

➡️ Click here to learn the guidelines for running a successful A/B or Split test on CCK

➡️ Click here to see which elements you should A/B test on a CCK page

➡️ Click here to learn the steps to run a Split or A/B Test on CCK