The SEO landscape has changed significantly over recent years. While websites once relied on keyword stuffing and large numbers of backlinks, quality content and user experience have taken over as the key components for SEO success. However, producing quality content and a positive user experience is easier said than done. You may think that you published a groundbreaking blog post or a share-worthy landing page, yet you can’t seem to find the traffic your content deserves. This is why it is critical to employ A/B testing and to implement optimizing measures on your site based on the results.
What is A/B Testing?
A/B testing is simply a way to test one version of a website against another. You have your control (A), which is the original version of your content, and you have your experimental version (B). If testing has been properly set up, half the visitors to your site will arrive on version A, and the other half will find themselves on version B.
Soon the data will begin rolling in, and you will have a number of valuable metrics to analyze. For example, you can see if visitors spend more time on version A or version B. You can look at sales conversions for the two versions. You can see which version has been shared more often on social media networks.
How is A/B implemented in Optimization?
Implementing this kind of testing does take some technical expertise. Since much of the content in versions A and B will be the same, you need to be careful so that search engines don’t penalize your site for duplicate content. This requires editing components of the meta data. Another technical aspect of setting up a test like this involves programming your site so that half the visitors will end up on version A, and the other half on version B. This needs to be random, and it needs to be seamless. Visitors should not be able to detect that there are multiple versions of the page.
What to do with the results?
The test provides a valuable set of data. Now you need to decide what to do with it. In some cases, more testing may be required. Perhaps the sample size is not large enough, or maybe you need to isolate certain components to see which element on your page is having a positive or a negative impact. Whether you need to refine your testing or not, the following step will be optimizing your page based on the results. For example, you may need to tweak your call to action, your headline, or an aspect of your copy on the original page.
Correctly implementing A/B testing, and optimizing your site based on the results, can have a substantial impact on your bottom line. Due to the technical requirements of setting up the test, and because the interpretation of the results may not be as straightforward as you may think, it’s best to hire a professional digital agency specializing in web and SEO work. We can set up an A/B test, monitor it, refine it – if needed – and make concrete recommendations based on the results. Nothing can replace hard data from your users. We can get this data for you, and we can help you decide what do with it.
For more information on A/B optimization for your website, call a Dependable Website Management representative today at (954) 740-7900
A/B Testing powered by Wikipedia
In marketing and business intelligence, A/B testing is jargon for a randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing with two variants leading to the technical term, two-sample hypothesis testing, used in the field of statistics. Other terms used for this method include bucket tests and split testing but these terms have a wider applicability to more than two variants. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g.,click-through rate for a banner advertisement). Formally the current web page is associated with the null hypothesis.