What are the differences between A/B and multivariate testing for UX in SEO?

Some argue A/B testing is time-consuming and requires significant traffic. But with focus, creativity, and patience, even small websites can benefit from gradual improvements.

SEO A/B testing differs from traditional UX testing as it's focused on the top of the funnel, organic traffic, not user behaviour. Unlike users, you can't split Googlebot in two and you definitely don't want to create duplicate versions of pages to see which ranks better. The approach for SEO A/B testing is template testing and the creation of statistically-similar control and variant groups which are measured over the course of ~3-4 weeks with adjustments to the forecast to compensate for seasonality, algorithm updates, competitor behaviour, etc., to answer the question of what specific impact did the site update have on organic traffic.

A/B testing isn't just for web pages; it's a mindset. It's an ongoing process, adaptable to various digital marketing aspects like email campaigns and social media.

This is actually a simple way for both experienced and new marketers to find out what works best for ads. What I find most marketers try to do is that they fail to isolate the elements of the experiment and attempt to test too much at once, perhaps due to time or budget constraints. This is detrimental in the long term because at the end of the day, what A/B testing does is that it identifies elements which your audience is most receptive to. By diluting it with too many changes, the results will be non conclusive.

Think of A/B testing like trying on two different hats to see which one looks better. You show one hat (version A) to some people and the other hat (version B) to others, then see which hat gets more compliments.

Efficiency is key for digital marketers, and multivariate testing saves time by allowing you to test multiple hypotheses in a single go. It streamlines the optimization process, making your campaigns more agile and responsive.

This is like trying on multiple accessories (hats, glasses, scarves) all at once to see which combination is the best. You mix and match, and test different combos to find out which full outfit gets the most "wow" reactions.

A/B testing and multivariate testing are both essential for optimizing UX in SEO, but they differ in approach. A/B testing compares two versions to refine specific elements incrementally, enhancing user satisfaction and potentially SEO performance. Multivariate testing examines the interaction of multiple elements simultaneously, helping identify which combinations work best together, enabling a more holistic optimization strategy for UX and SEO. The choice depends on the scope and complexity of changes being tested.

A/B testing changes one thing on your website to see if it improves how people feel about it or if it gets you more visitors from Google. Multivariate testing changes many things at once to see which whole set of changes makes the biggest impact.

A/B testing is like checking if a light switch turns on a bulb – pretty straightforward. Multivariate testing is like figuring out which switches control all the lights in a house – it's more complex and takes more time to understand.

Use A/B testing when you want a clear answer on whether a single change is good or not, like whether a red "Buy Now" button works better than a blue one.

Multivariate testing is like comparing fruit salads. You've got a whole bunch of different elements mixed in there. So, instead of just changing one thing, like in A/B testing, you're changing several elements at once to see how the different combinations perform. A multivariate test could switch up the headline, the images, and the call to action all at once, for instance. In terms of when you should use multivariate testing. Well, it's all about whether you're more interested in the forest or the trees. If you want to see the bigger picture, like how different elements interact with each other on a page, then multivariate testing is your friend. And that's about it. Happy testing!

Go for multivariate testing when you're ready to see how different changes work together, like if a red "Buy Now" button, combined with a picture of a happy customer, leads to more sales than other combinations.