A/B Testing

The ins and outs of A/B testing

Much has been written about A/B testing as a method of increasing conversion. But how useful is it?

A/B testing is a slightly scientific way of optimising the effectiveness of a website. It involves showing two groups of visitors to your site a different version of your page, to see which converts better.

When would we do this? Perhaps when you've broken away from a tried-and-tested page layout and want to try a brave new design. Using Aurora Optimiser, we can divide traffic between the old (A) and the new (B). We then observe their site visits for an agreed period and decide which page holds most conversion potential.

But an even more compelling reason is simply to eliminate the guesswork. How many times have you sat in a meeting in which everyone has an opinion about a web page, and yet nothing gets done? A/B testing makes everything so much easier, replacing subjectivity with science.

So, is everything in the A/B testing garden rosy? Well, not quite. A/B testing is slightly crude. It's hard to test every page element and we might have to do a few tests to find the winning element.

We'll also need to run tests over a decent period of time, to have any validity. It could take thousands of visits and hundreds of orders before your statistics have any real meaning. This can obviously take a long time if visitor numbers are low. We'll also have to factor in calendar variables, which can influence the outcomes.

Also, our A/B testing might show that our new page is a winner, but it's sometimes hard to see which page element has made the big difference. For example, we might have a new headline that's working wonders, but our new scrolling page layout could be a disaster. Our page could show better conversion, but we won't know to ditch the dodgy scrolling. This is where multivariate testing comes in.

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