What To Expect When You Are Testing?

We are committed to our A/B testing followers and, because of that, we love to share some of our latest findings and results with you here.

Most Popular A/B Experiments According to Our Customers

Split Testing of Pages and Heatmaps

There are two reasons that explain this situation:

— You want to start A/B testing your site, but you don’t know what to test. Here is were Heatmaps can make the difference. They’ll show you the hotspots in your site, and which are the most important elements to test. As easy as a pie!

— You know that most of the traffic starts in your landing pages. Therefore, you want to increase their conversion rate. Page Experiments are really helpful in these tasks, for they offer a combination of split testing analysis and heatmaps. With these experiments, you’ll be able to see the behavior of your visitors in both the original and the alternative content!

Pie chart with tests by type
Pie chart with screen resolution

Get Ready for Mobile, but Don’t Forget Desktop

Our world is now mobile. The power provided by these lightweight devices everyone owns is skyrocketing. That’s why your web needs to get ready to properly display your content in such devices.

But here’s the thing: don’t forget those that access your contents with a Desktop computer, because we’ve confirmed they are a big portion of the cake.

Through our gathering of heatmap data from your visitors, we’ve seen that more than a half of all the visitors tracked come from high-resolution devices. And that’s something you should take into account in your content strategy.

What Kind of Results Should I Expect?

Focus on Finding a Winner Alternative—It’s Worth It

Sometimes the benefits of A/B testing your site may be not clear enough. You may think you are wasting your time and efforts creating alternatives of your content and putting them under test.

However, the results we have found analyzing the experiments created with Nelio A/B Testing differ from this point of view. You won’t regret from split testing your site, because when you finally find a winning alternative, you’ll probably recover your previous investments.

In average, our customers are getting more than a 60% improvement in their conversion rates when some of their alternative versions wins the original. And our data says that almost half of the A/B testing experiments found a winner version.

Bar chart with average conversion rate improvements per test type
Bar chart with revenue improvement per test type

Taking the previous results in consideration, let’s assume our WordPress site has an actual conversion rate of 1%. That is, one visitor out of a hundred converts (the visitor does what we want him/her to do: for instance, visit a page, click on a link, or submit a form).

We can now determine the conversion rate after performing a successful A/B testing experiment simply by taking into account the improvement factor aforementioned. As you can see, the new conversion rates are brutally improving the performance of the element under test.

We can go further and study which is the real benefit for a situation like the one described before. Again, let’s assume that with the original 1% conversion rate the hypothetical website produces a revenue of US$1,000.

After applying the winner version instead of the original content, and assuming we get the new conversion rates indicated in the previous chart, we can calculate the extra benefits we may get.

In average, the final revenues obtained thanks to Nelio A/B Testing almost doubles the initial earnings.

Bar chart with revenue variation per test type
Bar chart with test duration (in days) per test type

How Much Time Requires an Experiment to Come to an End?

If you’re considering A/B testing your site, you may be wondering how much time experiments have to run before obtaining relevant results (from a statistical point of view). As you can imagine, this depends on the number of alternatives and the amount of visitors you have. We’ve investigated the amount of time the experiments of our customers have been running.

In average, all the experiments were running for less than a month and most of them didn’t surpass two weeks. In our opinion, that’s not too much regarding the benefits you can get in return!

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