A/B Testing: What is it? How to do it?

Digital marketing is constantly evolving, and it’s crucial to stay ahead to avoid being left behind by the competition.

One of the most effective tools for optimizing your conversions is A/B testing.

Let’s explore this powerful method together 🚀.

What is A/B Testing?

A/B testing, also called split testing, is a technique that involves comparing two versions of the same webpage or application to see which one performs better.

This involves simultaneously presenting two content variants to random segments of your target audience for a given period.

Then, conclusions are drawn based on the respective performances of these variants: generally to keep only the most effective version 😉.

Why is A/B Testing important in marketing?

A/B testing is an essential tool for optimizing user experience and boosting conversions on your landing pages.

Here are some of its benefits:

1. Resolve friction points

Is your conversion rate not optimal?

Thanks to A/B testing you can quickly test multiple changes on your page and easily see what helps reduce this friction.

2. Improve ROI

A/B testing also allows you to optimize the return on investment of your existing traffic, ensuring that each visitor has the best possible chance of conversion.

It is particularly useful for maximizing results without increasing the marketing budget.

It is often used in a CRO (Conversion Rate Optimization) strategy 😉.

3. Reduce Bounce Rate

The bounce rate is the proportion of visitors who leave your site after the current page.
They do not go further in your sales funnel.

It is therefore a rate that we seek to reduce as much as possible 😁.

A more attractive design, simplified navigation, and effective copywriting can significantly reduce the bounce rate, revealing to visitors the true potential of your offer.

Thanks to A/B testing you can conduct tests on these elements to understand what works best and thus reduce your bounce rate.

What elements to A/B test?

Every aspect of your site that interacts with the user can be tested: the layout, forms, calls to action, and even visual or written content.

The golden rule, however, is to test only one thing at a time in order not to skew the results.

Indeed, if you test a new CTA + a new title and your conversion rate increases, is it thanks to the new CTA? The new title? Both? Would you have had even better results without changing the CTA?

In short, only test one thing at a time 😉.

How to conduct an A/B Test?

Let’s take action with this step-by-step guide:

Step 1: Research

Before starting, use analytical tools to identify possible areas for improvement on your site.

Heatmaps and current conversion rates are good indicators to identify the conversion problems you may be having.

Step 2: Formulate your hypotheses

Based on your observations, develop clear hypotheses about:

1. Why is it not producing the expected results?
2. What could you do to improve these results?

Step 3: Create the “variants”

Do you have a hypothesis saying “test a larger CTA“?

Then you will launch an A/B test for this CTA.

You will create a variant B of your page in which you will modify your CTA (to enlarge it in this example).

Variant A was the current version of your page.

Step 4: Launch the tests

Randomly split the traffic between the two versions for a given period.

Make sure your two variants gather enough traffic for the results to be relevant.

Step 5: Analyze and adapt

Once the test is complete, gather the results and keep only the best variant.

A/B Testing and SEO

Use 302 Redirects

For your redirects to the right variant, prefer temporary redirects (302) which tell Google that the redirect is not permanent.

Which tool?

Nowadays, most page creation tools allow for A/B testing. See what’s possible with your favorite tool and the cost involved.

At HeroPage we also do A/B testing, even in the free version 😉.

Conclusion

In summary, A/B testing is a strategic tool for anyone looking to improve their conversions.

It’s fun, it’s simple, and most importantly, it’s effective!

It allows for changes based on concrete data to maximize your results while minimizing risks (your instinct is probably good, but nothing like confirming it with A/B testing 😉).