A/B Testing: Do you already know why and how to use it? Discover its benefits for a digital marketing strategy

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The position, color or design are often placed intentionally, and we need to know if that is achieving the initial objective.

If you want to know what is a good strategy for your company to know whether or not it is reaching your personas , that is, those who are your potential clients that you want to reach, you need to know about A/B testing.

But what are they? What are they for? How 99 acres database to use them? Stay with us and find out all about it.

What is a/b testing?
A/B testing aims to analyze which content best serves our audience.

For example, if you want to test the effectiveness of an Email Marketing, before sending it to everyone, send it to 5% of your database and wait.

After about 24 hours, you can analyze which campaign has been most effective so you can send it to the rest of your audience.

This type of action is widely used in marketing strategies, landing page campaigns , and others.

We can also use A/B testing by creating different versions of an element that will be randomly shown to users to see which one performs best.

We can also measure it in subscriptions, sales, etc. We can complement it by combining it with Google Analytics, knowing other information through the clicks made.

How to do good A/B tests?
There is no point in doing A/B tests that are not effective. Therefore, it is important to learn the best ways to do them so that they are applied efficiently to customers. But there is nothing wrong with that.

Here are some guidelines that can help you as a script of suggestions to get it right.

Success always goes to those who seek to improve their knowledge, and this is the goal of this text. Pay attention and good work.

1. Run one test at a time
If you do several tests at once, you have a high chance of getting confusing results. Be careful when selecting the strategic locations where you will apply them.

2. Evaluate one variant at a time
To evaluate the effectiveness of an element on your page, you need to isolate that variant in your A/B test. Test one element at a time.

3. Decide the level of importance needed before carrying out the tests
Before testing, it's important to think about how relevant you want your results to be in order to know whether or not those changes should be made to your site or campaign.

It is important to set a relevance goal for your winning variant before you start testing.

4. Decide what you want to evaluate
There are several things you can decide to try. You don't have to limit yourself to just images or text size, for example.

Look at the different elements of your marketing resources and their possible alternatives for design, wording and layout of elements.

5. Divide your sample group randomly
To have more conclusive results, it is important to evaluate two or more identical audiences.

You can, for example, automatically split traffic between variants so that each has a random sample of visitors.

6. Establish control versions and treatments
In any experiment, you need to maintain a version of the original item you are testing. When A/B testing, set your version without modifying the “control” version.

From it you can produce variants, which will be the landing pages that you will evaluate taking your control version as a reference.

7. Measure the lowest possible part of your funnel
A/B testing can have a significant effect on the bottom line. You might even find that a landing page that converted less actually generated more sales.

When running an A/B test, think about how it affects different metrics, such as clickthrough rate, leads, traffic-to-lead conversion rate, and demo requests.

8. Also evaluate secondary changes
In this case, less is also more. Small details are also very important, just as the big ones.

When creating your tests, you have to think that even a simple change can bring noticeable improvements. Even this concern is even easier to measure than big changes.

9. You can run an A/B test on an entire element
It is also important to focus on conversions with variants on the landing page, call to action or email. Instead of evaluating individual design elements, it would be interesting, for example, to design different pages and evaluate them against each other.

This type of testing yields the most significant improvements, so consider starting with it before continuing your optimization through small tweaks.

A/B Testing in Practice
To better understand what an A/B test is and how to perform it in practice, we will give you a fictitious case where different initiatives were carried out to increase the conversion rate of a landing page .

Let's imagine that the company ViajesTuMundo decides to run an A/B test to determine which of its two landing page options is more effective at driving conversions.

Test A
In version A, they decided to create designs in the form of conversation bubbles where the benefits of booking plane tickets and hotel rooms are explained, within a conversation between two people with stock images.

In addition, at the top of the page they position the title "Why have more than 5,000 people traveled with us?" and above that, a menu to take the user to other pages on the site.

At the bottom of the landing page, the benefits of having this organization as the organizer of these activities are explained, and it also offers these services for free if the user leaves their data.

The title of this segment is: "Try our services for free!", with the text "Isn't it time you stopped using companies that don't guarantee you comfort and make your life easier?"

Test B
In test B or version B, they choose a more "sober" design without a menu and where they position the attractive and precise title: "Let us take care of your trip!"

Then, they display a text where they highlight the authority of the brand saying: "ViajesTuMundo is a leading company in the Mexican tourism segment, helping thousands of people find destinations and prices in line with their expectations without the annoying reservation processes that are a headache for many."

Below, they list the brand's advantages in bullet form like this:

"Better prices: find reservations to your favorite destinations at a much lower price than what they show on their website
Travel in comfort: we guarantee you the best experience on your flights with business class seats at economy class prices.
All inclusive: all our hotel reservations include free food, drinks and entertainment."
And, next to the data sheet, a real photo of one of the brand's agents with the text: "Ana: Sales Executive who has facilitated the travel of more than 2,000 people since 2010."

The result?
In this hypothetical case, version B got 90% more conversions than version A.

Because?

First of all, a landing page has to communicate the brand's offer effectively and succinctly. This is done through attractive texts and typography that matches the message.

Case B combines these two factors better than the first test, especially the use of bullet points to display easy-to-read and impactful information considering the buyer persona .

Also, not using the menu eliminated the possibility of missing the conversion opportunity by clicking through to another page.

What are the biggest myths about AB testing?
Like any practice in digital marketing , AB testing has myths about its use. We are going to reveal some of them so that you can keep them in mind when you come across them.

Tests can be done at different times and not simultaneously.
When using an A and B test at different times or times, they may not perform as well as they should, and the data they return may be partial truth.

When doing an AB test, it is important to have tools that allow you to do it at the same time, for example:

Send an email to a specific database with two different subjects, to a similar audience segment and at the same time, since emails, like other consumer habits, are subject to schedules;
A landing page that contains two different layouts and you want to analyze, for example, which image or text is generating more conversions. When analyzing the different versions, it is important to look at the moment in which the content was published.
If you don't see results the next day, it's best to stop doing the test.
You may be running an AB test on a campaign that you urgently need to know the results of. However, in order to perform a more in-depth and accurate analysis, a larger number of results is needed, and this can only be achieved with time.

By leaving an AB test available for longer, you allow for more audience interactions with it, bringing more results, and thus, a greater possibility for data analysis.

Patience is a perfect ally when performing AB testing.

You can ignore the test and follow your intuition.
When you look at the results, it may seem best to set them aside and just follow your intuition about what seems to be the best option.

This is another AB testing myth, because for many it may be easier to stick to the option they had initially considered, but it is the data that the test provides that will tell us with certainty what is bringing better results and under what conditions.

AB testing doesn't work for websites
Here I can tell you that this depends on the context and the performance of the website to which the test is to be applied. Analyzing a website with 100,000 visits per month is not the same as analyzing one with 100,000 visits per month, since the intensity of interactions allows for more data to be interpreted.

For larger companies, it makes much more sense to implement it, however, performing AB testing on a small scale for a website with few visits can bring valuable insights , especially if you are working with a content marketing strategy .

Conclusion
The Internet is constantly updating its new features. For those who wish to innovate and improve their sites and the promotion of products or services, there is always a lot to discover and invest in your company.

In this text we learned about A/B testing, what it is, how it is used, and why to use it.

In addition, we also had some guidelines to help us like a script, guiding us towards the best suggestions to masterfully develop our A/B tests.

Do you have any other suggestions? Any questions? Any corrections?

Leave us a comment on our post! Your visits and comments are always very welcome. We hope that the text helps you and that you seek to heal other issues with us. See you soon!