How to Run an A/B Test in Your Campaign?

It is important to create compelling and engaging content for your customer base, be it a simple call to action on your emails or even an entire page of your website, higher customer engagement directly translates to better sales and more visitors to your website.

It isn’t very obvious, however, what content and in what format it should be in order to maximize these beneficial factors, if creating the most engaging content was easy then marketers would be out of a job. The truth is, creating the perfect content for your customer base is a difficult task.

Luckily, A/B testing can go a long way in helping you determine what the best course of action is, and it will not seriously have your time, but also provide powerful insights into how real customers are interacting with your content.

In this blog post, we’ll discuss what A/B Testing is, how it works, why it matters, and how to implement it into your marketing campaigns using best practices and proven industry standards.

What is A/B Testing?

A/B testing or otherwise known as split testing is a methodology digital marketers use to test different approaches and variations of content with their target audience. The core objective of A/B testing is to determine which version of the aforementioned content has higher performance for your specific parameters of success.

A/B testing can be used in various contexts and with different levels of complexity. There are various types of A/B testing you can use:

A/B

This compares version A and version B against each other, usually on an equally split 50/50 ratio. These simpler tests usually test a single variable to determine which of the two versions is more efficient in driving more engagement, conversion, or sales.

An example of this, is testing the same web page wherein version A has a specific call to action button placed in a more centralized location of the web page and version B has a different positioning for that same button. The idea in this example, would be to measure how this call to action’s placement would impact your audience.

A/B/N

A/B/N testing is a form of these tests that is a bit more complete. It adds more versions to the equation, while still retaining the single variable change. This allows you to test more than one hypothesis at the same time, saving you the time of running tests in pairs of two.

One example of A/B/N testing would be to take the last example about the placement of a call to action button on your website and instead of testing two different placements, you test 2+n amount of placements, with n being equal to any amount of placements you want to test.

Testing with more than one variable

This ia a more complex form of A/B testing. Essentially, for more experienced marketers, you can run tests on various different versions of content, with more than one variable being analyzed, when done correctly these types of tests tend to provide very important results and insights into how to build optimal content in order to achieve maximum efficiency during your campaigns.

An example of a more complex A/B test would be to take the previous examples and add the following variables: Not only are you testing different placements for your call to action button, but you’re also testing different colors based on the cultural preferences of various regions around the globe. In this scenario, you are now testing at least two variables (placement and color) all the while testing 2+n button placements.

For a more detailed and complex example, you could add a third variable to the previous example. Each time the person uses the call to action button they would either be redirected to a different URL for the checkout page or be kept on the same page to process payment. In this scenario, you’re adding another layer of complexity to the total amount of possibilities you need to test.

The best thing about multi-variable tests is that you can keep going as far as your imagination and ability to process results allow. Sky’s the limit.

You will find that most articles explaining A/B testing often give similar examples as the button placement/color and URL are really easy-to-understand explanations, but it is important to remember you can test all kinds of things with A/B testing on a multi-variable level:

Such as:

• Different fonts
• Completely different designs
• Written styles
• Punctuation
• Different designs
• Social media sharing options
• Use of certain images
• Reaction to specific languages
• Preference for native or foreign languages
• Certain ads
• Really, anything you can think of

What benefits can I expect from running A/B tests?

In reality, the benefits of A7B testing depend completely on how you use them. If you’re running little to no A/B tests during your campaigns it is unlikely that you will be able to see huge benefits. The advantage of A/b testing is that it is extremely cost-efficient to do - meaning as long as you’re willing to consider variables and put them to the test, the more valuable information you will be able to obtain from this marketing methodology.

However, generally speaking, here are some of the benefits you can expect from A/b Testing:

1. Increased engagement and conversion rates

A/B testing has been proven to allow you to grow your conversion rates by desirable values alongside optimizing engagement techniques

2. Increased website traffic and visitors

Through the results obtained from A/B testing you can adapt your website to become the most investing version of itself for your prospecting customers and new visitors.

3. Decreased rates of cart abandonment

The more optimal the call-to-action experience for your customers and visitors is, the more likely they are to follow through with their purchases on your website. A/B testing can help significantly reduce these rates.

4. Lower bounce rates

If your campaign is being optimized for your customer base, that means that more engagement, better deliverability rates, and higher conversion rates are all contributing to a better sender reputation, reducing all bounces from your campaign. All this through A/B testing.

How can I run a successful A/B test on my campaign?

Now that you understand what A/B testing is and how you can benefit from it, it’s time to brainstorm how you’re going to use this newfound knowledge to best benefit your online marketing campaigns.

Here are some steps you can take to prepare for a successful A/B testing campaign.

Determine which variables you want to work on

During an initial phase of A/B testing, it’s best to start small. We recommend you take some time to research some of the most common and popular A/B tests for companies within your industry and sector.

Often, you will start with single-variable tests and develop into more complex multi-variable tests as time advances and as you adapt to your simpler, albeit very important, test results.

One way of determining which variables you should consider at first is to go backward from your biggest problems at the moment.

For example, if your biggest problem at the moment is your cart abandonment rates, one way to determine which variables you should change is to consider the path the customer takes between landing on your website and getting to the cart.

Diagnosing this path will help you determine which variables could potentially be problematic, which immediately allows you to start testing.

Remember, start simple at first and slowly grow into more complex options and you gain confidence and lock down on certain content formats that you now, empirically know are optimal for your target audience.

Run your tests and determine what results you’re looking for

You can’t A/B tests forever, so it’s important to determine success thresholds for each test. By setting up definitive goals and specific objectives for your tests, you can much more easily move from one productive test to the next, without fear of losing the opportunity to run even more tests and find an even more optimized way of producing your content.

For example, if you set the objective that you want to increase calls to action on your web page, it would be wise to choose at which difference between version A, B, or N you choose to make the switch to a live production stage. Imagine you decide that whichever version has 20% more calls to action first than the other two, goes live. This is great, you’re now running a more efficient version of your website for your entire customer base.

This doesn’t mean you need to stop there, you can and should continue to test further and further.

Big companies will run thousands of A/B tests a day, there is no end to optimization.

The point in making decisions based on thresholds is that you maximize the optimization you can achieve.

Setting realistic goals will end up forcing you to use the most efficient content you know you can do, at that given moment, meaning that in practice you’re actually employing the results your tests provided you with - keeping up with this methodology will maximize your results.

Keep at it!

We sort of touched on this part during the last point. One of the advantages of A/B testing is that you can pretty much do it forever, on whichever scale best suits your needs.

This is, however, a very important point to isolate.

Continuing to run A/B testing in your campaigns is great for your overall results and performance. As you continue to use and learn about more complex techniques you will gain exponentially better results from your A/B testing.

Summary

In short, we hope this article has provided you with the basics of A/B testing. You can do all in this article and much more by running these tests. Through mixing and matching with other analysis tools and you will become the unstoppable digital marketing machine you were always meant to be.

Let’s so a quick revision of the content we discussed today

• What is A/B testing: Testing different types of content for the same audience.
• Types of tests: Equal splits, single variables, and multi variables.
• Benefits: Better conversion, more sales, more traffic, fewer bounces
• How to run a campaign: how to choose variables, the importance of realistic results and consistency in testing

See you next time!

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