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A/B Testing in Marketing Dos and Don'ts

Whether you’re a seasoned marketer or just getting started in the field, you’re likely familiar with the term A/B testing. Although applicable to much more than marketing, A/B testing has become a core practice and popular buzzword for marketers at companies of all sizes.

But, what is A/B testing in a marketing context and how do you do it effectively? In this blog post, we’ll answer these questions in an easily referenceable format split up by A/B testing dos and don’ts.

First, we’ll start with a section that explains exactly what A/B testing in marketing comprises, followed by a section of dos and a section of don’ts. We hope that through reading this comprehensive guide you will gain the insights you need to run a successful A/B testing program.

What is A/B Testing in Marketing?

A/B testing is a statistical methodology of comparing different versions of something to see which version performs better. In marketing, A/B testing practitioners typically test two marketing assets or elements (e.g. ads, website copy, subject lines, etc.)to see which one has a higher conversion rate—the percentage of users who have completed a desired action. 

Marketers have been using this scientific approach to problem-solving since the 1960s in direct marketing campaigns. However, the rise of digital marketing has led to the increased popularity of A/B testing, as the method makes it easy to launch and analyze experiments. In digital marketing, A/B testing is used to test different versions of webpages, email headlines, landing pages, ad copy, and other online content to determine which performs better.

When it comes to A/B testing websites, there are numerous components that marketers can test, including page layout, menu location, headlines, calls to action (CTAs), images, fonts, colors, and image sizes. A/B testing website elements ladders up into the larger practice of Conversion Rate Optimization (CRO), the process of increasing the percentage of website visitors who take a desired action or conversion goal. That desired action could be a purchase, a free trial sign-up, a demo request, a content download, or even just spending more time on a specific page.

While CRO comprises many strategies and practices, A/B testing is one of the most critical tools CRO practitioners have at their disposal. Its ubiquity in the world of website testing makes A/B testing table stakes for most high-performing marketing teams. It’s also important to note that while A/B testing is one of the most popular experimentation strategies for marketers, it is not the only one. Many marketers make use of multivariate testing, AI-driven optimization, or other forms of testing.

A/B Testing in Marketing Dos

While A/B testing is a foundational practice for many marketers, it’s not foolproof. There are many easy mistakes marketers and CRO geeks can make during even the most simple of A/B tests.

This section outlines A/B testing in marketing “dos” so you can make sure you’re on the right path the next time you run an experiment.

Do Be Aware of Potential Errors

Although A/B testing can be an invaluable tool when it comes to making decisions about your website and other marketing properties, it’s important to remember that in statistical hypothesis testing, no test can ever be 100% decisive. There are two types of errors associated with A/B tests, they are called type 1 errors and type 2 errors.

Type 1 and Type 2 errors

What is a Type 1 Error?

A type 1 error is when you reach a false positive. Sometimes a false positive can occur randomly, or there may be another variable that you didn’t originally account for that affects the outcome.

What is a Type 2 Error?

A type 2 error is essentially a false negative, meaning you’ve accepted the null hypothesis when there is a difference between the control group (null hypothesis) and the variation. This can occur when you don’t have a large enough sample size or your statistical power isn’t high enough.

Do Iterate off of Results

When running A/B tests, it can be tempting to stop testing a variable or page after collecting a handful of statistically significant results. Instead of looking at stat sig results as the end of a test, think of it as the inspiration for your next experimentation idea!

If you’ve tested out the color of a CTA button, maybe it’s time to now test out the shape. Or, maybe you’re happy with the results from your initial CTA button test and want to test out a new element altogether. In any case, keep the results of your previous experiments in mind as you come up with new testing ideas. This approach will allow you to uplevel your experimentation process, a key step to achieving experimentation maturity.

Do Consider the Limitations of A/B Testing 

Although A/B testing is considered to be a reliable form of experimentation, it may not be appropriate all of the time due to its limitations. Keep reading to learn about a few situations where an A/B test may not be worth your time.

Reasons Not to Run an A/B Test

Not enough traffic

If the website element or marketing asset you’re testing doesn’t receive a meaningful amount of traffic, it may not be worth A/B testing. To protect against the testing errors outlined earlier in this guide and achieve statistically significant results, you’ll need a healthy amount of traffic to see both of the variations in your A/B test. Consider making use of a stat sig calculator to get an idea of the traffic you’ll need to run a successful test.

No informed hypothesis

To run an effective A/B test you’ll need an informed hypothesis. What do you think will happen? If you can’t answer that question it’s time to pause and think it over before committing to the test.

Trying to achieve 1:1 personalization

A/B testing enables marketers to identify the highest converting versions of their digital marketing assets. In the case of websites, when an A/B test determines a winning site variation, marketers will bake the winning change into the website for future visitors to see. While A/B testing can enable marketers to create highly optimized sites, it cannot do so on a 1:1 level i.e. showing a visitor a version of the site that is most likely to get that specific visitor to convert. To achieve this level of personalization it’s best to leverage AI-driven optimization methods, which can serve the right version of the website to the right visitor at the right time.

A/B Testing in Marketing Don’ts

Use the below “don’ts” of A/B testing in marketing to guide your experimentation process.

Don’t Test Too Many Variables at Once

One of the most common mistakes when it comes to A/B testing is testing too many variables at once. Let’s imagine a scenario where you’re running multiple A/B tests on your homepage— you’re testing out different headline options, a new CTA button color, and a new hero image. While all of these tests are certainly worthwhile, once the tests conclude you will have trouble identifying which variables are responsible for the test results.

If you do want to test multiple variables at once, opt for multivariate testing, a method of statistical testing that involves multiple variables, each of which is modified as part of the experiment to test variations of the same idea. In such experiments, sets of variations are compared to one another to determine which set performs the best. 

Don’t Peek on Your Results Too Early

When it comes to A/B testing, patience is a virtue. While it can be tempting to peek at the results of your experiment well before they are statistically significant, do yourself a favor and don’t!

Known as the “peeking problem,” looking at A/B testing results before they are statistically significant is a mistake that even seasoned CROs make. This blunder can lead to assuming a variation that is showing early signs of winning is the winner of the experiment, even though showing the variation to a larger audience may prove the exact opposite. These false assumptions can lead to expensive marketing decisions down the line based on incomplete data, something you certainly don’t want to be responsible for.

Don’t Assume Results Will Stay the Same

Once you’ve ended an A/B test, complete with statistically significant results it’s time to bake that change into your website or digital marketing strategy. But, don’t set it and forget it!  

The winning variation of an A/B test represents consumer behavior from a specific period in time. This means that market conditions, political climate, or a slew of other external factors may have changed since you last tested what was deemed as the winner of the test.

Buyer behavior changes from day to day, even hour to hour— that’s why you should always be testing. When you’re done with an A/B test, start the next one and rerun previous tests to make sure the results are still valid. This approach to A/B testing will help you maintain the highest converting versions of your digital marketing properties.

Take These Tips on a Test Drive 

Although A/B testing is a foundational practice for many marketers, it doesn’t come without its challenges. Let these dos and don’ts of A/B testing in marketing guide you as you begin your next test or build your experimentation strategy from the ground up.

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