Common A/B Testing Mistakes in Email Marketing and How to Avoid Them

Did you hear about Knight Capital’s $440 million loss back in 2012? Yes, they lost that much in just 45 minutes because of a software hiccup. Interestingly, experts say it could’ve been avoided with proper testing. Of course, you are likely not going to mess up that badly in marketing, but skipping A/B testing could still cost you big time.

Here’s the funny thing about A/B testing in email marketing—it’s super helpful, no doubt, but it’s not foolproof. Just a simple mistake and you are already on your way to the wrong path, misreading data and missing out on golden opportunities. Before you know it, you’re dealing with fewer people opening your emails, less conversion, and a shrinking return on investment. That’s terrible, right?

But you don’t need to worry; some of us have made these mistakes, so you don’t have to make them anymore. In this article, we will show you some common A/B testing mistakes that can hurt your email marketing strategy. More importantly, we’ll provide you with practical strategies to sidestep these errors, ensuring your tests yield reliable insights. 

If you are ready, let’s dive in!

A/B Testing Mistakes to Avoid Before Testing

1. Not defining a clear hypothesis

It’s good that you have a gut feeling, but like the words of W.E. Deming, “Without data, you’re just another person with an opinion.” 

Imagine your computer is faulty, and the problem is with the keyboard cable. You suspect the problem is with the monitor (wrong hypothesis). And so you buy new cables and start to test them to see which one solves the problem. 

No matter how many monitor cables you buy or how many times you test your monitor, your conclusion will always be: there is no problem. However, when you hit the keyboard, the problem is still there!

The same applies to A/B testing in email marketing. A well-defined hypothesis serves as the backbone of your tests, guiding your test design and helping you interpret results accurately. As such, your hypothesis should be guided by research and focused on solving a particular problem.

Launching a test with vague goals like “improving open rates” without specifying how or why is a common A/B testing mistake in email marketing. This approach lacks clarity and direction, making it difficult to design an effective test and interpret the results meaningfully.

Instead, formulate your hypothesis like this: “Changing the subject line to include the recipient’s first name will increase open rates by at least 5%.” By doing this, you are clearly stating what you’re testing and what outcome you expect.

2. Starting tests with an inadequate list size

The reliability of your A/B test results heavily depends on having a sufficiently large sample size. Testing with too small a list can lead to statistically insignificant results, making it impossible to draw meaningful conclusions.

A statistically significant result means the effects you saw were due to the changes you made in your emails and not due to chance. 

For you to understand why your list size matters, imagine you want to know if chocolate is better than vanilla, so you collect responses from 10 people. 

If 7 people vote for chocolate, it might seem like their response is significant (70%). However, if you do the same survey with 1000 people and you get the same 7 responses for chocolate, the significance is almost 0 (0.07%).

To avoid making this mistake, you can find sample size calculators on the Internet. Some of these tools are free, and you can use them to calculate the minimum number of recipients you need for statistically significant results. 

If you discover your list does not meet the required number, focus on growing it before conducting extensive A/B tests.

3. Ignoring device and platform differences

As of 2023, data suggests that over 58% of people use the Internet from their phones (that’s minus tablets!). Unlike desktop users, mobile users typically interact with emails differently due to factors like touch navigation, screen size, and many others. 

All these means, as a marketer, your tests must consider how your emails will be rendered across the different devices that receive your emails. Failing to do this can skew your test results and lead to incomplete insights.

So, how do you avoid this costly error?

• Before you start your tests, ensure you see what your email looks like across multiple devices and email clients. The good thing is, there are lots of email testing tools out there that you can use to preview your emails.
• When testing, ensure you capture the differences in devices by segmenting your results by device type and email client. That way, you can easily identify any performance discrepancies.
• This last one is very important: ensure that both variants in your A/B test are optimized for mobile viewing. This is important because many of your recipients will likely read from their mobile devices.

A/B Testing Mistakes to Avoid During Testing

1. Conducting multivariable tests prematurely

Multivariable testing is simply when you test more than one element in your email at the same time. Don’t get it wrong; multivariable testing might save you time and offer some more insights. 

However, the complexity that comes with it can lead to major issues. First, you need a very large list to get meaningful results. Secondly, to make sense of the results, you may require expert analysis.

For example, imagine you are testing the subject line, CTA, and personalization at the same time and you get a 60% increase in CTA. To make sense of this test, you might have to answer the following difficult questions (you may not find answers).

• Which of these elements caused the 60% change?
• Was it a combination of all three, or was it just two? 
• If it was just two elements, which two? 

However, when you keep things simple—one change at a time—you can easily explain any change that you see. And if you must test multiple elements simultaneously, use a factorial design to isolate the impact of each variable.

2. Short testing periods

Everyone knows how frustrating it can be to wait, especially in business, where time is money. Sadly, when it comes to A/B tests, there are no two ways around it—you need to wait to get good results.

The problem with testing for a short time is that you will not get the full picture of your audience’s behavior. In most cases, your test will not account for normal fluctuations in engagement. 

To avoid this error, your test period should include different days of the week.

Ideally, you should run your tests for at least a week to account for daily variations in email engagement. For B2B emails, consider extending tests over two weeks to capture full business cycle behaviors.

3. Ignoring seasonal trends

No matter how good your email is, there is a great possibility that it would have lower open rates if you send it out on Christmas. The reason is obvious—most people are on vacation!

Here’s the thing: seasons and holidays can really affect how people interact with their emails. So if you’re not factoring that in, you might end up making conclusions that do not have any correlation with the true behavior of your audience. 

Fixing this error is simple:

• Be aware of seasonal trends that affect your industry and audience.
• Compare test results to performance data from similar time periods in previous years.
• If testing during an atypical period is unavoidable, note this in your documentation and consider re-testing during a more representative time.

A/B Testing Mistakes to Avoid after Testing

1. Insufficient documentation of test results

A lot of things happen during testing that even the smartest person may not be able to recount everything in detail. However, documents don’t forget.

With proper documentation, you can write all the important details and keep them for later. This ensures you are learning from your A/B tests and building institutional knowledge. Failing to record test details and results comprehensively can lead to repeated mistakes and missed insights.

2. Lack of iterative testing

Yes, you have concluded the test, but then people change. So, you must test your hypothesis as often as possible to capture the changes in the market and how people’s needs evolve over time.

Iterative testing is also important as it helps you improve your overall email marketing strategy.

To fix this:

• Develop a testing roadmap that outlines a series of related tests building on each other.
• Regularly review past test results to identify trends and generate new hypotheses.
• Create a culture of continuous experimentation within your team, encouraging ongoing testing and learning.

3. Excessive changes based on initial results

Just because something gave you good results does not mean it should be the new norm. This is because marketing is a complex field that is affected by a complex interplay of factors. 

So, before you throw your old strategies away outrightly, consider implementing the results you learned gradually. Start out with some segment of your audience before introducing the change to everyone.

By gently introducing your new strategy, you avoid potential anomalies that may affect your email marketing success.  

To Sum Up

Considering all of these potential mistakes that you can make, one might want to ask: Is A/B testing worth it? The answer is absolutely!

Like a multifunctional tool, you can use A/B testing on almost all the parts of your email. We’re talking subject lines, the email itself, how it looks, and even the day of the week when you send the email. 

Here’s the deal: if you stick to the playbook and dodge the mistakes above, A/B testing becomes your secret weapon. Otherwise, it would just be another tool—possibly one of those that supplies endless headaches.

However, if you do things right (like we explained), A/B testing will fuel long-term growth by helping you build stronger connections with your audience.

By embracing this method, you’re setting the stage for smarter, more impactful email marketing. It’s all about constant learning and fine-tuning to stay on top of your game.