Current A/B email testing tools are failing marketers. AI can fix that.

I've come across lots of email A/B testing tools in my career. Some are good. Most don't deliver on the reasons why you test in the first place: to get meaningful insights you can use to learn more about your customers, improve your messaging, and spend your marketing budgets wisely.

I see three common problems, each of which I'll address below:

1. They limit testing variables and often force marketers to declare winners too soon.
2. They don't account for complex user behavior that can exert unexpected influence and skew results.
3. They don't help marketers design strategy-based testing plans or analyses or relieve the burden of grunt work that testing and analysis entail.

The solution: AI-powered testing programs, like my Holistic Testing Methodology, which can

revolutionize the way marketers run and interpret A/B/n tests. I'll explain how AI can enhance test accuracy, reduce guesswork, and help marketers make data-driven decisions faster and more confidently.

The problem with today's A/B testing tools

A/B testing is crucial because it helps us make key decisions based on data from the test results, and not on gut reactions, personal or institutional biases, or the dreaded "we've always done it this way" excuse.

As I mentioned in an earlier OI post (5 ways to use advanced testing for better email results), an unofficial poll I ran on LinkedIn recently found that 68% of email marketers self-reported that they used A/B split testing for one reason that combines two goals: to gain uplifts in their email performance and to get more insights into their customers.

A/B testing itself isn't the problem. Rather, it's the way marketers set up and run their tests, analyze the results, and apply them in their email programs. Part of this stems from not knowing how to set up a scientifically valid, hypothesis-driven testing plan. (See "5 Things You're Probably Doing Wrong with Email Testing and How to Fix Them" for more on this problem.)

The tools that marketers use, especially the testing tools built into their platforms, don't help them overcome that knowledge gap. Here's what I see regularly:

1. Limited testing scope

ESP tools often test one message element at a time, like a subject line or call-to-action button copy. Email success comes from the interplay of multiple factors. They can include the subject line and CTA, but basing email success on just one element can lead you down a limiting path.

This limited scope forces you into this scenario and can reduce your chances of gaining a statistically significant result.

The answer is to test a control version of a multi-variable message against a variant instead of individual elements like subject lines. It’s crucial to understand that this multi-variable testing is acceptable within an A/B format as long as all of the elements in each message support your testing hypothesis.

2. Poor statistical guidance

Setting up a valid email test should be as scientific a process as creating a chemistry experiment. Key to these are knowing how many people you should send your test to, how long the test should run, and when you can assume your results will be statistically significant and not happening by chance or outside influences.

Most of the tools I've seen don't give marketers the recommendations or advice they need to make sure their tests are valid.

3. No educational support

Also key to a successful test is a hypothesis that outlines what you want to test, how you'll structure the test, and what you expect to learn from it. A good hypothesis is always tied to a strategic outcome. You don't test just because you should. ESP testing tools don't guide marketers either on creating their testing plans or how to analyze, interpret and apply the results.

How these limitations hold marketers back

I'm not saying these tools set up users to fail. Rather, they're too limited in what they offer to provide truly useful help and could damage your program by giving you incomplete or inaccurate results.

Surface-level insights can produce misleading conclusions. This happens more often than you might think, and it can stem from many different causes. I mentioned the problems that arise if you run a statistically invalid test, where random factors can influence results.

As an example, a tool that uses the 10-10-80 method will send your control message to 10% of your list and your variant to another 10%. It picks the so-called winner and then sends that version to the remaining 80% of your list – usually within 24 hours.

But you don't know that the customers in that 80% segment will be similar enough to those in your 10% segments to rule out unforeseen factors that could influence the results. Also, where clicks and conversions are concerned, anything less than 24 hours is too early to call a winner on just 20% of your audience. (Keep reading to learn how AI can help you choose a statistically valid 50-50 audience.)

You're testing the wrong things. As I mentioned above, platform-level tools isolate and test elements of a message instead of your messaging strategy. The subject line might have nothing to do with the email message's performance.

You can't do A/B/n testing of multiple variables or complex campaigns. Testing more than one element of a message and/or more than 2 variants can help you focus on strategy and understand why one message performed better than another.

"In the moment" testing doesn't offer long-term learning or encourage continuous optimization. Ad hoc or one-off testing tells you something about one message in one campaign at one time. Will that be the same every time you send a message? Probably not.

Traditional A/B testing tools rely on basic statistical comparisons, such as opens, clicks or conversions. But they don’t explain why one version outperformed another or highlight patterns across multiple tests.

The campaigns you run during peak holiday shopping season will likely be different from the general promotions you run at slower times of the year. Long-term testing can help you understand your audience's different motivations and predilections. What you learned in peak-season testing might not apply to messaging on all of your other campaigns.

They don't help you set up a valid test. As I mentioned above, the testing tools baked into ESP platforms are not intended to help you create a valid test. They will help you choose winners, not give you insights into why one test performed better than another. Nor will they help you choose statistically valid audience samples for testing

Validity is essential to reliable testing. For that reason, 50-50 testing will give you more reliable testing, as long as those samples are randomized so that they are as identical as possible. AI can give you those two randomized samples, allowing you to run the tests manually in your ESP and bypass those limited testing tools.

4 ways generative AI can help you find patterns and insights from A/B testing results 

This particular blog topic excites me because it unites two concepts that I'm passionate about into a solution that truly can help marketers do better.

As I've said, I'm an avid supporter of strategy-based email testing and even developed the Holistic Testing Methodology that blends scientific method, strategy, and A/B/n testing. I've also become a staunch advocate of using a well-trained bespoke GPT (we call our proprietary version of ChatGPT "Chad") to do much of the grunt work of structuring our testing frameworks.

This works because we have taken more than a year to train Chad up on Holistic Testing Methodology so he understands how and why we do things. When we ask him to create a test hypothesis now, he will deliver one that's usually 80% to 90% correct, requiring just a little refining and focusing to get it where we want it to be.

This comes with a big caution, of course. You need two things: a good understanding of how generative AI works, and a private GPT that can't be accessed by other GPTs for training. This means using a paid version of your large-language model and investing in the time and personal training you need to bring it up to speed.

Saving time is just one benefit. Incorporating generative AI in your testing protocol can also help with analyzing results and extracting useful insights, like these:

1. Identify deeper patterns across multiple tests

Most marketers conduct multiple A/B tests over time but struggle to connect results across different campaigns. GenAI can:

• Analyze historical test data to uncover patterns in audience behavior.
• Identify recurring trends, such as whether a particular CTA style consistently performs better.
• Detect seasonal variations, helping marketers adjust strategies based on past engagement cycles.

Example: If past tests show that personalized subject lines perform better only for specific audience segments (such as your VIP customers), GenAI can highlight this insight.  You can use it to inform and structure future tests.

2. Understand why results differ

Most A/B test results show which version performed better, but they don’t explain why. You need to know the "why" before you make substantial changes in your email messaging.

You can use GenAI for these tasks:

• Use Natural Language Processing (NLP) to analyze word choice and sentiment in email content.
• Compare performance metrics across multiple test variables to see which combination drives conversions.
• Cross-reference test results with customer personas to determine which messaging resonates with different audience types.

Example: You test a message focusing on loss aversion (subject line: "Act now – Only a few seats left!") against exclusivity (subject line: "Your Exclusive Spot is Waiting") among your most engaged customers. If the loss-aversion email doesn't perform as well as the softer approach, GenAI can detect that language might not work for this specific audience.

3. Predict future test outcomes

Rather than just analyzing past tests, GenAI can predict future test success by identifying likely winning variations before a campaign is launched. This can save you hours of combing through data and help you develop your testing structure faster. Here's how:

• Simulate audience reactions based on previous test data.
• Suggest optimized test variants before launching a new experiment.
• Highlight redundant tests, preventing wasted effort on experiments with predictable results.

Example: If GenAI detects that subject lines with emojis have never improved engagement for a B2B audience, it can recommend that you avoid this test and focus on variations that have delivered more impact, such as message tone, copy length, or personalization.

4. Automate post-test recommendations

Here's where GenAI can help you cut through all your data to find the insights that properly done testing should deliver. You can instruct your custom GPT to run these tasks after a test finishes:

• Automatically generate summary reports with key insights.
• Recommend clear next steps, such as whether to refine messaging, adjust targeting, or run additional tests.
• Rank the most valuable tests based on long-term impact rather than just immediate engagement.

Example: Instead of just stating, "Version B won with a 12% higher conversion rate,” GenAI can add: “Version B's success was driven by shorter, more direct CTAs. We recommend testing a refined CTA strategy in your next campaign to further optimize engagement."

Smarter, More Strategic A/B Testing with GenAI

To get the greatest benefit from GenAI and A/B testing, you will also need to invest time and money in a custom GPT and training in how generative AI works.

The benefits, however, could be massive. By analyzing historical data, identifying hidden patterns, predicting test outcomes, and automating insights, GenAI transforms A/B testing from a trial-and-error process into a data-driven strategy. This will help you create more accurate testing programs and deliver the insights and results that can transform your entire email program.

 Photo by CHUTTERSNAP on Unsplash