How A/B Testing Works: What Google and Meta Don’t Want You to Know

Key Takeaways

• Platform algorithms are quietly sabotaging your A/B tests

• Your "winning ads" reveal algorithm prowess, not creative excellence

• Research beats flawed testing every single time

A/B testing. The holy grail of digital marketing! The only way to optimize campaigns! The data-driven approach that works!

Not exactly.

I've seen brands invest considerable resources in A/B tests, believing they're following scientific principles, while the platforms quietly undermine their entire methodology. Google and Meta aren't being completely transparent about this, and it deserves more attention.

The Algorithm Reality No One's Talking About

When you launch an A/B test, you think you're conducting a controlled experiment. You create two ads and assume the platform shows each to a similar audience segment. That's how testing works, right?

Wrong.

Recent research published in the Journal of Marketing blew the lid off this assumption. The algorithms don't randomly assign audiences at all. They show different ads to entirely different user groups - based on their own criteria.

Why would platforms designed to help marketers actually sabotage testing? Because they were never designed to help you test – they were built to optimize engagement.

It's Not Your Test – It's Their Business Model

I've observed this pattern with several clients. They focus on creative elements, then (because of the platforms’ lack of transparency) misinterpret their results.

Here's what is really happening:

You create Ad A with a red background and Ad B with a blue background. You launch your test, expecting both to be shown to a representative mix of your target audience.

But the algorithm operates according to its own priorities, not your testing protocol.. It identifies users who historically engage with red-background ads and shows them Ad A. Then it hunts down blue-ad enthusiasts and serves them Ad B.

You haven't tested which color works better. You've tested how effectively the algorithm matched each ad to its ideal sub-audience.

Still Not Seeing It? Let Me Break It Down Further.

Say you want to test which ice cream flavor people prefer – chocolate or vanilla. But instead of asking the same group of people to try both flavors, you hunt down known chocolate lovers and ask them about chocolate, then find vanilla fans and ask them about vanilla.

What have you learned? Nothing about which flavor is generally preferred – only that chocolate lovers indeed like chocolate and vanilla fans like vanilla.

That's exactly what's happening with your A/B tests. And it's why you might see wildly different performance between two nearly identical ads.

The "Winner" That Isn't

This testing flaw creates a cascade of problems for your marketing strategy. When Ad A "wins," you think you've discovered an objective truth about your creative. You haven't.

You've simply learned that the algorithm found more red-background enthusiasts than blue ones within your audience parameters. That's useful knowledge for the platform, not so much for you.

(I’ve had several clients recently call for help, “dialing in” their marketing efforts for just this reason. It’s frustrating and wastes time and resources.)

Can You Salvage Platform Testing? Maybe.

While perfect A/B testing might be impossible on these platforms, you can mitigate the damage:

First, get crystal clear on what you're actually testing. Develop a specific hypothesis – it sounds basic, but most marketers skip this crucial step.

Second, test all possible combinations. If you're testing button colors (red/blue) and headlines ("Buy Now"/"Buy Today"), create all four variations. Half-measures produce garbage data.

Third, watch your variables. If your red button is round and your blue button is rectangular, you're testing multiple variables at once. Break these elements apart and test systematically.

Finally, run each ad as a separate campaign with identical targeting. It's not perfect, but it's cleaner than the platform's built-in testing tools.

Research: The Antidote to Testing Madness

I'm constantly surprised by how many brands – even the big ones – insist on reinventing the wheel with primary research when the answers already exist.

I'm not against primary research. I regularly conduct focus groups and interviews to understand behavior and motivation. But by the time I'm developing ads, I already know what will resonate.

Instead of wasting budget testing whether red or blue buttons drive more clicks, I look at what decades of research tells us:

"The average preference was 10% more for warm-colored advertisements over cool-colored advertisements..." (North & Ficorilli, 2017)

"Blue advertisements created positive feelings directly. Participants expected the message to be informative and trustworthy." (Krondahl & Nilsson, 2023)

This evidence-based approach delivers far more reliable guidance than platform testing ever could.

A Different Approach That Works

I recently worked with a digital marketer who had been religiously A/B testing every element of their ads. They had spreadsheets upon spreadsheets of results. When I shared the research on how algorithms influence test outcomes, there was stunned silence.

Now what?

Unfortunately, digital marketers are taught that A/B testing is infallible. (I can confirm this as it’s in every textbook I’ve reviewed for use in the classes I teach.) Switching to a new approach, grounded in established research, can be challenging. It requires a different skill set, but it can be taught.

Helping my client switch to a more strategic approach, based on large-scale research studies and deep audience understanding, increased conversions by 22% in a matter of weeks.

What’s Better than A/B Testing is Only a Keystroke Away

Thousands of studies are conducted each year on consumer behavior and preference. Tapping into this research can eliminate A/B testing at all or at least help allocate budget more effectively to avoid strategic missteps. While others might rely heavily on platform test results, you can develop campaigns based on marketing research that provides more reliable guidance.

The platforms are unlikely to admit that their algorithms undermine your tests – they're designed to drive engagement, not research validity. Now that you see what's really happening, you can approach your marketing with clearer eyes and stronger strategy.

In today's chaotic marketing landscape, the winners won't be those with the most data, but those who actually understand what their data is telling them.

And what much of the data suggests about typical A/B testing approaches is that it’s a lot less reliable than you think.

Maryanne Conlin is an award-winning, classically trained marketer and founder of NeuroD Marketing. She's worked with leading Fortune 500 brands, and helped over 500 clients build their businesses strategically for sustainable growth. An expert speaker and writer on targeting & positioning, she runs workshops and teaches internationally.

Contact me to learn how to optimize your marketing strategies in an economic downturn.