What is A/B testing (and why it’s underused)

At its core, A/B testing means comparing two versions of something - Version A vs. Version B - to see which one performs better based on a specific goal.

In performance marketing, that “something” could be anything from the headline in an ad to the CTA in a landing page, the offer in a creator campaign, or even the onboarding flow in an app.

And yet, many marketers still treat it like a checkbox: run two variations, wait a few days, and move on. But real A/B testing is about more than doubling your creatives, it’s about learning faster and acting smarter.

What turns a test into a strategy is the question behind it:

Are you trying to understand if a localized message drives more engagement? If a video format beats a static asset? If a new offer changes the conversion curve?

You need a clear hypothesis to test and stop guessing.

Why A/B testing matters in performance marketing

Every decision in performance marketing affects ROI and when you’re managing multiple channels, audiences, and creatives, the margin for guesswork disappears quickly.

A/B testing gives structure to that complexity: it helps you move from intuition to evidence.

Take affiliate marketing. You might be working with multiple publishers, some content-based, some cashback, some influencers. Do they convert better with a referral bonus or a one-time discount? Do short videos beat carousels?

In app growth, testing can reveal what really drives high-quality installs: Is it a direct-to-value onboarding? A more segmented registration flow? A sharper CTA? The answer often lies in small adjustments with big impacts on retention, IPM, or LTV.

The best-performing campaigns aren’t the ones that tried the most things. They’re the ones that learned fast and acted on what they saw.

How to run a test that actually teaches you something

Testing is about learning and learning requires structure.

Start with a clear hypothesis: “I believe Version A will outperform B because it simplifies the CTA.”

Define what you’ll measure: is it click-through rate, cost per registration, Day 1 retention, or return on ad spend?

Then make sure the test is set up to produce valid insights: enough traffic volume, a defined timeframe, and the patience to wait for real results.

A solid A/B testing framework can make all the difference. Here's how we approach it at Affluxo:

1. Collect baseline data: understand what’s currently working and where the gaps are.
2. Set a clear goal: like improving Day 1 retention or reducing CPA.
3. Formulate a hypothesis: a reasoned prediction: “If we reduce friction in step 2, more users will convert.”
4. Define your sample size: you can use sample size calculators such as this.
5. Design variations: two distinct, meaningful versions.
6. Run the experiment: with enough traffic and time for valid results.
7. Analyze: which one won and why.
8. Act or iterate: scale what works, pause what doesn’t, and feed learnings into the next test.

To prioritize what to test, many teams use the PIE framework (Potential, Importance, and Ease) to score test ideas. The higher the combined score, the more impact the test is likely to deliver. It’s a quick, pragmatic way to decide what’s worth testing now and what can wait.

But the most important step? What happens next.

Too many teams test and forget. They see a result, maybe tweak a creative… and never revisit the learning. That’s where growth stalls.

We believe the next move is just as important as the test itself.

“A/B testing lets us turn every hunch into something we can prove or disprove. And that’s the exciting part: when data turns into clarity, and clarity turns into decisions that scale.”

— Sebastián Sarbia, Head of Sales & Marketing at Affluxo

And that next move starts by asking yourself: What happens if this works? Can the winning variation scale across partners, regions, or channels? And what if it doesn’t: is it time to pause the idea, or test it in a different way? Most importantly, what did you learn that will help you build a stronger next test?

Even a failed variation teaches you something: it sharpens your next move, eliminates noise, and builds the path forward.

Real things you can start testing today

If you're not sure where to begin, start with what you're already running. Most of the time, there's no need to reinvent the wheel, just test it better. Here are a few high-impact areas we’ve seen A/B testing unlock real insights across both affiliate and app growth strategies:

🔸 Creative formats: short-form video vs. static banners

🔸 Offer types by publisher: cashback vs. trial vs. referral bonus

🔸 Mobile messaging: urgency-based CTAs vs. value-based headlines

🔸 Funnel variations: registration-first vs. explore-first onboarding

🔸 Localized vs. generic copy: adapting messaging by country or region

You don’t need a lab, you need a question and the discipline to run it properly.

Final thoughts

A/B testing helps you move faster by making smarter calls. Instead of relying on assumptions or gut feel, you’re building a feedback loop that sharpens your strategy with every campaign.

It reduces guesswork, it reveals insights and it turns campaigns into learning loops.

In a space where trends shift fast and channels evolve daily, the brands that stand out aren’t necessarily the ones doing the most. They’re the ones learning the fastest and using that knowledge to act with purpose.

At Affluxo, we don’t just run campaigns. We build testing muscles with our partners, so every move is smarter than the last

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Photo by Jason Dent on Unsplash