What Should You A/B Test First?

Key Takeaways:

  • The best experiments begin with opportunities rather than ideas.
  • High-traffic, high-value pages often produce the biggest optimization wins.
  • Visitor behavior provides critical evidence for prioritization.
  • Every experiment should answer one specific question.
  • Optimization success comes from consistently identifying and acting on meaningful opportunities.

Summary

Choosing what to test first is one of the biggest challenges for new optimization programs. This article introduces the Opportunity First Approach, showing readers how to identify high-impact pages, recognize friction through visitor behavior, and prioritize experiments that are most likely to produce meaningful business results.

One of the most common questions teams ask after deciding to start A/B testing isn’t how to run an experiment. It’s much simpler than that:

"What should we test first?"

It’s a fair question, but it’s also where many optimization programs go off track. Faced with an endless list of possibilities, teams often start testing whatever feels interesting. A different button color. A new headline. A redesigned hero section.

Sometimes those experiments uncover meaningful improvements. More often, they consume valuable time without answering an important business question.

The most successful optimization teams don’t start with ideas. They start with opportunities.

Opportunity First Optimization

Every website has opportunities to improve, but not every opportunity deserves to become your next experiment.

Before creating variations, step back and ask where the greatest opportunity exists. Is there a page that attracts significant traffic but rarely converts? Does a lead form have a high abandonment rate? Are visitors consistently leaving a pricing page without taking the next step?

Rather than guessing where to begin, let evidence guide your decisions.

That’s what we call the Opportunity First Approach. Instead of asking, “What should we test?” ask, “Where are visitors struggling the most?“

The Opportunity First Approach isn’t about finding the easiest page to change. It’s about finding the pages where better decisions are most likely to create measurable business impact. When you consistently start with evidence instead of ideas, your experiments become more meaningful, your hypotheses become stronger, and your optimization efforts become far more effective.

That one shift in perspective often leads to more meaningful experiments, stronger hypotheses, and better business results.

Start Where the Impact Is Greatest

Not every page deserves equal attention. If your goal is to improve conversions, begin with the pages that have the greatest influence on your business.

Good places to start include:

  • High-traffic landing pages
  • Pricing or product pages
  • Lead generation forms
  • Checkout or sign-up flows
  • Pages with unusually high exit rates

Improving a page that receives ten visitors each month probably won’t move the needle. Improving a page that thousands of visitors see every week might.

High-traffic pages give your experiments enough visitors to produce meaningful results more quickly. High-value pages directly influence revenue, leads, or sign-ups, making improvements on those pages far more impactful than optimizing low-traffic content. By focusing your efforts where they can make the biggest difference, you’ll learn faster and generate insights you can apply across the rest of your website.

Let Visitor Behavior Tell You Where to Look

Analytics tell you what happened. Visitor behavior helps explain why.

For example, imagine a landing page with strong traffic but very few demo requests. Analytics tell you the conversion rate is low, but they don’t explain the reason.

Reviewing session recordings may reveal visitors repeatedly scrolling up and down looking for information they can’t find. Heatmaps might show that very few people ever reach the primary call to action because it’s buried too far down the page.

Together, those insights provide a much stronger foundation for deciding what to test than simply guessing.

The same approach works for lead generation forms. Imagine your contact form receives hundreds of visitors every week, but very few submissions. Session recordings reveal that many visitors abandon the form after reaching the phone number field. Rather than redesigning the entire page, your first experiment could test whether making that field optional improves completion rates. Instead of guessing what might improve conversions, you’re using real visitor behavior to build a focused hypothesis.

If you’re new to interpreting visitor behavior, our CRO Insights Guide walks through practical techniques for identifying friction before building your next experiment.

Prioritize Before You Experiment

Once you’ve identified several opportunities, resist the temptation to test everything at once.

The strongest optimization opportunities usually share three characteristics:

1. High Visibility

Large numbers of visitors interact with the page, allowing you to gather meaningful insights more quickly.

2. High Business Impact

The page directly influences leads, revenue, sign-ups, or another important business goal.

3. Clear Evidence of Friction

Analytics, heatmaps, or session recordings suggest visitors are struggling to complete the next step.

When all three conditions exist together, you’ve likely identified a strong candidate for experimentation.

Every Experiment Should Answer One Question

One mistake many teams make is trying to solve several problems at once.

Imagine redesigning an entire landing page because conversions are low. You update the headline, replace the imagery, shorten the copy, move the form, and change every button.

If conversions improve, which change made the difference?

Instead, focus each experiment on answering a single question.

For example:

“Will moving our primary call to action above the fold increase demo requests because more visitors will see it without scrolling?“

That’s a focused hypothesis built around a specific opportunity.

If you’d like a step-by-step framework for building stronger hypotheses, our How to Run A/B Tests guide walks through the entire process.

Great Optimization Is Built One Opportunity at a Time

Organizations that consistently improve their websites rarely discover one magic change that transforms everything overnight.

Instead, they identify one opportunity, learn from it, apply those insights, and move to the next opportunity.

Over time, those improvements compound.

That’s why optimization is less about finding brilliant ideas and more about building a repeatable process for recognizing opportunities, validating assumptions, and continuously improving the visitor experience.

Better experiments begin with better opportunities. By learning to identify the pages where thoughtful improvements will have the greatest impact, optimization becomes less about guessing what to change and more about knowing where to focus next. That’s the mindset Bluvia A/B Testing is built to support.

Before You Decide What to Test

Before launching your next experiment, take a few minutes to evaluate whether you’ve identified the right opportunity. Ask yourself:

  • Is this page important to our business goals?
  • Do enough visitors interact with this page to produce meaningful results?
  • Is there clear evidence of visitor friction from analytics, heatmaps, or session recordings?
  • Can this experiment answer one specific question?

If you can confidently answer “yes” to each of these questions, you’ve likely found a strong candidate for your next A/B test. If not, spend a little more time understanding visitor behavior before building your experiment. The better you prioritize opportunities, the more valuable every experiment becomes.

Continue Your Optimization Journey

Finding the right opportunity is often more valuable than testing more ideas. Once you’ve learned how to identify the pages with the greatest potential, the next step is turning those opportunities into thoughtful experiments. These resources will help you continue that journey.

When you’re ready to turn opportunities into experiments, explore Bluvia A/B Testing and discover how WordPress teams build stronger optimization habits through continuous experimentation.

Why Most A/B Tests Fail Before They Begin

Key Takeaways:

  • Great experiments begin with curiosity, not assumptions.
  • Strong hypotheses are built from evidence and visitor behavior.
  • Testing too many changes at once makes results difficult to interpret.
  • Every experiment should teach you something, regardless of the outcome.
  • Continuous learning creates stronger optimization programs than isolated tests.

Summary

Most A/B tests fail long before anyone clicks “Launch.” Successful experimentation starts by identifying real problems, understanding visitor behavior, and developing thoughtful hypotheses. This article explains why better questions lead to better experiments and outlines the mindset and process that high-performing optimization teams use to make smarter decisions.

Most people think an A/B test begins when someone creates a variation and clicks Launch.

In reality, successful experiments start much earlier.

They begin when someone notices a problem, asks why it’s happening, and becomes curious enough to investigate before proposing a solution.

That’s where many A/B tests go wrong. Teams often jump straight to testing ideas before they’ve taken the time to understand the problem they’re trying to solve. The result isn’t just a disappointing experiment. It’s a missed opportunity to learn something meaningful about the people using the website.

If the goal of A/B testing is to make better decisions, then better decisions have to start with better questions. The quality of an experiment is rarely determined by the software you use. It’s determined by the quality of the thinking that happens before the first variation is ever created.

Great Experiments Start With Curiosity

It’s easy to become attached to ideas. Marketing wants a new headline. Design prefers a different layout. Leadership believes a larger call to action will improve conversions. Every suggestion is usually made with good intentions, but even the strongest ideas are still assumptions until they’re tested.

Instead of asking, “What should we test?” high-performing optimization teams ask a different question:

"What problem are we trying to solve?"

That subtle shift changes everything. Rather than searching for something to experiment with, teams begin looking for evidence that explains why visitors struggle. Understanding visitor behavior before proposing a solution often leads to stronger hypotheses and better experiments. The experiment becomes a way to validate what they’ve learned instead of proving someone’s opinion right.

Visitor Behavior Should Shape Every Hypothesis

A strong A/B testing hypothesis doesn’t appear out of thin air. It grows out of observation.

Before building variations, spend time understanding how visitors interact with your website. Analytics can identify pages with unusually high exit rates or low conversions. Session recordings can reveal hesitation, confusion, or repeated actions that suggest friction. Heatmaps help visualize where visitors focus their attention and, just as importantly, what they ignore.

For example, imagine a pricing page that receives plenty of traffic but consistently underperforms. Rather than immediately testing a new headline, your research reveals that most visitors never scroll far enough to see the pricing comparison table. Suddenly the problem isn’t the headline at all. It’s that the most important information appears too late in the experience. That insight leads to a much stronger hypothesis and a much more meaningful experiment.

If you’re new to interpreting visitor behavior, our CRO Insights Guide explains how to identify friction and prioritize what to test first.

The Best A/B Tests Don't Prove You're Right

One of the easiest traps to fall into is designing experiments to confirm an existing belief.

Maybe everyone agrees that a shorter form will convert better. Perhaps the team is convinced a different hero image will increase engagement. Those ideas may ultimately prove correct, but that’s not why experiments exist.

The purpose of an A/B test isn’t to prove you’re right. It’s to discover what’s true.

Sometimes the biggest lessons come from surprisingly small changes. One of the best-known examples comes from Microsoft’s Bing search engine, where a simple experiment testing alternative advertising headlines increased revenue by 12%, representing more than $100 million in additional annual revenue in the United States alone. The experiment didn’t succeed because the team was smarter than everyone else. It succeeded because they tested an idea instead of assuming they already knew the answer.

That’s the real value of experimentation. It allows teams to replace confidence with evidence and opinions with measurable results.

Approaching experiments with curiosity instead of certainty creates better outcomes because every result becomes useful. Winning experiments improve performance. Losing experiments improve understanding.

Simplicity Leads to Better Results

Another common mistake is changing too many things at once.

Imagine testing a new headline, different imagery, revised copy, updated colors, and a redesigned page layout in a single experiment. If conversions improve, which change made the difference? If performance declines, which element caused the problem?

Keeping experiments focused makes the results easier to interpret and the next decision easier to make.

If you’re unsure whether an experiment is focused enough, ask yourself whether you’ll know exactly what caused the result if the test succeeds. If the answer is no, simplify the experiment before launching it.

A simple hypothesis might look like this:

“We believe making our primary call to action more prominent will increase demo requests because visitors currently overlook it on mobile devices.“

If you’d like help writing stronger hypotheses, our How to Run A/B Tests guide walks through a practical framework for planning and evaluating experiments.

Notice how specific that is. It identifies a problem, proposes a solution, and explains why the change is expected to help.

That’s a much stronger starting point than, “Let’s see if a green button works better.“

Great Optimization Is Never Finished

Organizations that consistently improve their websites don’t chase one winning experiment after another. They build a repeatable process for learning.

They observe visitor behavior, identify friction, develop thoughtful hypotheses, and validate ideas through experimentation before using what they’ve learned to inform the next improvement. Over time, those individual steps become a repeatable optimization workflow. Instead of relying on isolated ideas or one-time redesigns, teams build a process that helps them continuously improve their websites based on real visitor behavior.

That’s why A/B testing is far more than a feature or a marketing tactic. It’s one part of a broader optimization workflow that helps organizations replace opinions with evidence and confidence. Every experiment adds another piece of understanding, making the next decision more informed than the last.

Great experiments don’t happen because teams have better instincts alone. They happen because teams build a repeatable process for learning. Bluvia A/B Testing was designed to support that process, helping WordPress teams validate ideas, learn from every experiment, and continuously improve over time.

Before You Launch Your Next Experiment

Before creating another variation, ask yourself these five questions:

  • What problem am I trying to solve?
  • What evidence suggests this problem exists?
  • What visitor behavior supports my hypothesis?
  • What single change am I testing?
  • What will I learn regardless of whether this experiment wins or loses?

If you can’t answer those questions clearly, you’re probably not ready to launch the experiment yet.

Continue Your Optimization Journey

Building stronger experiments starts long before you click “Launch.” The more you understand your visitors, develop thoughtful hypotheses, and learn from each experiment, the more effective your optimization process becomes. These resources can help you take the next step.

When you’re ready to move from assumptions to evidence, explore Bluvia A/B Testing and discover how WordPress teams build repeatable experimentation processes that support continuous optimization.