A/B Testing Your Design: What to Test First
A/B testing helps teams move beyond opinions by comparing design variations with real user data. This guide explains what to test first, how to prioritize experiments, and how to run reliable A/B tests that lead to meaningful conversion improvements.
You’ve designed a beautiful landing page. Your team loves it. But will your users? The only way to know for sure is to test it.
A/B testing—comparing two versions of a design to see which performs better—is the gold standard for data-driven design decisions. Yet many teams either skip testing entirely or test the wrong things in the wrong ways.
In this guide, we’ll cover everything you need to know about A/B testing your designs: what to test, how to prioritize, and how to avoid the mistakes that lead to misleading results.
What is A/B Testing?
A/B testing (also called split testing) is a method of comparing two versions of a webpage or design element to determine which performs better. You show version A to half your visitors and version B to the other half, then measure which version achieves your goal more effectively.
Why A/B Test Your Design?
Remove guesswork
Design opinions vary. Testing provides objective data.
Incremental improvements
Small changes can compound into significant gains. A 5% improvement in conversion rate might mean thousands of additional customers.
Reduce risk
Test changes on a portion of traffic before rolling out site-wide.
Learn about your audience
Test results reveal what your specific users respond to, not what “best practices” say they should.
Settle debates
End design disagreements with data, not opinions.
What Should You Test First?
Not all tests are created equal. Some changes can dramatically impact conversions, while others move the needle barely at all. Here’s how to prioritize.
The ICE Framework
Before testing, score each potential test on three criteria:
Impact
Confidence
Ease
Score each from 1-10 and average them. Test the highest-scoring ideas first.
High-Impact Elements to Test
Based on countless A/B tests across industries, these elements typically have the biggest impact on conversions.
Headlines and Value Propositions
Your headline is often the first (and sometimes only) thing visitors read. Testing different value propositions can dramatically affect whether users stay or bounce.
What to test:
Benefit-focused vs. feature-focused headlines
Specific numbers vs. vague claims
Question headlines vs. statement headlines
Short vs. long headlines
Different emotional angles
Example test:
Version A: Project Management Software for Teams
Version B: Ship Projects 2x Faster with Less Stress
Call-to-Action (CTA) Buttons
CTAs are conversion hotspots. Small changes can yield big results.
What to test:
Button text
Button color and contrast
Button size and padding
Button placement
Surrounding elements
Example test:
Version A: Submit
Version B: Get My Free Report
Hero Section Layout
The hero section sets the tone for the entire page. Testing different layouts can significantly impact scroll depth and engagement.
What to test:
Image placement
Video vs. static image
Screenshot vs. lifestyle imagery
Form placement
Single vs. multiple CTAs
Social Proof Placement
Social proof builds trust, but placement matters.
What to test:
Testimonials position
Logo placement
Ratings visibility
Specific vs. vague numbers
Video vs. text testimonials
Form Length and Fields
For lead generation, form design directly impacts submission rates.
What to test:
Number of fields
Required vs. optional fields
Single-step vs. multi-step forms
Field labels
Form placement
Important finding: Removing just one unnecessary form field can increase conversions by 5–10%.
Medium-Impact Elements
Navigation layout and labels
Page length
Content order
Image choices
Typography
Spacing and white space
Footer content and CTAs
Low-Impact Elements
Minor color variations
Subtle font changes
Small copy tweaks
Icon styles
Border radius and shadows
Note: Low impact does not mean no impact.
How to Run Valid A/B Tests
Running a test is easy. Running a valid test that produces reliable results is harder.
Define a Clear Hypothesis
If we change X, then metric Y will change because reason Z.
Choose the Right Metric
Primary metrics:
Conversion rate
Revenue per visitor
Click-through rate
Form submission rate
Secondary metrics:
Bounce rate
Time on page
Scroll depth
Pages per session
Avoid vanity metrics.
Calculate Required Sample Size
Ending tests too early leads to false positives.
Rule of thumb: At least 1,000 conversions per variation are needed for reliable conclusions.
Run the Test Properly
Randomize visitors
Keep experiences consistent
Run tests for full business cycles
Avoid early conclusions
Analyze Results Correctly
Check statistical significance
Evaluate practical impact
Review segment performance
Document learnings
Common A/B Testing Mistakes
Testing too many things at once
Ending tests too early
Ignoring seasonality
Testing on low-traffic pages
Ignoring the full funnel
Copying competitors
Testing without a hypothesis
Pre-Test Analysis: The Blur Test Method
Blur your design to identify visual hierarchy problems before testing.
How to use it:
Create variations
Blur both designs
Compare what stands out
Predict likely winners
Building a Testing Culture
Create a testing roadmap
Set a testing cadence
Share results widely
Iterate on winners
Learn from losses
Conclusion
A/B testing turns subjective design decisions into measurable outcomes. Focus on high-impact elements, use structured frameworks, and commit to continuous testing and learning.