Opinions about what makes a good website are cheap. Everyone has one. What actually works for your specific audience is what matters, and A/B testing is how you find out. Here's how to run tests that lead to meaningful improvements.
What Is A/B Testing?
A/B testing shows different versions of your website to different visitors and measures which performs better:
- Version A: Current design (control)
- Version B: Modified design (variant)
- Measure: Which gets more conversions?
You're not asking users which they prefer—you're measuring what they actually do.
This removes opinions and politics. The data decides.
Even small changes (button color, headline, image) can significantly impact results.
When to Use A/B Testing
A/B testing works when:
- You have measurable goals (signups, purchases, clicks)
- You have sufficient traffic (100+ conversions per month minimum)
- You have a specific hypothesis to test
- You can wait for statistical significance
A/B testing doesn't work well for:
- Low-traffic pages
- Major redesigns (too many changes at once)
- Subjective goals ("looks more modern")
Focus on high-impact pages: landing pages, pricing pages, checkout flows, signup forms.
What to Test
High-impact test ideas:
Headlines:
- Different value propositions
- Specific vs general messaging
- Benefit-focused vs feature-focused
Calls-to-action:
- Button text ("Get Started" vs "Start Free Trial")
- Button color and size
- Placement on page
Form fields:
- Number of required fields
- Field labels and placeholders
- Form length
Social proof:
- Testimonials vs no testimonials
- Customer logos vs numbers
- Specific vs vague claims
Pricing:
- Price anchoring
- Monthly vs annual display
- Discount presentation
Test one thing at a time. If you change five elements, you won't know what worked.
Running Tests Properly
Follow this process:
1. Form hypothesis
Example: "Adding customer testimonials will increase signup rate because it builds trust"
2. Determine sample size needed
Use a calculator—you need enough traffic for statistical significance
3. Split traffic evenly
50% see A, 50% see B
Randomize assignment
4. Run until significant
Don't stop early because one variant is winning
Typically 1-4 weeks depending on traffic
5. Analyze results
Is the difference statistically significant?
Does it align with your hypothesis?
6. Implement winner
Make the winning variant permanent
Move to next test
Tools for A/B Testing
You have several options:
Google Optimize (free):
- Integrates with Google Analytics
- Good for beginners
- Limited features
Optimizely:
- Powerful and flexible
- Expensive
- Best for serious testing programs
VWO (Visual Website Optimizer):
- Middle ground on price
- Good visual editor
- Solid feature set
For developers:
- Custom implementation
- Full control
- Requires more technical expertise
Start with Google Optimize if you're new to A/B testing. Upgrade when you outgrow it.
Common Mistakes to Avoid
Don't do this:
Stopping tests early:
- Wait for statistical significance
- Variance happens—don't call winners prematurely
Testing too many things:
- One test at a time on the same page
- Multiple simultaneous changes = can't isolate what worked
Ignoring statistical significance:
- Small sample sizes lead to false conclusions
- Use proper significance calculators
Testing without clear goals:
- Define success metric before starting
- Don't change metrics mid-test
Never testing:
- The biggest mistake is not testing at all
- Small improvements compound over time
Remember: Tests that don't show improvement still provide value—they prevent you from making changes that would hurt conversion.
A/B testing removes guesswork from website optimization. Instead of debating what might work, you test it with real users and let data guide decisions. Start with high-impact pages and one clear hypothesis at a time. Even small improvements to conversion rate can significantly impact revenue. The key is to keep testing—optimization is a continuous process, not a one-time project.