A/B Testing 101: What to Test & How to Read Results
In digital marketing, guessing is expensive. If you want to know what truly works—whether it’s a headline, a call-to-action, or a landing page—A/B testing gives you the data to make informed decisions.
This guide will walk you through the basics of A/B testing, what elements you can test, and how to read and act on the results.
What Is A/B Testing?
A/B testing (also known as split testing) is a method of comparing two versions of a webpage, email, ad, or other content to determine which performs better. Version A is the control (the original), and Version B is the variant (the new version with one change).
Both versions are shown to similar segments of your audience at the same time. You then analyze which version gets more conversions, clicks, opens, or other desired outcomes.
The goal is to make data-driven improvements that increase performance over time.
Why A/B Testing Matters
Every part of your digital presence affects your results. Even small changes—like button text or image placement—can impact how users behave.
Benefits of A/B testing:
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Increases conversion rates without extra traffic or cost
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Reduces bounce rates and improves user experience
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Minimizes risk by testing before full-scale changes
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Provides data to settle debates based on opinion
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Helps optimize for long-term growth
What Can You A/B Test?
You can test almost anything, but it’s most effective when focused on elements that influence user behavior. Here’s a list of popular things to test by platform:
1. Website or Landing Pages
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Headlines – Test different value propositions or tones
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Call-to-Action (CTA) Buttons – Wording, color, placement (e.g., “Get Started” vs. “Start Free Trial”)
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Images or Videos – Try lifestyle imagery vs. product-focused
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Form Fields – Fewer fields may boost completion rates
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Page Layout – Single column vs. multi-column; above-the-fold content
2. Email Marketing
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Subject Lines – Curiosity vs. urgency; emojis vs. none
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Sender Name – Personal name vs. brand name
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Email Content – Text-only vs. image-heavy; long vs. short format
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CTA Links or Buttons – Placement and wording
3. Paid Ads
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Headlines & Descriptions – Try different hooks or offers
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Visuals – Static image vs. video; color palettes
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Ad Copy – Emotional appeal vs. logical value
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Target Audience Segments – Age, interest, or behavior variations
4. Ecommerce Elements
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Pricing Models – Bundled offers vs. individual pricing
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Trust Badges or Reviews – Placement and style
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Product Descriptions – Long-form storytelling vs. bullet-point specs
How to Set Up an A/B Test
To ensure valid, actionable results, follow a structured process:
1. Set a Clear Goal
Before running a test, define what you want to improve. Your goal could be:
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Click-through rate (CTR)
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Conversion rate
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Email open rate
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Form submissions
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Add-to-cart actions
Be specific. “Increase landing page conversions by 10%” is more useful than “make the page better.”
2. Choose One Variable to Test
For accurate results, test one change at a time. If you change the headline and image simultaneously, you won’t know which one caused the difference in performance.
Start with high-impact areas like CTAs, headlines, or forms.
3. Split Your Audience Randomly
Your A and B versions must be shown to random, equally sized segments of your audience. Most platforms (like Google Optimize, Mailchimp, Meta Ads Manager) automatically do this.
Randomization removes bias and ensures clean data.
4. Run the Test Long Enough
The test needs enough traffic or impressions to produce reliable results. Don’t stop a test after a few hours just because one version is slightly ahead.
Follow these guidelines:
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For websites: run tests until you have at least 100 conversions per variant
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For emails: aim for statistical confidence (typically 95%) before declaring a winner
Use an A/B testing calculator to determine how long you should run your test based on your current traffic and conversion rate.
5. Avoid External Influences
Try to run tests when things are stable. Holidays, major promotions, or product launches can skew results. Also, avoid running multiple tests on the same page at once.
How to Read A/B Test Results
Once the test has run its course, it’s time to analyze the data and decide your next move.
Key Metrics to Review:
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Conversion Rate: The most common and important metric. Which version led to more people completing your desired action?
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Click-Through Rate: Useful for email or ad tests.
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Bounce Rate / Time on Page: For webpage-based tests.
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Revenue or ROI: For e-commerce A/B testing.
Understanding Statistical Significance
This is where many beginners get confused. A result is statistically significant when you can be confident the difference in performance is not due to chance.
Most testing tools will calculate this for you. Aim for at least 95% confidence before declaring a winner. Anything below that could be a fluke.
When a Winner Emerges
If the new version (B) outperforms the control (A) and reaches statistical significance:
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Implement the winning version permanently
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Consider creating a new test to improve further
If there’s no significant difference, keep the original or re-test with a new variable. Even “failed” tests provide valuable data.
Common A/B Testing Mistakes to Avoid
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Testing Too Many Variables at Once
This creates confusion and leads to inconclusive results. Stick to one variable at a time. -
Stopping Too Early
Allow enough time and volume. Early results often fluctuate. -
Not Defining Success Clearly
Without a clear goal, you won’t know what counts as a win. -
Ignoring User Segments
What works for new visitors may not work for returning users. Segment if needed. -
Making Emotional Decisions
Let the data drive your choices, not personal preference.
Conclusion: Test Smarter, Grow Faster
A/B testing isn’t just for big brands or tech-savvy marketers—it’s for anyone who wants to optimize outcomes through data, not guesswork. Whether you’re improving a landing page, an email campaign, or an ad, testing can lead to steady performance gains over time.
Start with simple tests, measure carefully, and build a habit of testing continuously. Over time, those small, informed decisions will compound into major business growth.