A/B testing is a method of comparing two versions of a marketing asset—like a landing page, headline, or CTA—to see which one performs better. It’s one of the most effective ways to improve conversion rates because it’s based on real user behavior, not assumptions.
Marketers use A/B tests to validate ideas, optimize campaigns, and make data-driven decisions. And since landing pages are built with a single goal in mind—whether it’s sign-ups, purchases, or demo requests—they’re the ideal place to run focused, high-impact experiments. Even small changes, like button copy or form layout, can lead to big results.
In this post, you’ll see real-world A/B test examples—from simple headline tweaks to complete layout overhauls—so you can learn what works, why it works, and how to apply it to your own landing pages.
What Is A/B Testing?
A/B testing is a data-driven method for comparing two or more versions of a webpage or app to determine which performs better. It’s commonly used to improve user experience and increase conversion rates by identifying the most effective design, copy, or layout.
According to Adelina Karpenkova in “A/B Testing in Marketing: The Best Practices Revealed,” A/B testing is a core part of the conversion rate optimization (CRO) process. It involves showing two versions of nearly identical content to similar audiences, with only one variable changed to measure impact.
Dennis van der Heijden, in CXL’s article “5 Things We Learned from Analyzing 28,304 Experiments,” reports that A/B tests account for 97.5% of all experiments on their platform. The popularity of A/B testing comes from its repeatable success and proven impact on revenue and conversion improvements.
Where Is A/B Testing Used?
A/B testing is widely used across industries to improve digital performance through real user data. Common areas of application include:
- E-commerce: Testing product page layouts, pricing displays, or CTA buttons.
- SaaS: Optimizing onboarding flows, landing pages, and feature descriptions.
- Publishing: Comparing headlines, article layouts, or subscription offers.
- Mobile apps: Testing navigation, feature placement, or notification timing.
- Email marketing: Refining subject lines, content order, or send times.
- Social media: Testing ad creatives, captions, or audience targeting strategies.
Creating and testing a new version of a page against the original helps businesses increase conversion rates and improve user experience. This is especially useful for sites with multiple pages, where consistent optimization can significantly impact overall performance.
What Are The Advantages of A/B Testing?
A/B testing helps eliminate guesswork and supports decisions based on measurable results. It allows businesses to optimize digital performance and drive better outcomes across websites, apps, and campaigns.
Here are ten key advantages of A/B testing:
- Improved User Experience (UX). Testing variations of content, layout, or features reveals what users find more intuitive. A better UX increases satisfaction and retention.
- Data-Driven Decisions. A/B testing replaces assumptions with actual performance data, allowing you to make informed changes that align with audience behavior.
- Increased Conversion Rates. By identifying high-performing page elements, you can increase sales, sign-ups, or other key actions.
- Reduced Bounce Rates. When content is better aligned with user expectations—especially on mobile—visitors stay longer, lowering bounce rates.
- Cost-Effective Optimization. Testing prevents waste by validating changes before full rollout, helping you avoid investing in ideas that don’t work.
- Lower Risk. Rather than launching major changes sitewide, A/B testing allows gradual rollouts, minimizing potential negative impact.
- Improved Content Engagement. Testing different headlines, visuals, or formatting helps determine what keeps users engaged and on the page.
- Better Return on Investment (ROI). Optimized landing pages and ads lead to higher conversion rates, making marketing spend more effective.
- Deeper Insight Into Audience Preferences. Over time, A/B testing reveals patterns in user behavior, enabling more targeted and effective strategies.
- Continuous Improvement. A/B testing encourages ongoing experimentation and refinement, leading to sustained performance gains.
According to CXL’s State of Conversion Optimization 2020 report, A/B testing ranks as one of the most effective CRO strategies. Smriti Chawla, in VWO’s CRO Industry Insights, notes that statistically significant A/B tests have increased conversion rates by an average of 49%.
What Are The Disadvantages of A/B Testing?
The main drawback of A/B testing is the risk of creating inconsistent user experiences, especially if tests are run too frequently or without proper planning. Poorly executed tests can confuse or frustrate users, ultimately reducing trust and engagement.
Common pitfalls include:
- Rushed Testing: Launching tests without careful design can lead to unreliable data and ineffective results.
- Ignoring Statistical Significance: Drawing conclusions from incomplete data can lead to decisions based on random variation rather than real differences.
- Acting on Inconclusive Results: Making changes before a test reaches valid conclusions can skew future tests and mislead performance tracking.
To avoid these issues, ensure each test runs long enough to reach statistical significance, uses a sufficient sample size, and is not altered mid-process. Patience and proper setup are essential for producing reliable and actionable insights.
Common Elements Tested in A/B Tests
A/B testing typically focuses on elements that directly affect user behavior and conversion rates. The most commonly tested components include:
- Headlines and Titles: These are often the first elements users see and can influence whether they continue engaging.
- Call-to-Action (CTA) Buttons: Variations in wording, color, size, and placement can significantly affect click-through rates.
- Landing Page Designs: Layout, imagery, and content hierarchy are frequently tested to determine what keeps users engaged.
- Email Campaign Variations: Subject lines, sender names, content structure, and timing can all influence open and click rates.
Other frequently tested elements include:
- Signup Forms: Adjusting the number of fields or field labels can impact completion rates.
- Social Proof Sections: Modifying reviews, testimonials, or trust badges may influence credibility and conversion.
- Ad Copy: Small wording changes can affect engagement and cost-efficiency in paid campaigns.
The key to successful A/B testing is prioritizing elements that are most likely to influence your specific conversion goals.
Let’s explore the most popular elements in more detail.
Website Headlines and Titles
A strong headline plays a critical role in attracting and retaining website visitors. A/B testing helps identify which wording and tone resonate best with your audience by allowing you to compare variations in style, messaging, and phrasing.
Optimizing headlines through testing can lead to higher click-through rates, better engagement, and improved site performance. According to HubSpot, testing headlines can increase click-through rates by up to 10%.
Call To Action (CTA) Buttons
CTA buttons are essential for guiding users toward conversion. A/B testing allows you to compare variations in button text, color, size, design, and placement to determine which version drives better results.
Even small adjustments can influence user behavior and improve conversion rates. According to ConvertVerve, testing CTA buttons can boost conversion rates by 14.79%.
Landing Page Designs
A well-structured landing page directly impacts user experience and conversion rates. A/B and multivariate tests often focus on elements such as:
- Page layout
- Headlines and subheadlines
- Body copy
- Pricing displays
- CTA buttons
- Signup flow
- Form length
- Visual elements
Testing different landing page designs helps identify which elements support user engagement and conversions, and which ones create friction or confusion. This continuous improvement process leads to better lead quality and higher conversion rates.
According to MarketingExperiments, optimizing landing page design can increase conversions by up to 30%.
Landingi, a dedicated landing page builder, supports this optimization process. Its intuitive interface and built-in testing tools make it easy to create, duplicate, and test variations to improve performance.
Email Campaign Variations
Email campaigns are a core component of digital marketing, and A/B testing helps optimize their performance. By testing different versions of subject lines, content structure, or send times, you can identify what drives higher open and click-through rates.
A/B testing refines your email strategy using real user data—eliminating guesswork and improving results.
Example:
A handmade jewelry brand wants to find the most effective subject line for its newsletter. It creates two email versions that are identical except for the subject line:
- Version A: “New Collection: Handcrafted Jewelry Just for You”
- Version B: “Discover Your New Favorite Piece”
Each version is sent to a different audience segment. The subject line with the higher open rate is used in future campaigns to boost engagement and potential sales.
According to Campaign Monitor, testing email variations can increase email revenue by up to 20%.
Why Landing Pages Are the Core of A/B Testing Strategies?
Landing pages are ideal for A/B testing because they provide a focused, conversion-driven environment. Whether you’re testing headlines, CTAs, form length, images, or color schemes, landing pages allow you to isolate variables and see which elements truly affect user behavior. These controlled conditions lead to clear, data-backed decisions rather than assumptions.
Because landing pages often represent the final step before conversion, even small improvements can produce an immediate impact on ROI.
Build variants, run A/B tests without developer support, and optimize pages based on real-time data.
1. Landing Page A/B Testing Example
Landing pages are among the most frequently tested assets in marketing. One notable example comes from ForestView, a digital agency based in Athens, Greece, which ran an A/B test to improve a client’s landing page performance.
The team hypothesized that reducing the need for excessive scrolling would help users find products more easily, thereby increasing form conversions. To test this, they redesigned the page with two key changes:
- Replaced a long product list with carousels
- Introduced multi-level filtering for dynamic product discovery
The A/B test ran for 14 days with over 5,000 visitors evenly split between the original (control) and the redesigned version (variation).
Results:
- Mobile form conversions: ↑ 20.45%
- Desktop form conversions: ↑ 8.50%
- User engagement: ↑ 70.92%
These results confirmed the hypothesis: streamlined navigation and focused filtering improved both usability and conversion rates.
2. SaaS A/B Testing Example
In the SaaS sector, A/B testing plays a key role in optimizing website design and improving conversions. A case study from Basecamp’s Signal v. Noise blog highlights split testing conducted on the Highrise marketing site.
The team hypothesized that a simplified layout—called the “Person Page”—would outperform a longer, more detailed “Long Form” version. To test this, they ran an A/B experiment comparing the two.
Key Results:
- The “Person Page” increased paid signups by 47% compared to the “Long Form” design.
- Compared to the original design (before either version), the increase was 102.5%.
- However, when additional content was added to the “Person Page,” conversions dropped by 22%.
In a separate test, the team examined the effect of using customer photos. They found that large, smiling customer images increased conversions, though the specific person pictured made no significant difference.
These A/B tests, conducted over several weeks, helped the team challenge assumptions and uncover which design elements truly influenced user behavior. The key takeaway: bucket testing reveals what works, even when it contradicts expectations.
3. Real Estate A/B Testing Example
The real estate industry depends heavily on optimized digital experiences. Leading platforms like Zillow, Trulia, and StreetEasy use A/B testing to improve usability and better meet user needs. As highlighted in Anadea’s article “How Effective A/B Testing Helps to Build a Cool Real Estate App,” StreetEasy ran tests to tailor its search experience to New York City users.
StreetEasy A/B Test Highlights:
The platform tested different search filter options and found that users preferred filtering by neighborhood and building type. In NYC, location and property style are major factors in decision-making. For example, someone familiar with Tribeca may not consider an apartment in Chinatown, even if it matches their other preferences.
Visual Content Matters:
Listing images are key to user engagement. StreetEasy observed that visitors spend about 60% of their time viewing listing photos, making image size and presentation a critical element to test and optimize.
Impact of Descriptive Language:
Zillow conducted a study of 24,000 home sales and found that specific keywords in listing descriptions—such as “luxurious,” “landscaped,” and “upgraded”—correlated with higher selling prices. This suggests that wording can directly influence conversion and perceived property value.
4. Mobile App A/B Testing Example
As mobile app optimization becomes increasingly important, A/B testing is playing a key role in shaping in-app experiences and monetization strategies. A notable case comes from AppQuantum and Red Machine, who collaborated on A/B testing for Doorman Story, a time management game where players run a hotel. This case is detailed in the Medium article “How to Conduct A/B Tests in Mobile Apps: Part I.”
Test Objective:
The team tested whether adding a paid game mechanic—a chewing gum machine—could become a viable monetization feature. Unlike similar games that offer such features for free, this version introduced the mechanic behind a paywall in select levels. Care was taken to avoid disrupting game balance.
Key Insight:
The goal was to measure how players responded to this paid element and whether it was perceived as valuable. A primary concern was user drop-off if players rejected paying for previously free tools.
Results:
The A/B test revealed that the simplest version of the mechanic performed best. This result challenged initial assumptions and demonstrated that simplicity can outperform more complex monetization models.
Conclusion:
This test highlights the importance of A/B testing in mobile apps—especially for monetization. By testing player behavior before a full rollout, the team avoided potentially alienating users and identified the most effective approach.
5. Email Marketing A/B Testing Example
MailerLite has conducted multiple A/B tests to improve email performance, as detailed in Jonas Fischer’s article, “A/B Testing Email Marketing Examples to Improve Campaigns, Landing Pages and More.” Their experiments focused on subject line length, use of emojis, question-based openings, image positioning, and automation performance.
Emoji Use in Subject Lines
In early tests, emojis had little effect. For example, in 2020:
- With emoji: 31.82% open rate
- Without emoji: 31.93% open rate
However, repeated testing showed a shift over time. In later experiments:
- With emoji: 37.33% open rate
- Without emoji: 36.87% open rate
This indicates that emoji effectiveness can evolve based on audience familiarity and broader email trends.
Subject Line Length
MailerLite also tested the impact of concise vs. long subject lines. Results showed that shorter subject lines led to stronger engagement, including:
- Up to 100% open rate
- 85.71% click rate
These findings highlight the importance of continuously testing even minor elements to stay aligned with audience preferences.
6. E-commerce A/B Testing Example
An article by Tomer Dean, “The Battle of Conversion Rates — User Generated Content vs Stock Photos,” explored how different types of imagery impact e-commerce performance, particularly in the fashion and apparel sector.
Test Focus:
The A/B testing campaign compared user-generated content (UGC)—photos of real people wearing the products—against traditional stock photography.
Key Results:
- For a Nike sports bra, a UGC image from Instagram achieved a 0.90% conversion rate, compared to 0.31% for the stock photo.
- A landing page for red high heels that combined a stock image with three UGC images significantly outperformed the version using only a stock photo.
- Additional tests with products like a Zara skirt and Nike running shoes showed similar trends, though with some variation by product type.
These tests demonstrated that authentic visuals can outperform polished stock imagery in driving conversions. However, the study also emphasized the need for ongoing testing and adherence to image licensing and copyright standards when using UGC.
According to Smriti Chawla in VWO’s “CRO Industry Insights from Our In-App Survey Results,” e-commerce sites generate an average of $3 per unique visitor, and a well-executed A/B test can boost that figure by up to 50%.
Can A/B testing be applied to every element on a website?
Technically, A/B testing can be applied to any element. However, it’s more effective to prioritize elements that have the greatest impact on user behavior and conversion rates—such as headlines, CTAs, forms, and key navigation features. Targeted testing ensures resources are focused where they deliver the most value.
Is there a risk of alienating users with frequent A/B tests?
Yes. If A/B tests are run too often or without consideration for user experience, they can cause confusion or frustration. To avoid this, test thoughtfully and maintain consistency across core site functions. Balance experimentation with a stable, user-friendly experience.
What is a control group in A/B testing?
A control group is the original version of a webpage, app, or element used as a baseline in an A/B test. It provides a reference point to measure the impact of changes made in the test variation.
What constitutes a statistically significant result in A/B tests?
A statistically significant result means the observed difference between the control and variation is unlikely to be due to chance. Most A/B tests use a 95% confidence level, meaning there’s only a 5% probability that the result occurred randomly.
What percentage of A/B/x tests fail?
According to VWO’s CRO Industry Insights by Smriti Chawla, only 14% of A/B tests result in statistically significant wins—meaning 86% fail to produce a measurable improvement in conversion rates. Research from Convert.com supports similar findings. However, failure rates may vary depending on the industry, audience size, and test complexity.
What are the popular tools used in A/B testing?
Commonly used A/B testing tools include:
- Optimizely
- VWO (Visual Website Optimizer)
- Google Optimize (sunset in 2023 but still referenced)
- Adobe Target
- AB Tasty
These platforms allow businesses to design, run, and analyze A/B tests—offering features such as targeting, multivariate testing, and detailed reporting.
Which has a more profound impact on user behavior: changing CTA color or its text?
CTA text typically has a more profound effect, as it directly influences how users interpret and respond to the action being asked. While color can enhance visibility, the wording shapes intent and urgency.
- A HubSpot study by Joshua Porter showed that changing a button’s color (red vs. green) increased conversions by 21%.
- Research by Melanie Deziel found that personalized CTA text can improve conversion rates by up to 202%.
In summary, while color matters visually, text has a greater influence on user behavior and overall conversion performance.
Which industries benefit the most from A/B testing?
Industries that gain the most from A/B testing include:
- E-commerce – optimizing product pages, pricing displays, and checkout flows
- SaaS – improving sign-up processes, onboarding flows, and feature messaging
- Digital marketing – refining email campaigns, landing pages, and ad performance
- Mobile app development – enhancing UI/UX, in-app purchases, and retention strategies
These industries benefit by using A/B testing to improve user experience, increase conversions, and make informed, data-driven decisions.
What is A/B testing in the marketing example?
A/B testing in marketing involves comparing different versions of campaigns—such as ad creatives, headlines, or messaging—to identify which performs better. By testing variations systematically, businesses can determine what resonates most with their audience and improves engagement or conversion rates. Frequent testing also helps refine strategy and make more effective, data-driven decisions.
What is an example of A/B testing on social media?
A/B testing on social media involves comparing variables like ad images, captions, or audience targeting. For example, a business might test two Facebook ads—one using a product photo and another using user-generated content—to see which version drives more clicks or conversions with a specific audience segment.
Why do we use A/B testing?
A/B testing helps marketers make data-driven decisions, improve user experience, and increase conversion rates. By testing different versions of webpages, apps, or campaigns, businesses can identify what works best, optimize performance, and improve return on investment.
Is A/B testing a KPI?
A/B testing is not a KPI itself but a method used to influence and measure KPIs such as conversion rate, click-through rate, or bounce rate. It provides insights into how specific changes affect user behavior and helps identify strategies that drive measurable results.
What is an example of A/B testing in real life?
A real-life example of A/B testing is comparing two product packaging designs to see which leads to more sales. Other examples include testing different store layouts or pricing strategies to determine which version improves customer experience or boosts revenue.
What are A/B samples?
A/B samples refer to the two versions used in a test: the control (original version) and the variation (modified version). Comparing their performance helps identify which version better supports goals like higher engagement or conversion.
How many companies use A/B testing?
According to the 2020 CXL Institute report State of Conversion Optimization, 44% of surveyed companies use A/B testing software. The study included 333 companies of varying sizes across multiple industries.
Adoption is especially common in sectors with strong digital operations, such as e-commerce and SaaS. A separate Econsultancy report from 2019 found that 77% of companies with structured CRO programs use A/B testing regularly.
How does Netflix use A/B testing?
Netflix uses A/B testing to improve user experience by testing elements such as:
- Thumbnails for shows and movies
- User interface layouts
- Personalized recommendation algorithms
- Preview autoplay settings
The goal is to identify which versions increase user engagement, retention, and overall viewing time.
Does YouTube have A/B testing?
YouTube does not offer A/B testing tools for creators. However, YouTube itself regularly conducts internal A/B tests to improve platform features and user experience. Creators who want to test content performance must rely on external tools or manual methods (e.g., testing thumbnails or titles over time).
Does Shopify have A/B testing?
Shopify does not include built-in A/B testing, but merchants can use third-party apps from the Shopify App Store to run tests. These tools allow for testing elements like product pages, pricing, and checkout flows.
Can Mailchimp do A/B testing?
Yes. Mailchimp offers A/B testing features that let users compare subject lines, email content, and send times to improve open and click-through rates.
Can Wix do A/B testing?
Wix does not offer native A/B testing. However, users can integrate third-party tools like Google Optimize to run tests on their websites. These tools allow Wix users to test layout, content, and design elements to enhance conversions and user engagement.
Does Google do A/B testing?
Yes. Google regularly conducts A/B and multivariate tests across its products and services to evaluate feature changes, interface updates, and algorithm improvements. These tests help ensure new implementations meet performance and user experience goals.
How to do A/B testing with Google Ads?
To run an A/B test in Google Ads:
- Select the variable you want to test (e.g., ad copy, keywords, or landing page).
- Create two ad versions—one original (control) and one variation.
- Run both ads simultaneously under similar conditions.
- Analyze performance data (CTR, conversions, etc.) to determine the winning version.
How to conduct an A/B test in Excel?
To run an A/B test in Excel:
- Use Excel’s T.TEST function to determine whether the difference between groups is statistically significant.
- Organize data into two groups: control (A) and variation (B).
- Calculate the mean and standard deviation for each group.
How much data is needed for the A/B test?
The required sample size depends on your desired confidence level, expected effect size, and traffic volume. As a general guideline, aim for at least 100 conversions per variation to detect meaningful differences. Higher traffic and longer test durations improve reliability.
Is A/B testing expensive?
Costs vary based on tools, complexity, and who runs the test.
- Free tools: Some platforms offer basic A/B testing features at no cost.
- Paid tools: Advanced tools may require a monthly or usage-based fee.
- In–house vs. outsourced: Running tests internally is often more cost-effective, while outsourcing may incur higher costs but offer strategic guidance.
Build Better Campaigns with Data-Driven A/B Tests on Landing Pages
A/B testing is more than a tool—it’s a mindset focused on continuous improvement. It enables marketers to make informed, data-backed decisions that refine campaigns and improve results. Whether testing a CTA button, a landing page layout, or an email variation, A/B testing helps identify what truly drives engagement and conversions.
As digital competition increases, the role of split testing in enhancing user experience and driving business outcomes continues to grow.
Platforms like Landingi have democratized access to this powerful tool, enabling businesses of all sizes to conduct insightful experiments. With Landingi, you can create a great campaign and optimize it with multivariate tests, campaign scheduler, dynamic text replacement, and personalization options to achieve the best results. The most exciting thing is that you can begin using the Landingi platform for free!
