Ankur Goyal
Ever wondered why your marketing campaigns aren’t generating any meaningful results, despite all the money and effort you put in? Crafting the perfect email, landing page, or ad can feel like shooting in the dark when you’re unsure what resonates with your audience.
That’s where split testing becomes useful. By testing different variations of your content, ads, or landing pages, you can pinpoint what truly resonates with your audience and make data-driven decisions that boost performance. But where do you begin?
In this guide, we’ll explain what split testing is, explore its types and benefits, and provide actionable steps to conduct tests efficiently. You’ll also discover the powerful split testing you can use for your experiments.
What is Split Testing?
Split testing is a conversion rate optimization method used to compare two or more versions of a webpage, email, ad, or other digital content to determine which one performs better and optimize your campaigns based on actionable data.
Unlike A/B testing, which typically compares two distinct versions (A and B) with different elements, split testing often involves dividing your audience into equal groups and showing each group a completely different version of the campaign.
For example, you might test two entirely separate landing pages with different designs, messaging, or layouts to see which one drives more conversions or engagement. The goal is to identify the version that resonates most with your audience and achieves your desired outcome, whether that’s clicks, sign-ups, or sales.
Split testing is particularly useful when you want to test broader changes rather than isolated elements.
Split Testing vs A/B Testing
A/B testing and split testing are often used interchangeably, but they have distinct differences in methodology and application.
Let me clarify the distinction for you:
A/B testing is a type of split testing that involves comparing two versions (A and B) of a single variable to see which performs better. It's typically binary—one variable is changed while all other conditions remain constant (e.g., two different headlines on the same landing page).
Key features of A/B testing
Tests two distinct versions (A and B) of a single element (e.g., a headline, button color, or layout).
Focuses on isolating and testing one variable at a time (e.g., changing only the call-to-action text).
Requires a control group (version A) and a variation (version B).
Often used for optimizing conversion rates, click-through rates, or other specific metrics.
For example, you could test a green "Buy Now" button (version A) against a red "Buy Now" button (version B) to see which generates more clicks.
On the other hand, split testing is broader and compares two entirely different experiences or pages. Instead of tweaking just one element, split testing often involves testing completely different designs, layouts, or concepts. For example, testing two completely different landing pages with unique designs and content.
Unlike A/B testing, split testing doesn't always focus on isolating a single variable; it can involve testing completely different designs or workflows.
Key features of split testing
Tests two or more entirely different versions of a page or product.
It can involve multiple variables changing at once (e.g., different layouts, images, and copy).
Often used for more radical changes rather than incremental optimizations.
Sometimes referred to as "multivariate testing" when multiple variables are tested simultaneously.
For instance, you could run a split test that compares two completely different landing page designs to see which one performs better overall.
Here is a summary of the key differences between split testing and A/B testing:
Aspect | Aspect A/B testing | Split testing |
|---|---|---|
Scope | Tests one variable at a time. | Can test multiple variables or entire designs. |
Focus | Focus Incremental changes. | Radical or holistic changes. |
Complexity | Simpler and more controlled. | Can be more complex and less controlled. |
Use Case | Optimizing specific elements. | Testing entirely different approaches. |
Ideally, split testing is useful when you want to test larger, more comprehensive changes (e.g., a completely new layout or design), whereas A/B testing is useful when you want to test small, specific changes to optimize performance (e.g., button color, headline text)
Types of Split Testing
The common types of split testing include A/B testing, A/B/n testing, and multivariate testing. Let’s explore them in depth.
1.A/B testing
A/B testing compares two versions (A and B) of a single element to determine which one performs better. To run the test, you divide your audience into two groups and show each group a different version of the same element, like a headline or a button. After gathering data, you can see which version drives more engagement, conversions, or clicks.
If you run an e-commerce website, you might test two product page headlines: “Limited-Time Offer – Shop Now” vs. “Exclusive Deals Just for You.” Then the headline that leads to more purchases becomes the clear winner.
For example, by changing three words on their CTA button from “SIGN UP FOR FREE” to “TRIAL FOR FREE,” Going increased conversion rates by 104%:
Source: Unbounce
2.A/B/n testing
The second type of split testing is A/B/n testing. It expands on A/B testing by comparing multiple variations (A, B, C, etc.) at once against a control.
Instead of just testing two versions, you can experiment with several to determine which one performs the best.
If you’re running a digital ad campaign, you might test three different headlines: “Get 50% Off Today,” “Exclusive Discount for Members,” and “Flash Sale – Limited Time Only.” By tracking click-through rates, you can quickly see which headline resonates most with your audience.
This method gives you more options to optimize, but requires a higher volume of traffic for accurate results. For example, Fab, an online retailer, increased cart adds by 49% by making their “Add To Cart” button clearer.
Source: Wishpond
They tested three variations of their “Add To Cart” button. Variation one (control) had only the cart image with a “+”. Then the second variation replaced the image with the text “Add To Cart,” and the third variation included a “+” and the word Cart. The button with the “Add To Cart” text increased conversions by 49%.
3. Multivariate testing
Multivariate testing lets you test multiple elements of a webpage, email, or ad simultaneously to find the best-performing combination. Instead of comparing just one element, you tweak several variables at once, like headlines, images, and buttons, and test different combinations.
For example, if you're optimizing a landing page, you might test three different headlines, two images, and two button styles. This setup would create several combinations as below:
Headline A + Image 1 + Button X
Headline B + Image 2 + Button Y
Headline C + Image 1 + Button Y
This split testing method helps uncover which combination of elements drives the best results, such as higher conversions or engagement.
While more complex than A/B or A/B/n testing, it’s powerful for optimizing entire pages or layouts by revealing how different elements work together to influence user behavior.
Note that multivariate testing can be complex due to the multiple elements involved.
Why not automate the process? With MAX: Experimentation Expert, one of FIBR’s innovative agents, you can test multiple elements simultaneously to find the best-performing combinations.
Split Testing Benefits
If done right, split testing can help you uncover useful insights to improve performance, enhance user experience, and drive growth. Here are additional benefits of running split tests:
1. Gaining a better understanding of your potential customers
Imagine if you could uncover what your customers want and how they want it. Every marketing campaign you’d run would be a sure bet. That’s the potential of split testing.
One of the most significant advantages of split testing is its ability to reveal deeper insights into your target audience. By testing different headlines, images, calls-to-action (CTAs), or value propositions, you can uncover what truly resonates with your potential customers.
For example, you might discover that a humorous headline outperforms a straightforward one, or that a specific image evokes a stronger emotional response. These insights go beyond guesswork—they are based on real user behavior and preferences.
Understanding your audience at this granular level allows you to tailor your landing pages, messaging, and design to align with their needs and desires.
Over time, this knowledge helps you create more personalized marketing campaigns, develop products that better meet customer expectations, and build stronger relationships with your audience.
2. Improving customer engagement rate
Customer engagement is a critical metric for any business, as it directly impacts retention and loyalty. Split testing helps you optimize your product, website, or onboarding process to create a more engaging experience for your users.
For instance, you might test different onboarding flows to see which one leads to higher user activation rates or test various email subject lines to determine which one drives more opens and clicks.
By identifying what keeps your customers engaged, you can refine your strategies to foster a more loyal customer base.
Engaged customers are more likely to return to your site, recommend your product to others, and become long-term advocates for your brand. Hence, split testing ensures that every touchpoint in the customer journey is optimized to maximize engagement.
3. Reducing bounce rates
Bounce rate is the percentage of visitors who leave your site after viewing only one page and can be a major concern for businesses. A high bounce rate may suggest that something on a page fails to capture your customer’s interest or meet their expectations.
Split testing helps to uncover what’s working and what’s not working by allowing you to experiment with different layouts, CTAs, images, and content to find the combination that keeps visitors on your site longer.
For example, you might test a simplified design versus a more detailed one, or a video-based landing page versus a text-heavy version.
By uncovering why customers are bouncing back, you can refine your website elements to create a seamless and compelling experience that encourages visitors to explore further instead of leaving immediately.
4. Reducing risk
Launching a new design, feature, or campaign can feel like a gamble, especially if you’re unsure how your audience will react. Split testing mitigates this risk by allowing you to test changes on a smaller scale before fully committing.
For example, you could test a new website layout with a portion of your traffic to see how it performs compared to the current version.
If the new design leads to higher conversions, you can roll it out with confidence. If it underperforms, you can make adjustments or revert to the original without significant consequences.
This approach minimizes the potential for costly mistakes and ensures that any major changes you implement are backed by data. It’s a low-risk, high-reward strategy that empowers businesses to innovate while maintaining stability.
5. Improving content and design
Split testing doesn’t just benefit your current campaigns—it also has a long-term impact on your creative processes. As your team conducts more tests and gathers data, they’ll start to identify patterns and preferences that resonate with your audience.
Over time, this knowledge informs the creation of future content and designs, ensuring they align more closely with your target audience’s preferences.
For instance, an email marketing team tests different subject lines to see which ones generate higher open rates. The design team tests various website layouts to determine which one provides the best user experience. Over time, these insights help teams naturally create content that is more engaging and effective.
6. Boosting conversion rates
At its core, split testing is about optimizing for conversions—whether that’s signing up for a free trial, making a purchase, or downloading a resource. By testing different elements of your landing pages, CTAs, forms, and more, you can identify the combinations that drive the highest conversion rates.
For instance, after a successful split test, you might find that a green button outperforms a red one, or that a shorter form leads to more submissions.
Even small improvements in conversion rates can have a significant impact on your bottom line, especially when scaled across your entire audience. Split testing ensures that you’re always moving closer to the most effective version of your marketing assets.
7. Improving ROI
Split testing is essential for maximizing return on investment (ROI) for your paid advertising campaigns. It helps you test variations in ad copy, keywords, targeting options, and visuals, to identify which combinations drive the most clicks and conversions.
This allows you to allocate your budget more effectively to avoid wasting money on underperforming ads.
For example, you might discover that a specific keyword generates a higher click-through rate (CTR) or that a particular demographic responds better to your messaging.
You can then use these insights to refine your campaigns and achieve better results with the same or even a smaller budget. Over time, this leads to a higher ROI and more efficient use of your marketing resources.
Split testing transforms marketing by helping you with actionable insights to optimize strategies, improve engagement, and increase conversions.
The key to successful split testing is continuous experimentation. By consistently testing, analyzing, and refining, you stay ahead of the competition and build marketing campaigns that truly resonate with your target audience.
With Fibr’s Experimentation Expert (MAX), you can run 24/7 experiments and continuously refine your website’s performance.
Here are the key capabilities of our experimentation agent
Hypothesis generation: The agent analyzes your website’s content, visuals, and conversion goals to generate data-driven hypotheses for testing.
Always-on testing: It ensures your website is in a constant state of optimization, running countless experiments to identify high-performing variations.
Want to see how our experimentation agent works? Book a demo call with our CRO experts today.
How to Conduct Split Testing?
Conducting effective split testing requires careful planning, execution, and analysis. Here is a step-by-step process to guide you.
Step 1: Define your split testing goals and objectives
Before starting a split test, you need to establish clear goals and objectives. Here are questions to ask yourself:
What are you trying to achieve?
Are you looking to increase conversions, improve click-through rates, reduce bounce rates, or boost engagement?
Do you want to test a new landing page, checkout process, or site navigation?
What key performance indicators (KPIs) will measure success (e.g., conversion rates, time on page, cart abandonment rates)?
Without clear goals, you won’t know what success looks like or how to measure it. Having a well-defined goal ensures your test has a clear direction and helps in interpreting results accurately. This will also help you avoid testing irrelevant elements.
For example, if you’re testing a landing page, your goal might be to increase the number of sign-ups. If you’re testing an email campaign, your objective could be to improve open rates or click-through rates.
Be specific about what you want to achieve, and ensure your goals are measurable and aligned with your broader business objectives as well.
Step 2: Define your hypothesis
A hypothesis is a smart guess about what changes will lead to better results. It’s the foundation of your split test and helps you focus on specific elements to test. Your hypothesis should be a clear statement predicting the impact of a change and also based on data, user behavior, or insights from previous tests.
Use this structure:
If [specific change] is made, then [expected outcome] will happen, because [reasoning].
Assuming you’re testing a call-to-action (CTA) button, your hypothesis might be: “Changing the CTA button color from blue to green will increase click-through rates because green is more visually appealing and stands out better against the background.”
For instance, if you suspect a shorter checkout process will improve conversions, your hypothesis might be:
If we reduce the number of checkout steps from five to three, then our conversion rate will increase by 15% because users will find it easier to complete the purchase.
Why not take your hypothesis generation a notch higher using AI?
With FIBR’s MAX, you can discover hidden patterns in historical data, user behavior and other insights to generate data-driven hypotheses for your split testing process.
The agent can also automate the process so you can focus on other business activities.
See what customers are saying about our MAX Experimentation Agent:
Book a demo call today to see how FIBR can help you execute successful, result-driven split tests.
Step 3: Calculate your sample size
Without a sufficient sample size, your results may not be reliable which can lead to incorrect conclusions and wasted effort. Equally, if your sample size is too small, results may be unreliable. If it’s too large, you may waste resources testing unnecessary data.
Therefore, to ensure your split test results are statistically significant, you need to determine the appropriate sample size. This is the number of users or visitors who will participate in the test. A sample size that’s too small may lead to unreliable results, while one that’s too large could waste resources.
Use a sample size calculator (available online) to calculate your testing sample size based on:
The number of participants you need based on your current conversion rate
Minimum detectable effect (the smallest improvement you want to detect)
Statistical significance (typically 95%)
Statistical power (usually 80%)
For example, if your current conversion rate is 5%, and you want to detect a 10% improvement with 95% confidence, you might need around 10,000 participants per variation.
Step 4: Create the variations
Once you’ve defined your goals, hypothesis, and sample size, it’s time to create the variations you want to test. These could include changes to design elements, copy, layout, images, CTAs, or even entire page structures.
The key is to test one variable at a time (univariate testing) or multiple variables simultaneously (multivariate testing), depending on your goals.
For example, if you’re testing a landing page, you might create three variations:
Variation A: Original design with a blue CTA button.
Variation B: Same design but with a green CTA button.
Variation C: A completely redesigned layout with a green CTA button.
Worth mentioning that, creating multiple variations manually can be time-consuming and hectic. To streamline this process, you can use FIBR’s visual editor to create variants. This feature can automatically generate and manage split testing variations.
The visual editor also allows you to track events—define, manage, and measure interactions like clicks, form submissions, and other key actions to optimize your website's performance without needing advanced technical skills.
FIBR also integrates with analytics tools like Google Analytics 4 for tracking performance.
Want to see FIBR’s visual editor in action? Book a demo call with our CRO experts.
Step 5: Run the test
With your variations ready, it’s time to launch the test. Use a split testing tool or platform to set up the experiment. The tool allows you to split your traffic evenly among the variations and ensure that each user sees only one version during the test.
Make sure the test runs long enough to collect sufficient data to make statistically significant conclusions. The duration will depend on your website traffic and the sample size you calculated earlier.
For example, if your site receives 1,000 visitors per day and you need 10,000 participants per variation, the test should run for at least 10 days.
Follow these best practices:
Test in a real-world environment: Ensure that the test runs on your live website with real visitors.
Keep external factors constant: Avoid running major marketing campaigns or making additional website changes during the test period.
Set a fixed duration: Tests should run for at least 2-4 weeks to gather meaningful data, depending on your traffic volume.
Step 6: Split traffic evenly
One of the most critical aspects of split testing is ensuring that traffic is divided evenly and randomly among the variations. This eliminates bias and ensures that external factors (like time of day or user demographics) don’t skew the results.
Most split testing tools handle this automatically, but it’s essential to double-check the settings. For example, if you’re testing three variations, the tool should allocate 33.3% of the traffic to each version. Avoid manually splitting traffic, as this can lead to inconsistencies and inaccurate results.
Use your split testing tool to:
Automatically divide traffic between variations
Ensure each visitor sees only one version
Track real-time performance metrics
Step 7: Analyze and optimize
Once the test is complete, it’s time to analyze the results. Look at the key metrics you defined in your goals, such as conversion rates, click-through rates, or engagement levels. Use statistical analysis to determine whether the differences between the variations are significant.
For example, if Variation B (green CTA button) has a 12% conversion rate compared to Variation A’s (blue CTA button) 10%, you’ll need to check if this difference is statistically significant. Most split testing tools like FIBR provide built-in analytics to help you interpret the data.
If one variation performs significantly better, implement it as the new default. If the results are inconclusive, consider running the test again with a larger sample size or refining your hypothesis. Remember, split testing is an iterative process. Even if you find a winning variation, there’s always room for further optimization.
Split Testing Best Practices
While there is no one-size-fits-all formula for running split tests, there are set practices that can guide you successfully. These include:
Keep split tests simple
One of the most important rules in split testing is to keep your tests simple. Overcomplicating your tests by introducing too many variables at once can make it difficult to pinpoint which factor is responsible for any differences in performance. Instead, focus on a single major change at a time to gain clear, actionable insights.
For example, if you’re testing a landing page, focus on one element at a time, such as the headline, call-to-action button, or imagery.
Here are actionable tips to follow:
Choose a single variable to test. Avoid modifying multiple elements simultaneously.
Make minor but meaningful changes. For example, if testing a headline, try a different phrase instead of completely rewriting the content.
Avoid testing too many variations at once. Limit yourself to two or three versions to maintain clarity in the results.
Use clear labels: Clearly label and document each variation to avoid confusion during analysis.
Isolate variables
Isolating variables is closely tied to keeping tests simple. When you change multiple elements simultaneously, it becomes nearly impossible to determine which change influenced the outcome.
For instance, if you alter both the color of a button and the text on it, you won’t know whether the color or the wording led to the improvement. To avoid this, test one variable at a time.
This means altering only one aspect at a time while keeping everything else constant. This way, you can confidently attribute any performance changes to the specific element you modified. Isolation ensures clarity and actionable insights.
Use the following tips when isolating variables:
Control external factors: Ensure that external elements (e.g., time of day, traffic sources, or device types) remain consistent across all test groups.
Use a control version. Always have a baseline to compare the new variation against.
Document changes meticulously: Keep a detailed log of what was changed in each variation to avoid overlap with other tests.
Ensure sufficient sample size
The sample size is critical in split testing. If your sample size is too small, the results may not be statistically significant. For example, testing a new feature with only 50 users might not provide enough data to determine its effectiveness.
Use statistical tools or calculators to determine the appropriate sample size based on your expected effect size and confidence level. A larger sample size increases the reliability of your results, ensuring that the insights you gain are accurate and actionable.
Use these tips when evaluating your split testing sample size:
Use a sample size calculator: Utilize an online sample size calculator to determine the minimum number of participants needed for statistically significant results.
Avoid premature conclusions: Wait until you’ve reached the required sample size before analyzing results to ensure reliability.
Segment your audience: If testing a niche audience, ensure the sample size is large enough within that segment to draw meaningful conclusions.
Allocate an adequate testing duration
Timing is another crucial factor in split testing. Running a test for too short a period can skew results due to external factors like day-of-week trends or seasonal fluctuations.
For example, if you run a test for only one day, the results might be influenced by a temporary spike in traffic or a holiday.
On the other hand, running a test for too long can delay decision-making and slow down progress. Aim for a testing duration that captures a full cycle of user behavior, such as a week or a month, depending on your traffic volume and business cycle.
Follow these tips:
Account for full business cycles: Run tests for at least one full business cycle (e.g., a week to capture weekday and weekend behavior).
Avoid seasonal bias: Avoid testing during holidays or special events unless they are part of the experiment.
Monitor traffic patterns: Ensure the test runs long enough to capture variations in traffic (e.g., morning vs. evening users).
Analyze results thoroughly before implementing changes
Once your split test is complete, take the time to analyze the results thoroughly. Don’t rush to implement changes based on surface-level observations. Dive deep into the data to understand not just what happened, but why it happened.
Look for statistical significance, patterns, and anomalies. For example, if one variation performed better, ask yourself whether the improvement was consistent across all user segments or only specific demographics. Thorough analysis ensures that your decisions are data-driven and aligned with your goals.
Iterate and optimize
Split testing is an ongoing process. Continuous iteration helps you stay ahead of changing user preferences and market trends. Use insights from one test to inform the next. For example, if a red CTA button performed well, test its placement or wording in the next round.
Also, review your performance metrics regularly to identify new areas for improvement and stay updated on industry trends and competitor strategies to inspire new test ideas.
Split Testing Tools to Run Experiment
When it comes to split testing, a handful of tools offer unique features to help businesses run effective split tests, optimize user experiences, and drive better results. Here are five that can help you:
1. Fibr AI
Fibr is an AI-powered conversion rate optimization solution offering a powerful split testing tool designed to help you optimize your landing pages, mobile apps, and other digital experiences to improve conversion rates.
At its core is Max, an AI-powered experimentation agent that automates split testing. Max runs always-on experiments, continuously testing and optimizing every element of your landing page, from headlines to CTAs, without manual intervention.
One of Fibr’s standout features is its hypothesis generation capability. Max analyzes your website’s content, visuals, and conversion goals to create data-driven hypotheses for testing. This ensures that every test is rooted in actionable insights, not guesswork.
Fibr also offers a visual editor, making it easy for users to create and deploy variations of their landing pages without needing technical expertise.
Additionally, it integrates seamlessly with Google Analytics 4 (GA4), allowing you to track the performance of your tests in real time and measure their impact on key metrics like conversions and engagement.
2. VWO
VWO is a comprehensive split testing tool that excels in split URL testing, allowing you to test entirely different versions of a webpage. Its intuitive interface and robust analytics make it easy to set up experiments, track performance, and derive actionable insights.
3. Dynamic Yield
Dynamic Yield focuses on personalization and optimization through split testing. It allows you to test different content variations across multiple channels, including web, email, and mobile.
4. GrowthBook
GrowthBook simplifies split testing for developers and marketers. It offers feature flagging, A/B testing, and analytics, making it ideal for teams looking to integrate testing into their development workflows.
5. Kameleoon
Kameleoon allows you to create and test variations of your website or app without coding, making it accessible to non-technical users. Its targeting and analytics capabilities help you deliver highly relevant experiences to your audience.
Conclusion
Split testing is not just a tool—it’s a game-changer for anyone serious about optimizing their marketing efforts and driving measurable results.
By testing and refining your campaigns, you can uncover what truly resonates with your audience, eliminate guesswork, and make data-driven decisions that boost engagement, conversions, and ROI.
Whether you’re tweaking a headline, redesigning a landing page, or overhauling an entire campaign, split testing empowers you to innovate with confidence.
The insights you gain from split testing can transform your marketing strategy, help you stay ahead of the competition, and deliver experiences that your audience loves.
Follow this guide, implement the steps, and run effective split tests.
If you aren’t sure where to start, we’re here to help you.
With FIBR’s conversion rate optimization solution, you get everything you need from tools to CRO experts who can guide you throughout the process.
Book a demo call today to see how we can help you.
FAQs
1.What challenges come with split testing?
Some of the challenges of split testing include the need for significant traffic to achieve statistical significance, potential implementation errors, and the time required to gather reliable data.
Other challenges include external factors like seasonality or user behavior changes which can skew results and make it harder to draw accurate conclusions.
2.What are the best tools for split testing?
The best split testing tools include Fibr AI which uses AI to automate split testing with Max, its experimentation agent. You can also use VWO for split URL testing or Dynamic Yield for personalizing web, email, and mobile channels. Other tools for running split tests include GrowthBook and Kameleoon.
3.How do you analyze split test results?
Analyze split test results by comparing key metrics like conversion rates, engagement, and revenue between variations. Use statistical significance to ensure results are reliable. You can use tools like GA4 or built-in analytics in your testing platform to track performance and identify winning variations.
4.Is split testing cost-effective?
Yes, split testing is cost-effective when done strategically. It helps optimize resources by identifying high-performing variations and reducing wasted spend on underperforming elements. However, costs can rise if tests are poorly designed or require extensive technical support.
5.How frequently should you conduct split tests?
Conduct split tests regularly, but avoid over testing. While testing, focus on testing significant changes or hypotheses and allow sufficient time for each test to gather meaningful data. Continuous testing is ideal for dynamic environments, but balance it with resource availability.
