Split testing

Boost business growth with strategic split testing

Split your traffic, analyze performance and find winning combinations. Make informed decisions to enhance user engagement, boost conversions and optimize your website for best performance.
No flickering: No flickering that can mess up your website's UX.
Performance: Test without impacting your website's performance.
Audience segmentation: Location, screen size, OS and browser.
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Split testing

What is split testing?

Split testing is a type of A/B test where you compare two different versions (A vs. B) to see which one performs better. Split testing is more extensive, here you change a whole page. You can test bigger changes, like the layout, the colors or even where certain elements on your website are placed. 

Your website visitors are randomly directed to one of these whole page variants and you measure which performs better. The goal here is to see which big changes lead people to do important actions more often, like clicking a button or signing up. These are called conversions and they tell you which variant works better.

The ultimate guide to A/B testing in Webflow

Follow this guide and test different versions of your site and discover the best performing ones to improve conversions.
Benefits

What are the advantages of split testing?

There are many advantages of split A/B testing that you can leverage as a business. If you want to optimize your website and stay competitive, testing is the way to go.

Increased conversions

Getting high-quality website traffic is often connected with significant expenses. However, by split testing you can maximize your current traffic without overspending money and resources. Even minor adjustments, like changing the CTA button, can bring you great ROI.

Visitor pain points

Visitors visit your website with a specific objective in mind, but they may encounter some common challenges during their journey. All this results in poor user experience and decreased conversion rates. So split testing enables you to quickly address and fix these pain points.

Reduce bounce rate

Different factors can contribute to a high bounce rate, including too many choices, unclear navigation or too complicated copy on your site. While there's no one-size-fits-all fix, A/B testing software provides a super simple way to find the most effective combination.

Low-risk changes

Split testing within Webflow allows for small adjustments rather than a complete page overhaul instantly. Making a lot of changes simultaneously can have unpredictable results, whereas implementing smaller tweaks based on statistical data is safer. 

Data-driven decisions

Because split testing relies solely on data rather than your intuition, you can identify a winning or losing variant faster. By depending on this data-driven approach, you mitigate any risks associated with your subjective judgments or punches in the dark because you guessed.

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How to

How to split test in Webflow?

The split testing framework is built of six essential steps that you need to take for a successful split test on your website or app. Ignoring them could compromise the accuracy of your test outcomes.

Research

Before outlining your split testing strategy, you must analyze your website's performance data. Take a look at the insights from analytics tools to pinpoint which pages you wish to test. Identify pages driving the most traffic and having the highest conversion rates. Prioritize high-potential or high-traffic pages to optimize the efficiency of your split tests.

Hypothesis

Clearly defining your objectives is crucial for assessing the effectiveness of your split tests. Draft a hypothesis, whether complex or more simple, like "Moving the CTA button to the middle of my landing page will increase the conversion rate by 5%." This step will lay the bricks for future improvements when you look back at the results.

Install Optibase

If you want to split test in Webflow, plenty of options are available, but only one seamlessly integrates with Webflow. Optibase, a Webflow app, lets you set up split tests in minutes without compromising website loading performance. Find Optibase in the Webflow Apps marketplace and install it in Designer.

Create variants

Once your split testing software is installed, go on and create a variant based on your hypothesis and split test it against the existing version. In Optibase, go to the "Tests" page and add a new variant. Also make sure to link it to the element in Webflow, so Optibase can know which HTML element belongs to each variant.

Execute your test

With your variants all set up, it's time to start the split testing process. But before you go on with the test, consider several factors to enhance the result accuracy. You should run your tests for enough time to gather accurate data. The average testing time in Webflow is 2 weeks, but you need to adapt it to your goals. Consider using the A/B test duration calculator to plan ahead.

Evaluate results

This is the final step in your split testing campaign, but it's also the most crucial. Split testing requires making continual changes based on data to enhance conversion rates. After your test is completed, analyze metrics like percentage increase, confidence level and the P2BB value that is provided in the testing software.
Features

Optibase split testing features

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Audience segmentation

Optibase offers precise audience segmentation settings, including geolocation, screen size, device type and operating system. By tailoring experiments to specific audiences, users can optimize user experience and conversions based on demographic and behavioral factors.

No flickering

Our testing platform ensures a smooth and consistent user experience by eliminating flickering during split tests. The experiment results are more reliable and users can maintain the integrity of the testing environment, enhancing trust among their target audience.

GDPR compliance

With strict adherence to GDPR regulations, Optibase prioritizes data privacy and security, providing users with peace of mind while conducting experiments. Compliance with the latest developments ensures that user data is handled ethically and responsibly at all times.

Cross-domain testing

Optibase supports testing across domains, allowing users to follow audiences across multiple domains. This experimentation technique across different web properties enables much better understanding of user behavior and their preferences.

No-code friendly

Optibase's intuitive interface and no-code approach make split testing accessible to users with different levels of technical expertise, letting them create and manage experiments without coding knowledge. Marketing teams can get results faster without engineering bottlenecks.

Traffic control

The flexible traffic allocation options in Optibase allow users to distribute traffic evenly or based on specific criteria across test variants. Maximize the impact of split tests with strategic experimentation and optimization of user engagement metrics.
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FAQ

Frequently asked questions

What is the difference between split testing and A/B testing?
The main difference between split testing and A/B testing is how extensive your changes are. If you want to test some smaller changes like the color or size of your CTA button, comparing a different headline or website copy, then it’s an A/B test. But if you compare a whole different website design or play with the layouts, then it’s a split test.
Why is split experimentation important?
Split A/B experimentation is important because you can make data-driven decisions, continuous optimization, mitigate the risks, it's cost efficient and it gives you a competitive advantage over others.
Does Webflow support split testing?
Yes, Webflow supports split testing with the app Optibase that you can find on the marketplace. The app lets you split test or A/B test designs, layouts, copy or just some elements in your Webflow build.
What are some best practices for designing split tests?
Best practices for designing split tests include testing one variable at a time, ensuring test variations are significantly different, avoiding bias in test setup and prioritizing tests based on their potential impact.