What is A/B testing?
A/B testing (also known as Split testing or Bucket testing) is the method of comparing two versions of a website or application against each other to determine which version performs better. This method works by randomly showing two variations of a page to users and using statistical analysis to determine which variation achieves better results for your conversion goals.
In fact, this is how A/B testing works:
- Create two versions of a page – the original version (control or A) and the modified version (variant or B)
- Randomly split your traffic between these versions
- Measure user engagement through dashboards
- Analyze the results to determine whether the changes have a positive, negative, or neutral impact
The changes you test can range from simple tweaks (like headers or buttons) to complete page redesigns. By measuring the impact of each change, A/B testing turns website optimization from guesswork to data-driven decisions, shifting conversations from “we think” to “we know” .
As visitors are served in a control or variable approach, their engagement with each experience is measured and collected in dashboards and analyzed through statistical tools. You can then determine whether changing the experience (change method or B) has a positive, negative, or neutral effect compared to the baseline version (control method or A).
“The concept of A/B testing is simple: Show different variations of your website to different people and measure which variation is most effective in converting them into customers.” By Dan Siroker and Pete Koomen (Book | A/B testing: The most powerful way to turn clicks into customers)
Why should you do A/B testing?
A/B testing allows individuals, teams, and companies to make careful changes to their user experience while collecting data on its impact. This allows them to build hypotheses and learn which elements and optimizations of their experience have the most impact on user behavior. In another way, they can be proven wrong – their opinion about the best experience for a given goal can be proven wrong through A/B testing.
More than just answering a question once or resolving a disagreement, A/B testing can be used to continuously improve a certain experience or improve a single goal like conversion rate optimization (CRO) over time.
Examples of A/B testing applications:
- Generate B2B leads: If you're a tech company, you can improve your landing pages by testing changes to titles, form fields, and CTAs. By testing each element one by one, you can determine which changes increase lead quality and conversion rates.
- Campaign performance: If you're a marketer running a product marketing campaign, you can optimize your ad spend by testing both ad copy and landing pages. For example, testing different layouts helps determine which version converts visitors into customers most effectively, helping to reduce the overall cost of customer acquisition.
- Product experience: Product teams across a company can use A/B testing to validate assumptions, prioritize important features, and deliver products without risk. From onboarding flows to in-product messaging, testing helps optimize user experiences while maintaining clear goals and hypotheses.
A/B testing helps transform decision making from opinion-based to data-based, challenging the term HiPPO (Highest Paid Person's Opinion).
As Dan Siroker notes, “We really don't know what's best, let's look at the data and use that to help guide us”.
How to do A/B testing
Here's an A/B testing framework you can use to start running tests:
1. Collect data
- Use analytics tools like Google Analytics to identify opportunities
- Focus on high traffic areas through heat maps
- Look for pages with high bounce rates
2. Set clear goals
- Identify specific metrics for improvement
- Set measurement criteria
- Set improvement goals
3. Create a test hypothesis
- Form clear predictions
- Based on existing data
- Prioritize according to potential impact
4. Design variations
- Make specific, measurable changes
- Ensure appropriate follow-up
- Check technical implementation
5. Run the test
- Split traffic randomly
- Track issues
- Collect data systematically
6. Analyze results
- Check statistical significance
- Consider all the metrics
- Document lessons learned
If your variation wins, great! Apply those insights on similar pages and keep iterating for success. But remember – not every test will come back positive, and that's completely normal.
In A/B testing, there are no failures, only opportunities to learn. Every test, whether the results are positive, negative, or neutral, provides valuable user insight and helps refine your testing strategy.
12 best A/B testing tools
1. Contentsquare
Contentsquare is an end-to-end experience intelligence platform that teams can use to track their website's digital experience. With both quantitative and qualitative tools and capabilities, this platform allows you to add deeper insights to your A/B tests and understand the motivations behind user actions.
2. VWO
Visual Website Optimizer (VWO) is a testing platform with a comprehensive CRO toolset that allows you to perform A/B testing of various elements of your website and mobile apps, such as headers, CTA buttons and images, to see which variation converts more users.
3. Omniconvert
Omniconvert is a website optimization platform with A/B testing, surveys, website personalization, customer segmentation, and behavioral targeting features.
4. Unbounce
Unbounce is landing page building software that includes analytics and A/B testing features that let you track key performance indicators (KPIs) and optimize conversion rates.
5. Crazy Egg
Crazy Egg is a website optimization tool that allows you to analyze user behavior on your website. This tool includes features like heat maps, scroll maps, and click reports to help you test different versions of your website to see which generates more engagement or conversions.
6. Kameleoon
Kameleoon is a web optimization platform with full-featured, web testing capabilities that allows you to run real-time A/B testing and gives you data-driven insights to make informed decisions. better product definition.
7. AB Tasty
AB Tasty is a web optimization platform that provides feature management, A/B testing, and personalization tools to help you improve conversion rates and customer experience in real time.
8. Google Optimize
Google Optimize is one of the most popular A/B testing solutions today. This solution is completely free and designed to work with other popular Google products like Google Analytics, Google Ads, and Firebase.
9. Firebase A/B Testing
Firebase is an application development platform created by Google. Firebase's A/B testing module can help you test changes to your app's features, user interface, or engagement campaigns.
10. Optimizely
Optimizely is a digital experience platform. It comes with A/B testing and multivariate capabilities, as well as CMS, site personalization features, feature toggle capabilities, etc.
11. Adobe Target
Adobe Target is an experimental platform – part of Adobe Experience Cloud. Like the entire experience cloud, Adobe Target is built for businesses, focusing on omnichannel user experiences and running tests on thousands or even millions of users.
12. Maxymiser
Maxymiser is a testing and optimization tool acquired by Oracle in 2015. Its main focus is to put testing and personalization in the hands of marketers by eliminating the need for development resources. development.
