Imagine developing multiple versions of a brand-new product page and watching your conversion rate drop by 20 percent. You thought the redesign looked amazing. Your team loved it. But customers started slipping away without buying.
It happens more often than you think. Companies spend thousands on redesigns based on gut feelings. Meanwhile, competitors are making smarter decisions using data. They are testing first, launching, and then winning the conversion fight.
Using A/B testing software will help your business avoid these costly mistakes by simply comparing two separate versions of a webpage, app, or campaign to find out which one converts better. The global A/B testing market is anticipated to increase from $9.41 billion in 2025 to $34.83 billion by 2034. Now, let’s discuss what A/B testing software is and why you should care.
What is A/B Testing Software?
A/B testing software is a tool that enables businesses to test two separate versions of a webpage, app screen, or feature to track which version is more effective. It randomly splits users into two groups. One group is given the current version, while the other group sees a modified version. The software will then monitor the test results for the version that gets better results.
These tools automatically log user activity. They gather data about clicks, conversions, and user engagement. You receive easy-to-read reports that clearly indicate which version wins. No assumptions are needed when making a decision.
What is A/B Testing in Software Development?
In software development, AB testing is about validating product decisions with actual user data. Teams are typically in the habit of creating a hypothesis about what they think will improve the user experience. Before implementing an idea in real life, they test it to validate their hypotheses with real users.
Software developers are using AB testing throughout their development cycle. They are testing new functionality, design modifications, and performance upgrades. Finally, they are using testing and human behaviour to help the product & sales teams refine their strategies based on performance versus gut feeling.
What are the examples of A/B Testing?
A very basic example is testing an email subject line. In version A, we write “New Products Available Now”. In version B, we write “Your Exclusive Early Access to New Products”. You send a copy of version A to half the email list and version B to the other half.
The software will track the number of opens per subject line. It can also track click-through rates and measure conversions. After you collect enough data, you may find out that version B’s subject line received a 25% higher open rate and had 15% more purchases.
Real-World A/B Testing Examples
Google’s Blue Link Experiment
Google ran an experiment to see which shade of blue would encourage users to click on ad links. They tested 41 different shades of blue, and when they found a blue that increased engagement to the tune of an estimated $200 million in revenue per year, they showed that even the smallest design details can have an effect on performance.
Amazon’s Button Color Test
Amazon experimented with changing its Add to Cart button from yellow to bright orange so it would stand out more against a white background. The orange button resulted in a 1% increase in clicks, which may seem very small, but since Amazon has millions of customers, this small increase turned into millions of dollars in additional revenue.
7 Steps for The Process of A/B Testing
- Develop a Hypothesis
Begin with a test hypothesis based on a strong theory about how you can improve performance. Your hypothesis should be specific and measurable and state exactly how you think you can improve performance. For example, if we make the CTA button bigger, we think we will see the clicks on it increase by 10%.
- Pick Your Variable
Select a single element you want to test. This could be your website headline, image, button colour, or even the length of a form. When you test one variable, your test results will be easy to read and simple to act on.
- Create Your Variants
You can build your versions of the test through your A/B testing software. Most of the popular testing tools today have visual editors available to you. You can go in and make the changes based on the variant without writing any code. Make at least two versions of your website or landing page.
- Set Your Goals
Establish what success looks like. Are you optimizing for clicks, sign-ups, purchases, or engagement? It is important to have established goals so you can measure results accurately and also make better decisions during the testing process.
- Split Your Traffic
The software will automatically split your audience based on your setup. Most of the time, you will have 50% of your users see version A, and 50% of your users see version B. You can also choose to split testing your traffic on the software differently in some test setups (i.e. if you have a higher volume of traffic, you may want to set up an 80/20 split between two tests).
- Run Your Test
You will need to leave your test running until you get enough test data to be confident in your results. This usually means putting your test into action for a one to two-week time frame. The length of time will depend on how much traffic you have and the size of the effect you are testing.
- Analyze Your Results
Once your test is finished, review the data. Look at your primary success metric and secondary metrics. Look for statistical significance. Make decisions based on strong evidence.
5 Types of A/B Test
- A/B Testing
A/B Testing involves evaluating two versions of a single element. You are testing version A against version B. This is the most classic and simplest type of test, yielding straightforward and actionable results quickly.
- Split Testing
Split testing works for any more extreme or exploratory changes such as testing a totally new landing page against the existing one, or a single user flow (e.g., a one page checkout process) against another (e.g., multi-step checkout).
- Multivariate Testing
Multivariate testing looks at multiple elements simultaneously, trying to find the best performing mix, for example, testing different headlines, images, CTA buttons, product descriptions, etc. This is an advanced testing methodology and should typically be reserved for more mature testing programmes with significant website traffic.
- Multi-Page Testing
Multi-page testing is a type of testing that evaluates changes across multiple pages in the user journey. You are testing like experiences throughout the funnel so that all phases are consistent. In summary, this type of testing encompasses whole user flows and not a singular page.
- A/B/n Testing
A/B/n testing compares more than two versions. The “n” indicates more than one challenger against the control version. An example would be testing four different button colors at the same time to see which is the winner.
What are the top 5 Key Features of A/B Testing Software?
Here are the top 5 key features of A/B Testing Software-
- Visual Editor
Most tools have drag-and-drop editors, meaning you can make updates and changes without ever having to touch code. This is a great way to make testing more pervasive among marketers and designers. Changes happen quickly, without requiring developer resources.
- Sufficient Traffic Splitting
The software automatically splits visitors between the two (or more) variants, assuring random assignments to eliminate bias in the results produced. You can specify split testing percentages as they pertain to your testing strategy and the risk you’re willing to take.
- Real-time Analytics
When the data begins coming in, you can view the results in real-time, tracking performance metrics as they come in and monitoring the test to catch any issues in real-time. Real-time data also allows rapid decision-making and continues optimizing.
- Segmentation
The software allows you to test how different audiences react to changes. Testing can be segmented by location, device, traffic source, or behavior which allows you to see what changes result in performance improvements for different user segments.
- Personalization
Some higher-level tools allow for testing combined with personalization, and automatically send the winning variants to specific audience segments. This strategy will maximize the performance of your test for the varying audience groups simultaneously.
What are the top A/B Testing Software Tools?
- VWO (Visual Website Optimizer)
VWO launched in 2010 as a simple drag-and-drop editor and today is a complete end-to-end experience optimization platform. VWO has several tools to experiment with mobile apps, landing pages, websites, and server-side testing, and it has incorporated generative AI to make suggestions for optimizations. It integrates with Google Analytics.
VWO has an all-encompassing understanding of user behavior, utilizing heat maps, funnels, and session recordings. You can create experiences directed toward specific visitor segments based on customer data.
- Optimizely
Optimizely has a robust AI assistant to facilitate generating ideas, text variations, and summaries of customers. The platform supports omnichannel experimentation, meaning it can optimize content across websites, mobile apps, and landing pages with a straightforward interface, plus all content is easy to structure without coding skills.
Optimizely is enterprise-grade software for serious testing programs. It has in-depth and complex features, extended integrations, and provides extensive support and advanced statistical modeling. It handles serious and complex experimentation at scale.
- AB Tasty
AB Tasty is the best A/B testing solution. It is anchored in a Bayesian statistics engine and uses advanced statistical methods for quick decision-making. AB Tasty combines testing with personalization and product recommendations. It integrates well with Google Analytics.
AB Tasty is a great fit for e-commerce brands, providing tools to increase conversion rates at every point along the customer journey. The company offers outstanding support and training resources.
- Crazy Egg
Crazy Egg offers A/B testing software with tools that are easy to use, such as heatmaps, click tracking, and recording user sessions, which allow actionable insights about precisely where visitors click, scroll, and engage with no coding necessary.
Crazy Egg is best suited for small to medium businesses. It combines testing with behavioral analytics, focusing on understanding why users behave in a certain way.
- Adobe Target
Adobe Target is a component of the Adobe Experience Cloud. If you utilize other products sold through Adobe, Target works seamlessly with Adobe’s other products. Adobe Target offers enterprise organizations advanced personalized and testing capabilities.
Adobe Target utilizes AI to automate optimization. It can predict what experience will result in the best performance. The platform offers personal content based on historical behavior and context in real time.
- Microsoft Clarity
Microsoft Clarity will remain free forever, with basic elements that do their job well. Microsoft Clarity offers heatmaps, session recordings, and basic A/B testing. This tool is perfect for startups and small businesses with a small budget.
Clarity provides behavioral insights at no cost or commitment, so you can see how users behave without needing to invest in advanced testing tools. The platform is easy to integrate with other Microsoft products.
- LaunchDarkly
LaunchDarkly is known for its good customer satisfaction ratings related to simple testing and feature flagging. This product was built for engineering teams. It allows for the ability to progressively roll out features and roll them back quickly.
LaunchDarkly is particularly strong with testing server-side. It enables engineers to test changes in the backend without fear. The separation of deployment with release reduces mistakes and enables faster deployments.
What are the Common A/B Testing Mistakes?
- Testing Too Many Variables
When you test too many changes at once, you will never know which variable (the change) caused the outcome of the test to occur. You should only have one variable when testing to develop actionable research results.
- Not Enough Sample Size
When you run a test and stop it, you will not have trustworthy research. Your sample size will produce unreliable results. Make sure you run a test long enough to collect enough surveys for statistical significance.
- Statistical Significance
When you act upon test results that are not statistically significant, you take a risk. Random chance may have caused the results, so you will be guessing. You should always wait for a confidence level above 95% before implementing a change.
- Testing Without a Hypothesis
Randomly testing solutions wastes time and company resources. Testing based on a hypothesis based upon user research or data provides some focus on what you are testing, which will lead to improvements in your research.
- Not Testing in a Continuous Way
Any one assay test will not provide that much value. If you carry out continuous tests, you will create continuous improvement. Experimenting should not just be a planned project. Make sure you are testing continuously.
7 Best Practices for A/B Testing
- Begin with Research
Gaining insight into user behavior online before testing demonstrates that the research phase is not distinct from testing but is simply the base for experimentation. Leverage analytics, heatmaps, and user feedback to find areas of the user experience that are worth testing.
- Test One Variable at a Time
A meta-analysis done on 2,732 A/B tests found that a test that focused on a single variable gave more reliable insights than tests that had more than one variable being tested at the same time.
- Test Elements Above-the-Fold
The above-the-fold area of the user experience contributes to first impressions and is a strong driver of engagement. Test elements that users see first and the most preferably.
- Set Clear Objectives
Prior to beginning tests, it is vital to ensure clarity around how your success will be measured. You must know what you are optimizing for. Make sure to measure not only primary metrics but secondary metrics too, to understand the full impact of a change.
- Do Not Stop Tests Early
Avoid stopping tests earlier than intended. Ensure you collect sufficient data to arrive at statistical significance. Consider user behaviour patterns that may be related to the day of the week, time of the year, or holidays.
- Document All Tests
Be sure to keep records for all tests that you run. Document your hypotheses, methodology and findings or results. This knowledge will be useful for future tests and prevents repeating failed experiments.
- Take Action
Recognize the winning variation of your test and use the information as soon as possible thereafter. Share what you learned and take accountability across the team or organization. Taking it a step further, use this knowledge to prompt future tests and to inform wider strategy thinking.
Top 3 Techniques for Choosing the Right A/B Testing Software
- Consider Your Budget
Prices can be very different by tool, ranging from free tools to thousands of dollars per month. Big enterprise tools also provide a lot of functionality that may be useful, but will come with a high price tag, so you want to make sure you’re paying for functionality that you will actually use. Start with more affordable options and then re-evaluate when you’re ready to move forward and the sophistication of your testing.
- Consider Ease of Use
An A/B testing tool should have a visual editor that is intuitive, allowing your team to create and use a test with minimal technical knowledge. If your tool is super complex, it will take longer to experiment and everyone is less likely to use it if its too complex.
- Consider Support and Training
Good support and training will make the testing easier than if you didn’t, or didn’t understand the capability of your tool. You should always look for documentation and tutorials, as well as a user-friendly customer service team. Some vendors offer dedicated training and onboarding as part of their services.
5 Benefits of A/B Testing Software
- Data-Informed Decisions
A/B testing brings data-informed decision-making by removing any guessing when trying to identify what is valuable to your audience. All changes and actions will be validated with the behaviour of real users before being fully implemented.
- Improved Conversion Rates
Businesses can run tests to determine what will foster the best conversion rate, enabling customers to buy quickly or subscribe more often, leading to an increased conversion rate and more revenue overall.
- Enhanced User Experience
Testing determines user preferences. You learn which designs, layouts, and features resonate best. This delivers experiences that users rave about and recommend to others.
- Quicker Optimizing
Testing in real-time saves companies hours of time by testing variations in real-time, compared to asking people to step aside for testing. Automated testing runs at all times without any human intervention.
- Lower Expense
Testing mitigates costly redesigns that were based on assumptions, being able to invest your resources in changes and designs that were proven to be effective. This leads to a better overall return on investment from all marketing efforts.
How to measure A/B Test Success?
- Primary Metrics
Keep the main focal point of your test in mind. This could be one of the things we typically look for such as conversion rate, click-through rate, or revenue per user. Keep this metric squarely in your primary focus throughout the test.
- Secondary Metrics
It is useful to monitor additional metrics to get a sense of the broader impact of the changes made. For example, a change may improve conversions but negatively affect other important metrics (bounce rate, time on site, engagement, etc.).
- Statistical Significance
Statistical significance is the A/B testing term for measuring how confident you are that the difference between the two versions wasn’t simply due to coincidence. The higher the statistical significance, the more confident you can be that the differences are genuine.
- Confidence Intervals
It is important to be able to articulate the degree of certainty around a potential outcome. For example, you can be 95% confident the true effect is somewhere in the range of outcomes if your 95% confidence interval, or related metric, falls within a given spread.
- Segment Analysis
You should always segment and analyze. For example, your test results may vary significantly depending on different segments of customers (defined by demographic attributes, prior transaction history, customer experience attributes, etc.). This tells you where you see opportunities for personalized marketing optimisation.
Conclusion
A/B testing software replaces blind judgment with data-informed decision-making as it compares versions and observes actual user behavior.
The marketplace is growing as more companies now understand that testing leads to optimization, which leads to quantifiable gains in conversion rates and customer satisfaction.
Whether you are testing the colour of a button or a total redesign, the right A/B testing tool will help you discover what works and continuously improve your digital experiences based on data rather than guessing.
FAQs
What is AB testing in software?
A/B testing is the process of comparing two or more versions of web-based software (UI, feature, flow) using live users to determine the best version.
Can you give an example of A/B testing?
Testing two separate CTAs on a landing page, or two separate onboarding flows in a mobile app see which flows to more signups.
What software do I need for A/B testing?
Common tools include options such as Optimizely, VWO, Firebase A/B Testing, AB Tasty, Crazy Egg and Unbounce.
Who completes the A/B testing?
Normally, product, design, data & marketing will be responsible for the A/B testing. The development team will implement any technical details, and the analytics team will review results.
What is the best tool for A/B testing?
The best is the tool that works for you. Traffic volume, web vs mobile vs backend, team structure, and budget will all play into choosing the right tool.
