TL;DR: What is A/B testing?

A/B testing is a user experience research method testing the difference in outcomes between two or more variants. 

A/B testing can be applied to almost anything - a website, online ad, or email headline. By comparing how users respond to two versions of a webpage or marketing asset, version A and version B, marketers can make data-driven decisions to improve strategy.

A/B testing allows businesses to improve conversion rates, reduce bounce rates, and increase user engagement. It provides actionable data that informs future marketing campaigns and helps optimize budget allocation. 

Benefits of A/B Testing

Improve Conversion Rate and Overall Marketing ROI

One of the primary benefits of A/B testing is its ability to improve conversion rates. By systematically testing different variations of elements on your website or advertisement—such as headlines, call-to-action buttons, and visuals—you can determine which variation resonates better with your audience. Through this process, you can make data-backed changes that lead to higher conversion rates and, ultimately, improved marketing return on investment (ROI).

Reduce Bounce Rate

Bounce rate refers to the percentage of visitors who leave a website without engaging further. A high bounce rate indicates a lack of relevance or an unappealing user experience. A/B testing enables you to identify the factors contributing to a high bounce rate and make necessary modifications. You may test different layouts, navigation options, and content placement to tailor a user’s experience and keep them on your site for longer. 

Increase User Engagement

Engaging site visitors is key to building a loyal customer base. A/B testing allows you to experiment with what drives user engagement. By testing variables like interactive elements, content formats, and personalized recommendations, you can identify the elements that resonate most with your audience, resulting in increased user engagement and interaction.

Access to Actionable Data

A/B testing provides actionable data that can guide your marketing strategies. Test results can provide valuable insight into customer preferences and behavior. These insights, recorded in buyer personas, can inform future campaigns, enabling you to create more targeted and effective messaging, appeal to specific segments of your audience, and improve ROI.

Optimized Budget Allocation

Budget allocation, particularly for advertising, can feel mysterious. Using systematic A/B testing, you can confidently redirect budgets to the highest-performing version of an ad or site. The time and expense of an A/B test are well worth the investment to wisely optimize marketing budgets going forward. 

A/B Testing Best Practices

To make the most of your A/B testing, follow these best practices:

Test Various Elements

Don't limit your A/B testing to just one aspect of your marketing strategy. Experiment with different elements such as headlines, images, button placements, or even entire landing pages to uncover new opportunities for optimization. 

However, be careful not to change more than one variable per test. Testing multiple elements will make it unclear which change led to the result. 

Consider Sample Sizes and Statistical Significance

Ensure that your sample sizes are large enough to provide statistically significant results. Smaller sample sizes can lead to unreliable data and inaccurate conclusions.

With that being said, gathering data from a large sample size will require more time and money than from a smaller sample size. 

As for calculating what that sample size may be? This requires some statistics and, to do it right, previous data about the variability of whatever metric you’re measuring.

Fortunately, there are lots of tools available to calculate all this for you. At 97th Floor we rely on VWO, a software company built entirely to help marketers complete experiments and optimize for conversions.

Avoid Common Mistakes in A/B Testing

Be cautious about changing multiple elements at once, as it can make it difficult to determine which specific change influenced the results. Additionally, avoid premature conclusions based on short testing durations. Give your tests enough time to gather sufficient data for analysis.

A/B Testing Process Overview

The A/B testing process typically involves the following steps:

Step 1: Set Clear Goals

Before beginning any A/B test, it is crucial to establish clear and measurable goals. Determine the specific metrics you want to improve, such as click-through rates, conversion rates, or average order value. By setting clear goals, you can monitor your progress and assess the effectiveness of your tests accurately.

Step 2: Identify Test Elements

Next, identify the elements you want to test within your digital marketing campaign. These elements could include headlines, call-to-action buttons, landing page layouts, imagery, or even color schemes, and more! Remember to choose elements that align with your goals and have the potential to make a significant impact on your desired metrics.

Step 3: Formulate Hypotheses

Once you have identified the elements to test, it's time to create hypotheses. Hypotheses are educated guesses about the potential impact that altering specific elements may have on your desired metrics. For example, you may hypothesize that a more prominent call-to-action button will increase conversion rates. The clearer and more specific your hypotheses, the easier it will be to measure and interpret your results.

Step 4: Create Variants

With your hypotheses in mind, it's time to create variants for your A/B test. Develop alternate versions of the elements you identified in step 2. Ensure that each variant represents a different approach or change from the original. For example, if you are testing headlines, create different versions with varying tones, lengths, or messaging.

Step 5: Randomize and Deploy

Before launching your A/B test, it is essential to ensure that your test groups are randomized. Randomization helps eliminate bias and ensures accurate results. Divide your audience into two or more groups, with each group receiving a different variant. Make sure each group is similar in size, characteristics, and behavior. Deploy the test and start collecting data.

Step 6: Collect Data

Collecting data is at the core of A/B testing. Monitor the performance of each variant over a period of time, paying close attention to the metrics you established as your goals. Use robust analytics tools to track and measure user behavior, conversions, and engagement. The larger the sample size and the longer the duration of the test, the more reliable and accurate your results will be.

Step 7: Analyze Results

After gathering sufficient data, it's time to analyze the results of your A/B test. Compare the performance of each variant against your established goals. Determine if any statistically significant differences exist between the variants. Statistical significance will indicate whether the observed differences are genuinely the result of the changes you made or merely due to chance.

Again, this is a spot where it is really great to have a tool like VWO.

Step 8: Implement Winning Variant

Based on your analysis, identify the winning variant that outperformed the others in achieving your goals. Implement the winning variant into your digital marketing campaign, replacing the original element you were testing. By doing so, you can ensure that you are continuously optimizing your campaigns and maximizing your marketing efforts.

Recommended Tools and Software


VWO offers a suite of conversion optimization tools, including A/B testing, heatmaps, and user behavior analysis. Its drag-and-drop editor and robust targeting options enable you to test and refine your digital marketing campaigns effectively.


Trusted by leading brands, Optimizely provides a comprehensive platform for A/B testing and personalization. With its visual editor and advanced targeting capabilities, you can effortlessly create and run experiments across different channels, including websites, mobile apps, and email campaigns.

Crazy Egg

Known for its heatmap and scroll map features, Crazy Egg allows you to visualize user behavior and identify areas for improvement on your website. By understanding how users interact with your pages, you can make informed decisions about where to focus your A/B testing efforts.


A/B testing is an indispensable tool for marketers seeking to optimize their digital marketing efforts.

By following the procedure outlined in this article, marketers can elevate their campaigns to new heights and achieve their objectives with confidence. Look at you go.


What is A/B testing and how does it work? 

A/B testing, also known as split testing, is a method used in digital marketing to compare two different versions of a web page or marketing asset. It works by dividing the audience into two groups and exposing each group to one version (A or B) of the asset. The performance of each version is then measured based on specific metrics, such as click-through rates or conversions. By analyzing the results, marketers can determine which version performs better and make data-driven decisions to optimize their marketing strategies.

What are the benefits of A/B testing for marketers? 

A/B testing offers several benefits for marketers. It helps improve conversion rates, reduce bounce rates, increase user engagement, and optimize overall marketing return on investment (ROI). By testing different elements, marketers can gain valuable insights into customer preferences, behavior, and preferences, which allows them to create more targeted and effective messaging for specific segments of the audience.

How can A/B testing improve conversion rates and overall marketing ROI?

 A/B testing allows marketers to identify which variations of marketing assets or web pages resonate better with the audience, leading to higher conversion rates. By systematically testing different elements like headlines, visuals, and call-to-action buttons, marketers can make data-backed changes that drive better results and ultimately improve the overall marketing ROI.

How does A/B testing help in reducing bounce rates on websites? 

A high bounce rate indicates that visitors are leaving a website without further engagement. A/B testing helps in reducing bounce rates by identifying factors that contribute to a poor user experience. By testing different layouts, navigation options, and content placement, marketers can optimize their websites to provide a more engaging and relevant user experience, encouraging visitors to stay longer and explore further.

Can A/B testing increase user engagement with marketing content? 

Yes, A/B testing can increase user engagement with marketing content. By experimenting with various interactive elements, content formats, and personalized recommendations, marketers can identify which elements resonate most with their audience. By implementing successful variations, they can drive higher user engagement and interaction with their content.

How does A/B testing provide actionable data for marketing strategies? 

A/B testing provides actionable data by presenting clear and measurable results for each tested variation. Marketers can analyze the data to gain valuable insights into customer preferences and behavior. This data guides future marketing campaigns, enabling marketers to create more targeted messaging and optimize their strategies based on evidence.

What are some best practices to follow while conducting A/B tests? 

Some best practices for conducting A/B tests include testing various elements to uncover new opportunities, ensuring sample sizes are large enough for statistically significant results, avoiding changing multiple elements at once to identify specific influences, and allowing sufficient time for tests to gather reliable data.

What is the process overview for conducting A/B testing in digital marketing campaigns? 

The A/B testing process involves setting clear goals, identifying test elements, formulating hypotheses, creating variants, randomizing and deploying the test, collecting data, analyzing results for statistical significance, and implementing the winning variant into the marketing campaign.

How do you analyze the results of an A/B test and determine the winning variant?

To analyze the results of an A/B test, marketers compare the performance of each variant against the established goals. They assess whether any statistically significant differences exist between the variants. Statistical significance helps determine whether the observed differences are genuinely the result of the changes made or merely due to chance.