A/B Testing: What It Is, How It Works, and Best Practices for 2024

A/B Testing: What It Is, How It Works, and Best Practices for 2024

Successful digital marketing campaigns generate leads, nurture prospects along the marketing funnel, and compel them to buy a product or service. Although it sounds straightforward, meeting or beating conversion goals while adequately managing the marketing budget and delivering a return on investment (ROI) can be challenging.

One way to maximize conversion rates is to use tools that help take the guesswork out of what will work best, such as A/B testing. A/B testing allows marketers to test digital marketing assets against one another to determine which delivers the best results.

Our guide to A/B testing reviews what it is, explains how to create A/B tests, and outlines some of the best A/B testing software tools.

What is A/B testing?

A/B testing is the process of comparing a control version (group A) of a digital marketing asset—such as an ad, a landing page, or an email—to a variant (group B) with one element changed.

Tests occur for a set duration and with a defined audience. You can use results indicating variances in key performance indicators (KPIs) to make data-driven adjustments, improve performance, and enhance the user experience.

Metrics to measure include but aren’t limited to conversion rate, click-through rate (CTR), page impressions, webpage bounce and exit rates, and simple revenue lift. Marketers often conduct A/B testing before a full campaign launch to determine the top performers and during a campaign to make changes that improve the results they want to achieve.

A/B testing is sometimes mistaken for split testing, but they’re different. Split testing compares two completely different versions of a marketing asset to determine which version performs best.

Why is A/B testing needed?

A/B testing lets you test multiple versions of a webpage or app to see which option is best received. You’ll run an experiment that shows both examples to various users at random. Statistical analysis will show you which option customers prefer based on factors like conversion.

A/B testing can be extremely beneficial for companies. You can decide which website version will generate the most engagement. The A/B testing process can help you evaluate multiple factors through case studies involving your homepage, checkout page, or CTA button.

When can A/B testing be most useful?

A/B testing can be helpful regardless of when you choose to collect data or run variations. However, some companies find it especially beneficial in situations like:

  • Launching a product. Use A/B testing to position yourself for a successful launch before bringing a new product to market.
  • Initiating an ad campaign. Use A/B testing to refine your landing page and discover which version of your new ad campaign will produce the best results.
  • Redesigning your website. A/B testing can improve your new webpage design, functionality, and navigation to increase conversions.
  • Installing a new feature. Want to know if a new feature or function will satisfy your audience? Use A/B testing in the development process.

Benefits of A/B testing

Those with the resources and knowledge base to conduct A/B testing can use this powerful tool to help ensure successful marketing efforts. A/B testing allows you to impact conversion rates and better manage your marketing budget. You can also apply what you learn to future marketing campaigns.

A/B testing benefits include but aren’t limited to:

  • Increased website traffic. Boosting website traffic, acquiring new customers, and growing your business usually occur simultaneously. You can use the most effective digital marketing assets determined by A/B testing to optimize CTR and generate more website traffic.
  • Higher conversion rates. Marketing efforts aim to add new customers or upsell existing ones and generate revenue. Optimizing landing pages, emails, digital ads, and other content increases the probability of higher conversion rates and a better ROI.
  • Higher engagement and lower bounce rate. A “bounce” is when a website visitor leaves without interacting with your site, such as visiting an additional page to gather more information or completing opt-in signup. Landing page optimization based on A/B testing results can boost engagement and reduce bounce.
  • Lower cart abandonment. Cart abandonment is when shoppers leave items in a website shopping cart without completing the purchase transaction. According to the Baymard Institute, the average cart abandonment rate is nearly 70%. Use A/B testing to determine which page designs and messaging result in more conversions and fewer abandoned carts.‍
  • Increased opt-ins. Opt-in marketing is when interested website visitors sign up to receive specific information. Opt-ins help you move prospects down the marketing funnel, build relationships with customers and potential customers over time, and collect contact information for lead generation. A/B testing can tell you which messages, positioning, and graphics work best to improve opt-in rates.

How does A/B testing work?

Those preparing to run A/B testing can follow these steps for maximum efficiency and value:

  1. Decide what to test. Which specific components of your app, website, ad campaign, or blog are you hoping to improve? Which page elements can you compare or contrast during testing?
  2. Choose specific KPIs to measure. KPIs need to be specific and quantifiable, such as  clicks, transactions, or revenue per user.
  3. Define your target audience. From among your audience and depending on what is being tested, you might target a custom subset of your market or a random sampling, or you might use cookies to target customers based on their on-page behavior.
  4. Create several test variants. An A/B test for a newsletter might try different greetings for subject lines. An A/B test for a website might direct random users between different pages that use different fonts, colors, numbering, or tone. What you test will depend on your product or service, but limiting the changes between variants is key—you need to limit the variables so that you know what is working and what isn’t.
  5. Identify the right time to test. As you prepare to run an A/B test, consider when your test will happen and how long it might last. However, the test shouldn’t end until you have a statistically significant (stat sig) result. Calculating stat sig can be complicated and needs to be done well to get unbiased results. If you need help, look for experienced professionals who specialize in A/B testing.
  6. Analyze the results. When analyzing A/B test results, consider how the different variations performed based on the KPIs you identified early in the process.
  7. Discuss the results with your team. As you process the test results, share the findings with your team and hear each person’s perspective on the data.

A/B testing examples

So far, we’ve discussed general steps and best practices for running A/B tests. In this section, we’ll introduce you to a few specific tests you can run and explain what you’ll learn in the process. For each example, we’ll walk you through the first four steps of the process.

Choosing a button color

You can enhance your conversion rate optimization by choosing the right color for your call-to-action button. If you’re unsure what color button to choose, you may decide to perform an A/B test.

  1. What are you testing? The purpose of the test is to determine which color button produces the best conversion rate.
  2. What KPIs are you measuring? You can measure KPIs like the number of clicks or number of forms submitted.
  3. Who’s your target audience? This test isn’t necessarily drawing new people to your website, but it’s impacting the user experience once someone visits your site. In that case, your target audience for this test would be potential customers you’ve already attracted to your site.
  4. What are your test variants? You’ll set up two to three webpages with different-colored buttons. Perhaps you choose a high-contrast color for one button that complements the other colors on the page and use a low-contrast color for your other CTA. Users who visit the site will randomly be directed to one of the variants.

After running the test long enough to achieve statistical significance, you’ll analyze the results to see which button received more clicks. You’ll better understand which color attracts more attention and motivates buyers to take the next step.

Altering headlines

You might understand that altering headlines can impact user behavior but are unsure which specific words or phrases will have the best result. Thankfully, evaluating multiple options using A/B testing is easy.

  1. What are you testing? You’re hoping to see which headline produces the best results by capturing users’ attention and motivating them to take some sort of action.
  2. What KPIs are you measuring? Relevant KPIs include the click-through rate and the amount of time each user spends on a page.
  3. Who’s your target audience? It depends on where you’re making the change. Is it on your webpage, or is it what the user will see on Google? Your approach might be different if you’re looking to gauge the response from users already on your site or from someone seeing your site in their search results.
  4. What are your test variants? You’ll create two (or more) versions of the same page with a different headline to see which option produces the greatest outcome.

Black-and-white logo

Brightly colored logos could distract from a CTA button or contact form. If you are ever in a situation like this, consider testing an alternate page with a black-and-white logo.

  1. What are you testing? The purpose of your test is to see whether a page with colorful or black-and-white logos results in more contact forms submitted.
  2. What KPIs are you measuring? For both pages, you’ll want to measure the number of visits to the page and the number of forms submitted.
  3. Who’s your target audience? If your website has a dedicated contact page, your target audience will be users who have clicked over to the page. If you have a contact form embedded on your homepage, the target audience will be site visitors scrolling through your page.
  4. What are your test variants? One variant will have colored logos and another will have only black-and-white logos.

You may find your test hypothesis is correct—the black-and-white page performs better—or you may have a higher confidence level in your original page with colored logos after finishing the A/B test.

Importance of A/B testing analysis

A/B testing analysis is important because it helps you understand your research findings while putting the data into its larger context. Let’s say you’re experimenting with a larger button on your website. You’ll run numbers for the control group and the variant.

If you find that the variant performs 3% better, switching is likely a good idea. However, this depends on the cost of updating your website and going with the new design. If you make the change and find it doesn’t work as well as you had hoped, returning to the old design is usually pretty easy when working digitally.

Running the test also allows your business to objectively evaluate different strategies rather than relying on intuition and assumptions. You’ll continually identify areas for improvement and optimize your online presence throughout this interactive process. The long-term result will likely be a better conversion rate.

Multivariate vs. A/B testing: What’s the difference?

A/B testing focuses on a single independent variable at a time, but what if you want to test the combined effect of several elements (e.g., a new landing page)? Unlike A/B testing, multivariate testing is designed to indicate how several components interact.

8 A/B testing tools

You must use the right tools to maximize A/B testing impact. We cover eight of the most popular A/B testing tools available.



Optimizely runs A/B and multivariate testing. The company’s Web Experimentation software includes a WYSIWYG visual editor that makes it easy for teams to collaborate on website changes. Optimizely also provides a Data Platform enhancement for building real-time segments based on customer behavior and attributes.

Adobe Target

Adobe Target

Adobe Target uses AI-powered testing to help you learn what your customers want. The platform includes multivariate testing capabilities that allow you to test two or more elements at the same time. Some options include images, layouts, background colors, and copy.

You can also take advantage of the software’s multiarmed bandit testing function to automatically send traffic to the most successful experience. This happens earlier in the process, so you’ll notice an increase in your conversion and revenue.



ABsmartly offers Group Sequential Testing to help users monitor data and save time while compiling data quickly and accurately. The platform fully integrates with your CMS tool, enabling your marketing team to run tests without negatively impacting developers. The software also includes REST API and SDK repositories that work out of the box with Java, JavaScript, Vue 2, Android, and iOS.

Google Optimize 360

Google Optimize 360

Google Optimize 360 helps companies create compelling website experiences for their customers. Optimize 360 software integrates with Analytics 360 to see where underperforming pages need improvement.

You’ll also have the capability of running up to 100 experiments at one time. In addition, you can improve the ROI of your Google Ads by creating and testing custom landing pages for specific campaigns and keywords. When you’re ready to analyze your data, export all information to BigQuery to generate deeper insights through large-scale data analysis.

After September 30, 2023, Google will no longer support Google Optimize 360. However, Google Analytics 4 will also support A/B testing.

AB Tasty

AB Tasty

AB Tasty helps companies improve their digital presence through A/B experimentation. The one-stop dashboard makes it easy to customize feature deployments and experiments while remaining agile.

The software implements AI-powered Dynamic allocation to automatically send visitors to the winning variations once they’re statistically reliable. It also equips its machine learning algorithms to analyze website traffic into four categories. This allows you to personalize each user’s experience based on their engagement level.



Convert allows you to choose from nine different experiments. Each one tracks a different metric, such as page visits or click goals.

Set better parameters around your test by using the drag-and-drop targeting engine to narrow your findings with over 40 filters. Each plan also comes with a debugging Chrome extension and a real-time Live Logs plug-in to identify potential mistakes or bugs in your experiment.



VWO includes web optimization features that allow you to tailor specific experiences to different customers and track visitor activity in a way that improves your conversion rate and user interface. The software also includes server-side optimization functions, such as feature rollouts with more control over launch features.

The 360-degree customer data platform allows you to see all your customer data in real time in the same location. Easily access data like each user’s attributes and an event timeline for the time they spent on your site.



Kameleoon uses an SPA-compatible smart graphic editor or code editor when building A/B tests, allowing users to build segments from their selection of more than 45 native targeting criteria.

The platform offers unlimited A/B and multivariate test variations, and you can choose between tracking one of the preloaded goals or creating your own goal. The smart graphics editor doesn’t require coding experience and enables you to quickly change text colors, add or delete images, or replace entire webpages.

Best practices when A/B testing

Before we wrap up, let’s cover a few best practices that can help you avoid some of the most common A/B testing mistakes.

Measure the right metrics

You can track two types of metrics: qualitative and quantitative. Quantitative data like conversions and time spent on the page are relatively simple to track when using A/B testing software. Most platforms include a test dashboard to view all your quantitative metrics.

Qualitative metrics aren’t as easy to track passively. You may need to set up a system where customers provide feedback on each variant to gather qualitative data through A/B testing.

Whatever data you decide to track, you must determine whether it corresponds with progress toward your desired outcome. For example, if you want to increase the number of customers who fill out a demo request form, choose data points that correspond with the form’s accessibility and presentation.

Choose the best sample size

You must put a large number of visitors through your test to get results of statistical significance. Your results may produce misleading information if your data doesn’t represent enough tests.

Confirm data accuracy

Don’t assume the results of your last A/B test are fully accurate without double-checking the results. Before analyzing your data, set aside some time to confirm the test ran properly and collected data correctly.

Be careful when scheduling tests

Running your A/B test for a longer period will better account for potential data variances. Factors like day of the week and time of day can skew your results. Ideally, your test should last at least a week, and shouldn’t be run during major holidays, to account for variations in user behavior.

Don’t make mid-test changes

Making mid-test changes is never a good idea. You’ll have a hard time determining how the change impacted your study’s results, and you might be uncertain about what to do with the results.

Even if you realize there’s an issue with your test after you begin, you’re better off restarting the test with the proper settings in place rather than making adjustments on the fly.

Isolate a single element

Choose one specific element to change or adjust in your variant, and the study’s data will tell you exactly how that variable impacted performance. Change multiple variables, and you may struggle to determine how each variable made a difference. However, if your goal is to see how different variables work together, you can run a multivariate test.

Get A/B testing help through Upwork

Despite reviewing best practices and current thoughts on how to develop winning marketing strategies that deliver results, you never know what will work best with your audience until you test it. A/B testing reduces the guesswork, enabling you to make insightful, data-driven decisions.

Thankfully, you can find top talent using Upwork’s network of A/B testing specialists. These skilled professionals will oversee your A/B testing so you can extract the most value from your test run to improve your conversion rate optimization and new visitor experience.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.



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A/B Testing: What It Is, How It Works, and Best Practices for 2024
The Upwork Team

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