Ulitmate starter guide to A/B testing with best practices

In the digital marketing world, every click and view, even every scroll, of digital content can provide valuable insight about a brand’s audience and customers. 

But how do we make sense of those content interactions? How do we know which pieces of digital content are working for the people engaging with them, and which aren’t?

The answer to those questions often comes from A/B testing. 

It's easy to rely on intuition when crafting digital content, such as landing pages, emails, or call-to-action (CTA) buttons. However, making content decisions based purely on gut feeling often leads us astray. It would be preferable to rely on hard data to guide our choices — which is where A/B testing comes in. 

If you haven’t dealt with content testing or experimentation before, diving into A/B testing can feel like venturing into a jungle without a map. Don’t worry: we’ve created this starter guide to help you take your first steps.

Not only will we break down the basics, but share best practices to ensure your first foray into A/B testing is a resounding success. Let’s get started. 

What is A/B testing?

A/B testing is a way to directly compare two versions of digital content in order to determine which performs better. 

That content might be a landing page on your website, an email you send out to customers, a feature on an app, a CTA button, or any other example of content displayed as part of a user touchpoint within your digital infrastructure. 

The A/B test itself involves you showing two variants of the content — let's call them A and B — to visitors (website browsers, app users, etc.), at the same time. You then collect data on your visitors' responses to the variants, analyze the test results to determine which has delivered the better conversion rate, and then implement that one on your live site. 

Three good reasons to start A/B testing

In an evolving digital landscape, businesses that stand still get left behind. To stay competitive and continuously improve, businesses must constantly test, learn, and adapt.

However, those requirements can complicate the testing process, and even prompt brands to consider other, potentially less data-intensive approaches — such as polling customers, copying competitors, or simply relying on intuition. 

If you're still uncertain whether the A/B testing method is what you need, let’s go over its key benefits:

1. Higher ROI from the same traffic

A/B testing is a cost-effective method of getting more value from your existing traffic. By making small tweaks as you conduct tests, and observing the results, you can significantly increase conversions without needing to attract more visitors, resulting in a higher return on investment (ROI) using existing assets. 

For example, if you have a bounce rate of 50% on your website, getting 50% more traffic would likely require you to spend significantly more to increase your reach. If, on the other hand, you could reduce the bounce rate to just 25% through A/B testing, the increase in conversions would be equivalent to having increased your traffic by 50%, for free.

2. Data-driven decision making

A/B testing enables you to make decisions based on real, actionable data. This means you're not just shooting in the dark with guesses and intuition about user behavior but can point to clear test results as proof — with every change you make backed by evidence and statistical analysis. In turn, this increases the likelihood of the success of the changes you implement, reduces the risk of costly mistakes, and even provides insight into what kind of future tests you might perform.

3. Learn more about your audience

Last but not least, A/B testing provides valuable insights into your audience's preferences and behavior. As you collect data, and test different content elements, you’ll learn what resonates with your visitors and what doesn't. This understanding allows you to tailor your user experience to better meet your audience's needs over the long term, thereby increasing satisfaction and loyalty.

To sum up those benefits, A/B testing is more than a nice-to-have; it's an essential tool for any brand seeking to optimize its digital presence, make informed decisions, and truly understand its audience.

What are the common A/B testing goals?

The most common goals of A/B testing include:

Higher conversion rate

Conversion rate optimization (CRO) is one of the primary goals of A/B testing.

As mentioned earlier, conversion rate could mean anything from getting sign-ups for a newsletter, increasing downloads for a specific digital product, or boosting general sales on an ecommerce website.

Lower bounce rate

Bounce rate refers to the percentage of visitors who leave a website after viewing only one page. A high bounce rate could indicate that your website's design, content, or usability is not appealing or intuitive for visitors.

A/B testing is a way to experiment with different layouts, colors, content, and more, to create a website that keeps visitors engaged, encourages them to explore more pages, and thereby lowers the bounce rate.

Lower cart abandonment

Cart abandonment happens when customers add products to their online shopping cart but leave the website without completing the purchase.

A/B testing can help identify and fix issues that might be causing cart abandonment. For example, you could test different checkout processes, payment options, shipping costs, or return policies to find out which content changes lead to higher completion rates.

What can you A/B test on websites?

A/B testing spans a wide variety of content testing elements, each of which could potentially impact a user's experience and consequent actions. Here are some key aspects you could consider for A/B testing.

Headline

Headlines are a crucial content component that can significantly impact engagement and conversion rates. When A/B testing headlines on your website or app (or other touchpoint), consider experimenting with the following variables:

• Length: The length of your headline can influence its readability and a user's understanding. Some users might prefer shorter, more concise headlines, while others might respond better to longer ones. Test different lengths to find out what works best for your audience.

• Tone of voice: The tone of your headline can set the mood for the rest of your content. Experimenting with different tones, such as formal, casual, humorous, or urgent, can help you understand what resonates most.

• Promise or value proposition: The promise in your headline tells your visitors what they stand to gain from your product, service, or content. Testing different promises can help you identify what your audience finds most appealing.

• Specific keywords: Including specific keywords in your headlines can improve SEO and attract more targeted traffic. Experiment with different keywords to see which have the most impact.

Call to action

When conducting A/B testing for the call to action (CTA) on your website, consider experimenting with these variables:

• Copy: Your CTA’s copy communicates the action that you want your visitors to take. Experiment with different verbs or action phrases to see which drives more clicks.

• Placement: The location of your CTA on the page can significantly impact its visibility and its effectiveness. Test different placements: above the fold, at the end of the page, in a sidebar, and so on, to find the optimal placement. 

• Size: The size of your CTA button can affect its visibility and its click-through rate. Larger buttons might be more noticeable, but they can also be overwhelming if not designed properly. Test different sizes to find a balance between visibility and aesthetics.

• Design: The design elements of your CTA, such as color, shape, and use of whitespace, can greatly influence its attractiveness and clickability. Experiment with different design elements to make your CTA stand out and appeal to your audience.

• Font: The font used in your CTA can impact its readability and perception. Test different fonts, font sizes, and text colors to ensure your CTA is easy to read and aligns with your brand image.

Design, layout, and navigation

• Original• Personalized

A website’s design and layout play a critical role in user engagement, conversion rates, and overall user experience. By experimenting with different designs and layouts, you’ll be able to identify what encourages users to stay longer, interact more, and ultimately convert.

When conducting A/B testing for the design and layout of your website, consider experimenting with the following variables: 

• Navigation structure: The navigation structure of your website can significantly affect user experience. Test different structures to see which is most intuitive and effective for your users.

• Page layout: The arrangement of elements on a page can impact how users interact with your content. Experiment with different layouts to find what works best.

• Color scheme: Colors can evoke different emotions and responses from users. Test different color schemes to see which is most appealing to your audience.

• Typography: The style and size of your text can influence readability and engagement. Test variables such as fonts, font sizes, and line spacing, to find what is most readable.

• Images vs. text: Some users might respond better to visual content, while others prefer text. Experiment with the balance between images and text to see what your audience prefers.

• Form design: If your site uses forms (for newsletter sign-ups, contact information, etc.), the design of these forms can impact conversion rates. Test different designs, form lengths, and form fields to optimize your forms.

• Buttons: The design of your buttons (including CTAs) can influence click-through rates. Experiment with different colors, shapes, and sizes to find what's most effective.

Price

A/B testing for price is a crucial strategy for optimizing business profits and customer satisfaction. Different pricing structures, levels, and strategies can significantly impact consumer behavior and purchase decisions.

Brands can test various price points, discounts, bundle options, and more to identify content that maximizes revenue and conversions without deterring potential customers. This approach can also help brands to understand customers' price sensitivity, which can inform future pricing decisions.

A/B testing for price is not just about increasing immediate sales; it's about gaining insights that can drive long-term business strategy and growth.

A/B test variables in emails

A/B testing in email marketing can significantly improve your open rates, click-through rates, and conversions. Consider the following variables: 

Subject line

Here are a few elements to consider when A/B testing your email subject lines:

• Numbers: Numbers stand out in text and can make your email subject line more eye-catching. Try testing subject lines with numbers against those without. For example, "5 ways to improve your customer experience with personalization" versus "How to improve your customer experience with personalization."

• Questions: Questions can spark curiosity and engage a reader's mind, making them more likely to open the email. Test a question-based subject line against a statement. For instance, "Want to boost your conversions with personalization?" versus "Boost your conversions with personalization."

• Emojis: Emojis can add personality and visual appeal to your subject lines, potentially increasing open rates. However, they might not be suitable for all audiences or brands. A/B testing can help you determine if emojis are effective for your specific audience. For example, "Generate more revenue with personalization 🌍" versus "Generate more revenue with personalization."

Design and layout

A/B testing in email design and layout can help you understand what elements engage your audience most, thereby leading to higher click-through rates and conversions. Consider the following:

• Email format: Test plain text emails against HTML emails. While HTML emails allow for more creativity and branding, plain text emails can sometimes feel more personal and less promotional, potentially leading to higher engagement.

• Content placement: The placement of your content can greatly impact how readers interact with your email. Test different layouts, such as single column vs. multi-column, or adjust the order of sections.

• Images and videos: Visuals can make your emails more engaging, but they can also distract from your message or lead to deliverability issues. Test emails with and without images or videos, or try different types and volumes of visuals.

• CTA design: Experiment with different CTA button colors, sizes, shapes, and text, to see what gets the most clicks.

• Typography and colors: The fonts and colors you use in your email can impact readability and mood. Try different font sizes, styles, and color schemes to see what your audience prefers.

• Personalization: Personalizing emails can increase relevance and engagement. Test different levels of personalization, such as using the recipient’s name, referencing past engagement with the brand, or tailoring content to their interests.

CTAs

When conducting A/B testing for the CTAs included in your emails, consider experimenting with the following content elements:

• Copy: The copy of your CTA is critical since it communicates the action that you want your readers to take. Experiment with different verbs or action phrases to see which drive more clicks.

• Placement: The location of a CTA on an email can significantly impact its visibility and, consequently, its effectiveness. Test different placements for optimal impact, for example: above the fold, at the end of the page, and so on.

• Size: The size of your CTA button can also affect its visibility and click-through rate. Larger buttons might be more noticeable, but they can also be overwhelming and off-putting if not designed properly. Test different sizes to find a balance between visibility and aesthetics.

• Design: The design elements of your CTA, such as color, shape, and use of whitespace, can greatly influence its impact on conversion rate. Experiment with different design elements to make your CTA stand out and appeal to your audience.

• Font: The font used in your CTA can impact its readability and perception. Test different fonts, font sizes, and text colors to ensure your CTA is easy to read and aligns with your brand image.

Need more A/B testing ideas?

If you’re still looking for inspiration, download our 26 A/B testing ideas ebook and kickstart your testing process today. 

What are the steps involved in an A/B test?

Step 1: Research

The research phase in the A/B testing is crucial because it lays the foundation for the entire test. This is the stage where you gather the valuable insights that will guide your testing strategy. Here's a closer look at what this step entails:

• Gathering performance data: Before you can improve, you need to understand your current situation. This involves collecting data about your website or marketing asset's performance. You might look at metrics such as bounce rates, conversion rates, time spent on a page, and so on. These metrics give you a baseline against which you can measure the impact of your A/B test.

• Understanding audience behavior: To create effective tests, you need to understand how your audience interacts with your website or marketing assets. This may involve using tools like heatmaps, session recordings, or user surveys, to gain insights into your audience's behavior. For example, are there parts of your webpage that users tend to ignore? Are there steps in your checkout process where users often drop off? These insights can help you identify potential areas for testing.

• Identifying areas for improvement: Once you've collected data and gained a better understanding of your audience's behavior, you can start to identify potential areas for improvement. These could be elements on your webpage that are underperforming, steps in your user journey that are causing friction, or opportunities that you're currently not taking advantage of.

• Studying industry trends: It's important to keep an eye on industry trends. What are your competitors doing? Are there new design trends, technologies, or strategies that could potentially improve your performance? By staying up to date with the latest industry developments, you can ensure that your A/B tests are not only optimizing your current performance but helping you stay ahead of the competition.

In summary, the research phase in A/B testing is all about gathering as much information as possible to inform your testing strategy. It's about understanding where you are now, what's working and what's not, and identifying opportunities for improvement. This step is crucial for ensuring that your A/B tests are focused, relevant, and likely to drive meaningful improvements in your performance.

Step 2: Formulate the hypothesis and identify the goal

After conducting thorough research, the next phase of the A/B testing method is to formulate a hypothesis and identify the goal of your test. This step is vital because it sets the direction for your test and defines what success will look like. 

Formulating the hypothesis: A hypothesis is a predictive statement that clearly expresses what you expect to happen during your A/B test. It's based on the insights gained during the research phase and should link a specific change (the variable in your test) to a predicted outcome. For example, "Changing the color of the 'Add to cart' button from blue to red will increase click-through rates." 

The hypothesis should be clear and specific, and it should be testable — that is, it should propose an outcome that can be supported or refuted by data. Formulating a strong hypothesis is crucial because it ensures that your A/B test is focused and has a clear purpose. It also provides a benchmark against which you can measure the results of your test.

Identifying the goal: Alongside the hypothesis, you need to clearly define the goal of your A/B test. The goal is the metric that you're aiming to improve with your test, and it should be directly related to the predicted outcome in your hypothesis. Common goals in A/B testing include increasing conversion rates, improving click-through rates, reducing bounce rates, boosting sales, or enhancing user engagement. 

Identifying the goal will give you a clear measure of success for your test, and ensures that you're not just making changes for the sake of it, but working towards a specific, measurable improvement in performance.

Step 3: Design the experiment

The design phase is where you plan the specifics of your A/B test, including the elements to be tested, the sample size, and the timeframe. It sets the groundwork for the execution of your test. 

Test elements that serve your business goals: One of the most important decisions you'll make during the design phase is choosing which elements to test. These elements could be anything from headlines, body text, images, CTA buttons, page layouts, or even entire workflows. 

The key here is to choose elements that have a direct impact on your business goals. For example, if your goal is to increase conversions, you might choose to test elements that directly influence the conversion process, such as the call-to-action button or the checkout process. It's also important to test only one element at a time (or a group of changes that constitute one distinct variant) to ensure that you can accurately attribute any changes in performance to the element you're testing.

Determine the correct sample size: Another key decision during the design phase is determining sample size, which refers to the number of users who will participate in your test. The correct sample size is crucial for ensuring that your test results are statistically significant. Factors that can influence the appropriate sample size include your website traffic, conversion rates, and the minimum effect you want to detect. Online sample size calculators can help you determine the right sample size for your test.

Remember, a small sample size might lead to inaccurate results, while an excessively large one could waste resources.

Step 4: Decide on timeframe

You need to decide how long your test will run. The duration of your test should be long enough to capture a sufficient amount of data but not so long that external factors (like seasonal trends) could influence the results.

A typical timeframe for an A/B test is at least two weeks, but this can vary depending on your website traffic and the nature of your business. For example, businesses with high daily traffic might achieve statistically significant results faster than those with lower traffic.

It's also crucial to run the test through complete business cycles to account for daily and weekly variations in user behavior.

Step 5: Create variants

Creating variants is a vital step in A/B testing. It involves making different versions of the webpage or marketing asset that you intend to test against each other. The purpose of this step is to see which version performs better in achieving your specified goal. 

Creating different versions: In the context of A/B testing, a variant refers to a version of your webpage or marketing asset that has been modified based on your hypothesis. 

Take our hypothesis from earlier, "Changing the color of the 'add to cart' button from blue to red will increase conversions;" here, you would create one variant of your webpage where the only change made is the color of the add to cart button. This variant will then be tested against the original version (often referred to as the “control”) to see if the change leads to an increase in conversions. If you're testing more than one change, you would create multiple variants, each incorporating a different proposed change.

Ensuring minimal changes between variants: It's crucial that the changes between your control and variants are minimal and isolated. This means that you should only test one change at a time so that you can accurately attribute any differences in performance to the specific element you're testing. If you were to change multiple elements at once, say, the color of the “add to cart” button and the headline text, and then saw an increase in conversions, you wouldn't be able to definitively say which change led to the improvement. 

By keeping changes minimal and isolated, you can ensure that your test results are accurate and meaningful.

Step 6: Run the test

Running the test is a key phase in the A/B testing process. This step involves using an A/B testing tool to present the two versions of your webpage or marketing asset (the control and the variants) to your audience, and then collecting data on user interaction. 

Serving different versions randomly: One of the fundamental aspects of A/B testing is that the versions of your webpage or marketing asset are served randomly to your audience. This means that when a visitor lands on your webpage, an A/B testing tool decides (based on a random algorithm) whether they see the control or one of the variants. 

Randomization is essential to ensure that there's no bias in who sees which version, which could otherwise skew the results. For instance, if visitors from a certain geographical area were more likely to see one version than another, this could influence the results. Randomization ensures that all visitors have an equal chance of seeing any version, making the test fair and the results reliable.

Collecting data on user interaction: While the A/B test is running, the testing tool collects data on how users interact with each version. This may include metrics such as the number of clicks, time spent on the page, conversions, bounce rate, and so on, depending on your testing goal. This data is crucial because it allows you to compare the performance of the control and the variants. By analyzing this data, you can determine which version led to better user engagement, higher conversion rates, or whatever your goal may be.

Step 7: Analyze the results

The next step in the A/B testing process is to analyze the test results. This phase involves interpreting the data collected during the test, evaluating the statistical significance, comparing the performance of the variants, and deciding on the next steps.

Step 8: Measure the significance

A/B test results analysis involves measuring statistical significance. This is done using a statistical significance calculator, which helps determine whether the differences observed between your control and variant(s) are due to the changes you made or just random chance. In A/B testing, a result is generally considered statistically significant if the p-value (the probability that the observed difference could have occurred by chance) is less than 0.05. This means there's less than a 5% chance that the observed difference is due to randomness, making it reasonable to attribute the difference to the changes made in the variant.

Step 9: Check goal metric and compare

Once you've established that your results are statistically significant, the next step is to check your goal metric for each variant. This could be conversion rate, click-through rate, time spent on page, bounce rate, etc., depending on what your original goal was. You'll need to compare these metrics for your control and variant(s) to see which version performed better.

For example, if your goal was to increase conversions, you'd compare the conversion rates of your control and variant(s) to see which led to a higher conversion rate.

Step 10: Take action based on your results

After analyzing and comparing your results, the final step is to take action by implementing a change on your live web page or app content.

If one variant clearly outperformed the others, you would typically implement that change on your website or marketing asset. However, if there's no clear winner, or if the results are not statistically significant, you would use the insights gained from the test to refine your hypothesis and design a new test. This could involve tweaking the changes made in the variant, testing a completely different element, or adjusting your sample size or test duration.

Step 11: Start planning the next test

A/B testing is an ongoing process of learning, improving, and optimizing your website or marketing assets. Once you've analyzed and implemented the results of one test, the next step is to start planning the next one.

Continuous learning and optimization: A/B testing is a cyclical process that involves constantly testing new hypotheses and implementing changes based on the results. It's a tool for continuous learning and optimization and each test you run gives you more insights into your audience's behavior and preferences, which you can use to make data-driven decisions and improve your website or marketing performance. For example, if an A/B test shows that changing the color of your “add to cart” button from blue to red increases conversions, you might then test different shades of red to see which one performs best.

Looking for new opportunities: After completing an A/B test, it's essential to reflect on the results and use them to identify new opportunities for testing. This could involve testing a different element of your webpage (like the headline, images, or layout), trying out a different change (such as changing the text of your call to action), or even testing a completely different hypothesis. 

The goal is always to learn more about your audience and find ways to improve your results. For instance, if your first test didn't yield a clear winner, you might use the insights gained to refine your hypothesis and design a new test.

Implementing a culture of testing: The most successful companies have a culture of testing, where every decision is backed by reliable data and every assumption is tested. By continuously planning and running new A/B tests, you can foster this culture in your own organization and ensure that your decisions are always data-driven.

What are the main challenges of A/B testing?

Deciding what to test

There are countless elements on a webpage that you could potentially test, from headlines and body text to images, buttons, layout, colors, and more. Determining which elements to focus on requires a solid understanding of your audience, your goals, and how different elements might impact user behavior.

Formulating hypotheses

Creating a valid and effective hypothesis for your A/B test is not always straightforward. It requires a deep understanding of your users, your product, and your market. Additionally, your hypothesis needs to be specific and measurable, and it should be based on data and insights rather than mere assumptions.

Locking in on sample size

Determining the right sample size for your A/B test can be tricky. If your sample size is too small, you might not get statistically significant results. If it's too large, you might waste resources on unnecessary testing.

You'll need to consider factors like your baseline conversion rate, the minimum detectable effect, and your desired statistical power and significance level.

Analyzing test results

Analyzing the results of an A/B test is more complex than just looking at which variant had a higher conversion rate. You need to calculate statistical significance to ensure that your results are not due to random chance.

Moreover, you should also consider other metrics and factors that might influence the results, such as seasonality or changes in user behavior over time.

Maintaining a testing culture/velocity

A/B testing is not a one-off activity but a continuous process of learning and optimization.

Maintaining a culture of testing and a high testing velocity can be a challenge, especially in larger organizations. It requires commitment from all levels of the organization, clear communication, and efficient processes for designing, implementing, and analyzing tests.

Potential effects on SEO

If not performed correctly, A/B testing can potentially have negative effects on SEO.

For example, if search engines perceive your A/B test as an attempt to present different content to users and search engines (a practice known as "cloaking"), they might penalize your site. To mitigate this risk, you should follow best practices for A/B testing and SEO, such as using the rel="canonical" link attribute and the "noindex" meta tag.

The relationship between A/B testing and personalization

A/B testing and personalization are two powerful tools in the digital marketer's toolbox, and they are closely related. Both are aimed at optimizing the user experience and increasing conversions, but they do so in different ways.

A/B testing is a method of comparing two or more versions of a webpage or other marketing asset to see which one performs better. It involves showing the two versions to different segments of your audience at random, and then using statistical analysis to determine which version leads to better performance on a specific goal (such as click-through rate or conversion rate).

Personalization is about tailoring the user experience based on individual user characteristics or behavior. This could involve showing personalized content or recommendations, customizing the layout or design of the site, or even delivering personalized emails or notifications.

So, how do A/B testing and personalization relate to each other?

Firstly, A/B testing can be used to optimize your personalization strategies. For example, you might have a hypothesis that a certain type of personalized content will lead to higher engagement. You can use A/B testing to test this hypothesis — by creating two versions of a page (one with the personalized content and one without) and seeing which one performs better.

Conversely, personalization can also enhance the effectiveness of your A/B tests. By segmenting your users based on their characteristics or behavior, you can run more targeted and relevant A/B tests. This can lead to more accurate results and more effective optimizations.

In summary, while A/B testing and personalization are different techniques, they complement each other well. By combining the iterative, data-driven approach of A/B testing with the targeted, user-centric approach of personalization, marketers can create more effective and engaging user experiences.

A/B testing with Contentful 

Are you ready to start A/B testing your content? You can do it all from the comfort of your Contentful account. Explore the experimentation capabilities of Contentful Personalization to begin your A/B testing journey. Our module is powered by AI  to take the friction out of the testing process, and help you choose the most impactful variables to include.

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