A/B Testing In Marketing: What It Is, How To Test, Examples, and Best Tips to Succeed

Digital marketing is full of decisions, and choosing the right subject line, button color, or page layout can be the difference between conversions and missed chances. The challenge is that assumptions rarely lead to consistent success. This is where A/B testing in marketing steps in!

This guide covers everything: what A/B testing is, how it works, metrics to track, common mistakes to avoid, a step-by-step testing process, A/B testing examples, and proven strategies.

Right now, let’s get into it!

An Overview of A/B Testing In Marketing

What is A/B testing?

A/B testing in marketing (split testing or bucket testing) is a randomized controlled experiment. You compare 2 versions of a marketing asset or campaign element, Version A (the control) and Version B (the variant), to determine which one performs better. By showing these versions to a similar, split audience at the same time, you accurately measure the impact of a single change.

A/B testing in marketing is to test two versions of one variant to find the best 

In fact, A/B testing is not about picking a winner; it’s about gaining a deeper understanding of your audience’s behaviour and preferences. Its results provide empirical evidence, moving your conversions from “we think” to “we know”. From that, you can ensure most marketing decisions are based on measurable data and recent updates rather than poor assumptions like before.   

There are 6 common targets of A/B testing in marketing that you should consider implementing: 

1. Website design

This is the most common application. It allows you to test different layouts, navigation menus, product page designs, or even your website's overall aesthetic. A simple change to a button color or the placement of a product image can have a massive impact on your conversion rates.

2. Email campaigns

Subject lines, sender names, preheaders, body copy, and calls-to-action (CTAs) are all prime candidates for A/B testing in marketing campaigns. The goal is to maximize open rates, click-through rates, and ultimately, conversions. For example, testing two different subject lines can reveal which resonates more with your audience and encourages them to open your email.

3. Sales campaigns 

When you're running a promotional campaign like Black Friday Sales, A/B testing in marketing can help you optimize elements such as offer headlines, discount percentages, or the urgency expressed in the copy. You can test which messaging strategy drives more sales or sign-ups.

A/B testings are often used for Back Friday sales to ensure the highest conversion rates and sales for your store

4. Newsletters

For this optimization, you can test the layout of your newsletters, the subject lines, and the types of content you feature (e.g., articles vs. product highlights). In addition to design elements, you can A/B test the frequency of your sends to identify what keeps your subscribers most engaged.

5. Paid internet advertising 

This A/B testing can include ad creative, copy, landing pages tied to ads, targeting, and CTA. You can test two versions to see which one has a lower cost per click (CPC) and a higher click-through rate (CTR). As a result, you can easily maximize your return on ad spend (ROAS).

6. Multimedia marketing strategies

The content you use on social media, in banner ads, and on landing pages is perfect for A/B testing. Do your customers respond better to images, animations, videos, or GIFs? Long or short headlines work more effectively? Testing varied formats can refine your creative strategy.

A great headline on the Shopify Trial landing page is a result of constant A/B testing 

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What does A/B testing involve? 

Each A/B test in marketing needs to be structured to provide reliable and actionable insights. Below are the three core components that marketers or solo merchants should consider to start: 

• A campaign to test: Decide which campaign, email, page, ad, etc., will be the subject. 

• Elements to test: What single variable(s) do you wish to change? They can include: CTAs, landing page layout, images, copy, navigation elements, headlines, and forms. 

• Defined goals: What do you want to increase or decrease? (e.g., conversion rate, click-through rate, abandoned cart rate, newsletter signups, and purchase completions).

Learn more: Top 20+ A/B Testing Ideas You Should Try [2025] 

Metrics for Quantifying the Impact of A/B Testing 

To assess the success of A/B testing in marketing, primary and supporting metrics are essential. The table below lists the eight most widely used names that marketers should know for tracking:

Learn more: 7 Essential Metrics for Comprehensive eCommerce Success Measurement

4 Common Types of A/B Testing

1. Split URL testing (Redirect testing) 

This type of test compares two different URLs. Instead of showing two versions of a single page, traffic is split between two separate pages (or URLs). For example, testing an entirely new checkout flow or a redesigned homepage would be an essential scenario for split URL testing.

Redirect or Split URL testing is known as the most widely used for web pages

When to use: This type of A/B testing in marketing is helpful for major design overhauls, testing new themes, or significant structural changes that are difficult to implement on a single page. More importantly, redirect testing can also be designed to help evaluate different user journeys.

2. Multivariate (MVT) testing 

MVT testing is a more complex form of A/B testing that tests multiple variables on a single page simultaneously. Like this A/B testing in marketing example, you could test 3 different headlines and 2 different images of a landing page at the same time. The test would create a combination of all possible variants ( 3 x 2 = 6 total versions) to choose which combination will perform best.

When to use: You should use MVT when you have significant traffic and want to understand how different elements interact with each other. It’s effective for fine-tuning a page that has already undergone previous optimization, and you want to find the best possible combination. The insights from MVT testing can be incredibly powerful but require a much larger sample size.

3. Multi-page funnel testing

This type of A/B testing in marketing applies a single change across multiple pages within a conversion funnel. For example, you might test a new navigation menu or a persistent hero image across the entire checkout process, from the shopping cart to the final confirmation page.

Multi-page funnel A/B testing helps identify the elements that influence final decisions

When to use: You can use multi-page to test elements that affect the entire customer journey. They can include a new CTA that appears on several pages, or a product suggestion change. Apparently, this method works well in optimizing the entire path, not only focusing on one point. 

4. A/B/n. testing

The final type of A/B testing is known as an extension of standard A/B testing, where you compare more than two variations against a control (e.g., A vs. B vs. C vs. D). This lets you test multiple new ideas at once and is often used to get a quick sense of which direction to pursue.

When to use: It’s recommended to use A/B/n when you have multiple ideas for a single element, like 4 different CTA button colors or 3 different product descriptions. While it can be faster than running individual A/B tests, it also requires a larger sample size for the best result. It's ideal for quickly identifying a winning variant before moving on to more granular optimization. 

Why Everyone Should A/B Test?

The benefits of A/B testing in marketing go far beyond improving a single metric for a campaign. They are a fundamental part of building a successful, data-driven business. Here’s why every marketer, especially those in eCommerce, should make it a core part of their long-term strategy. 

1. Unlock significant performance gains with minor tweaks 

It's a misconception that you need a complete website redesign to see a boost in conversions. In reality, some of the most dramatic increases come from seemingly minor, almost unnoticeable tweaks. Truly, A/B testing in marketing allows marketers to discover these high-impact changes.

A minor change on a CTA button from “Add to bag” to another version can help increase conversions on your product pages

A simple A/B testing in marketing example could be changing a CTA button from "Add to bag" to "Get Your Style." This could lead to a significant uplift as it speaks directly to visitors' desires. The true power lies in finding the right words or layout that resonates with your target audience. 

2. Minimize risks for higher rewards

Making a significant change to your website or an ad campaign without testing is truly a gamble. A new design might look great to you, but if it alienates your target audience, you could lose a significant amount of traffic and revenue. This is how A/B testing can help you mitigate this risk. 

By running a test on a small segment of your audience, you can validate your ideas with real data before rolling them out to everyone. This prevents costly mistakes and ensures that every change is an improvement, not a step backward. It's an investment in smart, sustainable growth.

3. Maximize the number of traffic and conversions

A/B testing in marketing is the primary engine that drives conversion rates. By regularly testing and optimizing, you can ensure that the traffic you already have is working as hard as possible. 

You're not only attracting visitors to your store, but you're also converting them into customers. Sometimes, a one percent increase in conversion rate can lead to a substantial boost in sales and revenue over time, all without spending a single extra dollar on attracting new traffic. This is the difference between a website that only exists and one that actively works for your business.

Step By Step To Run A/B Testing In Marketing (+Examples)

Step #1: Identify a specific goal 

Before you do anything else for your marketing plan, you need a clear, measurable objective, and running A/B tests is no exception. What is the most important metric you want to improve? Conversions are the most common objective, but you might consider other aspects, such as: 

• Decreasing abandoned carts

• Improving average order value

• Boosting newsletter signups

• Raising click rates on promotional emails

In fact, your objective should be based on data and an analysis of your website performance. We recommend that you work with Google Analytics, heatmaps tools, as well as user feedback.

Hotjar is a go-to heatmap platform for many marketers

Example: Our Shopify store's "Add to Cart" button on our product pages has a relatively low click-through rate. Therefore, we aim to increase this rate by 5% in the following three months. 

Learn more: GemPages x Heatmap: Third-Party Integration

Step #2: Choose a variable to test

Remember the golden rule of A/B testing in marketing campaigns: Test one variable at a time. This ensures that any change in performance can be directly attributed to the element you modified. If you change the headline and the CTA button, you won't know which change was responsible for the result. Consequently, you can’t get the most accurate result for this A/B test. 

Pro Tip: If you’re new to A/B testing, it’s better to start with a standard A/B rather than an A/B/n.  

Example: Based on our goal in the first step, we will test the color of the "Add to Cart" button. The current color is blue, and we’ll test an orange color for contrast with the green background. 

Step #3: Split your audience randomly

Most A/B testing apps for eCommerce stores can handle this perspective automatically. The key is to ensure that your audience is randomly and evenly split between the control (Version A) and the variant (Version B). This removes bias and ensures that both groups are statistically similar.

GemX supports Shopify marketers to split their audience for A/B tests automatically 

Example: Using a chosen A/B testing tool for our Shopify store, we’ll show 50% of our website visitors the product page with the blue button and 50% the page with the new orange button.

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Step #4: Run the test for a sufficient duration

The duration of A/B testing in marketing is critical. Ending a test too early can lead to false positives, while running it for too long can expose your business to unnecessary risks. Therefore, a good rule of thumb is to run the A/B test for at least one complete business cycle (e.g., a whole week or two) to account for day-of-the-week variations in traffic and behavior.

Example: Our marketers will test A/B for two whole weeks. This gathers a sample size that is significant enough to account for any weekend shopping habits that might differ from weekdays.

Step #5: Analyze the results

Once the test is complete, it's time to examine the data. Here, you can find the performance of each variant against your metric. Pay close attention to the statistical significance of the results.

Marketers often have an in-depth analysis of data to find helpful insights for A/B tests

In fact, your testing software can automatically collect and analyze the database for a full report. Unexpected things can completely occur in experiments, so whether you met your A/B testing objective or not, take full advantage of this time to dig into the analytics and see what happened. 

Example: After two weeks, the orange "Add to Cart" button version of the product page has recorded a 12% higher click-through rate than the blue button. The results are statistically significant, with a 98% confidence level. This clearly indicates that the orange button is a winner.

Step #6: Implement and Document 

If the variant wins, you could repeat an A/B test or permanently implement the change on your website (in case the current data is precise enough to conclude). Next, document your findings. 

• What was your hypothesis?

• What did you test?

• What were the results?

This creates a knowledge base for your team, preventing you from repeating A/B tests for similar types or variants further and helping you build a deeper understanding of your audience.

Example: We will change the "Add to Cart" button on all product pages to orange and add the results to our knowledge base, highlighting that a high-contrast CTA color improved efficiency.

Step #7: Keep Iterating

A/B testing in marketing is not a one-time event. The insights you gain from one test should inform the next. Now that you've optimized the button color, what's next? You can test the button copy or the placement of social proof. Continuous optimization is the key to sustained growth.

Example: Now that the orange button is live, we will formulate a new hypothesis: changing the button text from "Add to Cart" to "Secure Your Style" will increase click-throughs even further. Then, we'll start the process again, from the first step to the sixth step, to gather more findings. 

5 Best Strategies for Effective A/B Tests

While the steps for A/B testing are straightforward with clear examples, applying a strategic mindset can make a world of difference. Below are five tips to elevate your A/B testing process. 

1. Rely on representative samples of users

Your A/B test results are only as good as the audience you test them on. It’s crucial that the audience for your test is a true representation of your overall customer base. Therefore, let’s avoid A/B testing on a niche segment unless your goal is to optimize for that group specifically. A good A/B testing in marketing example is to run on all visitors, both new and returning ones.

2. Take full advantage of A/B testing solutions

Manually setting up and tracking A/B tests on your website often takes a lot of time and effort. That’s also when you need robust A/B testing apps like Gem X to automate the full process. These solutions are designed to help you run complex tests, track multiple metrics, and ensure your results are impactful without needing to write or support a single line of code from experts. 

3. Maximize your sample size

The larger your sample size, the more reliable your test results will be. A test that shows a 10% uplift with 100 visitors can not be as reliable as a test that shows a 1% uplift with 10,000 visitors. While you shouldn’t wait forever to achieve a massive sample, you should run your tests long enough for statistical significance. Most tools will recommend a confidence level of at least 95%. 

4. Run tests more than once

The results of a single test can sometimes be influenced by external factors you couldn’t control, like a specific holiday sale or a news event. To be certain of your findings, you should consider re-running your most important tests, especially if the initial results were surprising for analysis. In fact, this is particularly critical for A/B tests that directly impact your core conversion funnels.

5. Avoid common mistakes 

While A/B testing in marketing is a typical task, experienced marketers can fall into some traps: 

• Test too many variables at once

• End your test too early

• Ignore the surprising data

• Run a test on a low-traffic page

• Forget to document your findings

• Only use one A/B testing method for all campaigns. 

Simplify A/B Testing for eCommerce Store Design with Gem X

A/B testing enables businesses and solo merchants to understand their target audiences, enhance their digital experience, improve conversion rates, and stay ahead of industry trends. But running A/B tests can be time-consuming and technical if you don’t choose the right tools. 

That’s where Gem X comes in. Explicitly designed for Shopify A/B testing, this app streamlines the complete process. You can easily install Gem X and set up A/B tests, measure results in real time, and generate reliable reports that show which version truly drives most conversions.

Below are the key features that GemX empowers marketers to achieve the best performance: 

• Test headlines, CTAs, and layouts of landing pages to find the most-converting element. 

• Find out if default or custom designs perform better. 

• Allow for custom traffic routing to direct visitors seamlessly for accurate test results. 

• Support funnel analytics to gain deeper insights across the entire customer journey. 

• Work smoothly with top free landing page builders like GemPages. 

• Have a community with CRO experts for accessible guidance to scale growth. 

Read over our complete guide on how to run proper Shopify A/B testing for the best preparation. 

Learn more: Run A/B Testing or Split Testing My Page  

Conclusion

A/B testing in marketing is one of the most powerful levers you have as a marketer, especially in eCommerce and with platforms like Shopify. When done right, it reduces risk, improves performance, and gives deep insight into your customers. Following structured workflows, choosing proper types, avoiding pitfalls, and leveraging the right tools will help unlock growth.

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FAQs

What is an example of AB testing?

These are typical examples of A/B testing that you can get inspired by: testing two email subject lines to see which gets a higher open rate or testing a red vs. blue button for higher conversion. Based on your object, choose a proper variant and testing type, with an appropriate pathway.

What is alpha and beta testing in marketing?

Alpha / Beta testing usually refers to product development phases (pre-release or early release) rather than marketing experiments. These phases test functionality, bugs, and user acceptance, whereas A/B testing in marketing is about optimizing the performance of marketing assets.

Is AB testing a KPI?

No. A/B testing is not itself a KPI&semi it is a method. The KPIs are the metrics you measure (conversion rate, CTR, revenue per visitor, etc.). This test helps influence or improve your KPIs.

Topics: 

Conversion