What is A/B Testing?

A/B testing also known as split testing, is a marketing experiment in which you divide your audience into two groups to test different versions of a campaign and see which works best. In simple words, you target half of your audience with version A of your marketing content and the other half with version B. 

However, it is not limited to creating only two versions. A/B testing allows you to create multiple versions of a particular variable in a campaign to test its effectiveness. Hence, you can test various versions of emails, websites, product designs, and applications with the help of it.

A/B testing is used by businesses as a conversion rate optimization (CRO) technique to boost their conversions. Among CRO tools, A/B testing now ranks second, after analytics. This is because it enables businesses to approach marketing and design with a scientific mindset. Over time, A/B testing also helps businesses better understand their customers so they can make more effective choices.

Let’s look at the example of Amazon, which is the industry’s leader in eCommerce. Because of its huge volume and commitment to giving customers the greatest possible experience, Amazon leads in conversion optimization. After much testing and analysis, they launched their ‘1-Click Ordering’ feature back in the late 1990s, which enables customers to buy products without using the shopping cart at all.

Also, UNICEF, the United Nations agency for providing humanitarian and developmental aid to children, was able to increase donations by 51% using a progress bar. By displaying to contributors the amount they are contributing and the progress made towards that objective, it significantly raised the engagement from the user. Hence, both these cases and thousands of others are a clear indication that A/B testing plays a significant role in increasing conversions and user engagement.

Why Should We Consider A/B Testing? 

In today’s tech-driven world, A/B testing is one of the most promising tools available to test the effectiveness of various features. This is primarily because it increases conversion and user engagement and enables businesses to make decisions based upon data. 

Hence, we now see in detail the goals of A/B testing. 

● Data-Driven Decision Making

There is no guesswork while using A/B testing. Your audience's preferred layouts and colour schemes are known to your graphic designer. Additionally, your app developer is also fully aware of the kind of experience users anticipate from your branded app. This means that if you are running a data-driven business, A/B testing allows you to run a few tests on a constant basis, generating helpful data that you can use to quantify what works best. In this way, businesses can refine their strategy, identify the most effective variations, and develop customized products to improve client experiences.

However, even when the version is finalized, it is recommended to not quit testing. Once the new version goes live, start testing other web page elements to ensure that the most engaging version is served to the visitors.

● Increased Conversion Rates

Without having to spend extra money on obtaining new visitors, A/B testing helps you maximize the conversions from your current traffic. This becomes possible when sometimes the minutest of changes on your website can result in a significant increase in overall business conversions. This is evident in the recent report of InvespCRO which revealed that 60% of organizations find A/B testing to be highly valuable for optimizing their conversion rates. Moreover, reports have also shown that if A/B testing is implemented properly, it helps eCommerce websites generate an average revenue of $3 per unique visitor. 

● Increased engagement

A/B testing helps you test several variations of a website element until you identify the optimal one. This improves the overall experience of your website visitors, encouraging them to stay on your site longer and possibly become paying customers. It also helps you identify friction and user pain areas. However, on the other hand, if visitors bounce from your website within a few seconds, it means your brand is not interactive and engaging. 

● Helps with SEO

The best search engine optimizations are achieved by testing keyword efficiency, blog titles, meta tags, headings and subheadings, CTAs, and URL structures. eCommerce firms can test out various SEO techniques through A/B testing, allowing them to keep the strategies that perform well and discard the ones that do not. In this way, they are also able to target their resources for maximum output with minimal modifications, resulting in an increased ROI. 

How Should We Conduct an A/B Test?

A/B testing is a systematic and methodical approach to determining what functions well and what does not in a particular marketing campaign. A structured A/B testing program can make marketing efforts more profitable by pinpointing the most crucial problem areas that need optimization. 

Hence, for making the most out of this technique, the whole process should be carried out in the following steps to achieve maximum ROI:

Step 1: Define Your Hypothesis

The first and foremost step in A/B testing after deciding the variable to be tested is to decide on your goals. This includes conversions as the most common objective. However, you might also want to decrease your bounce rates and abandoned carts. For this purpose, first try to find your previous bounce rate and then compare it with the one after A/B testing. If there is a significant reduction, it means you are following the right track.

For developing your hypothesis, first collect information about your visitor behaviour. It is now up to you to interpret and evaluate that information. Utilizing all of the collected data is best achieved through analysis, observations, and the creation of user insights and websites. So when your hypothesis is ready, test it using a variety of criteria, such as the degree of confidence you have in its success, how it affects larger objectives, how simple it is to set up, and so forth.

And while doing so, if you ever face issues brainstorming ideas, Relevic can get you AI-generated testing ideas within minutes. 

Step 2: Select Elements to Test

Now start by choosing the key elements that could affect your goal, such as the CTA, color scheme, or layout. The next step is to create your variations. These variations stand for the several iterations of your elements that will be put to test against the existing version, also known as the control element. Remember, when making variations, concentrate on altering only one element at a time while maintaining consistency in other areas. The test findings may be diluted if there are several variables put under test. 

For example, if you are testing a subject line, you can create two or multiple variations with a slight change in their expression and wording. So, regardless of how you categorize each variation, make sure to isolate the modifications so you can accurately evaluate their impact on the intended outcome.

Step 3: Choose a Testing Tool

Once your element has been selected, you should choose the testing tool. There are several tools available now that help businesses with A/B testing. Some of the renowned ones that need extensive coding and are for large enterprises are VWO, Optimizely and Dynamic Yield. 

However, Relevic which has recently gained popularity because of its user-friendly and zero-coding features, also helps small and large businesses to A/B test their marketing campaigns at a deeper level. 

Now once you have chosen the A/B testing tool that suits you, fill out the sign-up form on the website and follow the instructions provided, as the process varies from tool to tool. 

Step 4: Identify Target Audience

Selecting the appropriate target audience or user sample size is necessary in order to precisely determine the success of the A/B test. 

Your choice of testing methodology (split, multipage, multivariate) will determine how many groups you need to use. For instance, in split testing, there should be only two groups, while in multivariate testing, the number of groups depends upon the number of versions to be tested.

However, even in split testing, it is to keep in mind that the two tested groups should belong to alike audiences with similar demographics and histories, otherwise, the results will be contradictory and skewed. 

Step 5: Run the Test

Once the A/B testing approach, variations, and sample size are ready, you can run the test using the tool you have selected. After that, give the test enough time to finish before you start analyzing the results. As far as the running time is concerned, it depends upon the instance when you have started yielding some statistically significant data. According to Google, an experiment should keep on running until it meets at least one of these conditions:

• Two weeks have passed to account for cyclical variations in web traffic during the week.
• At least one variant has a 95 percent probability of beating the baseline.

Step 6: Analyze the Results

Once your test is completed, your most important step begins, which is analyzing the results of the A/B test. For this purpose, the tool or software that you have chosen will extract all the results for you so you can track the performance differences between the two or more versions. 

Your next objective is to see whether the results are in accordance with your initial goal. First look at the statistics, such as conversion rate, percentage increase, confidence level, direct and indirect impact on other metrics, noticeable patterns, etc. Once you have taken these figures into account, deploy the winning variation if the test is successful. If the figures have come out to be unexpected, examine the data and determine what transpired. 

Let the statistics do all the talking because sometimes even accurate and reliable data tells you a different story than you have first imagined, so a pro tip is to use that data to help plan or change your campaigns.

Step 7: Implement Findings

The final step in a well-planned A/B test involves applying your results to your marketing strategy. Through data analysis, you are able to determine which A/B test versions worked better, allowing you to make the required adjustments to improve the user experience, increase conversions, or achieve specific goals. For example, if you find out that a particular promo code drastically increased the sales of the product and you have the data to verify that such adjustments will increase performance, then doing so is justified and advisable.

For now, we know that A/B testing and experimentation strategy are all about building feedback loops. Findings lead to more findings, optimization leads to further optimization, and rising customer engagement leads to more engagement. So once a particular feature is done, start testing the next feature so the momentum keeps going.

Types of A/B Testing

Let’s now jump to some of the most common types of A/B testing done to check the performance of web pages, URLs, or features.

1. Split URL Testing

Split URL testing is a process of experimentation in which a whole new URL for an existing web page is evaluated in order to determine which one works better.

Split testing is different from A/B testing in such a way that in split testing, audiences are sent to two different URLs (hidden from them). However, in classic A/B testing, audiences see different versions of the same URL. 

Advantages of Split URL Testing:

• Perfect for experimenting with bold new ideas and comparing them to the current page design. 
• Workflows can have a big impact on business conversions. By testing different paths before making permanent changes, you can spot any problem areas you might have missed.

2. Multivariate Testing

In multivariate testing (MVT), you can test many variations of an element (or more than one element) simultaneously. This type of testing helps to analyze which combination of variables performed the best out of all the possible permutations. 

It is best suited for advanced marketing, product, and development professionals.

Advantages of Multivariate Testing:

• It is time-saving as tests are conducted simultaneously with the same goal.
• It maps all the interactions between all the independent element variations (page headlines, banner images, etc.)

3. Multipage Testing

Multipage testing  is an experimental technique in which you test changes to certain variables, like the CTA button, headers, page titles etc, across several pages. Let’s take the example of the colour of a CTA button or a seasonal sales section that appears on several pages of your website. You can see how it affects your overall conversions. 

Advantages of Multipage Testing:

• It creates consistent experiences for your target audience. 
• It lets you apply the same update across multiple pages so that, when they navigate your website, users are not distracted and do not bounce off between different versions.

What Can You A/B Test?

All website elements that have the potential to influence the behavior of your visitors should be tested regularly. This is essential as it helps to modify website features to increase user engagement and conversion rate.

For your website to be optimized to its maximum potential, the following key site elements can be tested:

Headline and Subheadings

Headlines and subheadings are important as they grab the attention of the readers and compel them to keep reading. Hence, you should keep headings with interesting and easily understandable wording and test them with different font type and size to see which version performs better. For this purpose, you can also use Relevic’s AI-powered text generation system to generate recommendations for the existing copy on your website.

Images and Other Visuals

You can experiment with content images, above-the-fold photos, and background images. In addition, you can experiment by swapping out graphics for GIFs, pictures, or videos.

Navigation Bar

The home page is where the navigation on your website begins. All other pages link back to and originate from the home page, which serves as their parent page. Make sure your layout makes it easy for users to find what they're looking for and prevents them from becoming lost due to a malfunctioning navigation system. Every click needs to take users to the intended page.

Forms

Forms are important to collect personal information from your customers, but lengthy and tedious forms can be a red flag as they might frustrate the customer, making him leave your site. Make sure your forms are clear and precise to increase conversions, and therefore they can be tested for the following elements:

• Length of the form
• Color, font and other design elements 
• Adding or removing a progress bar
• Copy used on the “Submit” button

Call-to-Action buttons (CTAs)

CTAs are one of the most important elements that need regular testing to find out which colour scheme, font size, and CTA wording works best. This is important to attract customers to click that button, make the final purchase, and convert. Some of the appealing CTAs are as follows:

• Get started free
• Try for free
• Sign up
• Shop now
• Get the free guide
• Subscribe now
• Join our mission

Landing Pages

Do you know that landing pages have the highest conversion rate of all signup forms at 23%? These conversions can further go up if you A/B test your landing pages. When doing so, pay close attention to the headlines, graphics, layouts, call-to-actions, and other elements. Moreover, statistically analyze your conversion rate, bounce rate, and engagement to see which version performs better.

Social Proof

Social proof appears on your website in the form of shout-outs, testimonials, case studies, media mentions, awards and badges, and certificates. These are important as they build trust and credibility among website visitors. For social proof, A/B testing helps you decide whether these should be shown on the website or not. And if they should, what kinds of layout and placement will be most effective to increase conversions and user engagement.

Pop-ups

Pop-ups are an ideal way to capture your audience's attention. But too many popups might distract the customer, making him find the site spammy and leaving it right away.

Hence, A/B testing should be used to find out the right number of popups that should be displayed on a webpage, along with their design, placement and message.

What are the Best Practices for A/B Testing?

In order to conduct a true A/B test that yields accurate results and analysis, it is important that you follow some basic tips and best practices.

● Test One Variable at a Time:

For reliable results, testing one element at a time is key in A/B testing. You should either test for changes in the title, image, call to action, or number of form fields when testing for marketing purposes. Do not test all the variables simultaneously, as you will not be able to find out which variable has made a difference in your lead conversions. 

If you still need to test more than one element, then you need multivariate testing.

● Sample Size Considerations:

Another best practice for A/B testing is to choose the right sample size. For getting accurate results, the sample size should be big enough. For example, you might run two A/B tests for the form on your "Sign up" page. After two weeks of testing, you discover that Version A outperformed Version B by 25%. But your test sample is too small, thus, if each version received only 16 visitors over that period, you actually don't have enough data. Just 2 or 3 people filling out Version A is enough to completely sway your results.

● Check for Statistical Significance:

It is important to interpret your A/B testing results by checking their statistical significance. If the difference between the two versions tested is only 2%, you cannot say which one performed better. Hence, it is preferable that a difference of 5% is required to determine which version is superior. 

● Keep a Consistent Testing Environment:

Before running an A/B test, it is essential to decide on the starting and ending date of the test. This is necessary for the collection and analysis of data. It is preferred to keep a time of at least two to three weeks to gather meaningful data, but if you have a large sample size (audience), data of one week should also be enough for the analysis.

Moreover, once the test is in the running process, it is totally unadvisable to alter the experiment settings or the goal of the test. Changing the variable or the traffic allocations to variations will massively skew your test results.

What are the Common Pitfalls while A/B Testing?

A/B testing is one of the best methods to boost income inflow and move business metrics in the right direction. But as mentioned already, A/B testing necessitates preparation, persistence, and accuracy. 

Here is a list of some of the most frequent errors to keep in mind when doing an A/B test to help you avoid making mistakes: 

● Testing Too Many Variables

When you compare two or more variables at the same time, you are not able to detect which variable has resulted in the effect. For instance, you want to optimize a particular landing page, and for that purpose, instead of only testing the main body of content, you test the heading, CTA, and header images all at the same time. 

Now if the conversion rate goes up, you will not be able to find out which variable explicitly has resulted in this change. Hence, to get accurate results, considering only one variable while isolating the others is the right way to do the A/B test.

● Selecting an Insufficient Sample Size

It is a common mistake to select the wrong sample size for your A/B tests. In other words, businesses keep unbalanced traffic, which later yields insignificant results. Using lower or higher traffic than required for testing increases the chances of your campaign failing or generating inconclusive results. 

To further add, small samples can cause false positives and negatives, hence making it difficult to conclude if the differences are the result of your changes or by random chance. Imagine you are testing two versions of a landing page to see which one leads to higher conversion rates. You split the traffic but only end up with 100 visitors to Version A and 100 visitors to Version B. You might believe that Version A is superior if it has a 9% conversion rate, whereas Version B only has an 8% conversion rate. However, since each version only receives 100 visits, it is not statistically significant. It's likely that the outcomes would have been different if you had conducted the test with more participants.

● Ignoring Statistical Significance

If you ignore statistical significance and base your decisions only on intuition rather than calculations, you risk ending your test too soon before it produces statistically significant data. It also implies that you will receive inaccurate findings. For this reason, we suggest that you don’t assume that your experiment will always last two weeks. Instead, figure out how many visitors and conversions you require (for both the experiment and control versions) in order to ensure that your test is statistically significant. Coordinate with your team to plan, organize, execute, and evaluate A/B experiments using online calculators or A/B testing tools.

● Choosing the Wrong Hypothesis

Before you start an A/B test, you first decide on a hypothesis. A hypothesis basically explains which variable will be changed and how, and what will be its results and implications. A wrong hypothesis will yield incorrect and insignificant results, wasting your time, effort, and money. 

For this purpose, it is ideal to base your hypothesis on the data you gather from users. The goal of your test should be to solve a particular issue that users encounter. Typically, you find issues by speaking with users or by seeing how they behave. Moreover, the success of your A/B test depends upon choosing the right success metrics to measure before and after the test. 

● Keeping Short Testing Durations

In order to achieve statistical significance, it is important that you run the test for a certain length of time. This time depends upon your traffic and goals of the test. Running it for too long or too short can result in a failed test. For instance, if a version starts to perform really well within a week only, stopping the experiment early will give biased results. Hence, it is ideal to run the tests for at least two business cycles, as said by most experts. This is because of the following genuine reasons:

1. Time to decide: Some users need more time to think before making a decision.
2. Varied traffic sources: Your website may get visitors from different places (like social media, emails, or search engines), and they may behave differently.
3. Unexpected events: Things like a Friday newsletter can cause sudden spikes or dips in traffic, so running the test longer helps smooth out any unusual patterns.

Hence, two cycles give you a more complete view of how your audience behaves, ensuring more reliable insights.

● Using the Wrong Tools and Wrong Testing Type

One of the most critical aspects of A/B testing is choosing the right testing tool for your A/B tests. Due to the high demand and popularity of A/B testing, many tools are available in the digital world now. Out of these, certain tools can cause your website to load far more slowly than others, and some might fail to interface properly with essential qualitative tools (heatmaps, session recordings, etc.), deteriorating your data. 

Using such flawed tools for A/B testing puts your test's success at risk right from the start. Also, there are certain tools that change your parameters in between the tests without letting you know. Isn’t this risky?

Moreover, sticking to the typical A/B testing model also does not work every time. For instance, split testing should be used if you are going to completely redesign a page on your website. In the meantime, multivariate testing is ideal if you want to test different combinations of CTA buttons, their colours, the content, and the image of your page's banner.

How to Analyze A/B Test Results?

Once the procedure for A/B tests ends, the results achieved have to be analyzed first. The testing metrics vary for every business, as they mainly depend upon the goals of the company. For instance, an eCommerce website of a clothing brand might want to run an A/B test to decrease cart abandonment, while a tech-based company might use CTA variations on a landing page to boost free sign-ups. Hence, the following metrics need to be tracked and evaluated to make the most of each experiment.

1. Conversion Rate

The percentage of users who complete a desired action is determined by the conversion rate. The desired actions include making a purchase, subscribing to a newsletter, clicking on particular links, completing a form, and so forth. Since small gains frequently result in large profit growth, increasing the conversion rate is the main objective of A/B testing.

2. Bounce Rate

The percentage of users who leave a website after viewing just a single webpage is known as the bounce rate. To lower bounce rates and entice users to stay around, testers need to look at a variety of factors, including headlines, graphics, CTAs, and more. 

The bounce rate provides you with valuable information about the interest level of your visitors. This enables you to highlight issues related to your website design and content, and thus helps you improve the control and efficacy of your experiments.

3. Click-Through Rate (CTR)

CTR is the percentage of clicks on a specific link over the total number of views it received. CTR is used in A/B testing to assess how effective digital advertisements, messaging tactics, etc. are. To improve the CTR, we have to see which CTAs, colors, highlights, and images are the most engaging and persuasive.

4. Interpreting Statistical Significance

It is to keep in mind that statistical significance is not a metric; rather, it quantifies the accuracy of an A/B test. In a nutshell, statistical significance links a certain result from tested data to a particular cause. It is because sometimes it becomes challenging to decide if a result difference is due to the changes made or if they are occurring at random. Statistical significance then plays its role here! Higher the statistical significance, the more the data is reliable.

In A/B testing, the key indicators to see whether the differences are statistically significant or not are the P-value and confidence interval. The P-value is the probability of an event occurring, while the confidence interval refers to the uncertainty of a particular event. P-values that fall between 0.01 to 0.05 are considered ideal here, where 0.05 indicates a 95% confidence level and 0.01 indicates a 99% confidence level.

How to Deal with Inconclusive A/B Test Results?

When you’re faced with inconclusive A/B test results, the first thing to do is take a step back and review how the test was set up. Make sure you ran it for enough time, gathered enough data, and focused on the right metrics. Double-check that the variations you tested were meaningfully different and consider any outside factors, like seasonal trends or unexpected events, that could have influenced the results. Sometimes, the issue might just be that you didn’t collect enough data or need to rethink your hypothesis and adjust the variables you’re testing.

If the results are still unclear, don’t give up. Instead, try running follow-up tests with more specific adjustments. Rather than scrapping the test altogether, you can dig deeper by tweaking different elements or fine-tuning how you target your audience. You could also test on different audience segments or extend the test period to gather more data. In the end, inconclusive results aren’t a failure! They’re a chance to refine your approach and gather more meaningful insights for future tests.

A/B Testing Case Studies 

A/B testing depends upon the goals of a company. Regardless of the type of customers or industry, split testing can help you improve the customer experience. Let us now dive deeper to see which renowned brands worldwide were able to achieve their desired results with the help of A/B testing and which of them failed to do so:

1. Nissan

Nissan observed a decrease in their in-person encounters, so the company sought a better understanding of its target market. Nissan focused particularly on the content that increased sales to better understand its sales funnel.

The brand tested design components such as button shape, positioning, and body text using A/B and multivariate methods. Nissan saw a decrease in bounce rates and an increase in conversions as a result. Additionally, the open and click rates on its emails also doubled.

2. Save the Children

Not only businesses use A/B testing, it is also used by nonprofit organizations to increase donations. Save the Children shifted to digital programming and fundraising in response to COVID-19. The nonprofit conducted relatively little testing before the pandemic, but following a few A/B tests, it gained a deeper understanding of the needs of its donors.

A donation live feed that showed recent donations as social proof was tested by Save the Children. It also ran A/B tests on some of the material on its website. Save the Children saw an 85% increase in conversions as a result. In just two weeks, it raised £1.5 million for Ukraine and saw a 25% increase in revenue per visitor (RPV).

3. WorkZone

WorkZone, a US-based software company that offers comprehensive documentation collaboration tools, added a customer review area as a social proof marketing tactic next to the demo request form on the lead generation page. WorkZone soon discovered that visitors were being further distracted from completing the form by the overshadowing of customer testimonial branding. They decided to change customer testimonial logos from their original color to black and white and see whether the change would help increase the number of demo requests.

WorkZone discovered that the variant outperformed the control after the test ran for 22 days. It indicated a 99% statistical significance and predicted a 34% rise in form submissions.

4. Bing

Bing ran an A/B test to assess whether changing the number of ads shown at the top of search results would increase revenue per search (RPS). After running the test, the results initially appeared to support the hypothesis. There was an increase in ad clicks and, subsequently, a rise in revenue per search.

However, deeper analysis revealed that in the long run, users who saw more ads were less satisfied with the search results, leading to a higher bounce rate. 

The Bing’s team also learned that while A/B test results may show immediate positive metrics, a deeper look at secondary KPIs (e.g., bounce rate, user retention) is critical for understanding the broader impact of changes. Hence, this failed A/B test taught Bing that user experience should always be the primary focus.

A/B Testing Using Relevic

Your company will become more competitive and adaptable if you base your strategy on A/B testing and thorough data analysis. More importantly, you will have high-quality, data-backed feedback on your service's consumer satisfaction.

Using the right A/B testing tool will provide you with more concrete benefits, such as lower bounce rates and higher conversion rates, as well as fresh, insightful data on your customers' journey.

Use Relevic’s A/B Testing Checklist to make the right choice according to your needs and team skills.

Relevic provides more effective A/B testing solutions for growing brands as well as corporations. Keeping the user’s priorities in mind, it simplifies testing, analysis, and optimization without requiring any code knowledge. But how do you do that? Let me explain it to you in detail. 

Relevic allows you to create two versions using the A/B testing feature or more versions using the multi-test. You can first design two or more variations of your web page and then insert their URLs in the tab provided. Once done, you open Relevic’s campaign canvas, where you build your campaign by choosing the A/B test filter and drag and drop URLs of both variations. You can also further specify or limit your research to particular regions by using the location filter. 

Moreover, you can rename the campaign and add the starting and ending dates to run it. The slider bar enables you to decide which percentage of the audience should be exposed to the different variations, and in this way you can later use your findings to yield significant results. 

Relevic provides effective A/B testing solutions for growing brands as well as corporations. Keeping the user’s priorities in mind, it simplifies testing, analysis, and optimization without requiring any code knowledge.

Conclusion

We have tried our best to provide you with an extensive overview of A/B testing. By now, you should be all set and well-prepared to create your own optimization strategy using A/B testing for maximum conversion rate and user engagement on your website. For that purpose, pay close attention to every step involved and be cautious not to make any mistakes, especially the ones mentioned in the article, otherwise, you will get inaccurate results.

Moreover, when initializing your A/B testing, it is a good idea to begin it with small and simple experiments. You can start by testing one thing at a time so you get clear insights without the confusion of too many variables. As you gather results and learn what works, you can gradually increase the complexity of your tests and explore other areas. By taking this step-by-step approach, you will have a much better chance of making improvements that truly make a difference over time.

We wish you all the best with your A/B testing journey. May your A/B testing lead to outstanding breakthroughs! 

Frequently Asked Questions