How to increase conversion rates with A/B testing

No business wants to leave money on the table, but that’s what can happen if you’re not constantly testing your website and other channels to see how you can improve content experiences.

As the world of online marketing continues to evolve, so too do businesses’ strategies to increase their conversion rates. One of the most effective of those strategies is A/B testing.

In this post, we’ll discuss the A/B testing process, and explore how you can use it to boost conversion rates — and your bottom line. We’ll also provide some tips for getting started with the A/B testing process.

What is A/B testing?

A/B testing is a technique that allows businesses to compare two versions of website or app content in order to see which one performs better against a certain metric, with results measured via statistical test. 

Conducting A/B tests is relatively simple, and can be performed with digital tools, such as Ninetailed by Contentful 

Why perform A/B testing?

A/B testing allows businesses to make data-driven decisions about web content. Rather than relying on gut instinct, they can use real data to determine what works and what doesn’t. One of the great things about A/B testing is that it can be used to test pretty much anything within your digital framework, from the headline of an article to the color of a call-to-action (CTA) button.

Once you have decided what element you want to test, you create two versions of the page or app: a Version A and a Version B. You then send traffic to both versions, and see if there’s an observed difference in the outcomes you want to measure.

What is conversion rate?

Conversions are any action taken online that aligns with a business’s goals. This could be filling out a form, making a purchase, completing a survey, and so on.

Conversion rate refers to the number of conversions that occur in a given timeframe, and is usually expressed as a percentage. For example, if 100 visitors visit a website and 10 of them make a purchase, the conversion rate is 10%. The conversion rate metric helps businesses understand how effectively their website transforms visitors into customers. 

The process of pushing that metric as high as possible is known as conversation rate optimization (CRO).

A/B testing and conversion rate optimization 

A/B testing is a key tool for conversion rate optimization. By conducting A/B tests, businesses compare two versions of a web page or app, and decide which one delivers the better conversion rate.

Benefits of conversion rate optimization 

Conversion rate optimization offers numerous benefits, making it an essential part of any digital marketing strategy. Those benefits include:

• Increased revenue and sales: By optimizing conversion rates, businesses can turn more of their website visitors into paying customers, leading to higher overall revenue.

• Customer acquisition costs: When a higher percentage of visitors convert, businesses can achieve their sales goals without needing to invest as much in attracting new visitors, and so make substantial savings over time.

• Engagement: By making data-driven changes to a website, businesses can create a more intuitive and enjoyable experience for users, which not only increases the likelihood of conversions but also encourages repeat visits.

• Data and insight: By analyzing conversion data, businesses can better understand what their customers want and need, allowing them to tailor their offerings and marketing strategies more effectively. In a competitive market, these insights can be the difference between success and failure.

A/B testing for conversion rate optimization isn’t an exact science. Many factors affect conversion rates, such as a page’s design, the copy used, and the call to action. By testing different versions of these elements, businesses can find the combination that works best for their target audience.

With that in mind, it’s important to establish the statistical power of A/B testing results in order to detect true differences in conversion rates between variants. High statistical power typically minimizes the risk of Type II errors (false negatives) and Type I errors (false positives), and provides a better chance of accurately identifying significant differences during testing.

Supporting metrics

While we’re focusing on conversion rates in this post, there are other metrics that you can apply as part of your A/B testing plan, for example:

• Bounce rate: The rate at which visitors arrive on your website but leave without clicking any further links. 

• Click-through rate: The rate at which visitors click on a link that they have been shown. 

• Abandonment rate: The rate at which users abandon specific tasks on a website, such as data entry for a form, or the payment process.

• Scroll depth: The point at which users stop scrolling down through a page's content.

• Session duration: The average length of time that users spend visiting a website or page. 

• Retention rate: The rate at which users return to a page after leaving. 

The statistical significance of secondary metrics may support or clarify conclusions drawn about conversion rates during A/B testing, including the confidence intervals that you apply to results.

The A/B testing process

What does the A/B testing process look like from end to end?

Step 1: Set goals and the hypothesis

The first step in the testing process is to identify the goal of your A/B test, and state your hypothesis. In the example above, we’re entering the hypothesis into Ninetailed by Contentful’s A/B testing feature. 

Here, goal refers to the desired outcome of the test — that might be an increase in conversions, but could also include an increase in click-through rate, lead generation, and so on. Your hypothesis is what you think or predict will create that desired outcome, in other words: the change or variable that you will implement as part of your testing plan.

The goals of your A/B testing plan should be specific, measurable, and achievable — after all, you won’t be able to draw conclusions if there’s no observed difference. For example, if you’re trying to increase sales, you might set a goal of increasing conversion rates by a statistically significant 5%, with the hypothesis that changing the copy of a CTA will create that outcome.

By setting clear goals, and a clear hypothesis, you’ll be able to evaluate the statistical significance of your test results more effectively, and identify which version of your site you need to implement.

Step 2: Choose page elements to A/B test

The next step of the testing process is choosing which page elements to experiment with. This can be a daunting task, since there are a multitude of potential factors that could be affecting your conversion rates.

By taking a systematic approach, you can narrow the list of potential factors and identify the likely suspects. First, take a look at your website as a whole, and identify any areas that could be improved: are your calls to action clear and concise? Is your navigation easy to use? Once you’ve identified some general areas for improvement, you can start to narrow down the list of potential elements to test.

For example, if you’re unhappy with your conversion rate, you may want to experiment with your CTA button wording. Or, if you think that your navigation could be confusing, you may want to try out a new layout.

By carefully considering which elements to test, you can maximize the chances of increasing your conversion rates.

Common page elements to A/B test

• Original• Personalized

One of the great things about A/B testing is that you can test just about anything. From headlines and images to CTA buttons and copy, there’s a vast number of content possibilities that might deliver a real difference to conversion rates.

But this scope can be both a blessing and a curse. On the one hand, it means that you can fine-tune your website to find the perfect combination of elements. On the other, it can be tough to know where to start or what to test next.

To help you out, we've put together a list of some of the most common page elements that you can A/B test:

• Headlines: The headline is often the first thing visitors see when they land on your page, so it's important to ensure that it's optimized for maximum impact. Try testing different headlines to see which ones are most effective at grabbing attention and driving conversions.

• Images: Images are a powerful way to connect with visitors and convey your message. Swap out images to see which capture visitor attention most effectively.

• CTA buttons: The call-to-action button is one of the most important elements on your page, so it's crucial that it has an impact. Test different button colors, copy, and placement.

• Copy: Good copy can make a huge difference in how effectively your website converts visitors into customers. Test alternative versions of your copy to see which drive more conversions.

• Layout: The layout of your page can greatly affect how visitors interact with it. Try testing different layouts to see which ones generate more engagement.

Want to know where to start your A/B testing process? Download our ebook here. 

Step 3: Create variants

After you've built your A/B testing plan and created a hypothesis, it's time to create the content variants you'll be testing. For example, you might test different variations of headlines, images, or calls to action.

Through experimentation, you’ll find the combination of elements that work best for your business and your customers. And, once you've identified an optimal variant, you can implement it across your website for even better results.

Don't underestimate the power of content in your A/B testing efforts — it could be the key to achieving your desired conversion rate.

Step 4: Run the test

After you've determined your goals, designed your experiment, and created your variants, it's time for the next step: running the A/B test.

You’ll need to assign incoming traffic to the A/B variants that you’ve chosen to test, and then run your test for a period of time sufficient to generate accurate results.    

Don’t overlook how important it is to run your A/B test for sufficient time. If an experiment is run for too short a period of time, it may not be able to produce reliable results. Likewise, if an experiment is run for too long, it may no longer be representative of real-world conditions. The appropriate duration of a given test will depend on several factors, including sample size requirements, traffic volume, desired statistical significance, and the type of test being conducted.

With that in mind, businesses must strike a balance when conducting A/B tests, ensuring that they run experiments for long enough to produce accurate results without sacrificing accuracy.

Once your test is complete, you'll analyze the results to see if there was a statistical significance in conversion rates. If there was, you can then implement the more effective variant on your site.

Step 5: Analyze A/B test results

After conducting an A/B test, it’s critical to analyze the results accurately in order to determine whether the desired goal was met.

The first phase of your analysis should be to calculate the difference in conversion rate between the two groups.

The second step should be to determine whether this difference is statistically significant. This can be done by calculating a p-value, which represents the probability that the observed results occurred by chance. The difference is considered statistically significant if the p-value is less than 0.05.

Finally, you should calculate the effect size, which measures the practical significance of the results. The effect size can be calculated by dividing the absolute difference in conversion rate by the average conversion rate. Generally speaking, an effect size of 0.1 is considered to be small, 0.3 is moderate, and 0.5 is large.

Once all of these calculations have been made, businesses can determine whether their A/B test was successful, and make decisions accordingly.

A/B testing challenges

A/B testing isn’t always straightforward, and a number of factors can complicate or hinder the process. Some of the most challenging factors of A/B testing include:

Unclear hypothesis 

If you haven’t set out a clear hypothesis for your tests, you’ll struggle to understand the results that they deliver. Ultimately, this may undermine the statistical significance of any improvements because you don't fully understand what caused them.

Data segmentation

Effective data segmentation helps you interpret the results that your A/B testing generates. In practice, this means applying demographic or even behavioral segments in order to reveal useful patterns, or to reinforce the statistical significance of data.  

External variables

Carefully consider how external variables may affect your A/B testing process. For example, if you’re testing during fall or winter, any statistically significant differences may be being driven by holiday sales for Christmas or Black Friday, and won’t map effectively to “normal” page visits.   

Data depth

Numbers will be a big part of your A/B test plan, but while you may be seeing statistically significant results, don’t limit yourself to that quantitative data. Consider performing user surveys, and collecting feedback to support quantitative data with qualitative user data. 

Results focus

If conversion rates are the primary focus of your A/B testing plan, that’s fine — but don’t focus on that metric to the exclusion of other useful metrics, such as bounce rate, time on page, scroll depth, and so on. Integrating these secondary results into your testing conclusions may well support website optimization efforts. 

False positives and false negatives

Certain factors may create false negatives and false positives in testing results — and you must be alert to that risk during analysis. A false positive is the incorrect identification of a difference in conversion rates where none exists, while a false negative is the incorrect assumption that no difference exists. Managing false positive and false negative rates is essential to maintaining the reliability and credibility of your testing outcomes.

Choosing A or B: Which delivers more conversions?

Online businesses are always searching for ways to increase conversion rates and boost sales, and A/B testing is a powerful tool that can help achieve those goals.

Setting up your testing process effectively is the foundation for accurate, impactful results, so don't ignore the preparation process, and don’t avoid rigorous post-test analysis.

If you're not already using A/B testing as a way to boost conversions, we can help out. Check out how to apply AI-powered A/B testing to your digital content, and kickstart your journey today.

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