5 Steps to Design Effective Website Experiments

Running experiments on your website is not just a nice-to-have; it is a fundamental requirement for sustainable online growth. Whether you are testing the headline on a landing page or trying a new color for your call-to-action button, these tests provide the definitive answers you need to understand what works and what does not. Without this data, you are essentially guessing.

Experimental design can often seem over-complicated, overwhelming, and time-consuming. Many teams avoid it because they fear making a mistake that could negatively impact their conversion rates. The reality is that running experiments is only as complicated as you make it. If you understand the proper steps to running A/B tests on your website, you will be boosting conversions in no time.

At AEO/GEO, we believe that intelligent content creation must be paired with rigorous testing to ensure visibility and performance. We have walked through the basics of running an experiment. It is not rocket science. By following a structured approach, you can move from guesswork to data-driven decision-making.

Analyze Data to Identify Problem Areas

The first step in any successful experiment is to determine which part of your website needs the most improvement. This prevents you from wasting time testing elements that are already performing well or do not significantly impact your bottom line. You want to focus your efforts where they will have the highest leverage.

For example, if you have two landing pages, one with a 5% conversion rate and the other with a 30% conversion rate, you should seek to improve the 5% CVR page. The potential for growth is much higher there. If that seems obvious, let us look at a more nuanced case study to understand how to prioritize effectively.

Consider a hypothetical company, Example Enterprise. They generate 60,000 website visitors and 500 leads per month. Of those 60,000 visitors, 60% enter through the homepage, 20% through the blog, and 20% through various other pages. Interestingly, 83% of their leads converted on landing pages linked from the company blog. Only 17% came from their homepage.

Example Enterprise’s landing pages have an average conversion rate of 40%. From this data, we know they are getting a decent amount of traffic. Most leads come from the blog, even though it accounts for only 20% of traffic. The calls-to-action on the blog are performing very well. If you do the math, they have an 8.6% click-through rate. With that 40% landing page conversion rate, they are getting around 415 leads from the 12,000 blog visits. That is a strong performance.

So, what is the main problem? It is the homepage. The homepage generates 36,000 visits, but a mere 85 leads per month. Why is that? We know their landing pages are converting at a nice 40%, so the issue likely lies in how well they are driving traffic to their landing pages from the homepage. Their about pages are not performing well either, but the first priority should be to focus on where 60% of their website visitors are going and not converting.

Once you have narrowed down the most urgent problem, you can start asking specific questions. Is there a CTA above the fold on your homepage? Does it stand out enough? Does the copy create a sense of urgency? Are you surfacing the right offer? Is the design confusing? These are the variables you can now test to improve conversions.

Prioritizing High-Impact Areas

When analyzing data, it is easy to get distracted by minor tweaks on high-performing pages. However, the biggest opportunities often lie in the underperforming areas that receive significant traffic. By focusing on these problem areas, you ensure that your experimental efforts yield the highest return on investment.

You should also consider the user journey. Where do users drop off? Which pages have high bounce rates? These metrics can help you identify friction points that may not be immediately obvious from conversion rates alone. A comprehensive analysis will give you a clear roadmap for your testing strategy.

Decide Between Drastic and Incremental Tests

Once you have identified the problem area, you need to decide what kind of test to run. There are two main approaches: drastic A/B tests and incremental A/B tests. Each has its place, and the choice depends on your experience level and the specific goals of your experiment.

What is a Drastic A/B Test?

A drastic A/B test involves creating a variant that is completely different from your control. For example, you might completely redesign the layout of your homepage, or swap out a call-to-action that features an entirely different offer, design style, color scheme, and copy. This approach is high-risk, high-reward.

Here is an example of a drastic A/B test, which tests the layout of a thank-you page. By changing the entire structure and messaging, you can uncover insights that incremental changes might miss. This type of testing is ideal when you suspect that the current design is fundamentally flawed or when you want to explore a completely new creative direction.

What is an Incremental A/B Test?

An incremental A/B test involves changing only one variable in the variant page from your control. For example, you might swap out an image on a landing page, try new copy for a headline, or change the color of a call-to-action button. This approach is lower risk and allows you to isolate the impact of specific elements.

Here is an example of an incremental A/B test, which tests a landing page with no image versus having an image. This type of testing is ideal for fine-tuning your pages and making small, steady improvements over time. It is also a good starting point for teams that are new to experimental design.

Choosing the Right Approach

Should you choose to run a drastic or incremental A/B test? There is no wrong answer here. If you are new to experimental design, run a simple incremental test to get comfortable with running A/B tests while keeping your risk minimal. According to many best practices, you should run incremental tests, and the variant should only have one element that is different from your control.

However, if you are an experienced marketer, you might choose to go all in. If you want to find variations that make a huge impact on your click-through rate or conversion rate, you need to make some big changes. Do not be afraid to run big tests. That drastic A/B test above yielded a 125% increase in trial sign-up conversion rates. It would be hard to get those kinds of results after just tweaking the headline.

Choose the Right Testing Software

Now that you have decided on your test type, you need to choose software that makes it easy to test. You want a tool that lets you run your desired A/B test with minimal effort or coding know-how. The right software will streamline the process and allow you to focus on strategy rather than technical implementation.

We use HubSpot to run our own A/B tests. In HubSpot, you can test everything from:

• Your landing pages – compare form submissions, CVR, new contacts rates, and customers generated
• Your calls-to-action – compare clicks, CTRs, form submissions, and CVR
• Your email marketing sends – compare open rates, click-through rates, and unsubscribe rates

If you do not use HubSpot, there are several good alternatives for A/B testing website elements. Optimizely and Unbounce are popular choices. When selecting a tool, consider features like ease of use, reporting capabilities, and integration with your existing marketing stack.

Evaluating Testing Tools

When evaluating testing tools, look for platforms that offer robust statistical analysis and clear reporting. You want to be able to easily see which variant is winning and why. Additionally, consider the scalability of the tool. As your testing program grows, you will need a platform that can handle a larger volume of tests and traffic.

It is also important to consider the learning curve. A tool that is too complex may slow down your testing process, while a tool that is too simple may not offer the features you need. Find a balance that works for your team’s skills and resources.

Set Up Your Test and Let It Run

Now it is time to implement your test. As long as you have planned out what you want to test, this should be the easy part. The next step is to wait, which is necessary but often frustrating. You need to get the traffic volume to your page high enough to declare a winner with confidence in the test’s statistical significance.

The most important thing here is to not touch your test. If something on the variant or control is broken, such as a form submit button that does not work, fix whatever it is and restart the test. Otherwise, your data will not be sound. Any changes made during the test can skew the results and make it impossible to draw accurate conclusions.

It is excruciatingly frustrating to run a landing page A/B test, and then someone mistakenly edits the page to add a sentence or something. That completely nullifies the data from that point forward, because you have no idea if your test or their change is what influenced the final results. So just do not do it, and warn your colleagues not to touch the page you are testing without your permission first.

Maintaining Test Integrity

To maintain test integrity, establish clear protocols within your team. Communicate which pages are currently being tested and who is responsible for monitoring them. Use version control and documentation to track any changes made to the site. This will help ensure that your test results are reliable and actionable.

Additionally, be patient. Rushing a test to completion can lead to false positives or negatives. Allow the test to run for a sufficient period to account for variations in traffic and user behavior. This will give you a more accurate picture of how the variants perform over time.

Determine Statistical Significance

In the ideal experiment, only the variant is the variable, and all other conditions are constant. For example, the only difference will be the headline you are testing on your variant landing page, and all humans viewing the page will be perfect clones. Of course, this is impossible.

When conducting an A/B test, viewers are evenly split into two groups. But your viewers will not be perfect clones, and there will be external factors that play a role in your results. These include where the traffic came from, who is looking at your page, what time of day they are looking, whether it is a holiday or a workday, and even whether they have had coffee yet.

This is not much of a problem as long as the groups are split at random, but it does impact the data. This is why we need to test for statistical significance. Once something is statistically significant, you can be assured that your results weren’t just due to chance. Statistical significance gives you confidence that the observed difference between variants is real and not a fluke.

Calculating and Interpreting Results

You can use any simple A/B test calculator to determine your results’ statistical significance. For both the control and the variant, put in the total number of tries (landing page views, call-to-action impressions, email sent, etc.), and the number of goals it completed (form submissions, call-to-action clicks, emails clicked).

The calculator will tell you the confidence level your data produces for the winning variation. The closer you are to 100%, the more accurate the experiment results will be. You should aim for a value above 95%. If your confidence level is below 95%, you may need to run the test longer to gather more data.

In this example, the variant has a higher conversion rate, and the confidence level is 98%, which is above the 95% threshold. This means we can be confident that the variant is the winner.

Applying Insights for Future Tests

Once you have determined the winner, apply the insights to your future marketing efforts. If the variant won, implement the changes across your site. If the control won, analyze why the variant underperformed and use that knowledge to inform your next test. Continuous learning and improvement are key to a successful testing program.

By following these five steps, you can design effective website experiments that drive real results. Remember, testing is not a one-time event; it is an ongoing process. Keep testing, keep learning, and keep optimizing your website for better performance.