The Basics of AB Testing for Search Result Layouts

As a growth lead at Pareto, I’ve seen firsthand how ab testing can help early-stage startups grow by allowing them to understand their users and dial in their product/market fit. But AB testing isn’t just for startups - it’s an essential tool for any business looking to optimize their website and improve their search engine rankings.

In this article, I’ll explain what AB testing is, why it’s important, and how you can set up and analyze an AB test for search result layouts. I’ll also share some best practices and common mistakes to avoid, as well as some case studies of successful AB testing for search result layouts. By the end of this article, you’ll have a solid understanding of how AB testing can help you improve your website’s user experience and conversion rates.

What is AB Testing and Why is it Important?

AB testing, also known as split testing, is the process of comparing two versions of a webpage, email, or other marketing material to see which one performs better. In an AB test, you randomly divide your audience into two groups and present each group with a slightly different version of your content. You then track which version performs better in terms of user engagement, conversion rates, or other key metrics.

AB testing is important because it allows you to make data-driven decisions about your website’s design and content. Instead of relying on gut feelings or assumptions, you can test different variations of your website and see which ones resonate best with your audience. This can help you optimize your website for user experience, conversion rates, and other key performance indicators.

How to Set up an AB Test for Search Result Layouts

Setting up an AB test for search result layouts is relatively straightforward. Here are the basic steps:

Define your goal: Before you start your AB test, you need to define your goal. This could be increasing click-through rates, improving user engagement, or boosting conversion rates.
Create your variations: Next, you’ll need to create two different versions of your search result layout. These variations should be similar enough that they’re both plausible, but different enough that you can see a measurable difference in user behavior.
Randomly divide your audience: Once you have your variations, you’ll need to randomly divide your audience into two groups. You can do this using a tool like Google Optimize or Optimizely.
Run the test: Now it’s time to run the test. Present each group with a different version of your search result layout and track the results. Be sure to run the test for a long enough period to get statistically significant results.
Analyze the data: Once your test is complete, it’s time to analyze the data. Look at the key metrics you defined in step one and see which version of your search result layout performed better.
Implement the winning variation: Finally, implement the winning variation on your website and continue to monitor your key metrics. You may need to run additional tests to further optimize your search result layout.

Best Practices for AB Testing on Search Result Layouts

When running an AB test for search result layouts, there are a few best practices to keep in mind:

Test one thing at a time: To get accurate results, it’s important to test only one thing at a time. This could be the position of your search bar, the size of your thumbnail images, or the color of your call-to-action button. Testing multiple variables at once can make it difficult to determine which changes are driving the results.
Run the test for long enough: To get statistically significant results, you need to run your test for long enough. This will vary depending on your traffic volume, but as a general rule, you should aim for at least two weeks.
Keep your test sample size large enough: It’s important to have a large enough sample size to ensure that your results are statistically significant. This will also vary depending on your traffic volume, but as a general rule, you should aim for at least 100 conversions per variation.
Use a tool to track your results: There are many tools available to help you track your AB test results, such as Google Optimize, Optimizely, or VWO. These tools can help you set up your test, analyze the data, and implement the winning variation.

Measuring and Analyzing Results of AB Testing

When measuring and analyzing the results of your AB test, there are a few key metrics to look at:

Conversion rate: This is the percentage of visitors who took the desired action on your website, such as filling out a form or making a purchase.
Click-through rate: This is the percentage of visitors who clicked on one of your search results.
Bounce rate: This is the percentage of visitors who left your website after viewing only one page.
Time on page: This is the average amount of time visitors spent on your search results page.

By analyzing these metrics for each variation of your search result layout, you can determine which version performed better and make data-driven decisions about your website’s design and content.

Common Mistakes to Avoid in AB Testing for Search Result Layouts

When running an AB test for search result layouts, there are a few common mistakes to avoid:

Testing too many variations at once: As mentioned earlier, it’s important to test only one thing at a time to get accurate results. Testing multiple variations at once can make it difficult to determine which changes are driving the results.
Ending the test too soon: To get statistically significant results, you need to run your test for long enough. Ending the test too soon can result in inaccurate or inconclusive results.
Not having a large enough sample size: It’s important to have a large enough sample size to ensure that your results are statistically significant. Not having a large enough sample size can result in inaccurate or inconclusive results.
Ignoring qualitative feedback: While quantitative data is important, it’s also important to listen to qualitative feedback from your users. This can help you understand why one variation performed better than the other and make more informed decisions about your website’s design and content.

Case Studies: Successful AB Testing for Search Result Layouts

Here are a few examples of successful AB testing for search result layouts:

eBay: eBay ran an AB test to see which version of their search result layout led to more purchases. They tested two variations: one with more filters and one with fewer filters. The version with fewer filters resulted in a 10% increase in purchases.
HubSpot: HubSpot ran an AB test to see which version of their search result layout led to more click-throughs. They tested two variations: one with thumbnail images and one without thumbnail images. The version with thumbnail images resulted in a 27% increase in click-throughs.
Expedia: Expedia ran an AB test to see which version of their search result layout led to more bookings. They tested two variations: one with a grid layout and one with a list layout. The version with a grid layout resulted in a 13% increase in bookings.

Tools and Resources for AB Testing on Search Result Layouts

Here are a few tools and resources to help you get started with AB testing on search result layouts:

Google Optimize: Google Optimize is a free tool that allows you to run AB tests on your website, including search result layouts.
Optimizely: Optimizely is a popular AB testing tool that offers a range of features, including testing for search result layouts.
VWO: VWO is another popular AB testing tool that offers a range of features, including testing for search result layouts.
ConversionXL: ConversionXL is a website that offers a range of resources and courses on AB testing and conversion rate optimization.

Once you have a system bringing you leads on autopilot, the next step is to start optimizing your funnel. Optimizing your funnel starts by adopting a mindset of ‘this is what I think, but let’s test and see’. Because really, what are the chances that you have nailed the absolute optimal setup on the first try? There’s no chance, which means there is room for improvement, and AB testing is how we improve.

In conclusion, AB testing is an essential tool for improving your website’s user experience and conversion rates. By following best practices, avoiding common mistakes, and analyzing your results, you can make data-driven decisions about your search result layout and optimize your website for success.