A/B Testing Site Search

What is A/B testing site search?

A/B testing site search tests different variables to determine what search practices work best to improve the shopping experience on an eCommerce website. This approach involves offering customers different demographics and behaviors to see what resonates with them in terms of what they enjoy, what makes them click, and what ultimately leads to conversions.

By running A/B tests, eCommerce store owners can gain valuable insights into their customers’ behavior and preferences. These insights can then help improve the site search functionality. This type of A/B testing site search is essential since every eCommerce store is unique, and there is no one-size-fits-all formula for success.

Why is A/B testing site search important? 

A/B test your site search allows eCommerce store owners to understand which search experiences best support the customer journey. Site search plays a crucial role in the shopping experience and product discovery, and A/B testing can help clarify which search capabilities work best for your audience.

For instance, if you’re considering adding a visual search tool, you can test it among a small portion of your shoppers to understand its impact on key metrics like conversion rate, AOV, and the number of items added to cart.

Examples of how to do A/B testing site search on your eCommerce site

• Test the placement of the search bar: Test different locations for the search bar, such as in the header or the footer, to see which placement leads to more engagement and conversions.
• Test the search bar size: Test different size dimensions to determine which size leads to more usage and engagement.
• Test the search bar copy: Test different hint text for the search bar, such as “Search” versus “Find,” to see which copy motivates users to search more.
• Test the search bar color: Test different colors for the search bar to determine which color stands out more and leads to more engagement.
• Test the search bar shape: Test different design shapes for the search bar, such as a square versus a rounded rectangle, to see which shape catches users’ attention and leads to more usage.

• Test the autocomplete feature: This test is to see if it helps users find what they are looking for faster and leads to more engagement.
• Test the product suggestions: Test product suggestions when a user types in the search bar to see if it leads to more conversions and sales.
• Test the search results page: Test the layout and design of the search results page to determine which one leads to more engagement and conversions.
• Test the use of filters: Test this on the search results page to see if it helps users find what they are looking for faster and leads to more conversions.

Best practices to do A/B testing site search

Performing A/B testing for site search requires careful consideration since no universal method exists for conducting such tests. Store owners must determine which search success metric to compare, such as click-through rate (CTR), mean reciprocal rank (MRR) of clicks, conversion rate, revenue, or other relevant metrics.

Additionally, you must decide how long to run each test, considering the need to achieve statistical significance and the potential impact of the novelty effect.

A/B testing for site search is a scientific process that requires meticulous planning and execution rather than a simple switch that you can merely turn on and off.

When considering which search changes to launch, it’s important to use A/B testing to determine their effectiveness. The A/B testing approach taken can significantly impact the types of changes that are developed, tested, and ultimately implemented.

The target set for an A/B test is critical, as it determines the time needed to run the test before evaluating its success with statistical significance. More aggressive targets require less time to test, so it’s easier to determine if a test can double conversion rates than if it can increase conversion rates by just 1%.

Therefore, you need to carefully consider the target set for an A/B test to ensure enough time is allowed to achieve statistical significance while also aiming for a meaningful performance improvement.

When scoping the analysis of an A/B test for site search, it’s important to strike a balance between being too narrow and invalidating the test and being too broad and affecting queries outside the test’s intended scope.

It’s better to scope a search A/B test regarding search sessions rather than search queries. Scoping in this way ensures that improvements to search queries affected by the test don’t come at the expense of performance on other queries in the same search sessions.

However, it’s necessary to consider how the test may affect queries outside of its intended scope, including impacting behavior within a search session and even having long-term effects on searcher behavior.