SEO Split Testing Software

How Negative Review Snippets Affect SEO Performance

Before you get started: If you’re new to the principles of statistical SEO split testing and how SplitSignal works, it’s a good idea to get started. here Or request a demo of Split Signal.

beginning, I asked my Twitter followers Vote:


Here’s what other SEO experts need to share about this test:

Vladimir GartnerSoft Road Apps Senior Project Manager:

I would be surprised if a negative review variable increases ctr. It’s good to remove it.

Boss JantankoAPOLLO Insurance SEO Specialist:

Certainly the click rate! But SEO itself isn’t even called a lower ranking because of it!

Read the full analysis of this test to see if your followers were correct.

Rich results with star ratings are one of the most popular SERP features in many SEOs. The main reason for this is that if you are listed as a rich result, the searcher will notice you more because you stand out from other search results. This can result in higher click through rates (CTR). By providing the user with additional information about the entity (such as the product), the user can also make better decisions directly from the search results. This means that your rich results need to reach standards. Otherwise, it can backfire, but we’ll talk more about that later.

The richest results are produced using structured data. There are other important use cases for structured data, but the abundant results are one of the greatest driving forces for adopting structured data. Review snippets are snippets with short ratings such as products. When Google finds valid reviews or rating markup, it may display a wealth of results, including stars.


The most common rating displayed on SERP is AggregateRating property.

As mentioned earlier, if reviews aren’t up to date, users may not be able to click through your website from search results. Orange Valley I wanted to test this at one of the biggest e-commerce parties in the Netherlands.


The website in question marks all product pages as follows: Product structured data.. If the product had reviews, they were included in the markup, regardless of whether the review score was positive or negative.

Since the website sells more than 500,000 items, products with low review scores are inevitable. However, this means that some search results weren’t very attractive to click through.


We hypothesized that a low rating would adversely affect CTR and therefore organic traffic to the website. So I wanted to see what happens if I don’t include the aggregateRating property (and its nested objects and values) for products with a rating of less than 3 (out of 5) in the markup.

By doing this, we wanted to increase the likelihood that users would visit the website to learn everything about the product, rather than relying solely on the product’s review score. In addition, if the user is already visiting the website, they may be visiting other (related) product pages on the website instead of continuing to navigate on Google.


We used SplitSignal to set up and analyze the test. All product pages with a rating score of less than 3 were selected as variants or controls. We started the test and ran it for 21 days. We found that 98% of the pages we tested were accessed by Googlebot.



Removing the aggregateRating property (and its nested objects and values) for products with a rating of less than 3 (out of 5) increased clicks by 21%.

Only six days later, we were able to determine that this increase we saw was significant. If the blue shaded area is run below or above the y = 0 axis, the test is statistically significant at the 95% level. This means that we can be confident that the increase we are seeing is due to the changes we have made, not to other (external) factors.

Note that we are not comparing the actual control group page with the variant page, but the predictions based on historical data. Compare this with the actual data. Use a set of control pages to provide a model context for trends and external impacts. If something else changes during the test (such as seasonality), the model will detect and consider it. By filtering these external factors, you can gain insight into what the real impact of SEO changes is.

Analysis of results (why?)

This test shows that the abundant results themselves are not a formula of success. Showing a low rating score in search results can get false attention. Also, if you click from the search results to access the website, the opinions of other users may be important. Therefore, as SEO, you should carefully consider the information you provide directly to your users in search results.

As expected, CTR has increased dramatically on the tested product pages. However, while the results of this test were very positive, the total number of product pages with low rating scores was relatively small. This means that the absolute impact on traffic may not be that great compared to other possible SEO changes in the page or template that attract more traffic. Split testing not only helps clients find ways to improve organic traffic, but it also helps prove the impact of SEO changes by prioritizing them in the development queue.

Gaining additional organic traffic through split testing helps SEO build strong business cases to drive SEO changes.

The increase in traffic we saw on this website was invaluable as it gave them new insights into what their target audiences care about. Keep in mind that what works on one website may not work on another. The only way to know for sure is to test what works for you!

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