How Google Helps Users Find Products Faster

Shopping search results are looking more and more like feed than ranking results for some time. In December, I wrote about integrating Google’s shopping tab into the top results for shopping queries as e-commerce changes. The result is a marketplace that looks more like Amazon than web search results.

SERP features and query refinements play a big role in this transformation. They guide users from un-refined searches to finding products as quickly as possible, which has a huge impact on clicks and revenue.

In this in-depth study, I analyzed over 28,000 shopping SERP search results to understand how query refinement works and how ecommerce sites can leverage it.

It’s a bit early for the shopping season, but I write a lot about e-commerce because most stores take a while to make changes to their site (especially big ones). So if you want a fruitful 2024, now is the time to start working on your roadmap.

This article builds on two analyses I published previously:

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What are query enhancements

Query refinements are “pills” at the top of search results that help users refine their search. Query refinements are essentially product filters.

This was first announced in Google Search. In 2022, Google clarified that refinements (and filters on desktop) are in line with real-time search trends (emphasis mine):

Full-page shopping filters in Search are now dynamic and adapt based on real-time search trends. So if you’re shopping for jeans, you might see the filters “wide leg” and “bootcut” because those are the popular styles of jeans right now — but those can change over time, depending on what’s trendy.1

Query refinement examples. (Photo credit: Kevin Indig)

The goal of query refinement is to get users from the “messy middle” to conversion as quickly as possible. clean query. When you click on pillGoogle sends users to another search engine results page (SERP), just like when users click on the product filters on the left.

To understand how query refinement works and how e-commerce sites can use it to their advantage, I dug up some data.

How query refinements work

I analyzed 28,286 shopping keywords (US, desktop) with seoClarity and found that the query refinements:

• follow specific patterns that sites can use for keyword targeting.
• leads to search queries without search volume.
• launch new AI reviews on mobile devices.

Typical improvements

I analyzed which improvements appear most often in “position” one, two, and three. Think of position one in this context as the first refinement from the left, which is the most visible.

Most refinements specify gender. “Women” appears most often in the top three, but “men” appears most often in the first refinement. 45% of query refinements mention one gender at least once, 61.4% if you include children. That makes sense: Before you delve into product attributes like color or size, you want to make sure the product is “for you.”

The second most common group of refinements is location. Ten percent of the top three refinements include “nearby,” which is much more visible on mobile devices. Google defaults to showing maps on mobile devices because mobile users are more likely to be on the go.

The third group consists of attributes related to queries containing “for” or “from”, where users are trying to define use cases (9.8%), and the fourth group consists of attributes related to price (9% of refinements include the term “sale”).

Query refinements have a large overlap with product filters on desktop and often include the first few filters as refinements. Product filters do not exist on mobile, probably because users would expect a filter sidebar on desktop, but it makes no sense on mobile.

Sorting and visibility of refinements is different on mobile and desktop. Due to the difference in larger screen size, mobile search results show ~4 refinements upon loading, while desktop searches can show 10+.

Because query refinement relies on real-time search, it overlaps greatly with auto-suggestion.

Interesting discoveries

Three conclusions from the data surprised me:

First, Google keeps improvements strictly focused on product attributes, not user intent. I expected searchers to be interested in Reddit reviews and opinions, but neitherReddit“nor”Opinions”appeared as an improvement only once.

Two, query refinements exactly match the query, meaning you won’t find synonyms or closely related terms in them. As a result, brands also don’t show up in refinements.

Third, most of the query refinements do not have search volume or CPC. Only 10,696 / 27,262 keywords in the first refinement have search volume (median = 70), and only 6,514 / 27,262 keywords have CPC. Since query refinements are based on search behavior, we can conclude that search volume and other Keyword Planner metrics are very limited metrics.

AI Review Improvements

Of course, in my research I came across AI Overviews (AIO). For the queries I analyzed, mobile results returned AIO, but not desktop results. An example is brown mascara.

You probably noticed the AIO tabs in the screenshot above, which appear independently of the enhancements and explain common product attributes.

Note that AIO provides additional tips and information in tabs (see screenshot below).

At this stage, it is unclear whether citations in AIO tabs are good because they drive traffic to review articles, or bad because they give away the answer.

For other queries such as “air compressor”I was able to see improvements IN AI review instead of above it. Clicking on the AIO refinement leads to another search with the refinement in the query. For example, on the SERP for “air compressor”, one of the improvements is “for painting cars”. Clicking takes you to another SERP for the query “air compressor for car painting” (with a different AIO and tabs, but no refinement). Note that I was logged into the SGE beta, which means these features may not be available to every user yet.

5 lessons

From my analysis of over 28,000 purchase inquiries, I have drawn 5 key conclusions:

• If it is product-specific (e.g. fashion), create specific product and category pages for men/women/kids.
• Use query refinements and auto-suggestions to find relevant query variants for your keyword research (for example with seoClarity).
• Monitor rankings by query refinement to make faceted indexing decisions (e.g. Nike or Target). Refinements showing different URLs are an indicator of building specific facets.
• You need to identify searchers’ interests beyond search volume. The fact that more than half of queries have no search volume, but query refinements are optimized for search behavior, shows that you could be missing out on opportunities by limiting yourself to queries that do have search volume. Instead, leverage on-page search data, surveys, and qualitative research to improve your keyword targeting.
• Monitor and compare clicks across desktop and mobile results to understand the impact of product filters (desktop) and AIO (mobile).