Posted by TheMozTeam

From
carousel snippets
to related searches to “People also ask”
boxes
and
“People also search for” boxes
, the Google SERP is
jam-packed with features that not only aid in
keyword list creation
but can help you better understand the
topics your unique search landscape is structured around.

In fact, the increase of topics and entities as a way of
navigating and indexing the web was one of the biggest developments
in search in 2018. This is why we took 40,977 SERPS and stripped
out every term or phrase from the aforementioned features — a
small, first step toward making sense of Google’s organizational
skills.

We wanted to see how much overlap might exist across these
different SERP features. Does Google give us a lot of new keywords
to work with or just suggest the same stuff over and over again? Do
we need to pay attention to each SERP feature when building out our
SEO strategy or can we overlook a few? We dug into a bunch of data
in STAT to find out.

A little bit on topics and entities and SERP features

In September 2018,
Google announced
a new layer to its knowledge graph:

“The Topic Layer is built by analyzing all the content that
exists on the web for a given topic and develops hundreds and
thousands of subtopics. For these subtopics, we can identify the
most relevant articles and videos—the ones that have shown
themselves to be evergreen and continually useful, as well as fresh
content on the topic. We then look at patterns to understand how
these subtopics relate to each other, so we can more intelligently
surface the type of content you might want to explore next.”

But, even before Google came out with its Topic Layer, Cindy
Krum, CEO & Founder of MobileMoxie, was all about what
she called “entities”
as mobile-first indexing was
(finally) rolling out. See if you can spot the similarities:

“Entities can be described by keywords, but can also be
described by pictures, sounds, smells, feelings and concepts;
(Think about the sound of a train station – it brings up a
somewhat universal concept for anyone who might hear it, without
needing a keyword.) A unified index that is based on entity
concepts, eliminates the need for Google to sort through the
immense morass of changing languages and keywords in all the
languages in the world; instead, they can align their index based
on these unifying concepts (entities), and then stem out from there
in different languages as necessary.”

Bringing it back to SEO-specifics, Cindy explains that both
domains (traditionally associated with indexing) and the brands
that operate them can be considered entities. “Indexing based on
entities is what will allow Google to group all of a brand’s
international websites as one entity, and switch in the appropriate
one for the searcher, based on their individual country and
language.”

So, what does any of this have to do with our SERP features of
choice? Well, all of the suggested terms packed into them are the
direct result of Google’s endless topic analysing and organizing.
We might not be privy to every entity Google scrapes but we can
certainly take cues from how they choose to express the final
product on the SERP.

How we made the magic happen

In order to map the overlap in our particular query space, we
took the highly scientific word-bag approach. Operating on a
SERP-by-SERP level of analysis, we scooped each feature’s
suggestions into its own bag, filtered out any stop words, and then
compared one bag’s suggestions to another, looking for a match
and tallying as we went.

So, for example, we’d examine all the PAA questions on one
SERP against all the related searches on the same SERP. Each PAA
suggestion got its own bag, as did each related search, and we
removed the search term itself from all of the bags. If any
remaining words in the two bags matched, we counted it as an
overlap, divided it by the total number of possible overlaps, and
got the total entity overlap between these features. Phew!

In the end, after combing through 40,977 SERPs, we made roughly
forty-million word bag comparisons. No sweat.

What we found

Ultimately, there’s not a lot of overlap happening with our
four features. A measly average of 4 percent of the search
suggestions saw any duplication in terms. This tells us that
Google’s putting a lot of care and consideration into what each
SERP feature’s up to and we’d be wise to keep an eye on all of
them, even it means weeding out a few duplicate suggestions now and
then.

Here’s how things turned out when we looked at specific
pairings:

Carousel snippets

Carousel snippets hold the answers to many different questions
thanks to the “IQ-bubbles” that run along the bottom of them.
When you click a bubble, JavaScript takes over and replaces the
initial “parent” snippet with one that answers a brand new
query. This query is a combination of your original search term and
the text in the IQ-bubble. For this bit of research, we took the
bubble text and left the rest.

It turns out that carousel snippet IQ-bubbles had the least
amount of overlap with the other three SERP features. This is
likely because the bubbles, while topically related to the original
query, typically contain subcategories that live within the
high-level category introduced by the search term.

Take the above snippet for example. The query [savings account
rates] produces a SERP with organic results and other features that
provide general info on the subject of savings accounts. The
bubbles, however, name different banks that have savings accounts,
making them highly distinct keyword suggestions.

Other reasons to consider these terms when list-building and
content strategizing: Google keeps this snippet right at the top of
the SERP and doesn’t require clicking of any kind in order to
surface the bubbles, which means they’re one of the first things
Google makes sure a searcher sees.

The “People also ask” box

The “People also ask” box typically contains four questions
(before it gets infinite)
related to the searcher’s initial query, which then expand to
reveal answers that Google has pulled from other websites and links
that guide users to a SERP of the PAA question.

Not only are PAA questions excellent long-tail additions to your
keyword set, they’re also a great resource for content
inspiration. So we stripped them out and dumped them into our word
bags to analyse.

PAA questions ended up returning the second highest level of
duplication, though most of that was tied to terms we pulled from
the “People also search for” box — the two had a 10.41
percent overlap.

This makes sense as both ostensibly offer up other terms that
people either ask or search for. It could also be a result of the
longer length of both suggestions, which can create more
opportunity for matching.

Related searches

No less than eight related searches sit at the very bottom of
each SERP and, when clicked, become the search query of a new SERP.
These help to refine or expand on the original query.

We were surprised to see how little duplication related searches
had with the other SERP features — they were oddly unique. We say
“oddly unique” because these terms are usually shorter and more
iterative of the original query, tending to stay on topic and, as a
result, we expected them to show up more in the other features (the
carousel snippet perhaps being the only exception).

The “People also search for” box

In order to surface a “People also search for” box, you need
to do a little pogo-sticking. It’ll materialize after clicking an
organic search result and then navigating back to the SERP. Mobile
PASFs typically have eight topically-related terms that open up a
new SERP, while desktop PASFs usually have six.

Out of all our comparisons, PASF boxes had the most amount of
overlap, particularly with PAAs (which we noted above) and related
searches. Given that PASF terms are attached, both physically and
topically, to the organic result and not the search query, we
actually didn’t expect them to share this much.

One possible explanation would be the sheer volume of them. With
an average of
8.77 boxes per SERP
and six or eight terms per box, this would
lead to both a lot of duplication within the box itself and an
overall saturation of the topic field. But, when we think about
what PAAs and related searches attempt to do, PASFs do seem like a
mix of both.

Putting it all together

With not a lot of term overlap happening, it’s a good idea to
keep all of these features top of mind. Google may be running out
of unique-sounding names for them, but they’re not running out of
unique suggestions to stuff into them.

Even if understanding the topic hierarchies that rule your query
space is a little outside of your day-to-day concerns, if people
click on search suggestions rather than — or even in addition to
— organic results, then it stands to reason that you should at
least be trying to rank for these terms as well as the base
query.

If you’re super pressed for time or don’t have the resources
required to wade through each SERP feature’s suggestions and had
to pick just one, you could run with the PASF box (though we’d
still recommend you throw in any IQ-bubbles that show up) as it
returns the highest duplication.

Conversely, since STAT’s got super easy PAA and related
searches reports, you could quickly cover about as much ground with
those two. Want take those reports (and more) for a test drive? Say
hello and request a
demo
!

This post was originally published on
the STAT blog
.

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