[Build Backlinks Online] A New Analysis of Google SERPs Across Search Volume and Site Type

Build Backlinks Online has posted a new item, 'A New Analysis of Google SERPs
Across Search Volume and Site Type'


Posted by Matt Peters

At Moz, we have been following up on our 2013 Search Engine Ranking Factors
study by continuing to analyze interesting aspects of the data. One of our most
frequently asked questions is, "Do you see any systematic differences in
Google's search results across search volume or topic category?" By design, our
main study used a broad keyword set across all search volumes and industries to
capture Google's overall search algorithm. As a result, we weren't able to
answer this question since it requires segmenting the data into different
buckets. In this post, I'll do just that and dig into the data in an attempt to
answer this question.

Our approach

We used a subset of the data from our 2013 Ranking Factors study, focusing on
a few of the most important factors. In the main study, we collected the top 50
search results for about 15,000 keywords from Google, along with more then 100
different factors. These included links, anchor text, on-page factors, and
social signals, among others. Then, for each factor we computed the mean
Spearman correlation between the factor and search position. Here's a great
graphic from Rand that helps illustrate how to interpret the correlations:




In general, a higher correlation means that the factor is more closely related
to a higher ranking than a lower correlation. It doesn't necessarily mean that
there is causation!


In addition to search results and factors, we collected the categories from
AdWords (e.g. "Home and Garden") and the monthly US (local) search volume. This
allows us to examine correlations across these different segments.

Search volume

First up is search volume. We segmented each keyword into one of three buckets
depending on the average local (US) monthly search volume from AdWords: less
than 5,000 searches per month, 5,000-15,000 searches per month, and more than
15,000 searches per month.


To begin exploring the data, here is the median page and domain authority in
each bucket, along with the total percentage of results with a domain name
exactly matching the keyword:




Not too surprisingly, we see the overall page authority, domain authority and
the exact match domain (EMD) percentage all increase with search volume. This is
presumably because higher-volume queries are targeted by larger, more
authoritative sites.


Now, an overall higher page authority for high-volume queries doesn't
necessarily mean that the correlation with search position will be larger. The
correlation measures the extent to which page authority (or any other factor)
can predict the ordering. As a example, consider two three-result SERPs, one
with page authorities of 90, 92, and 88 for the first three positions; and
another with values of 30, 20, and 10. The first SERP has higher values overall,
but a lower correlation. To examine how these impact search ordering, we can
compute the mean Spearman correlation in each bucket:





And for those who prefer a chart:





From left to right, the table lists link-related factors (page authority,
domain authority, and exact match anchor text); a brand-related factor (number
of domain mentions in the last 30 days from Fresh Web Explorer); social factors
(number of Google +1s, Facebook shares, and tweets); and keyword-related factors
(keyword usage on the page, in the title, and EMD).


Looking at the data, we can see a few interesting things:


The correlations increase noticeably with search volume for link, brand, and
social media factors.
The correlations are mostly constant for keyword-related factors (keyword usage
on the page or in the domain name).

Primarily, point #1 says that these factors do a better job at predicting rank
as search volume increases. We'd expect to see a larger discrepancy in the link
or social metrics throughout the SERPs in higher volume queries than in
lower-volume queries. One corollary is that SERPs from lower-volume queries are
more heavily influenced by factors that aren't represented in the table (e.g.
positive or negative user signals).

One implication of point #2 is that Google's keyword-document relevance
algorithm is the same for high- and low-volume queries. That is, their method
for determining what a page is about doesn't depend the query popularity.


We can make this more concrete by considering two different queries and SERPs:
one high volume ("cheap flights" with more than 1 million searches per month),
and one low-volume ("home goods online" with less than 500 searches per month).
For reference, here are the top results for each search, with the page and
domain authority from the MozBar:




Above: Google SERP for "cheap flights"




Above: Google SERP for "home goods online"


When a user enters a query, Google first determines which of the many pages in
its index are relevant to the query, then ranks the results. A popular query
will likely have several relevant pages (or more) with many links, since they
are targeted by marketers. In this case, Google should have plenty of signals to
determine ranking. A relevant page with high page authority? Check, put it in
the top 10. On the other hand, pages in the dark corners of the internet with
relatively few links are likely most relevant to low-volume queries. In the
low-volume case, since the link signals aren't as clear, Google is forced to
rely more heavily on other signals to determine ranking, and the correlations
decrease. This example oversimplifies the complexity of the algorithm, but
provides some intuitive understanding of the data.

Site category

We can repeat the analysis for the different AdWords categories. First, the
median page and domain authority and EMD percentage:







And the mean Spearman correlations:





Overall, the trends are similar to search volume, with significant differences
in the link correlations, and smaller differences in the keyword-related
correlations. The explanation for these results is similar to the one above for
search volume. The industries with the largest link and social correlations
â "Health" and "Travel & Tourism" â tend to have broad-based queries
targeted by lots of sites. On the other hand, the industries near the bottom of
the table â "Apparel," "Dining & Nightlife," and "Retailers & General
Merchandise" â all tend to have specific or local intent queries that are
likely to be relevant to specific product pages or smaller sites.

Takeaways

In this post, we have explored how a few individual ranking factors vary
across search volume and keyword category. Correlations of link- and
social-related metrics increase with search volume, but correlations of
keyword-related factors (usage on page and in the domain name) are constant
across search volume. Taken together, this suggests that Google is using the
same query document relevance algorithm for both head and tail queries, but that
link metrics predict SERPs from popular queries better then tail queries. We see
something similar across site categories with the largest differences in link
related correlations. Industries like "Health" that have broad, informational
queries have higher correlations than industries like "Apparel" that tend to
have queries with specific product intent.

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Build Backlinks Online
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