Rand Fishkin, Wizard of Moz | @randfish | [email protected]
Search Ranking Factors 2015
What data, opinions, and testing have revealed
about how Google’s rankings operate.
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Slides Will Be Publicly Available
October 20th, 2015
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A look at Google’s algo
in 2015 according to
150 professional SEOs
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We used to show graphics like this to illustrate the relative
importance of different areas of optimization to Google’s
2013
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But a pie chart suggests
that you can only get so
much value from any
given set of features.
In reality, factors like higher
link authority on your domain
have as almost unlimited
ability to positively influence
rankings
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Thus, we’re switching up how we
illustrate opinions about the factors:
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No content
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Most interesting to me is what’s
happened to SEO professionals’
opinions over time…
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2015
(in blasphemous pie chart form
to illustrate comparative
change)
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2009 2011
2013 2015
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A few of the opinions about
factors in particular stand out:
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Page-Level Link Features
Domain-Level Link Features
Page-Level Keyword Features
2009 2011 2013 2015
43% 22% 19.15% 14.54%
2009 2011 2013 2015
24% 21% 20.94
%
14.60%
2009 2011 2013 2015
15% 14% 14.94% 13.97%
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1) Professional SEOs feel that, on
average, the algo is flattening, and the
days of a single factor having an
overwhelming impact are fading.
Takeaways:
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2) After years of dominating the algo,
links, while still powerful, don’t feel like
an overwhelming ranking force to SEOs.
Takeaways:
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Takeaways:
3) Engagement data is on the rise. If
growth rate continues, by our next
survey, it may be in the top two features.
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How do various web metrics
correlate with higher
Google rankings in 2015?
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An important reminder
about correlation:
Correlation DOESN’T tell us why
one page ranks higher than another.
It DOES tell us what features
higher-ranking pages tend to have
over their lower ranking peers.
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Do correlation coefficients in the 0.1 – 0.4 range
(typical for single factors in search engine studies)
mean anything?
Debunk
statements
about what’s
NOT causal in
rankings
3 Useful Applications:
Show
relative
potential
influence
ID factors for
more testing /
investigation
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Debunking myths with correlation data is easy:
Google are losers! The
more ads you show, the
higher they rank you.
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A negative correlation of -0.03
disproves the idea that more
ad slots = higher rankings.
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Coefficients can also be used to show
relative correlation:
The best SEOs use multiple repetitions of
keywords in their titles. I guarantee it works
better than some fancy LDA model.
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On average, content that better fits an LDA
topic model dramatically outperforms KW
repetition in the title
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Correlation numbers can lead us to interesting theories
that we can then validate through other means:
Could it be that partial match anchor text
now has equal or greater ranking influence
than exact match?
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Let’s go run some experiments
to see if this is true y’all!
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NOTE: In an algorithm with 100s – 1000s of ranking inputs,
we shouldn’t expect any single element to have the kinds of
high correlations seen in less complex input scenarios.
Single factors correlate with higher
Google rankings in this range.
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In May 2015, Moz collected 16,521
unique SERPs from Google.com (US).
Full methodology here
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Look Familiar?
Link metrics’ correlations w/ rankings
have remained similar for ~6 years
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Moz & Ahrefs
For the first time, we compared Mozscape’s link correlations
against Ahrefs… And found nearly identical results for both.
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Social Shares
Correlations are down ~10-15% from their high in 2013.
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Traffic & Engagement
For the first time, we measured usage data. While traffic looks
strongly correlated, engagement metrics have weaker numbers.
Traffic and engagement
metrics via
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Keyword Use & On-Page Optimization
As we get more sophisticated in our text-modeling
abilities, we’re seeing higher correlations (though still
low relative to links & social shares)
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For the first time, we also broke
correlations down by category of
keywords/SERPs
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Health websites
that link out more
tend to rank
higher.
Dining sites see almost no
correlation between
linking out & ranking.
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It tended be more present
in higher ranking sites for
these verticals
Anchor text had a
smaller relationship
w/ high rankings in
these verticals
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Those meager
restaurant websites?
Looks like Google
doesn’t mind much.
Buzzfeed &
Upworthy are
always showing
how lengthier
articles perform
better for them.
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Twitter & Facebook have very similar relative
correlations, which fits w/ Google’s statements
that they don’t directly use either.
In some verticals,
social sharing is
much less connected
to ranking positions
than others
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1) Correlations with links have
remained relatively similar, suggesting
that perhaps links haven’t faded in
influence as much as some in our
industry have suggested.
Takeaways:
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2) We need more sophisticated on-page
analysis tools. With the right algorithms/
software, we may find real opportunities
to improve rankings through content.
Takeaways:
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Takeaways:
3) Correlation is even more useful (and
interesting) on subsets of SERPs than
on an entire corpus. In the future,
calculating correlations for the SERPs
you/your company care about may
become standard.
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A Look at
Links & Social Shares
in Google’s Rankings
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We know that
links can still
overwhelm
other ranking
signals.
Via Rishi Lakhani on Refugeeks
Pointing a few anchor-text links at
this blocked-by-robots page on Matt’s
blog made it rank (even in 2015).
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20 Elements of a Link’s Ability to Influence Ranking:
1) Anchor Text
2) PageRank
3) Relevance
4) Domain
Authority
5) Location on
the Page
6) Internal vs. External
7) Quality of Other
Links on Page/Site
8) Editorial Weight
9) Engagement w/
Linking & Linked Pages
10) Follow vs. Nofollow
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20 Elements of a Link’s Ability to Influence Ranking:
11) Source Depth
12) Text vs. Img
13) Link Age
14) Topical
Authority of
Source
16) Spam Signals
17) Speed/Acceleration
of New Link Sources
18) Author Authority
19) 1st Link to Target on
Page vs Duplicate Links
20) Prior Links to
Target from
Source Domain
15) Javascript vs. HTML
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This stuff mattered a lot when we did
manual link building to move rankings
But today, many of us just let
content build links for us, right?
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Moz & Buzzfeed joined forces for a report
looking at 1 million pieces of content.
Data via Buzzsumo & Moz’s Joint Study
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Content + Social Sharing = Links?
Median # of links across a million
pieces of content in Buzzsumo’s
database?....
1 linking root domain.
Data via Buzzsumo & Moz’s Joint Study
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This is a power law distribution – the top content
gets the overwhelming majority of links and shares.
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The reality of social amplification
and earning links is…
0.028? That’s too close to 0 to infer any
consistent, direct influence.
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For the most heavily shared content, there’s a little bit
more of a correlation, but it’s small enough that relying
on social shares to earn your links is probably folly.
We tried segmenting the samples:
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This data shows why I can’t endorse either
of these common maxims in SEO and
content marketing:
Create good, unique content and Google
will figure out the rest.
The best way to earn links is to create
great content.
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In the past, I presented a concept that,
based on this data, now appears to be
fundamentally flawed:
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Publish
Amplify
Grow network Rank for slightly
more competitive
terms & phrases
Get links Grow authority
Earn search
traffic
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1) Social shares by themselves almost
never lead directly to the quantities of
links necessary to rank well.
Takeaways:
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2) Content that performs extraordinarily
well on social networks and ranks well in
search engines may not be benefitting
solely from links.
Takeaways:
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Validating Some of
Google’s Statements
On Secure Sites
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Via Google Webmaster Central Blog
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Via Rand’s Google+
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HTTPS URLs have a 0.04 correlation w/ higher
rankings… much lower than many features
Google says don’t impact rankings.
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Investigating SEOs’
Longstanding Theories re:
Raw URL Mentions
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Using data from Fresh Web Explorer,
we can see how many mentions a URL
receives in a given day/week/month
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The correlations w/ URL mentions are pretty
high – in the range of social shares and links
(0.19 for full domain, 0.17 for root domain)
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Via Stone Temple Blog (and IMEC Labs)
So, the crew at IMEC Labs ran a test!
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Via Stone Temple Blog (and IMEC Labs)
Not the easiest graph to read, but the results suggest
that raw URL mentions had no impact on rankings,
certainly nothing like the impact that links do.
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bit.ly/rankingfactors2015
All the data from the ranking factors
report can be found at: