Google's ranking factors 2011

Google’s Ranking Factors 2011
Early data from SEOmoz’s survey of 132 SEO professionals and
correlation data from 10,000+ keyword rankings

Download at:

http://bit.ly/rankfactorssydney
Rand Fishkin, SEOmoz CEO, April 2011

SEOmoz Makes Software! We don’t offer consulting.

Understanding, Interpreting & Using
Survey Opinion Data

Everybody’s wrong
sometimes, but there’s a lot we
can learn from the aggregation of
opinions

#1: Opinions are Not Fact
(these are smart people, but they can’t know everything about Google’s rankings)

#2: Not Everyone Agrees
(standard deviation can help show us the degree of consensus)

#3: Data is Still Preliminary
(these are raw responses without any filtering)

http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html
Many thanks to all who contributed their time to take the survey!

Understanding, Interpreting & Using
Correlation Data

This is powerful, useful information,
but with that power comes
responsibility to present it accurately

Methodology
10,271 Keywords, pulled from Google AdWords US Suggestions
(all SERPs were pulled from Google in March 2011, after the Panda/Farmer update)

Top 30 Results Retrieved for Each Keyword
(excluding all vertical/non-standard results)

Correlations are for Pages/Sites that Appear Higher in the Top 30
(we use the mean of Spearman’s correlation coefficient across all SERPs)

Results Where <2 URLs Contain a Given Feature Are Excluded
(this also holds true for results where all the URLs contain the same values for a feature)

More details, including complete documentation and the raw dataset will be
released in May with the published version of the 2011 Ranking Factors

Correlation & Dolphins

Dolphins who swim at the front of the pod tend to have larger dorsal fins, more muscular
tails and more damage on their flippers. The first two might have a causal link, but the
damaged flippers is likely a result of swimming at the front (i.e. having damaged flippers
doesn’t make a dolphin a better front-of-the-pod-swimmer). Likewise, with ranking
correlations, there’s probably many features that are correlated but not necessarily the
cause of the positive/negative rankings.

Correlation IS NOT Causation

Earning more linking root But, will adding more characters
domains to a URL may indeed to the HTML code of a page
increase that page’s ranking. increase rankings? Probably not.

Just because a feature is correlated, even very highly, doesn’t necessarily mean that
improving that metric on your site will necessarily improve your rankings.

How Confident Can We Be in the Accuracy
of these Correlations?

Because we have such a large data set, standard error is extremely low.
This means even for small correlations, our estimates of the mean
correlation are close to the actual mean correlation across all searches.

Standard error won’t be reported in this presentation, but it’s less than 0.0035 for all of
Spearman correlation results (so we can feel quite confident about our numbers)

Do Correlations in this Range Have
Value/Meaning?

Most of our data is A factor w/ 1.0 correlation
in this range would explain 100% of Google’s
algorithm across 10K+ keywords
A rough rule of thumb with linear fit numbers is that they explain the number squared of the
system’s variance. Thus, a factor with correlation 0.3 would explain ~9% of Google’s algorithm.

Overall Algorithmic Factors

These compare opinion/survey data
from 2009 vs. 2011

In 2009, link-based factors (page and domain-level) comprised 65%+ of voters’
algorithmic assessment

In 2011, link-based factors (page and domain-level) have shrunk in the voters’ minds to only
~45% of algorithmic components. Note: because the question options changed slightly (and more
options were added), direct comparison may not be entirely fair.

Page-Specific Link Signals

These metrics are based on links that
point specifically to the ranking page

Most Important Page-Level Link Factors
(as voted on by 132 SEOs)

My guess: Some voters
didn’t fully understand the
“linking c-blocks” choice

With opinion data, voters ordered the factors from most important to least. Thus, when looking
at opinion stats, the factor voters felt was most important will have the smallest rank.

In the rest of this
deck, we’ll use linking c-
blocks as a reference
point, hence the red 

This data is exactly what an SEO would expect – the more diverse the sources, the greater the
correlation with higher rankings. These numbers are relatively similar to June 2010 data.

Correlations of Page-Level, Anchor Text-Based Link Data

No Surprise: Total links (including internal)
w/ anchor text is less well-correlated than
external links w/ anchor text

Partial anchor text matches have greater correlation than exact match. This might be correlation
only, or could indicate that the common SEO wisdom to vary anchor text is accurate.

Rand’s Takeaways
#1: SEOs Believe the Power of Links Has Declined
(correlation of link data w/ rankings has fallen slightly from 2010 to 2011 as well)

#2: Diversity of Links > Raw Quantity
(This fits well with most SEOs expectations. Also helps me feel better about the correlation data)

#3: Exact Match Anchor Text Appears Slightly Less Well
Correlated than Partial Anchor Text in External Links
(This was surprising to me, though from Google’s perspective, it makes good sense. The
aggregated voter opinions agreed with this, too.)

These are my personal takeaways from the data; others’ interpretations may vary

Domain-Wide Link Signals

These metrics are based on links that point
to anywhere on the ranking domain

Most Important Domain-Level Link Factors

C-Blocks: Likely the same
vote interpretation issue
as with page-level

Voters seem to believe that diversity/quantity is more important that quality.

Correlation of Domain-Level Link Data

Nice Work! Excluding the
“c-blocks” issue, voters +
correlations match nicely.

Domain-level link data is surprisingly similar to page-level link data in correlation

Rand’s Takeaways

#1: Google May Rank Pages, But Domains Matter Too
(the closeness of correlation data and the opinions of voters both back this up)

#2: Link Velocity & Diversity of Link Types Would Be
Interesting to Measure Given Voters’ Opinions
(Hopefully we can look at these in future analyses)

#3: Correlations w/ “All” Links vs. Followed-Only is Odd
(Let’s take a closer link at these correlations)


Something Funny About Nofollows

These compare followed vs. nofollowed
links to the domain + page

Correlation of Followed vs. Nofollowed Links

Nofollowed Matters? Many SEOs have been
saying that nofollow links can help w/ rankings.
The correlation suggests maybe they’re right.

These numbers exhibit why we like to build ranking models using machine learning. Models can
help determine whether nofollowed links have a causal impact or whether it’s mere correlation.

Correlation of Followed Links to Nofollowed Links
(i.e. Are nofollowed links well correlated w/ rankings only because they’re indicative of followed links?)

Hard to know for sure, but based on this data, it could go either way – nofollowed links, in some
way, seem to have a positive impact on rankings. Some live tests are likely in order 

On-Page Signals

These metrics are based on keyword usage
and features of the ranking document

Most Important On-Page, Keyword-Use Factors

My guess: Some voters
didn’t fully understand
the internal/external
link anchors choice

NOTE: We surveyed SEOs about more on-page optimization features, but I didn’t include them all
on this chart as it would make the labels very tiny and hard to read 

Correlation of On-Page Keyword-Use Elements

Curious: Longer
documents seem to rank
better than shorter ones

Keyword-based factors are
generally less well correlated
w/ higher rankings than links.

This is just a sampling of the on-page elements we observed; some factors haven’t yet been
calculated and thus couldn’t be compared for this presentation. They’ll be in the full version.

Correlation of On-Page Keyword-Use Elements

The theory that AdSense
More reason to believe Google when they say
use boosts rankings isn’t
page load speed is a factor, but a very small one
supported by the data
There’s a longtime rumor that linking externally to Google.com (or Microsoft on Bing) helps with
rankings. It’s comforting to see that correlation-wise, linking to MS is better on Google 

Rand’s Takeaways
#1: Very Tough to Differentiate w/ On-Page Optimization
(as in the past, the data suggests that lots of results are getting on-page right)

#2: Longer/Larger Documents Tend to Rank Better
(It could be that post-Panda/Farmer update, robust content is rewarded more)

#3: Long Titles + URLs are Still Likely Bad for SEO
(In addition to the negative correlations, they’re harder to share, to type-in and to link to)

#4: Using Keywords Earlier in Tags/Docs Seems Wise
(Correlation backs up the common wisdom that keywords closer to the top matter more)

We definitely need to look at more on-page factors in the data for the full report, too.

Domain Name Match Signals

These signals are based on data
from users of Twitter, Facebook &
Google Buzz via their APIs

Domain Name Extensions in the Search Results:

Google may
not love .info
and .biz, but
they like them
better than
Canadians! 

Spearman’s Correlation with Google Rankings for
Exact Match Domain Names June 2010 vs. March 2011

Whoa! The influence of exact match domain names seems
to have waned considerably. Links… not so much.
The sample data sets are fairly comparable in every way – both come via Google AdWords
suggestions, both include approx. 10K keyword rankings and both were gathered from Google US.

Rand’s Takeaways

#1: Exact Match Domains May Not Be as Powerful
(though it’s possible that both number reflect correlation-only, not causation)

#2: Exact .coms Fell Farther than Any Other Factor
(Possibly a lot of gaming or manipulation happening w/ those sites?)

#3: Link Count Correlations Remain Similar
(This fits w/ my experience and makes me more comfortable comparing the data sets)

Domain names are still powerful (0.22 correlation for .com exacts), but perhaps losing ground.

Social Signals

These signals are based on data
from users of Twitter, Facebook &
Google Buzz via their APIs

Most Important Social Media-Based Factors
Curious: For Twitter, voters felt
authority matters more, while for
Facebook, it’s raw quantity (could
be because GG doesn’t have as
much access to FB graph data).

Although we didn’t ask voters for a cutoff on what they believe matters vs. doesn’t, I suspect
many/most would have said that Google Buzz and Digg/Reddit/SU aren’t used in the rankings.

Correlation of Social Media-Based Factors
(data via Topsy API & Google Buzz API)

Amazing: Facebook Shares
is our single highest
correlated metric with
higher Google rankings.

Although voters thought Twitter data / tweets to URLs were more influential, Facebook’s metrics
are substantially better correlated with rankings. Time to get more FB Shares!

Percent of Results (from our 10,200 Keyword Set) in Which the
Feature Was Present

It amazed me that
Facebook Share data was
present for 61% of pages
in the top 30 results

Forhttp:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html
most link factors, 99%+ of results had data from Linkscape; for social data, this was much
lower, but still high enough that standard error is below 0.0025 for each of the metrics.

Correlation of Social Metrics, Controlling for Links
(i.e. Are pages ranking well because of links and social metrics are simply good predictors of linking activity?)

Raw Correlations Correlations
Controlling for Links
Twitter’s correlation wanes dramatically, but Facebook features, while lower, still appear quite
influential. Facebook likely deserves much more SEO attention than it currently receives.

Rand’s Takeaways
#1: Social is Shockingly Well-Correlated
(it’s hard to doubt causation, particularly after reading the SearchEngineLand interview here)

#2: Facebook may be more influential than Twitter
(Or it may be that Facebook data is simply more robust/available for URLs in the SERPs)

#3: Google Buzz is Probably Not in Use Directly
(Since so many users simply have their Tweet streams go to Buzz, and correlation is lower)

#4: We Need to Learn More About How Social is Used
(Understanding how Google uses social metrics, parses “anchor text,” etc. looms large)

Expect more experimentation and, sadly, some gaming attempts w/
Twitter + Facebook by SEOs (and spammers) in the future.

Highest Positively + Negatively
Correlated Metrics Overall

These are the features most indicative
of higher vs. lower rankings

Top 8 Strongest Correlated Metrics

Exact match domain is actually not in the top 8, but I thought I should include it, as it was,
previously, one of the metrics most predictive of positive rankings.

Top 8 Most Negatively Correlated Metrics

Be concise and to-the-point;
it’s good for users and for
your rankings 

Long domain names, titles, URLs and domain names all had negative correlations with rankings.
Again, I’ve included # of words in title, which isn’t technically in the top 8, but still interesting

Top 8 Most Negatively Correlated Metrics

One of the most surprising finds in our
dataset. We double-checked to be sure.
40% of URLs in the set had only followed
links, and these tended to have lower Page
Authority (and lower rankings) than those
w/ both followed and nofollowed links.
Our data scientist thinks there’s some
correlation between having nofollowed
and other good/natural link signals.

Also note that % of followed links on a page has a slightly negative correlation with rankings.
Perhaps sites that make all their links out followed aren’t being careful about what they link to?

Which Domains Appeared Most
Frequently in Our 10K+ SERPs?

Top 20 Root Domains Most
Prevalent in our 10,200 keyword set
(top 30 rank positions)

SEOs may be disappointed to see
eHow.com performing so well, but
classic content aggregators like
About.com + Wikipedia still beat them.


What do the Experts think
the Future Holds?

What Do SEOs Believe Will Happen w/ Google’s Use of
Ranking Features in the Future?

While there was some significant contention about issues like paid links and ads vs. content, the
voters nearly all agreed that social signals and perceived user value signals have bright futures.

IMPORTANT!
Don’t Misuse or Misattribute Correlation Data!

Think of correlation data as a way of seeing features of sites that rank
well, rather than a way of seeing what metrics search engines are
actually measuring and counting.

A well-correlated metric can often be its own reward, even if it
doesn’t directly impact search engine rankings. Virtually all the data
in this report reflect the best practices of inbound marketing overall –
and using the data to help support these is an excellent application 

Thanks much!
Rand

We are looking forward to sharing the full data in the new version of the Search Ranking Factors
report coming in ay 2011. Lots more cool info along with the full dataset will be available then.

Q+A
Download at:

http://bit.ly/rankfactorssydney
You can now try SEOmoz PRO Free!
http://www.seomoz.org/freetrial

Rand Fishkin, CEO & Co-Founder, SEOmoz

• Twitter: @randfish
• Blog: www.seomoz.org/blog
• Email: rand@seomoz.org

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