Ranking Factors Data 2011: SMX Elite Sydney

Google’s Ranking Factors 2011,[object Object],Early data from SEOmoz’s survey of 132 SEO professionals and correlation data from 10,000+ keyword rankings,[object Object],Download at:,[object Object],http://bit.ly/rankfactorssydney,[object Object],Rand Fishkin, SEOmoz CEO, April 2011,[object Object]

SEOmoz Makes Software! We don’t offer consulting.,[object Object]

Understanding, Interpreting & Using Survey Opinion Data,[object Object],Everybody’s wrong sometimes, but there’s a lot we can learn from the aggregation of opinions,[object Object]

#1: Opinions are Not Fact,[object Object],(these are smart people, but they can’t know everything about Google’s rankings),[object Object],#2: Not Everyone Agrees(standard deviation can help show us the degree of consensus),[object Object],#3: Data is Still Preliminary,[object Object],(these are raw responses without any filtering),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],Many thanks to all who contributed their time to take the survey!,[object Object]

Understanding, Interpreting & Using Correlation Data,[object Object],This is powerful, useful information, but with that power comes responsibility to present it accurately,[object Object]

Methodology,[object Object],10,271 Keywords, pulled from Google AdWords US Suggestions,[object Object],(all SERPs were pulled from Google in March 2011, after the Panda/Farmer update),[object Object],Top 30 Results Retrieved for Each Keyword(excluding all vertical/non-standard results),[object Object],Correlations are for Pages/Sites that Appear Higher in the Top 30(we use the mean of Spearman’s correlation coefficient across all SERPs),[object Object],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),[object Object],More details, including complete documentation and the raw dataset will be released in May with the published version of the 2011 Ranking Factors,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation & Dolphins,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation IS NOT Causation,[object Object],Earning more linking root domains to a URL may indeed increase that page’s ranking.,[object Object],But, will adding more characters to the HTML code of a page increase rankings? Probably not.,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

How Confident Can We Be in the Accuracy of these Correlations?,[object Object],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.,[object Object],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),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Do Correlations in this Range Have Value/Meaning?,[object Object],A factor w/ 1.0 correlation would explain 100% of Google’s algorithm across 10K+ keywords,[object Object],Most of our data is in this range,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Are You Ready for Some Data?!,[object Object]

Overall Algorithmic Factors,[object Object],These compare opinion/survey data from 2009 vs. 2011,[object Object]

In 2009, link-based factors (page and domain-level) comprised 65%+ of voters’ algorithmic assessment,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Page-Specific Link Signals,[object Object],These metrics are based on links that point specifically to the ranking page,[object Object]

Most Important Page-Level Link Factors,[object Object],(as voted on by 132 SEOs),[object Object],My guess: Some voters didn’t fully understand the “linking c-blocks” choice,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

In the rest of this deck, we’ll use linking c-blocks as a reference point, hence the red ,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlations of Page-Level, Anchor Text-Based Link Data,[object Object],No Surprise: Total links (including internal) w/ anchor text is less well-correlated than external links w/ anchor text,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Rand’s Takeaways,[object Object],#1: SEOs Believe the Power of Links Has Declined,[object Object],(correlation of link data w/ rankings has fallen slightly from 2010 to 2011 as well),[object Object],#2: Diversity of Links > Raw Quantity,[object Object],(This fits well with most SEOs expectations. Also helps me feel better about the correlation data),[object Object],#3: Exact Match Anchor Text Appears Slightly Less Well Correlated than Partial Anchor Text in External Links,[object Object],(This was surprising to me, though from Google’s perspective, it makes good sense. The aggregated voter opinions agreed with this, too.),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],These are my personal takeaways from the data; others’ interpretations may vary,[object Object]

Domain-Wide Link Signals,[object Object],These metrics are based on links that point to anywhere on the ranking domain,[object Object]

Most Important Domain-Level Link Factors,[object Object],(as voted on by 132 SEOs),[object Object],C-Blocks: Likely the same vote interpretation issue as with page-level,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],Voters seem to believe that diversity/quantity is more important that quality.,[object Object]

Correlation of Domain-Level Link Data,[object Object],Nice Work! Excluding the “c-blocks” issue, voters + correlations match nicely.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],Domain-level link data is surprisingly similar to page-level link data in correlation,[object Object]

Rand’s Takeaways,[object Object],#1: Google May Rank Pages, But Domains Matter Too,[object Object],(the closeness of correlation data and the opinions of voters both back this up),[object Object],#2: Link Velocity & Diversity of Link Types Would Be Interesting to Measure Given Voters’ Opinions ,[object Object],(Hopefully we can look at these in future analyses),[object Object],#3: Correlations w/ “All” Links vs. Followed-Only is Odd,[object Object],(Let’s take a closer link at these correlations),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Something Funny About Nofollows,[object Object],These compare followed vs. nofollowed links to the domain + page,[object Object]

Correlation of Followed vs. Nofollowed Links,[object Object],Nofollowed Matters? Many SEOs have been saying that nofollow links can help w/ rankings. The correlation suggests maybe they’re right.,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation of Followed Links toNofollowedLinks(i.e. Are nofollowed links well correlated w/ rankings only because they’re indicative of followed links?),[object Object],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 ,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

On-Page Signals,[object Object],These metrics are based on keyword usage and features of the ranking document,[object Object]

Most Important On-Page, Keyword-Use Factors,[object Object],(as voted on by 132 SEOs),[object Object],My guess: Some voters didn’t fully understand the internal/external link anchors choice,[object Object],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 ,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation of On-Page Keyword-Use Elements,[object Object],Curious: Longer documents seem to rank better than shorter ones,[object Object],Keyword-based factors are generally less well correlated w/ higher rankings than links.,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation of On-Page Keyword-Use Elements,[object Object],The theory that AdSense use boosts rankings isn’t supported by the data,[object Object],More reason to believe Google when they say page load speed is a factor, but a very small one,[object Object],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 ,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Rand’s Takeaways,[object Object],#1: Very Tough to Differentiate w/ On-Page Optimization,[object Object],(as in the past, the data suggests that lots of results are getting on-page right),[object Object],#2: Longer/Larger Documents Tend to Rank Better,[object Object],(It could be that post-Panda/Farmer update, robust content is rewarded more),[object Object],#3: Long Titles + URLs are Still Likely Bad for SEO,[object Object],(In addition to the negative correlations, they’re harder to share, to type-in and to link to),[object Object],#4: Using Keywords Earlier in Tags/Docs Seems Wise,[object Object],(Correlation backs up the common wisdom that keywords closer to the top matter more),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],We definitely need to look at more on-page factors in the data for the full report, too.,[object Object]

Domain Name Match Signals,[object Object],These signals are based on data from users of Twitter, Facebook & Google Buzz via their APIs,[object Object]

Domain Name Extensions in the Search Results:,[object Object],Google may not love .info and .biz, but they like them better than Canadians! ,[object Object]

Spearman’s Correlation with Google Rankings for,[object Object],Exact Match Domain Names June 2010 vs. March 2011,[object Object],Whoa! The influence of exact match domain names seems to have waned considerably. Links… not so much.,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Rand’s Takeaways,[object Object],#1: Exact Match Domains May Not Be as Powerful,[object Object],(though it’s possible that both number reflect correlation-only, not causation),[object Object],#2: Exact .coms Fell Farther than Any Other Factor,[object Object],(Possibly a lot of gaming or manipulation happening w/ those sites?),[object Object],#3: Link Count Correlations Remain Similar,[object Object],(This fits w/ my experience and makes me more comfortable comparing the data sets),[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object],Domain names are still powerful (0.22 correlation for .com exacts), but perhaps losing ground.,[object Object]

Social Signals,[object Object],These signals are based on data from users of Twitter, Facebook & Google Buzz via their APIs,[object Object]

Most Important Social Media-Based Factors,[object Object],(as voted on by 132 SEOs),[object Object],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).,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Correlation of Social Media-Based Factors(data via Topsy API & Google Buzz API),[object Object],Amazing: Facebook Shares is our single highest correlated metric with higher Google rankings.,[object Object],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!,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Percent of Results (from our 10,200 Keyword Set) in Which the Feature Was Present,[object Object],It amazed me that Facebook Share data was present for 61% of pages in the top 30 results,[object Object],For 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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

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?),[object Object],Correlations Controlling for Links,[object Object],Raw Correlations,[object Object],Twitter’s correlation wanes dramatically, but Facebookfeatures, while lower, still appear quite influential. Facebook likely deserves much more SEO attention than it currently receives.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Rand’s Takeaways,[object Object],#1: Social is Shockingly Well-Correlated,[object Object],(it’s hard to doubt causation, particularly after reading the SearchEngineLand interview here),[object Object],#2: Facebook may be more influential than Twitter,[object Object],(Or it may be that Facebook data is simply more robust/available for URLs in the SERPs),[object Object],#3: Google Buzz is Probably Not in Use Directly,[object Object],(Since so many users simply have their Tweet streams go to Buzz, and correlation is lower),[object Object],#4: We Need to Learn More About How Social is Used,[object Object],(Understanding how Google uses social metrics, parses “anchor text,” etc. looms large),[object Object],Expect more experimentation and, sadly, some gaming attempts w/ Twitter + Facebook by SEOs (and spammers) in the future.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Highest Positively + Negatively Correlated Metrics Overall,[object Object],These are the features most indicative of higher vs. lower rankings,[object Object]

Top 8 Strongest Correlated Metrics,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Top 8 Most Negatively Correlated Metrics,[object Object],Be concise and to-the-point; it’s good for users and for your rankings ,[object Object],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,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Top 8 Most Negatively Correlated Metrics,[object Object],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.,[object Object],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?,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Which Domains Appeared Most Frequently in Our 10K+ SERPs?,[object Object]

Top 20 Root Domains Most Prevalent in our 10,200 keyword set,[object Object],(top 30 rank positions),[object Object],SEOs may be disappointed to see eHow.com performing so well, but classic content aggregators like About.com + Wikipedia still beat them.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

What do the Experts thinkthe Future Holds?,[object Object]

What Do SEOs Believe Will Happen w/ Google’s Use of Ranking Features in the Future?,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

IMPORTANT!,[object Object],Don’t Misuse or Misattribute Correlation Data!,[object Object],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.,[object Object],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 ,[object Object],Thanks much!,[object Object],Rand,[object Object],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.,[object Object],http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html,[object Object]

Q+A,[object Object],Download at:,[object Object],http://bit.ly/rankfactorssydney ,[object Object],You can now try SEOmoz PRO Free!,[object Object],http://www.seomoz.org/freetrial,[object Object],Rand Fishkin, CEO & Co-Founder, SEOmoz,[object Object],[object Object]

Ranking Factors Data 2011: SMX Elite Sydney

Ranking Factors Data 2011: SMX Elite Sydney

Recommended

More Related Content

What's hot

What's hot(20)

Viewers also liked

Viewers also liked(19)

Similar to Ranking Factors Data 2011: SMX Elite Sydney

Similar to Ranking Factors Data 2011: SMX Elite Sydney(20)

More from Rand Fishkin

More from Rand Fishkin(20)

Recently uploaded

Recently uploaded(20)

Ranking Factors Data 2011: SMX Elite Sydney