Rand Fishkin's presentation from SMX Munich sharing early data from SEOmoz's biennial search engine ranking factors survey and correlation data from 10K+ Google search results.

1. 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/rankfactorsmunich Rand Fishkin, SEOmoz CEO, April 2011

2. SEOmoz Makes Software! We don’t offer consulting.

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

4. #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!

5. Understanding, Interpreting & Using Correlation Data This is powerful, useful information, but with that power comes responsibility to present it accurately

6. 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 http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

7. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

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

9. 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) http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

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

11. Are You Ready for Some Data?!

12. Overall Algorithmic Factors These compare opinion/survey data from 2009 vs. 2011

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

14. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

15. Page-Specific Link Signals These metrics are based on links that point specifically to the ranking page

16. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

17. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

18. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

19. 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.) http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html These are my personal takeaways from the data; others’ interpretations may vary

20. Domain-Wide Link Signals These metrics are based on links that point to anywhere on the ranking domain

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

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

23. 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) http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

24. Something Funny About Nofollows These compare followed vs. nofollowed links to the domain + page

25. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

26. On-Page Signals These metrics are based on keyword usage and features of the ranking document

27. Most Important On-Page, Keyword-Use Factors (as voted on by 132 SEOs) 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  http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

28. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

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

30. 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) http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html We definitely need to look at more on-page factors in the data for the full report, too.

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

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

33. 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! http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

34. 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 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

35. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

36. Highest Positively + Negatively Correlated Metrics Overall These are the features most indicative of higher vs. lower rankings

37. Top 8 Strongest Correlated Metrics Exact match domain is actually not in the top 8, but I thought I should include it, as we haven’t discussed domain name matches yet. More data on that will be calculated/released in the full set. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

38. 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 http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

39. 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? http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

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

41. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

42. What do the Experts thinkthe Future Holds?

43. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

44. 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. http:/googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

46. Blog: www.seomoz.org/blog

47. Email: rand@seomoz.org