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Google Ranking Factors 2014: Correlations, Testing, & Hypotheses
Rand Fishkin discusses Google ranking factors based on correlation data, emphasizing that higher rankings are influenced by a variety of features beyond just individual page content. He highlights the decline of certain correlation factors like domain name keyword matching and suggests that social signals may influence search rankings. Fishkin shares insights from a recent SEO test regarding the importance of anchor text and geographic link placement while proposing hypotheses related to Google’s ranking algorithms.
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Google Ranking Factors 2014: Correlations, Testing, & Hypotheses
- 1. Rand Fishkin, Wizardof Moz | @randfish | rand@moz.com Google Ranking Factors: Correlations, Testing, & Hypotheses
- 2. This Presentation IsOnline Here: bit.ly/grankfactors2014
- 3. What does itmean? How should we apply the data? Correlation
- 4. Correlation does NOT saywhy these results rank higher than these results More on Rand’s Blog
- 5. Correlation tells uswhat features, on average, the results that rank higher have which the lower ranking results do not have. More on Rand’s Blog
- 6. Correlation tells uswhat features, on average, the results that rank higher have which the lower ranking results do not have. More on Rand’s Blog I’m actually MORE interested in this than I am in whatever Google’s actually using to rank the results!
- 7. Via Moz’s 2013Search Ranking Factors
- 8. Via Moz’s 2013Search Ranking Factors To me, this says individual pages still matter, but there’s a lot of weight on the hosting domain.
- 9. Via Moz’s 2013Search Ranking Factors MozRank used to be higher, and so did linking root domains. Google’s probably getting more complex.
- 10. Via Moz’s 2013Search Ranking Factors $100 says that if we could get more comprehensive brand mention data, this correlation would start to look a lot like links
- 11. Good discussion aboutGoogle+ correlations in this post Google+ is just too damn high.
- 12. Good discussion aboutGoogle+ correlations in this post Google: “Most of the initial discussion on this thread seemed to take from the blog post the idea that more Google +1s led to higher web ranking. I wanted to preemptively tackle that perception.”
- 13. Good discussion aboutGoogle+ correlations in this post To me, that’s Google working really hard to NOT say “we don’t use any data from Google+ (directly or indirectly) at all in our ranking algorithms.” I would be very surprised if they said that.
- 14. Good discussion aboutGoogle+ correlations in this post That said, all of the correlations with social are high. That tells me the things that make content have success on social probably have a lot of overlap with what makes content successful in Google.
- 15. Good discussion aboutGoogle+ correlations in this post Domain name keyword matching continues to show decline.
- 16. Via Mozcast PMD wasas high as 5% two years ago. EMD was almost 6%. Both have fallen precipitously.
- 17. Basic introduction toLDA and topic-modeling systems here. We were able to build a better keyword-modeling system in 2013, and correlations were higher than in past studies looking at raw keyword repetition or use in title elements.
- 18. More on rankingsand page load time here. Response time was interesting, but it’s likely a very small direct factor and relatively big indirect factor (i.e. users like fast-loading pages, and people link to/share what they like)
- 19. See How UniqueDoes Content Need to Be. Last, more content still seems to, on average, slightly overperform vs. less content. I’d question any causality here, though.
- 20. I hope tosee many, many more correlation tests and more things considered! Causal or not, correlation data is incredibly useful.
- 21. What can welearn from a recent SEO test? Testing
- 22. Hypothesis: It seems likeGoogle is starting to ignore or discount anchor text in links.
- 23. Here were thetest conditions: #1: Three-word keyword phrase in Google.com US #3: We pointed links with NO query-matching anchor text from 20 unique, not-particularly-on-topic, high DA domains at result A and EXACT-anchor-text match links from the same pages at result A. #2: At start of test, result A ranked #20, B ranked #13.
- 24. After 3 Weeks: Allof the links had been indexed by Google Result B (with exact-match anchor text) ranked #9 in Google.com US Result A (with non-query-matching anchor text) ranked #18 in Google.com US
- 25. Of Additional Interest: ResultB (with exact-match anchor text) ranked #4 in Google.co.uk Result A (with non-query-matching anchor text) ranked #19 in Google.co.uk ~5 of the 20 linking domains were from UK sites
- 26. Takeaways: #1) Anchor textstill matters #2) Geographic location of links matters
- 27. I’d love tosee lots more testing in the SEO world. Even imperfect tests are fascinating and useful, IMO.
- 28. Three guesses Randhas about what Google’s up to Hypotheses
- 29. Hypothesis #1: Carouselsand “Brand” are Connected However Google’s determining carousel placement is also connected to their entities and brand signals
- 30. Hypothesis #2: There’san aspect of mention frequency and mention source in Google’s brand/domain bias More and more, these queries return results that look like what you’d get if you polled people on the street to tell you what brands they most associated with the phrase “men’s sneakers”
- 31. Hypothesis #3: Googleis using search & visit patterns to connect words & phrases and rank results Why do they list these 3 in the top 10? My guess – it’s because they are most often visited by people who’ve done searchers around “luxury resorts Australia”
- 32. Hopefully, these hypothesescan lead to experiments, results, and more sharing
- 33. Rand Fishkin, Wizardof Moz | @randfish | rand@moz.com bit.ly/grankfactors2014