Rand Fishkin — ‘Search Ranking Factors in 2015: What Data, Opinions, and Testing Reveal’

Transcript

1. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com Search Ranking Factors 2015 What data, opinions, and testing have revealed about how Google’s rankings operate.
2. Slides Online at: Bit.ly/rankslides2015
3. A look at Google’s algorithm in 2015 according to 150 professional SEOs
4. We used to show graphics like this to illustrate the relative importance of different areas of optimization to Google’s algorithm. 2013
5. 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
6. Thus, we’ve got a new way to illustrate how ranking factors fit together:
8. Most interesting to me is what’s happened to SEO professionals’ opinions over time…
9. 2009 2011 2013
10. 2015 (in blasphemous pie chart form to illustrate comparative change)
11. 2009 2011 2013 2015
12. A few of the opinions about factors in particular stand out:
13. 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%
14. 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:
15. 2) After years of dominating the algo, links, while still powerful, don’t feel like an overwhelming ranking force to SEOs. Takeaways:
16. 3) Engagement data is on the rise. If growth rate continues, by our next survey, it may be in the top two features. Takeaways:
17. Correlation doesn’t imply causation… so why are we still talking about it in SEO?!
18. Because correlation tells us something else of great value: 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.
19. 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
20. Debunking myths with correlation data is easy: Google are losers! The more ads you buy, the higher they rank you.
21. Debunking myths with correlation data is easy: A negative correlation of -0.03 disproves the idea that more ad slots = higher rankings.
22. 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.
23. On average, content that better fits an LDA topic model dramatically outperforms KW repetition in the title Coefficients can also be used to show relative correlation:
24. 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?
25. Correlation numbers can lead us to interesting theories that we can then validate through other means: Let’s go run some experiments to see if this is true y’all!
26. 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.
27. How do various web metrics correlate with higher Google rankings in 2015?
28. In May 2015, Moz collected 16,521 unique SERPs from Google.com (US). Full methodology here
29. Look Familiar? Link metrics’ correlations w/ rankings have been similar for ~6 years
30. Moz & Ahrefs For the first time, we compared Mozscape’s link correlations against Ahrefs… And found nearly identical results for both.
31. Social Shares Correlations are down ~10-15% from their high in 2013.
32. 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
33. 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)
34. For the first time, we also broke correlations down by category of keywords/SERPs
35. Health websites that link out more tend to rank higher. Dining sites see almost no correlation between linking out & ranking.
36. It tended be more present in higher ranking sites for these verticals Anchor text had a smaller relationship w/ high rankings in these verticals
37. Those meager restaurant websites? Looks like Google doesn’t mind much. Buzzfeed & Upworthy are always showing how lengthier articles perform better for them.
38. 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
39. 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:
40. 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:
41. 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. Takeaways:
42. 3 Examples of What Correlation & Experimentation Can Do: #1: Help us validate what Google says #2: Verify theories about what’s in Google’s algo #3: Lead us to better tactical approaches
43. Validating Some of Google’s Statements On Secure Sites
44. Via Google Webmaster Central Blog
45. Via Rand’s Google+
46. HTTPS URLs have a 0.04 correlation w/ higher rankings… much lower than many features Google says don’t impact rankings.
50. Here’s another example of a potentially misleading statement, and we’ll be working to verify it, too: Via SERoundtable
51. Investigating SEOs’ Longstanding Theories re: Raw URL Mentions
52. Using data from Fresh Web Explorer, we can see how many mentions a URL receives in a given day/week/month
53. 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)
54. Via Stone Temple Blog (and IMEC Labs) So, the crew at IMEC Labs ran a test!
55. Results suggest raw URL mentions had no impact on rankings, certainly nothing like the impact of links.
56. A Look at Links & Social Shares in Google’s Rankings
57. 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).
58. We know about loads of link elements that influence rank-boosting ability: 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
59. 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 10) Prior Links to Target from Source Domain 15) Javascript vs. HTML We know about loads of link elements that influence rank-boosting ability:
60. 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?
61. Moz & Buzzfeed joined forces for a report looking at 1 million pieces of content. Data via Buzzsumo & Moz’s Joint Study
62. Content + Social Sharing = Links? Data via Buzzsumo & Moz’s Joint Study Median # of links across a million pieces of content in Buzzsumo’s database?.... 1 linking root domain.
63. This is a power law distribution – the top content gets the overwhelming majority of links and shares.
64. The reality of social amplification and earning links is… 0.028? That’s too close to 0 to infer any consistent, direct influence.
65. 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:
66. This data shows why I can’t endorse either of these common maxims in SEO/content marketing: Create good, unique content and Google will figure out the rest. The best way to earn links is to create great content.
67. In the past, I presented a concept that, based on this data, now appears to be fundamentally flawed:
68. Publish Amplify Grow network Rank for slightly more competitive terms & phrases Get links Grow authority Earn search traffic
69. Publish Amplify Grow network Rank for slightly more competitive terms & phrases Get links Grow authority Earn search traffic This doesn’t just happen. Link building – outreach, embeds, nudges, etc – are still essential.
70. 1) Social shares by themselves almost never lead directly to the quantities of links necessary to rank well. Takeaways:
71. 2) Content that performs extraordinarily well on social networks and ranks well in search engines may not be benefitting solely from links. Takeaways:
72. bit.ly/rankingfactors2015 All the data from the ranking factors report can be found at:
73. Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com Bit.ly/rankslides2015