Search ranking factors

Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com

Search Ranking Factors 2015What data, opinions, and testing have revealed

about how Google’s rankings operate.

Slides Online at:

Bit.ly/rankslides2015

A look at Google’s algorithmin 2015 according to150 professional SEOs

We used to show graphics like this to illustrate the relative importance of different areas of optimization to Google’s algorithm.

2013

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

Thus, we’ve got a new way to illustrate how ranking factors fit together:

Most interesting to me is what’s happened to SEO professionals’ opinions over time…

2009 2011 2013

2015(in blasphemous pie chart form to

illustrate comparative change)

2009 2011

2013 2015

A few of the opinions about factors in particular stand out:

Page-Level Link Features

Domain-Level Link Features

Page-Level Keyword Features

2009 2011 2013 201543% 22% 19.15% 14.54%

2009 2011 2013 201524% 21% 20.94% 14.60%

2009 2011 2013 201515% 14% 14.94% 13.97%

1) Professional SEOs feel that, on average, the algois flattening, and the days of a single factor having an overwhelming impact are fading.

Takeaways:

2) After years of dominating the algo, links, while still powerful, don’t feel like an overwhelming ranking force to SEOs.

Takeaways:

3) Engagement data is on the rise. If growth rate continues, by our next survey, it may be in the top two features.

Takeaways:

Correlation doesn’t imply causation… so why are we still talking about it in SEO?!

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.

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

Debunking myths with correlation data is easy:

Google are losers! The more ads you buy, the higher they rank you.

Debunking myths with correlation data is easy:

A negative correlation of -0.03 disproves the idea that more ad

slots = higher rankings.

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.

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:

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?

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!

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.

How do various web metrics correlate with higher Google rankings in 2015?

In May 2015, Moz collected 16,521 unique SERPs from Google.com (US). Full methodology here

Look Familiar?Link metrics’ correlations w/ rankings have

been similar for ~6 years

Moz & AhrefsFor the first time, we compared Mozscape’s link correlations against Ahrefs… And found nearly identical results for both.

Social SharesCorrelations are down ~10-15% from their high in 2013.

Traffic & EngagementFor the first time, we measured usage data. While traffic looks strongly

correlated, engagement metrics have weaker numbers.

Traffic and engagement metrics via

Keyword Use & On-Page OptimizationAs we get more sophisticated in our text-modeling abilities, we’re seeing higher

correlations (though still low relative to links & social shares)

For the first time, we also broke correlations down by category of keywords/SERPs

Health websites that link out more tend to

rank higher.

Dining sites see almost no correlation between linking

out & ranking.

It tended be more present in higher

ranking sites for these verticals

Anchor text had a smaller relationship w/ high rankings in these

verticals

Those meager restaurant websites? Looks like Google

doesn’t mind much.

Buzzfeed & Upworthy are always showing how

lengthier articles perform better for them.

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

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:

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:

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:

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

Validating Some of Google’s StatementsOn Secure Sites

Via Google Webmaster Central Blog

Via Rand’s Google+

HTTPS URLs have a 0.04 correlation w/ higher rankings… much lower than many features Google

says don’t impact rankings.

Here’s another example of a potentially misleading statement, and we’ll be working to verify it, too:

Via SERoundtable

Investigating SEOs’ Longstanding Theoriesre: Raw URL Mentions

Using data from Fresh Web Explorer, we can see how many mentions a URL receives in a

given day/week/month

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)

Via Stone Temple Blog (and IMEC Labs)

So, the crew at IMEC Labs ran a test!

Results suggest raw URL mentions had no impact on rankings, certainly nothing like the impact of links.

A Look atLinks & Social Sharesin Google’s Rankings

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).

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

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 Domain15) Javascript vs. HTML

We know about loads of link elements that influence rank-boosting ability:

This stuff mattered a lot when we did manual link building to move rankings

But today, many of us just letcontent build links for us, right?

Moz & Buzzfeed joined forces for a report looking at 1 million pieces of content.

Data via Buzzsumo & Moz’sJoint Study

Content + Social Sharing = Links?

Data via Buzzsumo & Moz’sJoint Study

Median # of links across a million pieces of content in Buzzsumo’s database?.... 1 linking root domain.

This is a power law distribution – the top content gets the overwhelming majority of links and shares.

The reality of social amplification and earning links is…

0.028? That’s too close to 0 to infer any consistent, direct influence.

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:

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.

In the past, I presented a concept that, based on this data, now appears to be fundamentally flawed:

Publish

Amplify

Grow network Rank for slightly more competitive terms & phrases

Get links Grow authority

Earn search traffic

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.

1) Social shares by themselves almost never lead directly to the quantities of links necessary to rank well.

Takeaways:

2) Content that performs extraordinarily well on social networks and ranks well in search engines may not be benefitting solely from links.

Takeaways:

bit.ly/rankingfactors2015

All the data from the ranking factors report can be found at:

Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com

Bit.ly/rankslides2015