Search Engine Ranking Factors. Explaining (Some of) Google's Algorithm with Pretty Charts & Math Stuff. (NOTE: This post is written by Ben Hendrickson and Rand Fishkin as a follow up to Ben's presentation at the Distilled/SEOmoz training seminar in London this week) Our web index, Linkscape, updated again recently, and in addition to provide the traditional stats, we thought we'd share some of the cutting edge research work we do here. Below, you'll find a post which requires extremely close and careful reading.
Correlation data doesn't have all the answers, but it's certainly very interesting. Likewise, the ranking models data provides a great deal of insight, but it would be dangerous to simply look at the charts without reading the post carefully. There's a number of caveats and information - raw lines can mislead by themselves, so please be diligent! [UPDATE Oct 26, 2009: There used to be a mistake.
First, some stats on the latest Linkscape index: Understanding the Charts: Mean Index By Value: These are used for the y-axises of many charts. Are Links Well Correlated with Rankings? P.s. Detailed SEO Industry Survey Results. All Links are Not Created Equal: 10 Illustrations on Search Engines' Valuation of Links. In 1997, Google's founders created an algorithmic method to determine importance and popularity based on several key principles: Links on the web can be interpreted as votes that are cast by the source for the target All votes are, initially, considered equal Over the course of executing the algorithm on a link graph, pages which receive more votes become more important More important pages cast more important votes The votes a page can cast are a function of that page's importance, divided by the number of votes/links it casts That algorithm, of course, was PageRank, and it changed the course of web search, providing tremendous value to Google's early efforts around quality and relevancy in results.
As knowledge of PageRank spread, those with a vested interest in influencing the search rankings (SEOs) found ways to leverage this information for their websites and pages. But, Google didn't stand still or rest on their laurels in the field of link analysis. P.s. Some Opinions on the SEO Myths & Realities Fight. A few weeks back, Stephan Spencer (one of my Art of SEO coauthors) authored a post for SearchEngineLand entitled 36 Myths that Won't Die But Need To. I certainly recommend checking out the post, but be warned of some highly contentious comments. The tweets and offline feedback were similarly up-in-arms and it's easy to understand why. SEO is a field where reputation is a huge part of your ability to perform well. Because the search engines don't publish comprehensive guidelines (or even guidelines that cover 1/10th of the material necessary for good SEO work), businesses rely on the savvy of individual consultants, contractors and employees.
The lack of standards sucks. This post is going to look at some of those nagging, lingering falsehoods that continue to thwart good SEO efforts, specifically those that Stephan called out and faced strong resistance. How Significantly Does Personalization Affect Rankings? Stephan Says: Comments Include: My Opinion - They're both right. Folly? The Science of Ranking Correlations: How Does PageRank Perform? I've been an SEO for a long while - nearly 8 years. In all that time, I still haven't been able to wean myself off the intoxicating drug dealt out by the Google toolbar - that "little green fairy dust" called PageRank. Intellectually, I know it's flawed in a multitude of ways, but so many people in our field (and in the broader webmaster/marketing community) still talk about "PR 4 websites" and how "I have a PR6 but he's still outranking me.
" I find myself thinking about it, using it in conversations and yes, even considering it as a metric for rankings. There's so many reasons why PageRank shouldn't be a primary metric for SEO: Infrequently updated - Google updates the PR scores in the toolbar 2-4X each year on an unpredictable and unpublished schedule. But perhaps none of these are as compelling as the data put together by our in-house correlation, machine-learning & ranking model expert, Ben Hendrickson. How Well Does PageRank Correlate to Rankings? Where/How to Access These Metrics. Google vs. Bing: Correlation Analysis of Ranking Elements. Earlier this year, Danny Sullivan of Third Door Media asked me if SEOmoz could put together some data comparing ranking elements of Google against those of Bing to help illustrate the potential biases SEOs might face when optimizing for the two engines. Today at SMX Advanced in Seattle, I presented the following data, compiled by our own Ben Hendrickson with help from the entire SEOmoz engineering team (particularly Phil & Chas on the Linkscape side).
The results I'm sharing match those in the presentation, with a bit more detail added in for those interested. Rather than include the entire slide deck, I've taken the charts, graphs and data directly from the presentation so those of you seeking to convince clients or motivate internal teams can use them in your own presentations. But, before we begin with the data, I'd like to share a few critical notes about this research that shouldn't be ignored. Goals of the Correlation Data Research Methodology Understanding Correlation Significance. Are Exact Match Domains Too Powerful? Is Their Time Limited? Last night at the SEOmoz meetup in Avi Wilensky's incredible office space, a frequent topic of discussion both during the presentations/Q+A and in small group networking before and after was the propensity for Google (and Bing) to bias towards exact match domains in the rankings. How big an issue is exact-match domains? Let's look at some data from our correlation analysis from SMX Advanced earlier this year: Just by itself, exact match is remarkably high in correlation to rankings.
No other on-site/on-page factor we examined even came close. Granted, that's not causation, and it could be other factors influencing those impressively high rankings. Let's get a bit deeper and more granular around the issue: Holy what?! We can also look at the raw prominence (less interesting for determining what might help a page/site rank, but useful for this application: That's saying that more than 1/4 and nearly 1/3 SERPs contain an exact match domain in the top 10.
A completely unrelated p.s. Perfecting Keyword Targeting & On-Page Optimization. (Last Updated: October 24, 2014 by Rand) How do I build the perfectly optimized page? This is a challenging question for many in the SEO and web marketing fields. There are hundreds of "best practices" lists for where to place keywords and how to do "on-page optimization," but as search engines have evolved and as other sources of traffic — social networks, referring links, email, blogs, etc. — have become more important and interconnected, the very nature of what's "optimal" is up for debate.
My perspective is certainly not gospel, but it's informed by years of experience, testing, failure, and learning alongside a lot of metrics from Moz's phenomenal data science team. I don't think there's one absolute right way to optimize a page, but I do think I can share a lot about the architecture of how to target content and increase the likelihood that it will: larger version In the old days of SEO, "on-page optimization" referred merely to keyword placement. Uniquely valuable Keyword-targeted.