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As SEOs, we are interested in the most influential factors in commercial web search results. Accordingly, we have been conducting ongoing studies looking at the relationship between web search results and link metrics/anchor text from Linkscape, social media signals from Facebook and Twitter, and on-page/URL/domain keyword factors. This document explains our methods, including the construction of the data set and statistical analysis. It is structured as follows: the following section includes some details on the dataset itself, from the choice of the keyword list to data sources and feature extraction.The last section describes our statistical analysis methods. Before diving into the details, we want to mention several important things about the analysis.
( 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!
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.
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. If your boss reads Stephan's article and sees him contradicting advice that you've been giving for years, faith erodes and with it, job security.
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.
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.
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:
How Do I Build the Perfectly Optimized Page? If you're in SEO, you probably hear this question a lot. Sadly, there's no cut and dry answer, but there are sets of best practices we can draw from and sharpen to help get close. In this blog post, I'm going to share our top recommendations for achieving on-page, keyword-targeting "perfection," or, at least, close to it. Some of these are backed by data points, correlation studies and extensive testing while others are simply gut-feelings based on experience.