Spam Score: Moz’s New Metric to Measure Penalization Risk

Posted by randfish

Today, I’m very excited to announce that Moz’s Spam Score, an R&D project we’ve worked on for nearly a year, is finally going live. In this post, you can learn more about how we’re calculating spam score, what it means, and how you can potentially use it in your SEO work.

How does Spam Score work?

Over the last year, our data science team, led by 
Dr. Matt Peters, examined a great number of potential factors that predicted that a site might be penalized or banned by Google. We found strong correlations with 17 unique factors we call “spam flags,” and turned them into a score.

Almost every subdomain in 
Mozscape (our web index) now has a Spam Score attached to it, and this score is viewable inside Open Site Explorer (and soon, the MozBar and other tools). The score is simple; it just records the quantity of spam flags the subdomain triggers. Our correlations showed that no particular flag was more likely than others to mean a domain was penalized/banned in Google, but firing many flags had a very strong correlation (you can see the math below).

Spam Score currently operates only on the subdomain level—we don’t have it for pages or root domains. It’s been my experience and the experience of many other SEOs in the field that a great deal of link spam is tied to the subdomain-level. There are plenty of exceptions—manipulative links can and do live on plenty of high-quality sites—but as we’ve tested, we found that subdomain-level Spam Score was the best solution we could create at web scale. It does a solid job with the most obvious, nastiest spam, and a decent job highlighting risk in other areas, too.

How to access Spam Score

Right now, you can find Spam Score inside 
Open Site Explorer, both in the top metrics (just below domain/page authority) and in its own tab labeled “Spam Analysis.” Spam Score is only available for Pro subscribers right now, though in the future, we may make the score in the metrics section available to everyone (if you’re not a subscriber, you can check it out with a free trial). 

The current Spam Analysis page includes a list of subdomains or pages linking to your site. You can toggle the target to look at all links to a given subdomain on your site, given pages, or the entire root domain. You can further toggle source tier to look at the Spam Score for incoming linking pages or subdomains (but in the case of pages, we’re still showing the Spam Score for the subdomain on which that page is hosted).

You can click on any Spam Score row and see the details about which flags were triggered. We’ll bring you to a page like this:

Back on the original Spam Analysis page, at the very bottom of the rows, you’ll find an option to export a disavow file, which is compatible with Google Webmaster Tools. You can choose to filter the file to contain only those sites with a given spam flag count or higher:

Disavow exports usually take less than 3 hours to finish. We can send you an email when it’s ready, too.

WARNING: Please do not export this file and simply upload it to Google! You can really, really hurt your site’s ranking and there may be no way to recover. Instead, carefully sort through the links therein and make sure you really do want to disavow what’s in there. You can easily remove/edit the file to take out links you feel are not spam. When Moz’s Cyrus Shepard disavowed every link to his own site, it took more than a year for his rankings to return!

We’ve actually made the file not-wholly-ready for upload to Google in order to be sure folks aren’t too cavalier with this particular step. You’ll need to open it up and make some edits (specifically to lines at the top of the file) in order to ready it for Webmaster Tools

In the near future, we hope to have Spam Score in the Mozbar as well, which might look like this: 

Sweet, right? 🙂

Potential use cases for Spam Analysis

This list probably isn’t exhaustive, but these are a few of the ways we’ve been playing around with the data:

  1. Checking for spammy links to your own site: Almost every site has at least a few bad links pointing to it, but it’s been hard to know how much or how many potentially harmful links you might have until now. Run a quick spam analysis and see if there’s enough there to cause concern.
  2. Evaluating potential links: This is a big one where we think Spam Score can be helpful. It’s not going to catch every potentially bad link, and you should certainly still use your brain for evaluation too, but as you’re scanning a list of link opportunities or surfing to various sites, having the ability to see if they fire a lot of flags is a great warning sign.
  3. Link cleanup: Link cleanup projects can be messy, involved, precarious, and massively tedious. Spam Score might not catch everything, but sorting links by it can be hugely helpful in identifying potentially nasty stuff, and filtering out the more probably clean links.
  4. Disavow Files: Again, because Spam Score won’t perfectly catch everything, you will likely need to do some additional work here (especially if the site you’re working on has done some link buying on more generally trustworthy domains), but it can save you a heap of time evaluating and listing the worst and most obvious junk.

Over time, we’re also excited about using Spam Score to help improve the PA and DA calculations (it’s not currently in there), as well as adding it to other tools and data sources. We’d love your feedback and insight about where you’d most want to see Spam Score get involved.

Details about Spam Score’s calculation

This section comes courtesy of Moz’s head of data science, Dr. Matt Peters, who created the metric and deserves (at least in my humble opinion) a big round of applause. – Rand

Definition of “spam”

Before diving into the details of the individual spam flags and their calculation, it’s important to first describe our data gathering process and “spam” definition.

For our purposes, we followed Google’s definition of spam and gathered labels for a large number of sites as follows.

  • First, we randomly selected a large number of subdomains from the Mozscape index stratified by mozRank.
  • Then we crawled the subdomains and threw out any that didn’t return a “200 OK” (redirects, errors, etc).
  • Finally, we collected the top 10 de-personalized, geo-agnostic Google-US search results using the full subdomain name as the keyword and checked whether any of those results matched the original keyword. If they did not, we called the subdomain “spam,” otherwise we called it “ham.”

We performed the most recent data collection in November 2014 (after the Penguin 3.0 update) for about 500,000 subdomains.

Relationship between number of flags and spam

The overall Spam Score is currently an aggregate of 17 different “flags.” You can think of each flag a potential “warning sign” that signals that a site may be spammy. The overall likelihood of spam increases as a site accumulates more and more flags, so that the total number of flags is a strong predictor of spam. Accordingly, the flags are designed to be used together—no single flag, or even a few flags, is cause for concern (and indeed most sites will trigger at least a few flags).

The following table shows the relationship between the number of flags and percent of sites with those flags that we found Google had penalized or banned:

ABOVE: The overall probability of spam vs. the number of spam flags. Data collected in Nov. 2014 for approximately 500K subdomains. The table also highlights the three overall danger levels: low/green (< 10%) moderate/yellow (10-50%) and high/red (>50%)

The overall spam percent averaged across a large number of sites increases in lock step with the number of flags; however there are outliers in every category. For example, there are a small number of sites with very few flags that are tagged as spam by Google and conversely a small number of sites with many flags that are not spam.

Spam flag details

The individual spam flags capture a wide range of spam signals link profiles, anchor text, on page signals and properties of the domain name. At a high level the process to determine the spam flags for each subdomain is:

  • Collect link metrics from Mozscape (mozRank, mozTrust, number of linking domains, etc).
  • Collect anchor text metrics from Mozscape (top anchor text phrases sorted by number of links)
  • Collect the top five pages by Page Authority on the subdomain from Mozscape
  • Crawl the top five pages plus the home page and process to extract on page signals
  • Provide the output for Mozscape to include in the next index release cycle

Since the spam flags are incorporated into in the Mozscape index, fresh data is released with each new index. Right now, we crawl and process the spam flags for each subdomains every two – three months although this may change in the future.

Link flags

The following table lists the link and anchor text related flags with the the odds ratio for each flag. For each flag, we can compute two percents: the percent of sites with that flag that are penalized by Google and the percent of sites with that flag that were not penalized. The odds ratio is the ratio of these percents and gives the increase in likelihood that a site is spam if it has the flag. For example, the first row says that a site with this flag is 12.4 times more likely to be spam than one without the flag.

ABOVE: Description and odds ratio of link and anchor text related spam flags. In addition to a description, it lists the odds ratio for each flag which gives the overall increase in spam likelihood if the flag is present).

Working down the table, the flags are:

  • Low mozTrust to mozRank ratio: Sites with low mozTrust compared to mozRank are likely to be spam.
  • Large site with few links: Large sites with many pages tend to also have many links and large sites without a corresponding large number of links are likely to be spam.
  • Site link diversity is low: If a large percentage of links to a site are from a few domains it is likely to be spam.
  • Ratio of followed to nofollowed subdomains/domains (two separate flags): Sites with a large number of followed links relative to nofollowed are likely to be spam.
  • Small proportion of branded links (anchor text): Organically occurring links tend to contain a disproportionate amount of banded keywords. If a site does not have a lot of branded anchor text, it’s a signal the links are not organic.

On-page flags

Similar to the link flags, the following table lists the on page and domain name related flags:

ABOVE: Description and odds ratio of on page and domain name related spam flags. In addition to a description, it lists the odds ratio for each flag which gives the overall increase in spam likelihood if the flag is present).

  • Thin content: If a site has a relatively small ratio of content to navigation chrome it’s likely to be spam.
  • Site mark-up is abnormally small: Non-spam sites tend to invest in rich user experiences with CSS, Javascript and extensive mark-up. Accordingly, a large ratio of text to mark-up is a spam signal.
  • Large number of external links: A site with a large number of external links may look spammy.
  • Low number of internal links: Real sites tend to link heavily to themselves via internal navigation and a relative lack of internal links is a spam signal.
  • Anchor text-heavy page: Sites with a lot of anchor text are more likely to be spam then those with more content and less links.
  • External links in navigation: Spam sites may hide external links in the sidebar or footer.
  • No contact info: Real sites prominently display their social and other contact information.
  • Low number of pages found: A site with only one or a few pages is more likely to be spam than one with many pages.
  • TLD correlated with spam domains: Certain TLDs are more spammy than others (e.g. pw).
  • Domain name length: A long subdomain name like “bycheapviagra.freeshipping.onlinepharmacy.com” may indicate keyword stuffing.
  • Domain name contains numerals: domain names with numerals may be automatically generated and therefore spam.

If you’d like some more details on the technical aspects of the spam score, check out the 
video of Matt’s 2012 MozCon talk about Algorithmic Spam Detection or the slides (many of the details have evolved, but the overall ideas are the same):

We’d love your feedback

As with all metrics, Spam Score won’t be perfect. We’d love to hear your feedback and ideas for improving the score as well as what you’d like to see from it’s in-product application in the future. Feel free to leave comments on this post, or to email Matt (matt at moz dot com) and me (rand at moz dot com) privately with any suggestions.

Good luck cleaning up and preventing link spam!



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What Deep Learning and Machine Learning Mean For the Future of SEO – Whiteboard Friday

Posted by randfish

Imagine a world where even the high-up Google engineers don’t know what’s in the ranking algorithm. We may be moving in that direction. In today’s Whiteboard Friday, Rand explores and explains the concepts of deep learning and machine learning, drawing us a picture of how they could impact our work as SEOs.

For reference, here’s a still of this week’s whiteboard!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we are going to take a peek into Google’s future and look at what it could mean as Google advances their machine learning and deep learning capabilities. I know these sound like big, fancy, important words. They’re not actually that tough of topics to understand. In fact, they’re simplistic enough that even a lot of technology firms like Moz do some level of machine learning. We don’t do anything with deep learning and a lot of neural networks. We might be going that direction.

But I found an article that was published in January, absolutely fascinating and I think really worth reading, and I wanted to extract some of the contents here for Whiteboard Friday because I do think this is tactically and strategically important to understand for SEOs and really important for us to understand so that we can explain to our bosses, our teams, our clients how SEO works and will work in the future.

The article is called “Google Search Will Be Your Next Brain.” It’s by Steve Levy. It’s over on Medium. I do encourage you to read it. It’s a relatively lengthy read, but just a fascinating one if you’re interested in search. It starts with a profile of Geoff Hinton, who was a professor in Canada and worked on neural networks for a long time and then came over to Google and is now a distinguished engineer there. As the article says, a quote from the article: “He is versed in the black art of organizing several layers of artificial neurons so that the entire system, the system of neurons, could be trained or even train itself to divine coherence from random inputs.”

This sounds complex, but basically what we’re saying is we’re trying to get machines to come up with outcomes on their own rather than us having to tell them all the inputs to consider and how to process those incomes and the outcome to spit out. So this is essentially machine learning. Google has used this, for example, to figure out when you give it a bunch of photos and it can say, “Oh, this is a landscape photo. Oh, this is an outdoor photo. Oh, this is a photo of a person.” Have you ever had that creepy experience where you upload a photo to Facebook or to Google+ and they say, “Is this your friend so and so?” And you’re like, “God, that’s a terrible shot of my friend. You can barely see most of his face, and he’s wearing glasses which he usually never wears. How in the world could Google+ or Facebook figure out that this is this person?”

That’s what they use, these neural networks, these deep machine learning processes for. So I’ll give you a simple example. Here at MOZ, we do machine learning very simplistically for page authority and domain authority. We take all the inputs — numbers of links, number of linking root domains, every single metric that you could get from MOZ on the page level, on the sub-domain level, on the root-domain level, all these metrics — and then we combine them together and we say, “Hey machine, we want you to build us the algorithm that best correlates with how Google ranks pages, and here’s a bunch of pages that Google has ranked.” I think we use a base set of 10,000, and we do it about quarterly or every 6 months, feed that back into the system and the system pumps out the little algorithm that says, “Here you go. This will give you the best correlating metric with how Google ranks pages.” That’s how you get page authority domain authority.

Cool, really useful, helpful for us to say like, “Okay, this page is probably considered a little more important than this page by Google, and this one a lot more important.” Very cool. But it’s not a particularly advanced system. The more advanced system is to have these kinds of neural nets in layers. So you have a set of networks, and these neural networks, by the way, they’re designed to replicate nodes in the human brain, which is in my opinion a little creepy, but don’t worry. The article does talk about how there’s a board of scientists who make sure Terminator 2 doesn’t happen, or Terminator 1 for that matter. Apparently, no one’s stopping Terminator 4 from happening? That’s the new one that’s coming out.

So one layer of the neural net will identify features. Another layer of the neural net might classify the types of features that are coming in. Imagine this for search results. Search results are coming in, and Google’s looking at the features of all the websites and web pages, your websites and pages, to try and consider like, “What are the elements I could pull out from there?”

Well, there’s the link data about it, and there are things that happen on the page. There are user interactions and all sorts of stuff. Then we’re going to classify types of pages, types of searches, and then we’re going to extract the features or metrics that predict the desired result, that a user gets a search result they really like. We have an algorithm that can consistently produce those, and then neural networks are hopefully designed — that’s what Geoff Hinton has been working on — to train themselves to get better. So it’s not like with PA and DA, our data scientist Matt Peters and his team looking at it and going, “I bet we could make this better by doing this.”

This is standing back and the guys at Google just going, “All right machine, you learn.” They figure it out. It’s kind of creepy, right?

In the original system, you needed those people, these individuals here to feed the inputs, to say like, “This is what you can consider, system, and the features that we want you to extract from it.”

Then unsupervised learning, which is kind of this next step, the system figures it out. So this takes us to some interesting places. Imagine the Google algorithm, circa 2005. You had basically a bunch of things in here. Maybe you’d have anchor text, PageRank and you’d have some measure of authority on a domain level. Maybe there are people who are tossing new stuff in there like, “Hey algorithm, let’s consider the location of the searcher. Hey algorithm, let’s consider some user and usage data.” They’re tossing new things into the bucket that the algorithm might consider, and then they’re measuring it, seeing if it improves.

But you get to the algorithm today, and gosh there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, “Hey, we only ant to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this.” What does that even mean? But that’s what the algorithm is designed to do. The machine learning system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.

Then, over time, the idea is that in the future even the inputs aren’t given by human beings. The machine is getting to figure this stuff out itself. That’s weird. That means that if you were to ask a Google engineer in a world where deep learning controls the ranking algorithm, if you were to ask the people who designed the ranking system, “Hey, does it matter if I get more links,” they might be like, “Well, maybe.” But they don’t know, because they don’t know what’s in this algorithm. Only the machine knows, and the machine can’t even really explain it. You could go take a snapshot and look at it, but (a) it’s constantly evolving, and (b) a lot of these metrics are going to be weird conglomerations and derivatives of a bunch of metrics mashed together and torn apart and considered only when certain criteria are fulfilled. Yikes.

So what does that mean for SEOs. Like what do we have to care about from all of these systems and this evolution and this move towards deep learning, which by the way that’s what Jeff Dean, who is, I think, a senior fellow over at Google, he’s the dude that everyone mocks for being the world’s smartest computer scientist over there, and Jeff Dean has basically said, “Hey, we want to put this into search. It’s not there yet, but we want to take these models, these things that Hinton has built, and we want to put them into search.” That for SEOs in the future is going to mean much less distinct universal ranking inputs, ranking factors. We won’t really have ranking factors in the way that we know them today. It won’t be like, “Well, they have more anchor text and so they rank higher.” That might be something we’d still look at and we’d say, “Hey, they have this anchor text. Maybe that’s correlated with what the machine is finding, the system is finding to be useful, and that’s still something I want to care about to a certain extent.”

But we’re going to have to consider those things a lot more seriously. We’re going to have to take another look at them and decide and determine whether the things that we thought were ranking factors still are when the neural network system takes over. It also is going to mean something that I think many, many SEOs have been predicting for a long time and have been working towards, which is more success for websites that satisfy searchers. If the output is successful searches, and that’ s what the system is looking for, and that’s what it’s trying to correlate all its metrics to, if you produce something that means more successful searches for Google searchers when they get to your site, and you ranking in the top means Google searchers are happier, well you know what? The algorithm will catch up to you. That’s kind of a nice thing. It does mean a lot less info from Google about how they rank results.

So today you might hear from someone at Google, “Well, page speed is a very small ranking factor.” In the future they might be, “Well, page speed is like all ranking factors, totally unknown to us.” Because the machine might say, “Well yeah, page speed as a distinct metric, one that a Google engineer could actually look at, looks very small.” But derivatives of things that are connected to page speed may be huge inputs. Maybe page speed is something, that across all of these, is very well connected with happier searchers and successful search results. Weird things that we never thought of before might be connected with them as the machine learning system tries to build all those correlations, and that means potentially many more inputs into the ranking algorithm, things that we would never consider today, things we might consider wholly illogical, like, “What servers do you run on?” Well, that seems ridiculous. Why would Google ever grade you on that?

If human beings are putting factors into the algorithm, they never would. But the neural network doesn’t care. It doesn’t care. It’s a honey badger. It doesn’t care what inputs it collects. It only cares about successful searches, and so if it turns out that Ubuntu is poorly correlated with successful search results, too bad.

This world is not here yet today, but certainly there are elements of it. Google has talked about how Panda and Penguin are based off of machine learning systems like this. I think, given what Geoff Hinton and Jeff Dean are working on at Google, it sounds like this will be making its way more seriously into search and therefore it’s something that we’re really going to have to consider as search marketers.

All right everyone, I hope you’ll join me again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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How to Defeat Duplicate Content – Next Level

Posted by EllieWilkinson

Welcome to the third installment of Next Level! In the previous Next Level blog post, we shared a workflow showing you how to take on your competitors using Moz tools. We’re continuing the educational series with several new videos all about resolving duplicate content. Read on and level up!


Dealing with duplicate content can feel a bit like doing battle with your site’s evil doppelgänger—confusing and tricky to defeat! But identifying and resolving duplicates is a necessary part of helping search engines decide on relevant results. In this short video, learn about how duplicate content happens, why it’s important to fix, and a bit about how you can uncover it.

Next Level – Identifying Duplicate_pt1

[
Quick clarification: Search engines don’t actively penalize duplicate content, per se; they just don’t always understand it as well, which can lead to a drop in rankings. More info here.]

Now that you have a better idea of how to identify those dastardly duplicates, let’s get rid of ’em once and for all. Watch this next video to review how to use Moz Analytics to find and fix duplicate content using three common solutions. (You’ll need a Moz Pro subscription to use Moz Analytics. If you aren’t yet a Moz Pro subscriber, you can always try out the tools with a
30-day free trial.)

Workflow summary

Here’s a review of the three common solutions to conquering duplicate content:

  1. 301 redirect. Check Page Authority to see if one page has a higher PA than the other using Open Site Explorer, then set up a 301 redirect from the duplicate page to the original page. This will ensure that they no longer compete with one another in the search results. Wondering what a 301 redirect is and how to do it? Read more about redirection here.
  2. Rel=canonical. A rel=canonical tag passes the same amount of ranking power as a 301 redirect, and there’s a bonus: it often takes less development time to implement! Add this tag to the HTML head of a web page to tell search engines that it should be treated as a copy of the “canon,” or original, page:
    <head> <link rel="canonical" href="http://moz.com/blog/" /> </head>

    If you’re curious, you can
    read more about canonicalization here.

  3. noindex, follow. Add the values “noindex, follow” to the meta robots tag to tell search engines not to include the duplicate pages in their indexes, but to crawl their links. This works really well with paginated content or if you have a system set up to tag or categorize content (as with a blog). Here’s what it should look like:
    <head> <meta name="robots" content="noindex, follow" /> </head>

    If you’re looking to block the Moz crawler, Rogerbot, you can use the robots.txt file if you prefer—he’s a good robot, and he’ll obey!
    More about meta robots (and robots.txt) here.

Can’t get enough of duplicate content? Want to become a duplicate content connoisseur? This last video explains more about how Moz finds duplicates, if you’re curious. And you can read even more over at the
Moz Developer Blog.

We’d love to hear about your techniques for defeating duplicates! Chime in below in the comments.

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BruteForce EVO2 | Optimization Software | Seo Software

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Is It Possible to Have Good SEO Simply by Having Great Content – Whiteboard Friday

Posted by randfish

This question, posed by Alex Moravek in our Q&A section, has a somewhat complicated answer. In today’s Whiteboard Friday, Rand discusses how organizations might perform well in search rankings without doing any link building at all, relying instead on the strength of their content to be deemed relevant and important by Google.

For reference, here’s a still of this week’s whiteboard!

Video transcription

Howdy Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re chatting about is it possible to have good SEO simply by focusing on great content to the exclusion of link building.

This question was posed in the Moz Q&A Forum, which I deeply love, by Alex Moravek — I might not be saying your name right, Alex, and for that I apologize — from SEO Agencias in Madrid. My Spanish is poor, but my love for churros is so strong.

Alex, I think this is a great question. In fact, we get asked this all the time by all sorts of folks, particularly people in the blogging world and people with small and medium businesses who hear about SEO and go, “Okay, I think can make my website accessible, and yes, I can produce great content, but I just either don’t feel comfortable, don’t have time and energy, don’t understand, or just don’t feel okay with doing link building.” Link acquisition through an outreach and a manual process is beyond the scope of what they can fit into their marketing activities.

In fact, it is possible kind of, sort of. It is possible, but what you desperately need in order for this strategy to be possible are really two things. One is content exposure, and two you need time. I’ll explain why you need both of these things.

I’m going to dramatically simplify Google’s ranking algorithm. In fact, I’m going to simplify it so much that those of you who are SEO professionals are going to be like, “Oh God, Rand, you’re killing me.” I apologize in advance. Just bear with me a second.

We basically have keywords and on-page stuff, topical relevance, etc. All your topic modeling stuff might go in there. There’s content quality, all the factors that Google and Bing might measure around a content’s quality. There’s domain authority. There’s link-based authority based on the links that point to all the pages on a given domain that tell Google or Bing how important pages on this particular domain are.

There are probably some topical relevance elements in there, too. There’s page level authority. These could be all the algorithms you’ve heard of like PageRank and TrustRank, etc., and all the much more modern ones of those.

I’m not specifically talking about Moz scores here, the Moz scores DA and PA. Those are rough interpretations of these much more sophisticated formulas that the engines have.

There’s user and usage data, which we know the engines are using. They’ve talked about using that. There’s spam analysis.

Super simplistic. There are these six things, six broad categories of ranking elements. If you have just these four — keywords, on-page content quality, user and usage data, spam analysis, you’re not spammy — without these, without any domain authority or any page authority, it’s next to impossible to rank for competitive terms and very challenging and very unlikely to rank even for stuff in the chunky middle and long tail. Long tail you might rank for a few things if it’s very, very long tail. But these things taken together give you a sense of ranking ability.

Here’s what some marketers, some bloggers, some folks who invest in content nearly to the exclusion of links have found. They have had success with this strategy. They’ve basically elected to entirely ignore link building and let links come to them.

Instead of focusing on link building, they’re going to focus on product quality, press and public relations, social media, offline marketing, word of mouth, content strategy, email marketing, these other channels that can potentially earn them things. Advertising as well potentially could be in here.

What they rely on is that people find them through these other channels. They find them through social, through ads, through offline, through blogs, through very long tail search, through their content, maybe their email marketing list, word of mouth, press. All of these things are discovery mechanisms that are not search.

Once people get to the site, then these websites rely on the fact that, because of the experience people have, the quality of their products, of their content, because all of that stuff is so good, they’re going to earn links naturally.

This is a leap. In fact, for many SEOs, this is kind of a crazy leap to make, because there are so many things that you can do that will nudge people in this link earning direction. We’ve talked about a number of those at Moz. Of course, if you visit the link building section of our blog, there are hundreds if not thousands of great strategies around this.

These folks have elected to ignore all that link building stuff, let the links come to them, and these signals, these people who visit via other channels eventually lead to links which lead to DA, PA ranking ability. I don’t think this strategy is for everyone, but it is possible.

I think in the utopia that Larry Page and Sergey Brin from Google imagined when they were building their first search engine this is, in fact, how they hoped that the web would work. They hoped that people wouldn’t be out actively gaming and manipulating the web’s link graph, but rather that all the links would be earned naturally and editorially.

I think that’s a very, very optimistic and almost naive way of thinking about it. Remember, they were college students at the time. Maybe they were eating their granola, and dancing around, and hoping that everyone on the web would link only for editorial reasons. Not to make fun of granola. I love granola, especially, oh man, with those acai berries. Bowls of those things are great.

This is a potential strategy if you are very uncomfortable with link building and you feel like you can optimize this process. You have all of these channels going on.

For SEOs who are thinking, “Rand, I’m never going to ignore link building,” you can still get a tremendous amount out of thinking about how you optimize the return on investment and especially the exposure that you receive from these and how that might translate naturally into links.

I find looking at websites that accomplish SEO without active link building fascinating, because they have editorially earned those links through very little intentional effort on their own. I think there’s a tremendous amount that we can take away from that process and optimize around this.

Alex, yes, this is possible. Would I recommend it? Only in a very few instances. I think that there’s a ton that SEOs can do to optimize and nudge and create intelligent, non-manipulative ways of earning links that are a little more powerful than just sitting back and waiting, but it is possible.

All right, everyone. Thanks for joining us, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

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