Stop Ghost Spam in Google Analytics with One Filter

Posted by CarloSeo

The spam in Google Analytics (GA) is becoming a serious issue. Due to a deluge of referral spam from social buttons, adult sites, and many, many other sources, people are starting to become overwhelmed by all the filters they are setting up to manage the useless data they are receiving.

The good news is, there is no need to panic. In this post, I’m going to focus on the most common mistakes people make when fighting spam in GA, and explain an efficient way to prevent it.

But first, let’s make sure we understand how spam works. A couple of months ago, Jared Gardner wrote an excellent article explaining what referral spam is, including its intended purpose. He also pointed out some great examples of referral spam.

Types of spam

The spam in Google Analytics can be categorized by two types: ghosts and crawlers.

Ghosts

The vast majority of spam is this type. They are called ghosts because they never access your site. It is important to keep this in mind, as it’s key to creating a more efficient solution for managing spam.

As unusual as it sounds, this type of spam doesn’t have any interaction with your site at all. You may wonder how that is possible since one of the main purposes of GA is to track visits to our sites.

They do it by using the Measurement Protocol, which allows people to send data directly to Google Analytics’ servers. Using this method, and probably randomly generated tracking codes (UA-XXXXX-1) as well, the spammers leave a “visit” with fake data, without even knowing who they are hitting.

Crawlers

This type of spam, the opposite to ghost spam, does access your site. As the name implies, these spam bots crawl your pages, ignoring rules like those found in robots.txt that are supposed to stop them from reading your site. When they exit your site, they leave a record on your reports that appears similar to a legitimate visit.

Crawlers are harder to identify because they know their targets and use real data. But it is also true that new ones seldom appear. So if you detect a referral in your analytics that looks suspicious, researching it on Google or checking it against this list might help you answer the question of whether or not it is spammy.

Most common mistakes made when dealing with spam in GA

I’ve been following this issue closely for the last few months. According to the comments people have made on my articles and conversations I’ve found in discussion forums, there are primarily three mistakes people make when dealing with spam in Google Analytics.

Mistake #1. Blocking ghost spam from the .htaccess file

One of the biggest mistakes people make is trying to block Ghost Spam from the .htaccess file.

For those who are not familiar with this file, one of its main functions is to allow/block access to your site. Now we know that ghosts never reach your site, so adding them here won’t have any effect and will only add useless lines to your .htaccess file.

Ghost spam usually shows up for a few days and then disappears. As a result, sometimes people think that they successfully blocked it from here when really it’s just a coincidence of timing.

Then when the spammers later return, they get worried because the solution is not working anymore, and they think the spammer somehow bypassed the barriers they set up.

The truth is, the .htaccess file can only effectively block crawlers such as buttons-for-website.com and a few others since these access your site. Most of the spam can’t be blocked using this method, so there is no other option than using filters to exclude them.

Mistake #2. Using the referral exclusion list to stop spam

Another error is trying to use the referral exclusion list to stop the spam. The name may confuse you, but this list is not intended to exclude referrals in the way we want to for the spam. It has other purposes.

For example, when a customer buys something, sometimes they get redirected to a third-party page for payment. After making a payment, they’re redirected back to you website, and GA records that as a new referral. It is appropriate to use referral exclusion list to prevent this from happening.

If you try to use the referral exclusion list to manage spam, however, the referral part will be stripped since there is no preexisting record. As a result, a direct visit will be recorded, and you will have a bigger problem than the one you started with since. You will still have spam, and direct visits are harder to track.

Mistake #3. Worrying that bounce rate changes will affect rankings

When people see that the bounce rate changes drastically because of the spam, they start worrying about the impact that it will have on their rankings in the SERPs.

bounce.png

This is another mistake commonly made. With or without spam, Google doesn’t take into consideration Google Analytics metrics as a ranking factor. Here is an explanation about this from Matt Cutts, the former head of Google’s web spam team.

And if you think about it, Cutts’ explanation makes sense; because although many people have GA, not everyone uses it.

Assuming your site has been hacked

Another common concern when people see strange landing pages coming from spam on their reports is that they have been hacked.

landing page

The page that the spam shows on the reports doesn’t exist, and if you try to open it, you will get a 404 page. Your site hasn’t been compromised.

But you have to make sure the page doesn’t exist. Because there are cases (not spam) where some sites have a security breach and get injected with pages full of bad keywords to defame the website.

What should you worry about?

Now that we’ve discarded security issues and their effects on rankings, the only thing left to worry about is your data. The fake trail that the spam leaves behind pollutes your reports.

It might have greater or lesser impact depending on your site traffic, but everyone is susceptible to the spam.

Small and midsize sites are the most easily impacted – not only because a big part of their traffic can be spam, but also because usually these sites are self-managed and sometimes don’t have the support of an analyst or a webmaster.

Big sites with a lot of traffic can also be impacted by spam, and although the impact can be insignificant, invalid traffic means inaccurate reports no matter the size of the website. As an analyst, you should be able to explain what’s going on in even in the most granular reports.

You only need one filter to deal with ghost spam

Usually it is recommended to add the referral to an exclusion filter after it is spotted. Although this is useful for a quick action against the spam, it has three big disadvantages.

  • Making filters every week for every new spam detected is tedious and time-consuming, especially if you manage many sites. Plus, by the time you apply the filter, and it starts working, you already have some affected data.
  • Some of the spammers use direct visits along with the referrals.
  • These direct hits won’t be stopped by the filter so even if you are excluding the referral you will sill be receiving invalid traffic, which explains why some people have seen an unusual spike in direct traffic.

Luckily, there is a good way to prevent all these problems. Most of the spam (ghost) works by hitting GA’s random tracking-IDs, meaning the offender doesn’t really know who is the target, and for that reason either the hostname is not set or it uses a fake one. (See report below)

Ghost-Spam.png

You can see that they use some weird names or don’t even bother to set one. Although there are some known names in the list, these can be easily added by the spammer.

On the other hand, valid traffic will always use a real hostname. In most of the cases, this will be the domain. But it also can also result from paid services, translation services, or any other place where you’ve inserted GA tracking code.

Valid-Referral.png

Based on this, we can make a filter that will include only hits that use real hostnames. This will automatically exclude all hits from ghost spam, whether it shows up as a referral, keyword, or pageview; or even as a direct visit.

To create this filter, you will need to find the report of hostnames. Here’s how:

  1. Go to the Reporting tab in GA
  2. Click on Audience in the lefthand panel
  3. Expand Technology and select Network
  4. At the top of the report, click on Hostname

Valid-list

You will see a list of all hostnames, including the ones that the spam uses. Make a list of all the valid hostnames you find, as follows:

  • yourmaindomain.com
  • blog.yourmaindomain.com
  • es.yourmaindomain.com
  • payingservice.com
  • translatetool.com
  • anotheruseddomain.com

For small to medium sites, this list of hostnames will likely consist of the main domain and a couple of subdomains. After you are sure you got all of them, create a regular expression similar to this one:

yourmaindomain\.com|anotheruseddomain\.com|payingservice\.com|translatetool\.com

You don’t need to put all of your subdomains in the regular expression. The main domain will match all of them. If you don’t have a view set up without filters, create one now.

Then create a Custom Filter.

Make sure you select INCLUDE, then select “Hostname” on the filter field, and copy your expression into the Filter Pattern box.

filter

You might want to verify the filter before saving to check that everything is okay. Once you’re ready, set it to save, and apply the filter to all the views you want (except the view without filters).

This single filter will get rid of future occurrences of ghost spam that use invalid hostnames, and it doesn’t require much maintenance. But it’s important that every time you add your tracking code to any service, you add it to the end of the filter.

Now you should only need to take care of the crawler spam. Since crawlers access your site, you can block them by adding these lines to the .htaccess file:

## STOP REFERRER SPAM 
RewriteCond %{HTTP_REFERER} semalt\.com [NC,OR] 
RewriteCond %{HTTP_REFERER} buttons-for-website\.com [NC] 
RewriteRule .* - [F]

It is important to note that this file is very sensitive, and misplacing a single character it it can bring down your entire site. Therefore, make sure you create a backup copy of your .htaccess file prior to editing it.

If you don’t feel comfortable messing around with your .htaccess file, you can alternatively make an expression with all the crawlers, then and add it to an exclude filter by Campaign Source.

Implement these combined solutions, and you will worry much less about spam contaminating your analytics data. This will have the added benefit of freeing up more time for you to spend actually analyze your valid data.

After stopping spam, you can also get clean reports from the historical data by using the same expressions in an Advance Segment to exclude all the spam.

Bonus resources to help you manage spam

If you still need more information to help you understand and deal with the spam on your GA reports, you can read my main article on the subject here: http://www.ohow.co/what-is-referrer-spam-how-stop-it-guide/.

Additional information on how to stop spam can be found at these URLs:

In closing, I am eager to hear your ideas on this serious issue. Please share them in the comments below.

(Editor’s Note: All images featured in this post were created by the author.)

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Reblogged 3 years ago from tracking.feedpress.it

​The 2015 Online Marketing Industry Survey

Posted by Dr-Pete

It’s been another wild year in search marketing. Mobilegeddon crushed our Twitter streams, but not our dreams, and Matt Cutts stepped out of the spotlight to make way for an uncertain Google future. Pandas and Penguins continue to torment us, but most days, like anyone else, we were just trying to get the job done and earn a living.

This year, over 3,600 brave souls, each one more intelligent and good-looking than the last, completed our survey. While the last survey was technically “2014”, we collected data for it in late 2013, so the 2015 survey reflects about 18 months of industry changes.

A few highlights

Let’s dig in. Almost half (49%) of our 2015 respondents involved in search marketing were in-house marketers. In-house teams still tend to be small – 71% of our in-house marketers reported only 1-3 people in their company being involved in search marketing at least quarter-time. These teams do have substantial influence, though, with 86% reporting that they were involved in purchasing decisions.

Agency search marketers reported larger teams and more diverse responsibilities. More than one-third (36%) of agency marketers in our survey reported working with more than 20 clients in the previous year. Agencies covered a wide range of services, with the top 5 being:

More than four-fifths (81%) of agency respondents reported providing both SEO and SEM services for clients. Please note that respondents could select more than one service/tool/etc., so the charts in this post will not add up to 100%.

The vast majority of respondents (85%) reported being directly involved with content marketing, which was on par with 2014. Nearly two-thirds (66%) of agency content marketers reported “Content for SEO purposes” as their top activity, although “Building Content Strategy” came in a solid second at 44% of respondents.

Top tools

Where do we get such wonderful toys? We marketers love our tools, so let’s take a look at the Top 10 tools across a range of categories. Please note that this survey was conducted here on Moz, and our audience certainly has a pro-Moz slant.

Up first, here are the Top 10 SEO tools in our survey:

Just like last time, Google Webmaster Tools (now “Search Console”) leads the way. Moz Pro and Majestic slipped a little bit, and Firebug fell out of the Top 10. The core players remained fairly stable.

Here are the Top 10 Content tools in our survey:

Even with its uncertain future, Google Alerts continues to be widely used. There are a lot of newcomers to the content tools world, so year-over-year comparisons are tricky. Expect even more players in this market in the coming year.

Following are our respondents’ Top 10 analytics tools:

For an industry that complains about Google so much, we sure do seem to love their stuff. Google Analytics dominates, crushing the enterprise players, at least in the mid-market. KISSmetrics gained solid ground (from the #10 spot last time), while home-brewed tools slipped a bit. CrazyEgg and WordPress Stats remain very popular since our last survey.

Finally, here are the Top 10 social tools used by our respondents:

Facebook Insights and Hootsuite retained the top spots from last year, but newcomer Twitter Analytics rocketed into the #3 position. LinkedIn Insights emerged as a strong contender, too. Overall usage of all social tools increased. Tweetdeck held the #6 spot in 2014, with 19% usage, but dropped to #10 this year, even bumping up slightly to 20%.

Of course, digging into social tools naturally begs the question of which social networks are at the top of our lists.

The Top 6 are unchanged since our last survey, and it’s clear that the barriers to entry to compete with the big social networks are only getting higher. Instagram doubled its usage (from 11% of respondents last time), but this still wasn’t enough to overtake Pinterest. Reddit and Quora saw steady growth, and StumbleUpon slipped out of the Top 10.

Top activities

So, what exactly do we do with these tools and all of our time? Across all online marketers in our survey, the Top 5 activities were:

For in-house marketers, “Site Audits” dropped to the #6 position and “Brand Strategy” jumped up to the #3 spot. Naturally, in-house marketers have more resources to focus on strategy.

For agencies and consultants, “Site Audits” bumped up to #2, and “Managing People” pushed down social media to take the #5 position. Larger agency teams require more traditional people wrangling.

Here’s a much more detailed breakdown of how we spend our time in 2015:

In terms of overall demand for services, the Top 5 winners (calculated by % reporting increase – % reporting decrease were):

Demand for CRO is growing at a steady clip, but analytics still leads the way. Both “Content Creation” (#2) and “Content Curation” (#6) showed solid demand increases.

Some categories reported both gains and losses – 30% of respondents reported increased demand for “Link Building”, while 20% reported decreased demand. Similarly, 20% reported increased demand for “Link Removal”, while almost as many (17%) reported decreased demand. This may be a result of overall demand shifts, or it may represent more specialization by agencies and consultants.

What’s in store for 2016?

It’s clear that our job as online marketers is becoming more diverse, more challenging, and more strategic. We have to have a command of a wide array of tools and tactics, and that’s not going to slow down any time soon. On the bright side, companies are more aware of what we do, and they’re more willing to spend the money to have it done. Our evolution has barely begun as an industry, and you can expect more changes and growth in the coming year.

Raw data download

If you’d like to take a look through the raw results from this year’s survey (we’ve removed identifying information like email addresses from all responses), we’ve got that for you here:

Download the raw results

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Reblogged 3 years ago from tracking.feedpress.it

A Vision for Brand Engagement Online, or "The Goal"

Posted by EricEnge

Today’s post focuses on a vision for your online presence. This vision outlines what it takes to be the best, both from an overall reputation and visibility standpoint, as well as an SEO point of view. The reason these are tied together is simple: Your overall online reputation and visibility is a huge factor in your SEO. Period. Let’s start by talking about why.

Core ranking signals

For purposes of this post, let’s define three cornerstone ranking signals that most everyone agrees on:

Links

Links remain a huge factor in overall ranking. Both Cyrus Shepard and Marcus Tober re-confirmed this on the Periodic Table of SEO Ranking Factors session at the SMX Advanced conference in Seattle this past June.

On-page content

On-page content remains a huge factor too, but with some subtleties now thrown in. I wrote about some of this in earlier posts I did on Moz about Term Frequency and Inverse Document Frequency. Suffice it to say that on-page content is about a lot more than pure words on the page, but also includes the supporting pages that you link to.

User engagement with your site

This is not one of the traditional SEO signals from the early days of SEO, but most advanced SEO pros that I know consider it a real factor these days. One of the most popular concepts people talk about is called pogo-sticking, which is illustrated here:

You can learn more about the pogosticking concept by visiting this Whiteboard Friday video by a rookie SEO with a last name of Fishkin.

New, lesser-known signals

OK, so these are the more obvious signals, but now let’s look more broadly at the overall web ecosystem and talk about other types of ranking signals. Be warned that some of these signals may be indirect, but that just doesn’t matter. In fact, my first example below is an indirect factor which I will use to demonstrate why whether a signal is direct or indirect is not an issue at all.

Let me illustrate with an example. Say you spend $1 billion dollars building a huge brand around a product that is massively useful to people. Included in this is a sizable $100 million dollar campaign to support a highly popular charitable foundation, and your employees regularly donate time to help out in schools across your country. In short, the great majority of people love your brand.

Do you think this will impact the way people link to your site? Of course it does. Do you think it will impact how likely people are to be satisified with quality of the pages of your site? Consider this A/B test scenario of 2 pages from different “brands” (for the one on the left, imagine the image of Coca Cola or Pepsi Cola, whichever one you prefer):

Do you think that the huge brand will get a benefit of a doubt on their page that the no-name brand does not even though the pages are identical? Of course they will. Now let’s look at some simpler scenarios that don’t involve a $1 billion investment.

1. Cover major options related to a product or service on “money pages”

Imagine that a user arrives on your auto parts site after searching on the phrase “oil filter” at Google or Bing. Chances are pretty good that they want an oil filter, but here are some other items they may also want:

  • A guide to picking the right filter for their car
  • Oil
  • An oil filter wrench
  • A drainage pan to drain the old oil into

This is just the basics, right? But, you would be surprised with how many sites don’t include links or information on directly related products on their money pages. Providing this type of smart site and page design can have a major impact on user engagement with the money pages of your site.

2. Include other related links on money pages

In the prior item we covered the user’s most directly related needs, but they may have secondary needs as well. Someone who is changing a car’s oil is either a mechanic or a do-it-yourself-er. What else might they need? How about other parts, such as windshield wipers or air filters?

These are other fairly easy maintenance steps for someone who is working on their car to complete. Presence of these supporting products could be one way to improve user engagement with your pages.

3. Offer industry-leading non-commercial content on-site

Publishing world-class content on your site is a great way to produce links to your site. Of course, if you do this on a blog on your site, it may not provide links directly to your money pages, but it will nonetheless lift overall site authority.

In addition, if someone has consumed one or more pieces of great content on your site, the chance of their engaging in a more positive manner with your site overall go way up. Why? Because you’ve earned their trust and admiration.

4. Be everywhere your audiences are with more high-quality, relevant, non-commercial content

Are there major media sites that cover your market space? Do they consider you to be an expert? Will they quote you in articles they write? Can you provide them with guest posts or let you be a guest columnist? Will they collaborate on larger content projects with you?

All of these activities put you in front of their audiences, and if those audiences overlap with yours, this provides a great way to build your overall reputation and visibility. This content that you publish, or collaborate on, that shows up on 3rd-party sites will get you mentions and links. In addition, once again, it will provide you with a boost to your branding. People are now more likely to consume your other content more readily, including on your money pages.

5. Leverage social media

The concept here shares much in common with the prior point. Social media provides opportunities to get in front of relevant audiences. Every person that’s an avid follower of yours on a social media site is more likely to show very different behavior characteristics interacting with your site than someone that does not know you well at all.

Note that links from social media sites are nofollowed, but active social media behavior can lead to people implementing “real world” links to your site that are followed, from their blogs and media web sites.

6. Be active in the offline world as well

Think your offline activity doesn’t matter online? Think again. Relationships are still most easily built face-to-face. People you meet and spend time with can well become your most loyal fans online. This is particularly important when it comes to building relationships with influential people.

One great way to do that is to go to public events related to your industry, such as conferences. Better still, obtain speaking engagements at those conferences. This can even impact people who weren’t there to hear you speak, as they become aware that you have been asked to do that. This concept can also work for a small local business. Get out in your community and engage with people at local events.

The payoff here is similar to the payoff for other items: more engaged, highly loyal fans who engage with you across the web, sending more and more positive signals, both to other people and to search engines, that you are the real deal.

7. Provide great customer service/support

Whatever your business may be, you need to take care of your customers as best you can. No one can make everyone happy, that’s unrealistic, but striving for much better than average is a really sound idea. Having satisfied customers saying nice things about you online is a big impact item in the grand scheme of things.

8. Actively build relationships with influencers too

While this post is not about the value of influencer relationships, I include this in the list for illustration purposes, for two reasons:

  1. Some opportunities are worth extra effort. Know of someone who could have a major impact on your business? Know that they will be at a public event in the near future? Book your plane tickets and get your butt out there. No guarantee that you will get the result you are looking for, or that it will happen quickly, but your chances go WAY up if you get some face time with them.
  2. Influencers are worth special attention and focus, but your relationship-building approach to the web and SEO is not only about influencers. It’s about the entire ecosystem.

It’s an integrated ecosystem

The web provides a level of integrated, real-time connectivity of a kind that the world has never seen before. This is only going to increase. Do something bad to a customer in Hong Kong? Consumers in Boston will know within 5 minutes. That’s where it’s all headed.

Google and Bing (and any future search engine that may emerge) want to measure these types of signals because they tell them how to improve the quality of the experience on their platforms. There are may ways they can perform these measurements.

One simple concept is covered by Rand in this recent Whiteboard Friday video. The discussion is about a recent patent granted to Google that shows how the company can use search queries to detect who is an authority on a topic.

The example he provides is about people who search on “email finding tool”. If Google also finds that a number of people search on “voila norbert email tool”, Google may use that as an authority signal.

Think about that for a moment. How are you going to get people to search on your brand more while putting it together with a non-branded querly like that? (OK, please leave Mechanical Turk and other services like that out of the discussion).

Now you can start to see the bigger picture. Measurements like pogosticking and this recent search behavior related patent are just the tip of the iceberg. Undoubtedly, there are many other ways that search engines can measure what people like and engage with the most.

This is all part of SEO now. UX, product breadth, problem solving, UX, engaging in social media, getting face to face, creating great content that you publish in front of other people’s audiences, and more.

For the small local business, you can still win at this game, as your focus just needs to be on doing it better than your competitors. The big brands will never be hyper-local like you are, so don’t think you can’t play the game, because you can.

Whoever you are, get ready, because this new integrated ecosystem is already upon us, and you need to be a part of it.

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Reblogged 3 years ago from tracking.feedpress.it

The Meta Referrer Tag: An Advancement for SEO and the Internet

Posted by Cyrus-Shepard

The movement to make the Internet more secure through HTTPS brings several useful advancements for webmasters. In addition to security improvements, HTTPS promises future technological advances and potential SEO benefits for marketers.

HTTPS in search results is rising. Recent MozCast data from Dr. Pete shows nearly 20% of first page Google results are now HTTPS.

Sadly, HTTPS also has its downsides.

Marketers run into their first challenge when they switch regular HTTP sites over to HTTPS. Technically challenging, the switch typically involves routing your site through a series of 301 redirects. Historically, these types of redirects are associated with a loss of link equity (thought to be around 15%) which can lead to a loss in rankings. This can offset any SEO advantage that Google claims switching.

Ross Hudgens perfectly summed it up in this tweet:

Many SEOs have anecdotally shared stories of HTTPS sites performing well in Google search results (and our soon-to-be-published Ranking Factors data seems to support this.) However, the short term effect of a large migration can be hard to take. When Moz recently switched to HTTPS to provide better security to our logged-in users, we saw an 8-9% dip in our organic search traffic.

Problem number two is the subject of this post. It involves the loss of referral data. Typically, when one site sends traffic to another, information is sent that identifies the originating site as the source of traffic. This invaluable data allows people to see where their traffic is coming from, and helps spread the flow of information across the web.

SEOs have long used referrer data for a number of beneficial purposes. Oftentimes, people will link back or check out the site sending traffic when they see the referrer in their analytics data. Spammers know this works, as evidenced by the recent increase in referrer spam:

This process stops when traffic flows from an HTTPS site to a non-secure HTTP site. In this case, no referrer data is sent. Webmasters can’t know where their traffic is coming from.

Here’s how referral data to my personal site looked when Moz switched to HTTPS. I lost all visibility into where my traffic came from.

Its (not provided) all over again!

Enter the meta referrer tag

While we can’t solve the ranking challenges imposed by switching a site to HTTPS, we can solve the loss of referral data, and it’s actually super-simple.

Almost completely unknown to most marketers, the relatively new meta referrer tag (it’s actually been around for a few years) was designed to help out in these situations.

Better yet, the tag allows you to control how your referrer information is passed.

The meta referrer tag works with most browsers to pass referrer information in a manner defined by the user. Traffic remains encrypted and all the benefits of using HTTPS remain in place, but now you can pass referrer data to all websites, even those that use HTTP.

How to use the meta referrer tag

What follows are extremely simplified instructions for using the meta referrer tag. For more in-depth understanding, we highly recommend referring to the W3C working draft of the spec.

The meta referrer tag is placed in the <head> section of your HTML, and references one of five states, which control how browsers send referrer information from your site. The five states are:

  1. None: Never pass referral data
    <meta name="referrer" content="none">
    
  2. None When Downgrade: Sends referrer information to secure HTTPS sites, but not insecure HTTP sites
    <meta name="referrer" content="none-when-downgrade">
    
  3. Origin Only: Sends the scheme, host, and port (basically, the subdomain) stripped of the full URL as a referrer, i.e. https://moz.com/example.html would simply send https://moz.com
    <meta name="referrer" content="origin">
    

  4. Origin When Cross-Origin: Sends the full URL as the referrer when the target has the same scheme, host, and port (i.e. subdomain) regardless if it’s HTTP or HTTPS, while sending origin-only referral information to external sites. (note: There is a typo in the official spec. Future versions should be “origin-when-cross-origin”)
    <meta name="referrer" content="origin-when-crossorigin">
    
  5. Unsafe URL: Always passes the URL string as a referrer. Note if you have any sensitive information contained in your URL, this isn’t the safest option. By default, URL fragments, username, and password are automatically stripped out.
    <meta name="referrer" content="unsafe-url">
    

The meta referrer tag in action

By clicking the link below, you can get a sense of how the meta referrer tag works.

Check Referrer

Boom!

We’ve set the meta referrer tag for Moz to “origin”, which means when we link out to another site, we pass our scheme, host, and port. The end result is you see http://moz.com as the referrer, stripped of the full URL path (/meta-referrer-tag).

My personal site typically receives several visits per day from Moz. Here’s what my analytics data looked like before and after we implemented the meta referrer tag.

For simplicity and security, most sites may want to implement the “origin” state, but there are drawbacks.

One negative side effect was that as soon as we implemented the meta referrer tag, our AdRoll analytics, which we use for retargeting, stopped working. It turns out that AdRoll uses our referrer information for analytics, but the meta referrer tag “origin” state meant that the only URL they ever saw reported was https://moz.com.

Conclusion

We love the meta referrer tag because it keeps information flowing on the Internet. It’s the way the web is supposed to work!

It helps marketers and webmasters see exactly where their traffic is coming from. It encourages engagement, communication, and even linking, which can lead to improvements in SEO.

Useful links:

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Reblogged 3 years ago from tracking.feedpress.it

5 Spreadsheet Tips for Manual Link Audits

Posted by MarieHaynes

Link auditing is the part of my job that I love the most. I have audited a LOT of links over the last few years. While there are some programs out there that can be quite helpful to the avid link auditor, I still prefer to create a spreadsheet of my links in Excel and then to audit those links one-by-one from within Google Spreadsheets. Over the years I have learned a few tricks and formulas that have helped me in this process. In this article, I will share several of these with you.

Please know that while I am quite comfortable being labelled a link auditing expert, I am not an Excel wizard. I am betting that some of the things that I am doing could be improved upon if you’re an advanced user. As such, if you have any suggestions or tips of your own I’d love to hear them in the comments section!

1. Extract the domain or subdomain from a URL

OK. You’ve downloaded links from as many sources as possible and now you want to manually visit and evaluate one link from every domain. But, holy moly, some of these domains can have THOUSANDS of links pointing to the site. So, let’s break these down so that you are just seeing one link from each domain. The first step is to extract the domain or subdomain from each url.

I am going to show you examples from a Google spreadsheet as I find that these display nicer for demonstration purposes. However, if you’ve got a fairly large site, you’ll find that the spreadsheets are easier to create in Excel. If you’re confused about any of these steps, check out the animated gif at the end of each step to see the process in action.

Here is how you extract a domain or subdomain from a url:

  • Create a new column to the left of your url column.
  • Use this formula:

    =LEFT(B1,FIND(“/”,B1,9)-1)

    What this will do is remove everything after the trailing slash following the domain name. http://www.example.com/article.html will now become http://www.example.com and http://www.subdomain.example.com/article.html will now become http://www.subdomain.example.com.

  • Copy our new column A and paste it right back where it was using the “paste as values” function. If you don’t do this, you won’t be able to use the Find and Replace feature.
  • Use Find and Replace to replace each of the following with a blank (i.e. nothing):
    http://
    https://
    www.

And BOOM! We are left with a column that contains just domain names and subdomain names. This animated gif shows each of the steps we just outlined:

2. Just show one link from each domain

The next step is to filter this list so that we are just seeing one link from each domain. If you are manually reviewing links, there’s usually no point in reviewing every single link from every domain. I will throw in a word of caution here though. Sometimes a domain can have both a good link and a bad link pointing to you. Or in some cases, you may find that links from one page are followed and from another page on the same site they are nofollowed. You can miss some of these by just looking at one link from each domain. Personally, I have some checks built in to my process where I use Scrapebox and some internal tools that I have created to make sure that I’m not missing the odd link by just looking at one link from each domain. For most link audits, however, you are not going to miss very much by assessing one link from each domain.

Here’s how we do it:

  • Highlight our domains column and sort the column in alphabetical order.
  • Create a column to the left of our domains, so that the domains are in column B.
  • Use this formula:

    =IF(B1=B2,”duplicate”,”unique”)

  • Copy that formula down the column.
  • Use the filter function so that you are just seeing the duplicates.
  • Delete those rows. Note: If you have tens of thousands of rows to delete, the spreadsheet may crash. A workaround here is to use “Clear Rows” instead of “Delete Rows” and then sort your domains column from A-Z once you are finished.

We’ve now got a list of one link from every domain linking to us.

Here’s the gif that shows each of these steps:

You may wonder why I didn’t use Excel’s dedupe function to simply deduplicate these entries. I have found that it doesn’t take much deduplication to crash Excel, which is why I do this step manually.

3. Finding patterns FTW!

Sometimes when you are auditing links, you’ll find that unnatural links have patterns. I LOVE when I see these, because sometimes I can quickly go through hundreds of links without having to check each one manually. Here is an example. Let’s say that your website has a bunch of spammy directory links. As you’re auditing you notice patterns such as one of these:

  • All of these directory links come from a url that contains …/computers/internet/item40682/
  • A whole bunch of spammy links that all come from a particular free subdomain like blogspot, wordpress, weebly, etc.
  • A lot of links that all contain a particular keyword for anchor text (this is assuming you’ve included anchor text in your spreadsheet when making it.)

You can quickly find all of these links and mark them as “disavow” or “keep” by doing the following:

  • Create a new column. In my example, I am going to create a new column in Column C and look for patterns in urls that are in Column B.
  • Use this formula:

    =FIND(“/item40682”,B1)
    (You would replace “item40682” with the phrase that you are looking for.)

  • Copy this formula down the column.
  • Filter your new column so that you are seeing any rows that have a number in this column. If the phrase doesn’t exist in that url, you’ll see “N/A”, and we can ignore those.
  • Now you can mark these all as disavow

4. Check your disavow file

This next tip is one that you can use to check your disavow file across your list of domains that you want to audit. The goal here is to see which links you have disavowed so that you don’t waste time reassessing them. This particular tip only works for checking links that you have disavowed on the domain level.

The first thing you’ll want to do is download your current disavow file from Google. For some strange reason, Google gives you the disavow file in CSV format. I have never understood this because they want you to upload the file in .txt. Still, I guess this is what works best for Google. All of your entries will be in column A of the CSV:

What we are going to do now is add these to a new sheet on our current spreadsheet and use a VLOOKUP function to mark which of our domains we have disavowed.

Here are the steps:

  • Create a new sheet on your current spreadsheet workbook.
  • Copy and paste column A from your disavow spreadsheet onto this new sheet. Or, alternatively, use the import function to import the entire CSV onto this sheet.
  • In B1, write “previously disavowed” and copy this down the entire column.
  • Remove the “domain:” from each of the entries by doing a Find and Replace to replace domain: with a blank.
  • Now go back to your link audit spreadsheet. If your domains are in column A and if you had, say, 1500 domains in your disavow file, your formula would look like this:

    =VLOOKUP(A1,Sheet2!$A$1:$B$1500,2,FALSE)

When you copy this formula down the spreadsheet, it will check each of your domains, and if it finds the domain in Sheet 2, it will write “previously disavowed” on our link audit spreadsheet.

Here is a gif that shows the process:

5. Make monthly or quarterly disavow work easier

That same formula described above is a great one to use if you are doing regular repeated link audits. In this case, your second sheet on your spreadsheet would contain domains that you have previously audited, and column B of this spreadsheet would say, “previously audited” rather than “previously disavowed“.

Your tips?

These are just a few of the formulas that you can use to help make link auditing work easier. But there are lots of other things you can do with Excel or Google Sheets to help speed up the process as well. If you have some tips to add, leave a comment below. Also, if you need clarification on any of these tips, I’m happy to answer questions in the comments section.

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Reblogged 3 years ago from tracking.feedpress.it

How to Use Server Log Analysis for Technical SEO

Posted by SamuelScott

It’s ten o’clock. Do you know where your logs are?

I’m introducing this guide with a pun on a common public-service announcement that has run on late-night TV news broadcasts in the United States because log analysis is something that is extremely newsworthy and important.

If your technical and on-page SEO is poor, then nothing else that you do will matter. Technical SEO is the key to helping search engines to crawl, parse, and index websites, and thereby rank them appropriately long before any marketing work begins.

The important thing to remember: Your log files contain the only data that is 100% accurate in terms of how search engines are crawling your website. By helping Google to do its job, you will set the stage for your future SEO work and make your job easier. Log analysis is one facet of technical SEO, and correcting the problems found in your logs will help to lead to higher rankings, more traffic, and more conversions and sales.

Here are just a few reasons why:

  • Too many response code errors may cause Google to reduce its crawling of your website and perhaps even your rankings.
  • You want to make sure that search engines are crawling everything, new and old, that you want to appear and rank in the SERPs (and nothing else).
  • It’s crucial to ensure that all URL redirections will pass along any incoming “link juice.”

However, log analysis is something that is unfortunately discussed all too rarely in SEO circles. So, here, I wanted to give the Moz community an introductory guide to log analytics that I hope will help. If you have any questions, feel free to ask in the comments!

What is a log file?

Computer servers, operating systems, network devices, and computer applications automatically generate something called a log entry whenever they perform an action. In a SEO and digital marketing context, one type of action is whenever a page is requested by a visiting bot or human.

Server log entries are specifically programmed to be output in the Common Log Format of the W3C consortium. Here is one example from Wikipedia with my accompanying explanations:

127.0.0.1 user-identifier frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
  • 127.0.0.1 — The remote hostname. An IP address is shown, like in this example, whenever the DNS hostname is not available or DNSLookup is turned off.
  • user-identifier — The remote logname / RFC 1413 identity of the user. (It’s not that important.)
  • frank — The user ID of the person requesting the page. Based on what I see in my Moz profile, Moz’s log entries would probably show either “SamuelScott” or “392388” whenever I visit a page after having logged in.
  • [10/Oct/2000:13:55:36 -0700] — The date, time, and timezone of the action in question in strftime format.
  • GET /apache_pb.gif HTTP/1.0 — “GET” is one of the two commands (the other is “POST”) that can be performed. “GET” fetches a URL while “POST” is submitting something (such as a forum comment). The second part is the URL that is being accessed, and the last part is the version of HTTP that is being accessed.
  • 200 — The status code of the document that was returned.
  • 2326 — The size, in bytes, of the document that was returned.

Note: A hyphen is shown in a field when that information is unavailable.

Every single time that you — or the Googlebot — visit a page on a website, a line with this information is output, recorded, and stored by the server.

Log entries are generated continuously and anywhere from several to thousands can be created every second — depending on the level of a given server, network, or application’s activity. A collection of log entries is called a log file (or often in slang, “the log” or “the logs”), and it is displayed with the most-recent log entry at the bottom. Individual log files often contain a calendar day’s worth of log entries.

Accessing your log files

Different types of servers store and manage their log files differently. Here are the general guides to finding and managing log data on three of the most-popular types of servers:

What is log analysis?

Log analysis (or log analytics) is the process of going through log files to learn something from the data. Some common reasons include:

  • Development and quality assurance (QA) — Creating a program or application and checking for problematic bugs to make sure that it functions properly
  • Network troubleshooting — Responding to and fixing system errors in a network
  • Customer service — Determining what happened when a customer had a problem with a technical product
  • Security issues — Investigating incidents of hacking and other intrusions
  • Compliance matters — Gathering information in response to corporate or government policies
  • Technical SEO — This is my favorite! More on that in a bit.

Log analysis is rarely performed regularly. Usually, people go into log files only in response to something — a bug, a hack, a subpoena, an error, or a malfunction. It’s not something that anyone wants to do on an ongoing basis.

Why? This is a screenshot of ours of just a very small part of an original (unstructured) log file:

Ouch. If a website gets 10,000 visitors who each go to ten pages per day, then the server will create a log file every day that will consist of 100,000 log entries. No one has the time to go through all of that manually.

How to do log analysis

There are three general ways to make log analysis easier in SEO or any other context:

  • Do-it-yourself in Excel
  • Proprietary software such as Splunk or Sumo-logic
  • The ELK Stack open-source software

Tim Resnik’s Moz essay from a few years ago walks you through the process of exporting a batch of log files into Excel. This is a (relatively) quick and easy way to do simple log analysis, but the downside is that one will see only a snapshot in time and not any overall trends. To obtain the best data, it’s crucial to use either proprietary tools or the ELK Stack.

Splunk and Sumo-Logic are proprietary log analysis tools that are primarily used by enterprise companies. The ELK Stack is a free and open-source batch of three platforms (Elasticsearch, Logstash, and Kibana) that is owned by Elastic and used more often by smaller businesses. (Disclosure: We at Logz.io use the ELK Stack to monitor our own internal systems as well as for the basis of our own log management software.)

For those who are interested in using this process to do technical SEO analysis, monitor system or application performance, or for any other reason, our CEO, Tomer Levy, has written a guide to deploying the ELK Stack.

Technical SEO insights in log data

However you choose to access and understand your log data, there are many important technical SEO issues to address as needed. I’ve included screenshots of our technical SEO dashboard with our own website’s data to demonstrate what to examine in your logs.

Bot crawl volume

It’s important to know the number of requests made by Baidu, BingBot, GoogleBot, Yahoo, Yandex, and others over a given period time. If, for example, you want to get found in search in Russia but Yandex is not crawling your website, that is a problem. (You’d want to consult Yandex Webmaster and see this article on Search Engine Land.)

Response code errors

Moz has a great primer on the meanings of the different status codes. I have an alert system setup that tells me about 4XX and 5XX errors immediately because those are very significant.

Temporary redirects

Temporary 302 redirects do not pass along the “link juice” of external links from the old URL to the new one. Almost all of the time, they should be changed to permanent 301 redirects.

Crawl budget waste

Google assigns a crawl budget to each website based on numerous factors. If your crawl budget is, say, 100 pages per day (or the equivalent amount of data), then you want to be sure that all 100 are things that you want to appear in the SERPs. No matter what you write in your robots.txt file and meta-robots tags, you might still be wasting your crawl budget on advertising landing pages, internal scripts, and more. The logs will tell you — I’ve outlined two script-based examples in red above.

If you hit your crawl limit but still have new content that should be indexed to appear in search results, Google may abandon your site before finding it.

Duplicate URL crawling

The addition of URL parameters — typically used in tracking for marketing purposes — often results in search engines wasting crawl budgets by crawling different URLs with the same content. To learn how to address this issue, I recommend reading the resources on Google and Search Engine Land here, here, here, and here.

Crawl priority

Google might be ignoring (and not crawling or indexing) a crucial page or section of your website. The logs will reveal what URLs and/or directories are getting the most and least attention. If, for example, you have published an e-book that attempts to rank for targeted search queries but it sits in a directory that Google only visits once every six months, then you won’t get any organic search traffic from the e-book for up to six months.

If a part of your website is not being crawled very often — and it is updated often enough that it should be — then you might need to check your internal-linking structure and the crawl-priority settings in your XML sitemap.

Last crawl date

Have you uploaded something that you hope will be indexed quickly? The log files will tell you when Google has crawled it.

Crawl budget

One thing I personally like to check and see is Googlebot’s real-time activity on our site because the crawl budget that the search engine assigns to a website is a rough indicator — a very rough one — of how much it “likes” your site. Google ideally does not want to waste valuable crawling time on a bad website. Here, I had seen that Googlebot had made 154 requests of our new startup’s website over the prior twenty-four hours. Hopefully, that number will go up!

As I hope you can see, log analysis is critically important in technical SEO. It’s eleven o’clock — do you know where your logs are now?

Additional resources

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Reblogged 3 years ago from tracking.feedpress.it

How Much Has Link Building Changed in Recent Years?

Posted by Paddy_Moogan

I get asked this question a lot. It’s mainly asked by people who are considering buying my link building book and want to know whether it’s still up to date. This is understandable given that the first edition was published in February 2013 and our industry has a deserved reputation for always changing.

I find myself giving the same answer, even though I’ve been asked it probably dozens of times in the last two years—”not that much”. I don’t think this is solely due to the book itself standing the test of time, although I’ll happily take a bit of credit for that 🙂 I think it’s more a sign of our industry as a whole not changing as much as we’d like to think.

I started to question myself and if I was right and honestly, it’s one of the reasons it has taken me over two years to release the second edition of the book.

So I posed this question to a group of friends not so long ago, some via email and some via a Facebook group. I was expecting to be called out by many of them because my position was that in reality, it hasn’t actually changed that much. The thing is, many of them agreed and the conversations ended with a pretty long thread with lots of insights. In this post, I’d like to share some of them, share what my position is and talk about what actually has changed.

My personal view

Link building hasn’t changed as much we think it has.

The core principles of link building haven’t changed. The signals around link building have changed, but mainly around new machine learning developments that have indirectly affected what we do. One thing that has definitely changed is the mindset of SEOs (and now clients) towards link building.

I think the last big change to link building came in April 2012 when Penguin rolled out. This genuinely did change our industry and put to bed a few techniques that should never have worked so well in the first place.

Since then, we’ve seen some things change, but the core principles haven’t changed if you want to build a business that will be around for years to come and not run the risk of being hit by a link related Google update. For me, these principles are quite simple:

  • You need to deserve links – either an asset you create or your product
  • You need to put this asset in front of a relevant audience who have the ability to share it
  • You need consistency – one new asset every year is unlikely to cut it
  • Anything that scales is at risk

For me, the move towards user data driving search results + machine learning has been the biggest change we’ve seen in recent years and it’s still going.

Let’s dive a bit deeper into all of this and I’ll talk about how this relates to link building.

The typical mindset for building links has changed

I think that most SEOs are coming round to the idea that you can’t get away with building low quality links any more, not if you want to build a sustainable, long-term business. Spammy link building still works in the short-term and I think it always will, but it’s much harder than it used to be to sustain websites that are built on spam. The approach is more “churn and burn” and spammers are happy to churn through lots of domains and just make a small profit on each one before moving onto another.

For everyone else, it’s all about the long-term and not putting client websites at risk.

This has led to many SEOs embracing different forms of link building and generally starting to use content as an asset when it comes to attracting links. A big part of me feels that it was actually Penguin in 2012 that drove the rise of content marketing amongst SEOs, but that’s a post for another day…! For today though, this goes some way towards explain the trend we see below.

Slowly but surely, I’m seeing clients come to my company already knowing that low quality link building isn’t what they want. It’s taken a few years after Penguin for it to filter down to client / business owner level, but it’s definitely happening. This is a good thing but unfortunately, the main reason for this is that most of them have been burnt in the past by SEO companies who have built low quality links without giving thought to building good quality ones too.

I have no doubt that it’s this change in mindset which has led to trends like this:

The thing is, I don’t think this was by choice.

Let’s be honest. A lot of us used the kind of link building tactics that Google no longer like because they worked. I don’t think many SEOs were under the illusion that it was genuinely high quality stuff, but it worked and it was far less risky to do than it is today. Unless you were super-spammy, the low-quality links just worked.

Fast forward to a post-Penguin world, things are far more risky. For me, it’s because of this that we see the trends like the above. As an industry, we had the easiest link building methods taken away from us and we’re left with fewer options. One of the main options is content marketing which, if you do it right, can lead to good quality links and importantly, the types of links you won’t be removing in the future. Get it wrong and you’ll lose budget and lose the trust if your boss or client in the power of content when it comes to link building.

There are still plenty of other methods to build links and sometimes we can forget this. Just look at this epic list from Jon Cooper. Even with this many tactics still available to us, it’s hard work. Way harder than it used to be.

My summary here is that as an industry, our mindset has shifted but it certainly wasn’t a voluntary shift. If the tactics that Penguin targeted still worked today, we’d still be using them.

A few other opinions…

I definitely think too many people want the next easy win. As someone surfing the edge of what Google is bringing our way, here’s my general take—SEO, in broad strokes, is changing a lot, *but* any given change is more and more niche and impacts fewer people. What we’re seeing isn’t radical, sweeping changes that impact everyone, but a sort of modularization of SEO, where we each have to be aware of what impacts our given industries, verticals, etc.”

Dr. Pete

 

I don’t feel that techniques for acquiring links have changed that much. You can either earn them through content and outreach or you can just buy them. What has changed is the awareness of “link building” outside of the SEO community. This makes link building / content marketing much harder when pitching to journalists and even more difficult when pitching to bloggers.

“Link building has to be more integrated with other channels and struggles to work in its own environment unless supported by brand, PR and social. Having other channels supporting your link development efforts also creates greater search signals and more opportunity to reach a bigger audience which will drive a greater ROI.

Carl Hendy

 

SEO has grown up in terms of more mature staff and SEOs becoming more ingrained into businesses so there is a smarter (less pressure) approach. At the same time, SEO has become more integrated into marketing and has made marketing teams and decision makers more intelligent in strategies and not pushing for the quick win. I’m also seeing that companies who used to rely on SEO and building links have gone through IPOs and the need to build 1000s of links per quarter has rightly reduced.

Danny Denhard

Signals that surround link building have changed

There is no question about this one in my mind. I actually wrote about this last year in my previous blog post where I talked about signals such as anchor text and deep links changing over time.

Many of the people I asked felt the same, here are some quotes from them, split out by the types of signal.

Domain level link metrics

I think domain level links have become increasingly important compared with page level factors, i.e. you can get a whole site ranking well off the back of one insanely strong page, even with sub-optimal PageRank flow from that page to the rest of the site.

Phil Nottingham

I’d agree with Phil here and this is what I was getting at in my previous post on how I feel “deep links” will matter less over time. It’s not just about domain level links here, it’s just as much about the additional signals available for Google to use (more on that later).

Anchor text

I’ve never liked anchor text as a link signal. I mean, who actually uses exact match commercial keywords as anchor text on the web?

SEOs. 🙂

Sure there will be natural links like this, but honestly, I struggle with the idea that it took Google so long to start turning down the dial on commercial anchor text as a ranking signal. They are starting to turn it down though, slowly but surely. Don’t get me wrong, it still matters and it still works. But like pure link spam, the barrier is a lot more lower now in terms what of constitutes too much.

Rand feels that they matter more than we’d expect and I’d mostly agree with this statement:

Exact match anchor text links still have more power than you’d expect—I think Google still hasn’t perfectly sorted what is “brand” or “branded query” from generics (i.e. they want to start ranking a new startup like meldhome.com for “Meld” if the site/brand gets popular, but they can’t quite tell the difference between that and https://moz.com/learn/seo/redirection getting a few manipulative links that say “redirect”)

Rand Fishkin

What I do struggle with though, is that Google still haven’t figured this out and that short-term, commercial anchor text spam is still so effective. Even for a short burst of time.

I don’t think link building as a concept has changed loads—but I think links as a signal have, mainly because of filters and penalties but I don’t see anywhere near the same level of impact from coverage anymore, even against 18 months ago.

Paul Rogers

New signals have been introduced

It isn’t just about established signals changing though, there are new signals too and I personally feel that this is where we’ve seen the most change in Google algorithms in recent years—going all the way back to Panda in 2011.

With Panda, we saw a new level of machine learning where it almost felt like Google had found a way of incorporating human reaction / feelings into their algorithms. They could then run this against a website and answer questions like the ones included in this post. Things such as:

  • “Would you be comfortable giving your credit card information to this site?”
  • “Does this article contain insightful analysis or interesting information that is beyond obvious?”
  • “Are the pages produced with great care and attention to detail vs. less attention to detail?”

It is a touch scary that Google was able to run machine learning against answers to questions like this and write an algorithm to predict the answers for any given page on the web. They have though and this was four years ago now.

Since then, they’ve made various moves to utilize machine learning and AI to build out new products and improve their search results. For me, this was one of the biggest and went pretty unnoticed by our industry. Well, until Hummingbird came along I feel pretty sure that we have Ray Kurzweil to thank for at least some of that.

There seems to be more weight on theme/topic related to sites, though it’s hard to tell if this is mostly link based or more user/usage data based. Google is doing a good job of ranking sites and pages that don’t earn the most links but do provide the most relevant/best answer. I have a feeling they use some combination of signals to say “people who perform searches like this seem to eventually wind up on this website—let’s rank it.” One of my favorite examples is the Audubon Society ranking for all sorts of birding-related searches with very poor keyword targeting, not great links, etc. I think user behavior patterns are stronger in the algo than they’ve ever been.

– Rand Fishkin

Leading on from what Rand has said, it’s becoming more and more common to see search results that just don’t make sense if you look at the link metrics—but are a good result.

For me, the move towards user data driving search results + machine learning advanced has been the biggest change we’ve seen in recent years and it’s still going.

Edit: since drafting this post, Tom Anthony released this excellent blog post on his views on the future of search and the shift to data-driven results. I’d recommend reading that as it approaches this whole area from a different perspective and I feel that an off-shoot of what Tom is talking about is the impact on link building.

You may be asking at this point, what does machine learning have to do with link building?

Everything. Because as strong as links are as a ranking signal, Google want more signals and user signals are far, far harder to manipulate than established link signals. Yes it can be done—I’ve seen it happen. There have even been a few public tests done. But it’s very hard to scale and I’d venture a guess that only the top 1% of spammers are capable of doing it, let alone maintaining it for a long period of time. When I think about the process for manipulation here, I actually think we go a step beyond spammers towards hackers and more cut and dry illegal activity.

For link building, this means that traditional methods of manipulating signals are going to become less and less effective as these user signals become stronger. For us as link builders, it means we can’t keep searching for that silver bullet or the next method of scaling link building just for an easy win. The fact is that scalable link building is always going to be at risk from penalization from Google—I don’t really want to live a life where I’m always worried about my clients being hit by the next update. Even if Google doesn’t catch up with a certain method, machine learning and user data mean that these methods may naturally become less effective and cost efficient over time.

There are of course other things such as social signals that have come into play. I certainly don’t feel like these are a strong ranking factor yet, but with deals like this one between Google and Twitter being signed, I wouldn’t be surprised if that ever-growing dataset is used at some point in organic results. The one advantage that Twitter has over Google is it’s breaking news freshness. Twitter is still way quicker at breaking news than Google is—140 characters in a tweet is far quicker than Google News! Google know this which is why I feel they’ve pulled this partnership back into existence after a couple of years apart.

There is another important point to remember here and it’s nicely summarised by Dr. Pete:

At the same time, as new signals are introduced, these are layers not replacements. People hear social signals or user signals or authorship and want it to be the link-killer, because they already fucked up link-building, but these are just layers on top of on-page and links and all of the other layers. As each layer is added, it can verify the layers that came before it and what you need isn’t the magic signal but a combination of signals that generally matches what Google expects to see from real, strong entities. So, links still matter, but they matter in concert with other things, which basically means it’s getting more complicated and, frankly, a bit harder. Of course, on one wants to hear that.”

– Dr. Pete

The core principles have not changed

This is the crux of everything for me. With all the changes listed above, the key is that the core principles around link building haven’t changed. I could even argue that Penguin didn’t change the core principles because the techniques that Penguin targeted should never have worked in the first place. I won’t argue this too much though because even Google advised website owners to build directory links at one time.

You need an asset

You need to give someone a reason to link to you. Many won’t do it out of the goodness of their heart! One of the most effective ways to do this is to develop a content asset and use this as your reason to make people care. Once you’ve made someone care, they’re more likely to share the content or link to it from somewhere.

You need to promote that asset to the right audience

I really dislike the stance that some marketers take when it comes to content promotion—build great content and links will come.

No. Sorry but for the vast majority of us, that’s simply not true. The exceptions are people that sky dive from space or have huge existing audiences to leverage.

You simply have to spend time promoting your content or your asset for it to get shares and links. It is hard work and sometimes you can spend a long time on it and get little return, but it’s important to keep working at until you’re at a point where you have two things:

  • A big enough audience where you can almost guarantee at least some traffic to your new content along with some shares
  • Enough strong relationships with relevant websites who you can speak to when new content is published and stand a good chance of them linking to it

Getting to this point is hard—but that’s kind of the point. There are various hacks you can use along the way but it will take time to get right.

You need consistency

Leading on from the previous point. It takes time and hard work to get links to your content—the types of links that stand the test of time and you’re not going to be removing in 12 months time anyway! This means that you need to keep pushing content out and getting better each and every time. This isn’t to say you should just churn content out for the sake of it, far from it. I am saying that with each piece of content you create, you will learn to do at least one thing better the next time. Try to give yourself the leverage to do this.

Anything scalable is at risk

Scalable link building is exactly what Google has been trying to crack down on for the last few years. Penguin was the biggest move and hit some of the most scalable tactics we had at our disposal. When you scale something, you often lose some level of quality, which is exactly what Google doesn’t want when it comes to links. If you’re still relying on tactics that could fall into the scalable category, I think you need to be very careful and just look at the trend in the types of links Google has been penalizing to understand why.

The part Google plays in this

To finish up, I want to briefly talk about the part that Google plays in all of this and shaping the future they want for the web.

I’ve always tried to steer clear of arguments involving the idea that Google is actively pushing FUD into the community. I’ve preferred to concentrate more on things I can actually influence and change with my clients rather than what Google is telling us all to do.

However, for the purposes of this post, I want to talk about it.

General paranoia has increased. My bet is there are some companies out there carrying out zero specific linkbuilding activity through worry.

Dan Barker

Dan’s point is a very fair one and just a day or two after reading this in an email, I came across a page related to a client’s target audience that said:

“We are not publishing guest posts on SITE NAME any more. All previous guest posts are now deleted. For more information, see www.mattcutts.com/blog/guest-blogging/“.

I’ve reworded this as to not reveal the name of the site, but you get the point.

This is silly. Honestly, so silly. They are a good site, publish good content, and had good editorial standards. Yet they have ignored all of their own policies, hard work, and objectives to follow a blog post from Matt. I’m 100% confident that it wasn’t sites like this one that Matt was talking about in this blog post.

This is, of course, from the publishers’ angle rather than the link builders’ angle, but it does go to show the effect that statements from Google can have. Google know this so it does make sense for them to push out messages that make their jobs easier and suit their own objectives—why wouldn’t they? In a similar way, what did they do when they were struggling to classify at scale which links are bad vs. good and they didn’t have a big enough web spam team? They got us to do it for them 🙂

I’m mostly joking here, but you see the point.

The most recent infamous mobilegeddon update, discussed here by Dr. Pete is another example of Google pushing out messages that ultimately scared a lot of people into action. Although to be fair, I think that despite the apparent small impact so far, the broad message from Google is a very serious one.

Because of this, I think we need to remember that Google does have their own agenda and many shareholders to keep happy. I’m not in the camp of believing everything that Google puts out is FUD, but I’m much more sensitive and questioning of the messages now than I’ve ever been.

What do you think? I’d love to hear your feedback and thoughts in the comments.

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Reblogged 3 years ago from tracking.feedpress.it

​The 3 Most Common SEO Problems on Listings Sites

Posted by Dom-Woodman

Listings sites have a very specific set of search problems that you don’t run into everywhere else. In the day I’m one of Distilled’s analysts, but by night I run a job listings site, teflSearch. So, for my first Moz Blog post I thought I’d cover the three search problems with listings sites that I spent far too long agonising about.

Quick clarification time: What is a listings site (i.e. will this post be useful for you)?

The classic listings site is Craigslist, but plenty of other sites act like listing sites:

  • Job sites like Monster
  • E-commerce sites like Amazon
  • Matching sites like Spareroom

1. Generating quality landing pages

The landing pages on listings sites are incredibly important. These pages are usually the primary drivers of converting traffic, and they’re usually generated automatically (or are occasionally custom category pages) .

For example, if I search “Jobs in Manchester“, you can see nearly every result is an automatically generated landing page or category page.

There are three common ways to generate these pages (occasionally a combination of more than one is used):

  • Faceted pages: These are generated by facets—groups of preset filters that let you filter the current search results. They usually sit on the left-hand side of the page.
  • Category pages: These pages are listings which have already had a filter applied and can’t be changed. They’re usually custom pages.
  • Free-text search pages: These pages are generated by a free-text search box.

Those definitions are still bit general; let’s clear them up with some examples:

Amazon uses a combination of categories and facets. If you click on browse by department you can see all the category pages. Then on each category page you can see a faceted search. Amazon is so large that it needs both.

Indeed generates its landing pages through free text search, for example if we search for “IT jobs in manchester” it will generate: IT jobs in manchester.

teflSearch generates landing pages using just facets. The jobs in China landing page is simply a facet of the main search page.

Each method has its own search problems when used for generating landing pages, so lets tackle them one by one.


Aside

Facets and free text search will typically generate pages with parameters e.g. a search for “dogs” would produce:

www.mysite.com?search=dogs

But to make the URL user friendly sites will often alter the URLs to display them as folders

www.mysite.com/results/dogs/

These are still just ordinary free text search and facets, the URLs are just user friendly. (They’re a lot easier to work with in robots.txt too!)


Free search (& category) problems

If you’ve decided the base of your search will be a free text search, then we’ll have two major goals:

  • Goal 1: Helping search engines find your landing pages
  • Goal 2: Giving them link equity.

Solution

Search engines won’t use search boxes and so the solution to both problems is to provide links to the valuable landing pages so search engines can find them.

There are plenty of ways to do this, but two of the most common are:

  • Category links alongside a search

    Photobucket uses a free text search to generate pages, but if we look at example search for photos of dogs, we can see the categories which define the landing pages along the right-hand side. (This is also an example of URL friendly searches!)

  • Putting the main landing pages in a top-level menu

    Indeed also uses free text to generate landing pages, and they have a browse jobs section which contains the URL structure to allow search engines to find all the valuable landing pages.

Breadcrumbs are also often used in addition to the two above and in both the examples above, you’ll find breadcrumbs that reinforce that hierarchy.

Category (& facet) problems

Categories, because they tend to be custom pages, don’t actually have many search disadvantages. Instead it’s the other attributes that make them more or less desirable. You can create them for the purposes you want and so you typically won’t have too many problems.

However, if you also use a faceted search in each category (like Amazon) to generate additional landing pages, then you’ll run into all the problems described in the next section.

At first facets seem great, an easy way to generate multiple strong relevant landing pages without doing much at all. The problems appear because people don’t put limits on facets.

Lets take the job page on teflSearch. We can see it has 18 facets each with many options. Some of these options will generate useful landing pages:

The China facet in countries will generate “Jobs in China” that’s a useful landing page.

On the other hand, the “Conditional Bonus” facet will generate “Jobs with a conditional bonus,” and that’s not so great.

We can also see that the options within a single facet aren’t always useful. As of writing, I have a single job available in Serbia. That’s not a useful search result, and the poor user engagement combined with the tiny amount of content will be a strong signal to Google that it’s thin content. Depending on the scale of your site it’s very easy to generate a mass of poor-quality landing pages.

Facets generate other problems too. The primary one being they can create a huge amount of duplicate content and pages for search engines to get lost in. This is caused by two things: The first is the sheer number of possibilities they generate, and the second is because selecting facets in different orders creates identical pages with different URLs.

We end up with four goals for our facet-generated landing pages:

  • Goal 1: Make sure our searchable landing pages are actually worth landing on, and that we’re not handing a mass of low-value pages to the search engines.
  • Goal 2: Make sure we don’t generate multiple copies of our automatically generated landing pages.
  • Goal 3: Make sure search engines don’t get caught in the metaphorical plastic six-pack rings of our facets.
  • Goal 4: Make sure our landing pages have strong internal linking.

The first goal needs to be set internally; you’re always going to be the best judge of the number of results that need to present on a page in order for it to be useful to a user. I’d argue you can rarely ever go below three, but it depends both on your business and on how much content fluctuates on your site, as the useful landing pages might also change over time.

We can solve the next three problems as group. There are several possible solutions depending on what skills and resources you have access to; here are two possible solutions:

Category/facet solution 1: Blocking the majority of facets and providing external links
  • Easiest method
  • Good if your valuable category pages rarely change and you don’t have too many of them.
  • Can be problematic if your valuable facet pages change a lot

Nofollow all your facet links, and noindex and block category pages which aren’t valuable or are deeper than x facet/folder levels into your search using robots.txt.

You set x by looking at where your useful facet pages exist that have search volume. So, for example, if you have three facets for televisions: manufacturer, size, and resolution, and even combinations of all three have multiple results and search volume, then you could set you index everything up to three levels.

On the other hand, if people are searching for three levels (e.g. “Samsung 42″ Full HD TV”) but you only have one or two results for three-level facets, then you’d be better off indexing two levels and letting the product pages themselves pick up long-tail traffic for the third level.

If you have valuable facet pages that exist deeper than 1 facet or folder into your search, then this creates some duplicate content problems dealt with in the aside “Indexing more than 1 level of facets” below.)

The immediate problem with this set-up, however, is that in one stroke we’ve removed most of the internal links to our category pages, and by no-following all the facet links, search engines won’t be able to find your valuable category pages.

In order re-create the linking, you can add a top level drop down menu to your site containing the most valuable category pages, add category links elsewhere on the page, or create a separate part of the site with links to the valuable category pages.

The top level drop down menu you can see on teflSearch (it’s the search jobs menu), the other two examples are demonstrated in Photobucket and Indeed respectively in the previous section.

The big advantage for this method is how quick it is to implement, it doesn’t require any fiddly internal logic and adding an extra menu option is usually minimal effort.

Category/facet solution 2: Creating internal logic to work with the facets

  • Requires new internal logic
  • Works for large numbers of category pages with value that can change rapidly

There are four parts to the second solution:

  1. Select valuable facet categories and allow those links to be followed. No-follow the rest.
  2. No-index all pages that return a number of items below the threshold for a useful landing page
  3. No-follow all facets on pages with a search depth greater than x.
  4. Block all facet pages deeper than x level in robots.txt

As with the last solution, x is set by looking at where your useful facet pages exist that have search volume (full explanation in the first solution), and if you’re indexing more than one level you’ll need to check out the aside below to see how to deal with the duplicate content it generates.


Aside: Indexing more than one level of facets

If you want more than one level of facets to be indexable, then this will create certain problems.

Suppose you have a facet for size:

  • Televisions: Size: 46″, 44″, 42″

And want to add a brand facet:

  • Televisions: Brand: Samsung, Panasonic, Sony

This will create duplicate content because the search engines will be able to follow your facets in both orders, generating:

  • Television – 46″ – Samsung
  • Television – Samsung – 46″

You’ll have to either rel canonical your duplicate pages with another rule or set up your facets so they create a single unique URL.

You also need to be aware that each followable facet you add will multiply with each other followable facet and it’s very easy to generate a mass of pages for search engines to get stuck in. Depending on your setup you might need to block more paths in robots.txt or set-up more logic to prevent them being followed.

Letting search engines index more than one level of facets adds a lot of possible problems; make sure you’re keeping track of them.


2. User-generated content cannibalization

This is a common problem for listings sites (assuming they allow user generated content). If you’re reading this as an e-commerce site who only lists their own products, you can skip this one.

As we covered in the first area, category pages on listings sites are usually the landing pages aiming for the valuable search terms, but as your users start generating pages they can often create titles and content that cannibalise your landing pages.

Suppose you’re a job site with a category page for PHP Jobs in Greater Manchester. If a recruiter then creates a job advert for PHP Jobs in Greater Manchester for the 4 positions they currently have, you’ve got a duplicate content problem.

This is less of a problem when your site is large and your categories mature, it will be obvious to any search engine which are your high value category pages, but at the start where you’re lacking authority and individual listings might contain more relevant content than your own search pages this can be a problem.

Solution 1: Create structured titles

Set the <title> differently than the on-page title. Depending on variables you have available to you can set the title tag programmatically without changing the page title using other information given by the user.

For example, on our imaginary job site, suppose the recruiter also provided the following information in other fields:

  • The no. of positions: 4
  • The primary area: PHP Developer
  • The name of the recruiting company: ABC Recruitment
  • Location: Manchester

We could set the <title> pattern to be: *No of positions* *The primary area* with *recruiter name* in *Location* which would give us:

4 PHP Developers with ABC Recruitment in Manchester

Setting a <title> tag allows you to target long-tail traffic by constructing detailed descriptive titles. In our above example, imagine the recruiter had specified “Castlefield, Manchester” as the location.

All of a sudden, you’ve got a perfect opportunity to pick up long-tail traffic for people searching in Castlefield in Manchester.

On the downside, you lose the ability to pick up long-tail traffic where your users have chosen keywords you wouldn’t have used.

For example, suppose Manchester has a jobs program called “Green Highway.” A job advert title containing “Green Highway” might pick up valuable long-tail traffic. Being able to discover this, however, and find a way to fit it into a dynamic title is very hard.

Solution 2: Use regex to noindex the offending pages

Perform a regex (or string contains) search on your listings titles and no-index the ones which cannabalise your main category pages.

If it’s not possible to construct titles with variables or your users provide a lot of additional long-tail traffic with their own titles, then is a great option. On the downside, you miss out on possible structured long-tail traffic that you might’ve been able to aim for.

Solution 3: De-index all your listings

It may seem rash, but if you’re a large site with a huge number of very similar or low-content listings, you might want to consider this, but there is no common standard. Some sites like Indeed choose to no-index all their job adverts, whereas some other sites like Craigslist index all their individual listings because they’ll drive long tail traffic.

Don’t de-index them all lightly!

3. Constantly expiring content

Our third and final problem is that user-generated content doesn’t last forever. Particularly on listings sites, it’s constantly expiring and changing.

For most use cases I’d recommend 301’ing expired content to a relevant category page, with a message triggered by the redirect notifying the user of why they’ve been redirected. It typically comes out as the best combination of search and UX.

For more information or advice on how to deal with the edge cases, there’s a previous Moz blog post on how to deal with expired content which I think does an excellent job of covering this area.

Summary

In summary, if you’re working with listings sites, all three of the following need to be kept in mind:

  • How are the landing pages generated? If they’re generated using free text or facets have the potential problems been solved?
  • Is user generated content cannibalising the main landing pages?
  • How has constantly expiring content been dealt with?

Good luck listing, and if you’ve had any other tricky problems or solutions you’ve come across working on listings sites lets chat about them in the comments below!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Using Term Frequency Analysis to Measure Your Content Quality

Posted by EricEnge

It’s time to look at your content differently—time to start understanding just how good it really is. I am not simply talking about titles, keyword usage, and meta descriptions. I am talking about the entire page experience. In today’s post, I am going to introduce the general concept of content quality analysis, why it should matter to you, and how to use term frequency (TF) analysis to gather ideas on how to improve your content.

TF analysis is usually combined with inverse document frequency analysis (collectively TF-IDF analysis). TF-IDF analysis has been a staple concept for information retrieval science for a long time. You can read more about TF-IDF and other search science concepts in Cyrus Shepard’s
excellent article here.

For purposes of today’s post, I am going to show you how you can use TF analysis to get clues as to what Google is valuing in the content of sites that currently outrank you. But first, let’s get oriented.

Conceptualizing page quality

Start by asking yourself if your page provides a quality experience to people who visit it. For example, if a search engine sends 100 people to your page, how many of them will be happy? Seventy percent? Thirty percent? Less? What if your competitor’s page gets a higher percentage of happy users than yours does? Does that feel like an “uh-oh”?

Let’s think about this with a specific example in mind. What if you ran a golf club site, and 100 people come to your page after searching on a phrase like “golf clubs.” What are the kinds of things they may be looking for?

Here are some things they might want:

  1. A way to buy golf clubs on your site (you would need to see a shopping cart of some sort).
  2. The ability to select specific brands, perhaps by links to other pages about those brands of golf clubs.
  3. Information on how to pick the club that is best for them.
  4. The ability to select specific types of clubs (drivers, putters, irons, etc.). Again, this may be via links to other pages.
  5. A site search box.
  6. Pricing info.
  7. Info on shipping costs.
  8. Expert analysis comparing different golf club brands.
  9. End user reviews of your company so they can determine if they want to do business with you.
  10. How your return policy works.
  11. How they can file a complaint.
  12. Information about your company. Perhaps an “about us” page.
  13. A link to a privacy policy page.
  14. Whether or not you have been “in the news” recently.
  15. Trust symbols that show that you are a reputable organization.
  16. A way to access pages to buy different products, such as golf balls or tees.
  17. Information about specific golf courses.
  18. Tips on how to improve their golf game.

This is really only a partial list, and the specifics of your site can certainly vary for any number of reasons from what I laid out above. So how do you figure out what it is that people really want? You could pull in data from a number of sources. For example, using data from your site search box can be invaluable. You can do user testing on your site. You can conduct surveys. These are all good sources of data.

You can also look at your analytics data to see what pages get visited the most. Just be careful how you use that data. For example, if most of your traffic is from search, this data will be biased by incoming search traffic, and hence what Google chooses to rank. In addition, you may only have a small percentage of the visitors to your site going to your privacy policy, but chances are good that there are significantly more users than that who notice whether or not you have a privacy policy. Many of these will be satisfied just to see that you have one and won’t actually go check it out.

Whatever you do, it’s worth using many of these methods to determine what users want from the pages of your site and then using the resulting information to improve your overall site experience.

Is Google using this type of info as a ranking factor?

At some level, they clearly are. Clearly Google and Bing have evolved far beyond the initial TF-IDF concepts, but we can still use them to better understand our own content.

The first major indication we had that Google was performing content quality analysis was with the release of the
Panda algorithm in February of 2011. More recently, we know that on April 21 Google will release an algorithm that makes the mobile friendliness of a web site a ranking factor. Pure and simple, this algo is about the user experience with a page.

Exactly how Google is performing these measurements is not known, but
what we do know is their intent. They want to make their search engine look good, largely because it helps them make more money. Sending users to pages that make them happy will do that. Google has every incentive to improve the quality of their search results in as many ways as they can.

Ultimately, we don’t actually know what Google is measuring and using. It may be that the only SEO impact of providing pages that satisfy a very high percentage of users is an indirect one. I.e., so many people like your site that it gets written about more, linked to more, has tons of social shares, gets great engagement, that Google sees other signals that it uses as ranking factors, and this is why your rankings improve.

But, do I care if the impact is a direct one or an indirect one? Well, NO.

Using TF analysis to evaluate your page

TF-IDF analysis is more about relevance than content quality, but we can still use various precepts from it to help us understand our own content quality. One way to do this is to compare the results of a TF analysis of all the keywords on your page with those pages that currently outrank you in the search results. In this section, I am going to outline the basic concepts for how you can do this. In the next section I will show you a process that you can use with publicly available tools and a spreadsheet.

The simplest form of TF analysis is to count the number of uses of each keyword on a page. However, the problem with that is that a page using a keyword 10 times will be seen as 10 times more valuable than a page that uses a keyword only once. For that reason, we dampen the calculations. I have seen two methods for doing this, as follows:

term frequency calculation

The first method relies on dividing the number of repetitions of a keyword by the count for the most popular word on the entire page. Basically, what this does is eliminate the inherent advantage that longer documents might otherwise have over shorter ones. The second method dampens the total impact in a different way, by taking the log base 10 for the actual keyword count. Both of these achieve the effect of still valuing incremental uses of a keyword, but dampening it substantially. I prefer to use method 1, but you can use either method for our purposes here.

Once you have the TF calculated for every different keyword found on your page, you can then start to do the same analysis for pages that outrank you for a given search term. If you were to do this for five competing pages, the result might look something like this:

term frequency spreadsheet

I will show you how to set up the spreadsheet later, but for now, let’s do the fun part, which is to figure out how to analyze the results. Here are some of the things to look for:

  1. Are there any highly related words that all or most of your competitors are using that you don’t use at all?
  2. Are there any such words that you use significantly less, on average, than your competitors?
  3. Also look for words that you use significantly more than competitors.

You can then tag these words for further analysis. Once you are done, your spreadsheet may now look like this:

second stage term frequency analysis spreadsheet

In order to make this fit into this screen shot above and keep it legibly, I eliminated some columns you saw in my first spreadsheet. However, I did a sample analysis for the movie “Woman in Gold”. You can see the
full spreadsheet of calculations here. Note that we used an automated approach to marking some items at “Low Ratio,” “High Ratio,” or “All Competitors Have, Client Does Not.”

None of these flags by themselves have meaning, so you now need to put all of this into context. In our example, the following words probably have no significance at all: “get”, “you”, “top”, “see”, “we”, “all”, “but”, and other words of this type. These are just very basic English language words.

But, we can see other things of note relating to the target page (a.k.a. the client page):

  1. It’s missing any mention of actor ryan reynolds
  2. It’s missing any mention of actor helen mirren
  3. The page has no reviews
  4. Words like “family” and “story” are not mentioned
  5. “Austrian” and “maria altmann” are not used at all
  6. The phrase “woman in gold” and words “billing” and “info” are used proportionally more than they are with the other pages

Note that the last item is only visible if you open
the spreadsheet. The issues above could well be significant, as the lead actors, reviews, and other indications that the page has in-depth content. We see that competing pages that rank have details of the story, so that’s an indication that this is what Google (and users) are looking for. The fact that the main key phrase, and the word “billing”, are used to a proportionally high degree also makes it seem a bit spammy.

In fact, if you look at the information closely, you can see that the target page is quite thin in overall content. So much so, that it almost looks like a doorway page. In fact, it looks like it was put together by the movie studio itself, just not very well, as it presents little in the way of a home page experience that would cause it to rank for the name of the movie!

In the many different times I have done an analysis using these methods, I’ve been able to make many different types of observations about pages. A few of the more interesting ones include:

  1. A page that had no privacy policy, yet was taking personally identifiable info from users.
  2. A major lack of important synonyms that would indicate a real depth of available content.
  3. Comparatively low Domain Authority competitors ranking with in-depth content.

These types of observations are interesting and valuable, but it’s important to stress that you shouldn’t be overly mechanical about this. The value in this type of analysis is that it gives you a technical way to compare the content on your page with that of your competitors. This type of analysis should be used in combination with other methods that you use for evaluating that same page. I’ll address this some more in the summary section of this below.

How do you execute this for yourself?

The
full spreadsheet contains all the formulas so all you need to do is link in the keyword count data. I have tried this with two different keyword density tools, the one from Searchmetrics, and this one from motoricerca.info.

I am not endorsing these tools, and I have no financial interest in either one—they just seemed to work fairly well for the process I outlined above. To provide the data in the right format, please do the following:

  1. Run all the URLs you are testing through the keyword density tool.
  2. Copy and paste all the one word, two word, and three word results into a tab on the spreadsheet.
  3. Sort them all so you get total word counts aligned by position as I have shown in the linked spreadsheet.
  4. Set up the formulas as I did in the demo spreadsheet (you can just use the demo spreadsheet).
  5. Then do your analysis!

This may sound a bit tedious (and it is), but it has worked very well for us at STC.

Summary

You can also use usability groups and a number of other methods to figure out what users are really looking for on your site. However, what this does is give us a look at what Google has chosen to rank the highest in its search results. Don’t treat this as some sort of magic formula where you mechanically tweak the content to get better metrics in this analysis.

Instead, use this as a method for slicing into your content to better see it the way a machine might see it. It can yield some surprising (and wonderful) insights!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it