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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Your Daily SEO Fix: Week 2

Posted by Trevor-Klein

Last week, we began posting short (< 2-minute) video tutorials that help you all get the most out of Moz’s tools. Each tutorial is designed to solve a use case that we regularly hear about from Moz community members—a need or problem for which you all could use a solution.

Today, we’ve got a brand-new roundup of the most recent videos:

  • How to Examine and Analyze SERPs Using New MozBar Features
  • How to Boost Your Rankings through On-Page Optimization
  • How to Check Your Anchor Text Using Open Site Explorer
  • How to Do Keyword Research with OSE and the Keyword Difficulty Tool
  • How to Discover Keyword Opportunities in Moz Analytics

Let’s get right down to business!

Fix 1: How to Examine and Analyze SERPs Using New MozBar Features

The MozBar is a handy tool that helps you access important SEO metrics while you surf the web. In this Daily SEO Fix, Abe shows you how to use this toolbar to examine and analyze SERPs and access keyword difficulty scores for a given page—in a single click.

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Fix 2: How to Boost Your Rankings through On-Page Optimization

There are several on-page factors that influence your search engine rankings. In this Daily SEO Fix, Holly shows you how to use Moz’s On-Page Optimization tool to identify pages on your website that could use some love and what you can do to improve them.

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Fix 3: How to Check Your Anchor Text Using Open Site Explorer

Dive into OSE with Tori in this Daily SEO Fix to check out the anchor text opportunities for Moz.com. By highlighting all your anchor text you can discover other potential keyword ranking opportunities you might not have thought of before.

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Fix 4: How to Do Keyword Research with OSE and the Keyword Difficulty Tool

Studying your competitors can help identify keyword opportunities for your own site. In this Daily SEO Fix, Jacki walks through how to use OSE to research the anchor text for competitors websites and how to use the Keyword Difficulty Tool to identify potential expansion opportunities for your site.

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Fix 5: How to Discover Keyword Opportunities in Moz Analytics

Digesting organic traffic that is coming to your site is an easy way to surface potential keyword opportunities. In this Daily SEO Fix, Chiaryn walks through the keyword opportunity tab in Moz Analytics and highlights a quick tip for leveraging that tool.

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Looking for more?

We’ve got more videos in last week’s round-up! Check it out here.


Don’t have a Pro subscription? No problem. Everything we cover in these Daily SEO Fix videos is available with a free 30-day trial.

Sounds good. Sign me up!

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!

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​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!

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Spam Finder

This is an incredibly easy way to get a list of spammy domains that link to any site. It’s a new feature, since it was only possible since we improved our sorting filters and integrated our Top Backlink functionality. Here’s what we think is a list of spammy looking domains that link to our own…

The post Spam Finder appeared first on Majestic Blog.

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Location is Everything: Local Rankings in Moz Analytics

Posted by MatthewBrown

Today we are thrilled to launch 
local rankings as a feature in Moz Analytics, which gives our customers the ability to assign geo-locations to their tracked keywords. If you’re a Moz Analytics customer and are ready to jump right in, here’s where you an find the new feature within the application:

Not a Moz Analytics customer? You can take the new features for a free spin…

One of the biggest SEO developments of the last several years is how frequently Google is returning localized organics across a rapidly increasing number of search queries. It’s not just happening for “best pizza in Portland” (the answer to that is
Apizza Scholls, by the way). Searches like “financial planning” and “election guide” now trigger Google’s localization algorithm:

local search results election guide

This type of query underscores the need to track rankings on a local level. I’m searching for a non-localized keyword (“election guide”), but Google recognizes I’m searching from Portland, Oregon so they add the localization layer to the result.

Local tends to get lost in the shuffle of zoo animal updates we’ve seen from Google in the last couple of years, but search marketers are coming around to realize the 2012 Venice update was one of the most important changes Google made to the search landscape. It certainly didn’t seem like a huge deal when it launched; here’s how Google described Venice as part of the late lamented
monthly search product updates they used to provide:

  • Improvements to ranking for local search results. [launch codename “Venice”] This improvement improves the triggering of Local Universal results by relying more on the ranking of our main search results as a signal.

Seems innocent enough, right? What the Venice update actually kicked off was a long-term relationship between local search results (what we see in Google local packs and map results) and the organic search results that, once upon a time, existed on their own. “Localized organics,” as they are known, have been increasingly altering the organic search landscape for keywords that normally triggered “generic” or national rankings. If you haven’t already read it, Mike Ramsey’s article on
how to adjust for the Venice update remains one of the best strategic looks at the algorithm update.

This jump in localized organic results has prompted both marketers and business owners to track rankings at the local level. An increasing number of Moz customers have been requesting the ability to add locations to their keywords since the 2012 Venice update, and this is likely due to Google expanding the queries which trigger a localized result. You asked for it, and today we’re delivering. Our new local rankings feature allows our customers to track keywords for any city, state, or ZIP/postal code.

Geo-located searches

We can now return rankings based on a location you specify, just like I set my search to Portland in the example above. This is critical for monitoring the health of your local search campaigns, as Google continues to fold the location layer into the organic results. Here’s how it looks in Moz Analytics:

tracking local keyword ranking

A keyword with a location specified counts against your keyword limit in Moz Analytics just like any other keyword.

The location being tracked will also be displayed in your rankings reports as well as on the keyword analysis page:

local keyword difficulty

The local rankings feature allows you to enter your desired tracking location by city, state, neighborhood, and zip or postal code. We provide neighborhood-level granularity via dropdown for the United States, United Kingdom, Canada and Australia. The dropdown will also provide city-level listings for other countries. It’s also possible to enter a location of your choice not on the list in the text box. Fair warning: We cannot guarantee the accuracy of rankings in mythical locations like Westeros or Twin Peaks, or mythical spellings like Pordland or Los Andules.

An easy way to get started with the new feature is to look at keywords you are already tracking, and find the ones that have an obvious local intent for searchers. Then add the neighborhood or city you are targeting for the most qualified searchers.

What’s next?

We will be launching local rankings functionality within the Moz Local application in the first part of 2015, which will provide needed visibility to folks who are mainly concerned with Local SEO. We’re also working on functionality to allow users to easily add geo-modifiers to their tracked keywords, so we can provide rankings for “health club Des Moines” alongside tracking rankings for “health clubs” in the 50301 zip code.

Right now this feature works with all Google engines (we’ll be adding Bing and Yahoo! later). We’ll also be keeping tabs on Google’s advancements on the local front so we can provide our customers with the best data on their local visibility.

Please let us know what you think in the comments below! Customer feedback, suggestions, and comments were instrumental into both the design and prioritization of this feature.

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Meet New “Not Provided Keyword Traffic” Dashboard in SEO PowerSuite!

Google Analytics keeps hiding more and more of your valuable keyword data under the “Not provided” label. This surely cuts you from access to crucial SEO stats!

How can you fill this huge gap in your analytics? Now there’s an easy way to unlock “Not provided” keywords with SEO PowerSuite!

Welcome the “not provided” solution in SEO PowerSuite:

Unlock your “not provided” traffic
Get access to the data you’ve been missing!

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