seo

Posted by ryanwashere

This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author’s views are entirely his or her own and may not reflect the views of Moz, Inc.

I was furious when keyword data disappeared from Google Analytics (GA).

I mean, how could I possibly optimize a website without keyword data?!?!

It didn’t take me long to realize I was overreacting. In fact, I quickly realized how trivial keyword data was.

Search engines are pretty damn good at what they do. If you properly optimize your content, people will find it with the keywords you intended. (You should set up an SEO dashboard in GA to verify your results.)

The truly valuable keywords are the ones visitors use within your site.

When mined correctly, internal terms uncover how and why users engage with content. These insights provide clear direction to improve content, SEO, and the user journey (resulting in increased conversions, leads, and sales).

In this post, I’ll cover three things:

1. How to set up internal search reporting in GA
2. How to access and analyze five internal search reports in GA
3. Two client case studies using internal search data

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Prepping your analytics account

Before I get into the details, make sure you have the following set up in your GA account:

1. Exclude internal traffic (filter). You wouldn’t believe how many organizations don’t do this. This simple filter makes all the difference when it comes to data quality. Make sure your website is excluding all internal traffic (step-by-step directions: how to set up internal filters in GA.)
2. Goals, events and conversions. In order to discover user intent, we need to be able to segment reports by conversions. Make sure that your website has clearly defined key performance indicators (KPIs) that are represented by goals in GA (step by step directions: how to set up goals in GA.)

Supplemental reading: How to set up Google Analytics on your website

Setting up GA site search reporting

Standard GA implementation doesn’t have internal search reporting configured. In order to get the data, we need to input some information into GA manually.

Follow these steps to get it up and running:

1. Navigate to the “Admin” tab
2. Click “View Settings”
3. Go to the bottom, where you’ll find “Site Search Settings”
4. Click the button so that its setting is “On”

In order to complete the tracking, you’ll need to locate your site’s query parameter.

1. In a new browser tab, open your website
2. In your website’s internal search bar, type the word “seo” and click “search”
3. You will be redirected to your website’s internal search landing page
4. Look at the URL on the landing page (see screenshot below)
5. You will see your search term, along with these characters: “?”, “random letter”, and “=”
6. The letter before the equal sign (“=”) is your website’s query parameter
7. Enter this value into the appropriate box in GA
8. Click save

EXAMPLE

Search query: seo
Landing URL: http://webris.org/?s=seo
Parameter : ?s=seo
What to enter in GA: s

GA will not post-date searches. In other words, searches that took place before you set up reporting won’t populate. You will only get data from searches occur going forward.

For this reason, you’ll need to wait about 30 days after setting up site search tracking in GA before analyzing the site search data. Otherwise, you won’t have sufficient data to conduct meaningful analysis.

Analyzing the site search data

To access your site search data, navigate to Behavior > Behavior Flow > Site Search in GA.

There are five reports under Site Search:

1. Overview
2. Usage
3. Search Terms
4. Pages
5. Any/All Reports (Segments)

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Report #1: Overview

How to get there: Behavior > Behavior Flow > Site Search > Overview
What the report tells us: Lists the high-level metrics related to your site’s internal search
Potential insights:

• Visits With Site Search, % Search Exits, and % Search Refinements: When looked at together, these metrics can tell you a lot about how visitors are finding content. If all three numbers are high, it likely means users can’t find what they‘re looking for.
• Time after Search and Average Search Depth: Conversely, if these two metrics are high, it probably means users find a lot of value in your site search.
• Overview (graph): Pay close attention to spikes and surges in internal searches. Were you running campaigns during this time? Use traffic segments to dig into causation.

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Report #2: Usage

How to get there: Behavior > Behavior Flow > Site Search > Usage
What the report tells us: User journeys that used site search vs. those who didn’t
Potential insights:

• Pages/Session, Average Session Duration: If the pages viewed and session duration is higher with visitors using site search, this indicates your website has the right content (i.e., users are finding the content they are searching for). Keep a close eye on these metrics and test widgets, sidebars and “suggested article” plugins to help you figure out how to improve navigation.
• Goal Completions: These are important metrics. Plain and simple, this tells us whether or not site search helps drive goal completions. If so, you may want to consider making your site search more prominent, or make it stand out with specific calls to action.
• Secondary dimension: You can add a number of dimensions to this report to get deeper insight. I like to add “Medium”—it gives you a breakdown of each traffic medium, segmented by Visits With Site Search and Visits Without Site Search.

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Report #3: Search terms

How to get there: Behavior > Behavior Flow > Site Search > Search Terms
What the report tells us: Lists the most used search terms with corresponding engagement metrics
Potential Insight :

• Look at each engagement metric for discrepancies between search terms. If one search term has an abnormally high % Search Exits or % of Search Refinements, then you most likely don’t have content those visitors are looking for.
• Look at the complete list of terms—are these included in your PPC and SEO keyword targeting strategies? If not, they should be. These are the terms your visitors expect to see on your site.
• Add traffic channel segments to see which channel drives the most internal searches. These terms should match up with your PPC and SEO strategies. If a visitor is using site search to refine what they’re looking for, it could mean that they didn’t find your site from the right landing page.

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Report #4: Pages

How to get there: Behavior > Behavior Flow > Site Search > Pages
What the report tells us: The pages users made their queries on
Potential insights:

• Overall: Looking at the overall picture of the data will show you where users are having problems finding content. Take a closer look at how your top pages are structured—can users find what they need?
• Secondary dimension: I like to layer on the “Previous Page Path” dimension. This helps create a greater context for the problems users are have navigating your site.

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Report #5: Segments

How to get there: Behavior > Behavior Flow > Site Search > Any/All Reports

What the report tells us: Segments add additional depth and value. I often use the following segments to drive more insights:

• Mobile traffic: Segmenting by mobile allows you to see visitors are using site search more from mobile. This can yield insights into mobile design and layout.
• Converters or Made a purchase: Is site search driving conversions or adding roadblocks?
• Organic traffic: What percentage of users that find your website through search engines need to refine their searches? The internal keyword searches are the keywords that users are really looking for when they find your site.
• Returning users: Returning users are loyal—they enjoy your content enough to return for more. Use the internal search data to find out what content you need to best serve them.

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Case Studies: Driving action from internal search

The internal site search reports described above are high-level. Sometimes it takes seeing them in action to understand how to truly apply them.

As such, I’ve included two case studies that show exactly how I’ve used internal search data to drive meaningful action.

Case study #1

Site: Pop culture publisher (online only)
Marketing channels: SEO, social, and content

Problem:

• The site drives traffic from five to eight daily blog updates about niche pop culture celebrities
• In November, traffic stagnated, and then started to decline

Research:

• The site thrives by creating content about niche celebrities, the ones few other sites write about. This gave them the monopoly on both the SERPs and avid social media fans
• Digging in further, I found social traffic was steadily declining, while organic was remaining nearly the same, month-over-month
• A full-scale content analysis was completed, finding that more and more content was being created about the same niche celebrities. This was causing diminishing returns on social and organic traffic.
• The site suffered from content exhaustion: Writers were covering the same topics over and over.
• In order to build traffic, they needed to scale efforts horizontally by creating content around new niche celebrities.

Solution:

• I consulted the Search Terms report (Behavior > Behavior Flow > Site Search > Search Terms) to see what visitors were looking for on the site
• By adding a filter for “no-results”, I could see what content visitors were searching for on the site that turned up no results
• I dumped this list into Excel, and had the writers create new content based on the search terms in the report

Results:

After launch of the strategy, the site saw amazing results:

• 201.05% increase in month-over-month traffic
• 210.99% increase in month-over-month pageviews
• 3.30% increase in pages per session
• 3.15% increase in session duration
• 4.75% decrease in bounce rate

Up and to the right!

Case study #2

Site: Online travel site
Marketing channels: SEO, PPC, email, social, content, display, TV, radio, and print

Problem:

• Large spike in month-over-month internal searches on client’s site, with poor metrics for actions following internal searches
• Both the search volume and search rate had nearly doubled (35,457 to 65,032; and 4.37% to 8.56%, respectively) month-over-month

Research:

• Digging in, I found traffic on-site increased by 40,000 month-over-month; when segmented, I found the increase was strictly organic traffic
• Consulted GA Landing Pages report with Organic Segment to find which pages the increase in traffic was going to
  • (Behavior > Site Content > Landing Pages > Organic Segment)
• This showed that 100% of the increase in month-over-month traffic went to the home page
  • This was out of the ordinary, as 80% of organic traffic generally goes deep into the site, not to the home page

• Next, I consulted the Google Webmaster Tools (GWT, recently rebranded as Google Search Console) Search Analytics report to see what keywords were driving the increase
  • (GWT > Search Traffic > Search Analytics)

• GWT analysis showed the increase came from queries consisting of branded keywords + “giveaway” (e.g., client giveaway promotion and client giveaway)

Solution:

• I reported the findings to the client, and found out they’d been running a series of offline ads promoting a giveaway in attempts to generate email leads
  • Note: Large organizations often have employees, agencies, contractors, and consultants running for multiple efforts. It’s not uncommon for efforts to operate in silos.
• The giveaway was set up on a landing page that was difficult to find unless typed in directly (e.g., clientsite.com/giveaway)
• I recommended that the client include a call-to-action on the home page that linked to the giveaway

Results:

• Sessions with search decreased by nearly 10%
• Results after search increased by 6.45%
• Search depth increased by 9.01%
• Most importantly, users were able to find the giveaway. Email leads increased by 245%!

Closing

When mined properly, internal search data will give you the information you need to greatly improve your web content, design, and search engine optimization efforts.

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Posted by russangular

Given this blog’s readership, chances are good you will spend some time this week looking at backlinks in one of the growing number of link data tools. We know backlinks continue to be one of, if not the most important parts of Google’s ranking algorithm. We tend to take these link data sets at face value, though, in part because they are all we have. But when your rankings are on the line, is there a better way to get at which data set is the best? How should we go about assessing these different link indexes like Moz, Majestic, Ahrefs and SEMrush for quality? Historically, there have been 4 common approaches to this question of index quality…

• Breadth: We might choose to look at the number of linking root domains any given service reports. We know that referring domains correlates strongly with search rankings, so it makes sense to judge a link index by how many unique domains it has discovered and indexed.
• Depth: We also might choose to look at how deep the web has been crawled, looking more at the total number of URLs in the index, rather than the diversity of referring domains.
• Link Overlap: A more sophisticated approach might count the number of links an index has in common with Google Webmaster Tools.
• Freshness: Finally, we might choose to look at the freshness of the index. What percentage of links in the index are still live?

There are a number of really good studies (some newer than others) using these techniques that are worth checking out when you get a chance:

• BuiltVisible analysis of Moz, Majestic, GWT, Ahrefs and Search Metrics
• SEOBook comparison of Moz, Majestic, Ahrefs, and Ayima
• MatthewWoodward study of Ahrefs, Majestic, Moz, Raven and SEO Spyglass
• Marketing Signals analysis of Moz, Majestic, Ahrefs, and GWT
• RankAbove comparison of Moz, Majestic, Ahrefs and Link Research Tools
• StoneTemple study of Moz and Majestic

While these are all excellent at addressing the methodologies above, there is a particular limitation with all of them. They miss one of the most important metrics we need to determine the value of a link index: proportional representation to Google’s link graph . So here at Angular Marketing, we decided to take a closer look.

Proportional representation to Google Search Console data

So, why is it important to determine proportional representation? Many of the most important and valued metrics we use are built on proportional models. PageRank, MozRank, CitationFlow and Ahrefs Rank are proportional in nature. The score of any one URL in the data set is relative to the other URLs in the data set. If the data set is biased, the results are biased.

A Visualization

Link graphs are biased by their crawl prioritization. Because there is no full representation of the Internet, every link graph, even Google’s, is a biased sample of the web. Imagine for a second that the picture below is of the web. Each dot represents a page on the Internet, and the dots surrounded by green represent a fictitious index by Google of certain sections of the web.

Of course, Google isn’t the only organization that crawls the web. Other organizations like Moz, Majestic, Ahrefs, and SEMrush have their own crawl prioritizations which result in different link indexes.

In the example above, you can see different link providers trying to index the web like Google. Link data provider 1 (purple) does a good job of building a model that is similar to Google. It isn’t very big, but it is proportional. Link data provider 2 (blue) has a much larger index, and likely has more links in common with Google that link data provider 1, but it is highly disproportional. So, how would we go about measuring this proportionality? And which data set is the most proportional to Google?

Methodology

The first step is to determine a measurement of relativity for analysis. Google doesn’t give us very much information about their link graph. All we have is what is in Google Search Console. The best source we can use is referring domain counts. In particular, we want to look at what we call referring domain link pairs. A referring domain link pair would be something like ask.com->mlb.com: 9,444 which means that ask.com links to mlb.com 9,444 times.

Steps

1. Determine the root linking domain pairs and values to 100+ sites in Google Search Console
2. Determine the same for Ahrefs, Moz, Majestic Fresh, Majestic Historic, SEMrush
3. Compare the referring domain link pairs of each data set to Google, assuming a Poisson Distribution
4. Run simulations of each data set’s performance against each other (ie: Moz vs Maj, Ahrefs vs SEMrush, Moz vs SEMrush, et al.)
5. Analyze the results

Results

When placed head-to-head, there seem to be some clear winners at first glance. In head-to-head, Moz edges out Ahrefs, but across the board, Moz and Ahrefs fare quite evenly. Moz, Ahrefs and SEMrush seem to be far better than Majestic Fresh and Majestic Historic. Is that really the case? And why?

It turns out there is an inversely proportional relationship between index size and proportional relevancy. This might seem counterintuitive, shouldn’t the bigger indexes be closer to Google? Not Exactly.

What does this mean?

Each organization has to create a crawl prioritization strategy. When you discover millions of links, you have to prioritize which ones you might crawl next. Google has a crawl prioritization, so does Moz, Majestic, Ahrefs and SEMrush. There are lots of different things you might choose to prioritize…

• You might prioritize link discovery. If you want to build a very large index, you could prioritize crawling pages on sites that have historically provided new links.
• You might prioritize content uniqueness. If you want to build a search engine, you might prioritize finding pages that are unlike any you have seen before. You could choose to crawl domains that historically provide unique data and little duplicate content.
• You might prioritize content freshness. If you want to keep your search engine recent, you might prioritize crawling pages that change frequently.
• You might prioritize content value, crawling the most important URLs first based on the number of inbound links to that page.

Chances are, an organization’s crawl priority will blend some of these features, but it’s difficult to design one exactly like Google. Imagine for a moment that instead of crawling the web, you want to climb a tree. You have to come up with a tree climbing strategy.

• You decide to climb the longest branch you see at each intersection.
• One friend of yours decides to climb the first new branch he reaches, regardless of how long it is.
• Your other friend decides to climb the first new branch she reaches only if she sees another branch coming off of it.

Despite having different climb strategies, everyone chooses the same first branch, and everyone chooses the same second branch. There are only so many different options early on.

But as the climbers go further and further along, their choices eventually produce differing results. This is exactly the same for web crawlers like Google, Moz, Majestic, Ahrefs and SEMrush. The bigger the crawl, the more the crawl prioritization will cause disparities. This is not a deficiency; this is just the nature of the beast. However, we aren’t completely lost. Once we know how index size is related to disparity, we can make some inferences about how similar a crawl priority may be to Google.

Unfortunately, we have to be careful in our conclusions. We only have a few data points with which to work, so it is very difficult to be certain regarding this part of the analysis. In particular, it seems strange that Majestic would get better relative to its index size as it grows, unless Google holds on to old data (which might be an important discovery in and of itself). It is most likely that at this point we can’t make this level of conclusion.

So what do we do?

Let’s say you have a list of domains or URLs for which you would like to know their relative values. Your process might look something like this…

• Check Open Site Explorer to see if all URLs are in their index. If so, you are looking metrics most likely to be proportional to Google’s link graph.
• If any of the links do not occur in the index, move to Ahrefs and use their Ahrefs ranking if all you need is a single PageRank-like metric.
• If any of the links are missing from Ahrefs’s index, or you need something related to trust, move on to Majestic Fresh.
• Finally, use Majestic Historic for (by leaps and bounds) the largest coverage available.

It is important to point out that the likelihood that all the URLs you want to check are in a single index increases as the accuracy of the metric decreases. Considering the size of Majestic’s data, you can’t ignore them because you are less likely to get null value answers from their data than the others. If anything rings true, it is that once again it makes sense to get data from as many sources as possible. You won’t get the most proportional data without Moz, the broadest data without Majestic, or everything in-between without Ahrefs.

What about SEMrush? They are making progress, but they don’t publish any relative statistics that would be useful in this particular case. Maybe we can hope to see more from them soon given their already promising index!

Recommendations for the link graphing industry

All we hear about these days is big data; we almost never hear about good data. I know that the teams at Moz, Majestic, Ahrefs, SEMrush and others are interested in mimicking Google, but I would love to see some organization stand up against the allure of more data in favor of better data—data more like Google’s. It could begin with testing various crawl strategies to see if they produce a result more similar to that of data shared in Google Search Console. Having the most Google-like data is certainly a crown worth winning.

Credits

Thanks to Diana Carter at Angular for assistance with data acquisition and Andrew Cron with statistical analysis. Thanks also to the representatives from Moz, Majestic, Ahrefs, and SEMrush for answering questions about their indices.

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Posted by Trevor-Klein

In late 2013, we asked you all about your experience with the Moz Blog. It was the first time we’d collected direct feedback from our readers in more than three years—an eternity in the marketing industry. With the pace of change in our line of work (not to mention your schedules and reading habits) we didn’t want to wait that long again, so we’re taking this opportunity to ask you how well we’re keeping up.

Our mission is to help you all become better marketers, and to do that, we need to know more about you. What challenges do you all face? What are your pain points? Your day-to-day frustrations? If you could learn more about one or two (or three) topics, what would those be?

If you’ll help us out by taking this five-minute survey, we can make sure we’re offering the most useful and valuable content we possibly can. When we’re done looking through the responses, we’ll follow up with a post about what we learned.

Thanks, everyone; we’re excited to see what you have to say!

Can’t see the survey? Click here to take it in a new tab.

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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Posted by Casey_MerazCompetition in local search is fierce. While it’s typical to do some surface level research on your competitors before entering a market, you can go much further down the SEO rabbit hole. In this article we will look at how you can… Continue reading →

Posted by randfishA recent patent from Google suggests a new kind of influence in the rankings that has immense implications for marketers. In today’s Whiteboard Friday, Rand discusses what it says, what that means, and adds a twist of his own to get u… Continue reading →