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How To Remarket 70+ Ways Using Segments and Conditions in Google Analytics

May 12, 2014 By Glenn Gabe 5 Comments

Remarketing in Google Analytics Using Conditions and Segments

I know what you’re thinking. Can you really remarket more than 70 different ways using segments in Google Analytics? Yes, you can! Actually, when you combine the methods I’ll cover today, there are many more types of Remarketing lists you can build! So the total number is much greater than 70.

My post today is meant to introduce you to segments in Google Analytics (GA), explain how you can use them to remarket to people who already visited your site, and provide important Remarketing tips along the way. I hope once you read this post, you’re ready to kick off some Remarketing campaigns to drive more sales, leads, phone calls, etc.

What Are Segments in Google Analytics?
Many digital marketers know about Remarketing already. That’s where you can reach people that already visited your website via advertising as they browse the web. For example, if John visited Roku’s website, browsed various products, and left, then Roku could use Remarketing to advertise to John as he browses the Google Display Network (GDN). The Google Display Network is a massive network of sites that run Google advertising, and includes Google-owned properties like YouTube, Google Maps, Gmail, etc. According to Google, the GDN reaches 90% of internet users worldwide.

Remarketing via The Google Display Network (GDN)

By the way, if you’ve ever visited a website and then saw ads from that website as you browsed the web, then you’ve been remarketed to. As you can guess, this can be an incredibly powerful way to drive more sales, leads, etc. It can also be extremely frustrating and/or shocking to users. So be careful when crafting your Remarketing strategy!

When Remarketing first rolled out, you could only set up Remarketing lists in the AdWords interface. That was ok, but didn’t provide a massive amount of flexibility. That’s when Google enabled marketers to set up Remarketing lists via Google Analytics. That opened up an incredible amount of opportunity to slice and dice visitors to create advanced-level Remarketing lists. For example, you could create Remarketing lists based on users who visited a certain section of your website, or lists based on users completing a certain conversion goal, etc. Needless to say, tying Google Analytics to Remarketing was an awesome addition.

Now, I started using Google Analytics Remarketing functionality immediately to help clients build advanced Remarketing lists, but I had a feeling that Google was going to make it even more powerful. And they did.

Along Came Segments… Remarketing Options Galore
You might already be familiar with segments in Google Analytics, which was originally named “Advanced Segmentation”. In July of 2013, Google released a new version in Google Analytics and simply called it “Segments”. But don’t get fooled by the simpler name. Segments enable marketers to slice and dice their users and traffic to view reporting at a granular level. For example, I often set up a number of segments for clients, based on their specific goals. Doing so enables me to quickly view granular reporting by removing a lot of the noise residing in standard reports.

Using Segments to Create Remarketing Lists in Google Analytics

But starting in January of 2014, Google rolled out an update that enabled marketers to use those segments to create Remarketing lists. Yes, now marketers had an incredible number of options available when creating Remarketing lists. In addition, you could easily import segments you are already using! This means you could leverage the hard work you’ve already put in when creating segments in Google Analytics.

Although I thought I had a lot of flexibility in creating Remarketing lists leading up to that point, the ability to use segments opened the targeting flood gates. I remember checking out the list of options when segments for Remarketing first launched, and I was blown away.

For example, using segments you could create Remarketing lists based on:

Demographics like age, gender, language, location, and more.
Technology options like operating system, browser, device category, mobile device model or branding, and more.
Behavior like the number of sessions per user, days since last session, transactions, and session duration.
“Date of First Session” where you could create lists based on the initial session date or a range (sessions that started between two dates).
Traffic Sources based on campaign, medium, source, or keyword.
Ecommerce options like transaction id, revenue, days to transaction, product purchased, or product category.
And you can combine any of these options to create even more advanced Remarketing lists.

Now, the options listed above are based on the major categories of segments in Google Analytics. But you can also set Remarketing lists based on conditions. Using conditions, you could leverage many of the dimensions or metrics available in Google Analytics to build advanced Remarketing lists. Actually, there are so many options via “conditions” that I can’t even list them all here in this post.

For example, there are eight major categories of dimensions and metrics you could choose from, including Acquisition, Advertising, Behavior, Custom Variables, Ecommerce, Time, Users, and Other. And each category has a number of dimensions or metrics you can select to help craft your Remarketing lists.

Using Conditions to Create Remarketing Lists in Google Analytics

Note, it can definitely be overwhelming to review the list of options when you first check this out. Don’t worry, I provide some tips for getting started later in this post. For now, just understand that you can use segments and conditions in Google Analytics to craft Remarketing lists based on a number of factors (or a combination of factors). Basically, you have the power to remarket however you like. And that’s awesome.

Examples of What You Can Do
Enough with the introduction. Let’s get specific. I’m sure you are wondering how segments in Google Analytics can be used in the real-world. I’ll provide a few examples below of Remarketing lists you can build to get back in front of people who already visited your website. Note, the lists you build should be based on your specific business and website. I’m just covering a few options below so you can see the power of using segments to build Remarketing lists.

Example 1: Remarket to users who came from a specific referral path (page).
Imagine you knew that certain referring webpages drove a lot of high-quality traffic on a regular basis. Based on the quality of traffic coming through those referring pages, you decide that you would love to remarket to those users as they browse the web (since you have a strong feel for the type of user they are based on the content at hand).

Using segments, you could create a Remarketing list based on the original referral path (i.e. the referring pages). And once that list reaches 100 members, then you can start getting targeted ads in front of those users and driving them to your preferred landing page (whether that’s current content, campaign landing pages, etc.)

Using Referring Path to Create Remarketing Lists

And if you find several referring pages that target similar categories of content, then you could use Boolean operators to combine those pages from across different websites. For example, {referring path A} AND {referring path B}. For example, if three referring pages are all about Category A, then you could combine them to create a Remarketing list. You can also use regular expressions to match certain criteria. Yes, the sky’s the limit.

Using Boolean Operators to Create Advanced Remarketing Lists

Example 2: Reach a certain demographic that has visited your website.
Let’s say you just launched a new product targeting 18-25 year olds and wanted to remarket to users who already visited your website that fit into this category. You know they showed some interest in your company and products already (since they already visited your site), so you want to reach them via display advertising as they browse the web.

Using segments, you could create a Remarketing list based on age using the Demographics category. Simply click the checkbox next to the age category you want to target.

Creating Remarketing Lists Based on Demographics

Or to get even more targeted, you could combine age with gender to test various messaging or visuals in your ads. Going even further, you could add location as another selection to target users based on age, gender, and geographic location (down to the city level if you wanted).

Combining Demographics to Create Advanced Remarketing Lists

Example 3: Target users of specific campaigns, ad groups, or keywords.
Let’s say you are already using AdWords to drive targeted users to your website. Using segments in Google Analytics, you could build a Remarketing list based on specific campaigns, ad groups, or keywords. For example, if you have an ad group targeting a specific category or product, then you could create a list containing the users that already searched Google and clicked through your ads related to that category. It’s a great way to get back in front of a targeted audience.

Creating Remarketing Lists Based on Previous Campaigns

And by combining the targeting listed above with ecommerce conditions like the number of transactions or amount of revenue generated, you could create advanced Remarketing lists targeting very specific types of users.

Creating Remarketing Lists Based on Revenue

Example 4: Pages or Page Titles
If you have been building a lot of new content and want to reach those visitors as they browse the web, then you could create a Remarketing list based Pages or Page Titles. For example, let’s say you have 25 blog posts about a certain category of content. They rank very well, have built up a nice amount of referral traffic, etc. You could build a Remarketing list by select a grouping of pages via urls or via page titles. Then you could reach those users as they browse the web and drive them to a targeted landing pages, knowing they were interested in a certain post (or group of posts) about a certain subject.

Creating Remarketing Lists Based on Page Titles

And you can combine those pages with conversion goals to add users to a list that completed some type of important action on the site. For example, users that signed up for your email newsletter, users that triggered an event, downloaded a study, etc.

Creating Remarketing Lists Based on Page Titles and Conversion

Remarketing Tips

Based on the examples listed above, I hope you see the power in using segments and conditions to craft Remarketing lists. But as I said earlier, it can quickly become overwhelming (especially for marketers new to Remarketing). Below, I’ve listed several important tips to keep in mind while crafting your campaigns.

Remarketing Lists Require 100 Members
A list requires at least 100 members before you can start showing ads to users. Keep this in mind when building lists to ensure you can reach that number. If not, you will never get back in front of those users.
Start Simple, Then Increase in Complexity
Based on the 100 member requirement, start with simpler Remarketing lists and increase your targeting as you get more comfortable with Remarketing. Don’t start with the most granular targeting possible, only to have a list of 3 people.
Refine Your Tracking Snippet
Google requires that you refine your Google Analytics tracking code in order take advantage of Remarketing. Review the documentation to ensure you have the proper technical setup.
Craft a Strategy First, and Your Lists Should Support Your Strategy
Don’t create lists for the sake of creating lists. Always start by mapping out a strong Remarketing strategy before jumping into list creation. Your strategy should dictate your Remarketing lists, and not the other way around. Spend time up front mapping out who you want to target, and why. And once you have a solid plan mapped out, you can easily build your lists via Google Analytics segments and conditions.
Use Display Advertising In Addition to Text Ads
Remarketing enables you to use both image ads and text ads. Definitely use both when crafting your campaigns. There are a number of sizes and formats you can use. I recommend hiring a designer to build your ads unless you have in-house staff that is capable of designing high-quality ads. Use image ads where possible to grab the user’s attention and provide text ads as a backup when a site doesn’t support image ads. You don’t have to choose one or the other.
Measure Your Results! Don’t “Set It and Forget It”.
Remarketing is advertising. And advertising campaigns should have a goal. Don’t simply set up Remarketing without knowing the intended action you want users to take. Instead, make sure you set up conversion goals to track how those users convert. Do not set up the campaign and let it run without analyzing the results. Understand the ROI of the campaign. That’s the only way you’ll know if it worked, if the campaign should keep running, and if you should base other campaigns on the original.

Summary – New and Powerful Ways to Remarket
After reading this post, I hope you see the power in using segments and conditions for creating Remarketing lists. In my opinion, too many marketers keep going after new eyeballs and easily forget about the eyeballs that already showed an interest in their company, products, or services. I believe that’s a mistake. Instead, marketers can craft advanced Remarketing lists to get back in front of a targeted audience. Doing so provides another chance at converting them.

Remember, a warm lead is always more powerful than a cold call. Good luck.

I’m Speaking at the Weber Shandwick Data Salon on April 24th – Learn About Google Algorithm Updates, Manual Penalties, and More

April 16, 2014 By Glenn Gabe Leave a Comment

Weber Shandwick Data Salon on April 24, 2014

I’m excited to announce that I’ll be speaking at the Weber Shandwick Data Salon on Thursday, April 24th in New York City (from 6:00PM to 7:30PM). Each month, Weber Shandwick invites leaders from various areas of digital marketing to speak, to spark conversation, and to share ideas. I’m thrilled to be presenting next week to speak about the latest in SEO.

My presentation will cover some extremely important topics that I’m neck deep in on a regular basis, including Google algorithm updates, manual penalties, and the war for organic search traffic that’s going on each day. I’ll be introducing various algorithm updates like Panda and Penguin, explain what manual actions are, and provide case studies along the way. I’ll also introduce the approach that Google is using to fight webspam algorithmically, while also covering how manual penalties work, how to recover from them, and how to ensure websites stay out of the danger zone.

My goal is to get the audience thinking about content quality, webspam, unnatural links, and webmaster guidelines now before any risky tactics being employed can get them in trouble. Unfortunately, I’ve spoken with hundreds of companies over the past few years that were blindsided by algo updates or manual actions simply because they never thought about the repercussions of their tactics, didn’t understand Google’s stance on webspam, or the various algorithm updates it was crafting. Many of them learned too late the dangers of pushing the envelope SEO-wise.

So join me next Thursday, April 24th at 6PM for a deep dive on algorithm updates, manual penalties, and more from the dynamic world of SEO. You can register today via the following link:

Register for Weber Shandwick’s Data Salon on April 24th:
https://www.surveymonkey.com/s/N6G5K5B

Below I have provided the session overview. I hope to see you there!

Weber Shandwick Data Salon #3
April 24, 2014 from 6:00PM to 7:30PM
Speaker: Glenn Gabe of G-Squared Interactive
Moderator: Kareem Harper of Weber Shandwick
909 Third Avenue, 5th Floor
*Refreshments will be available starting at 6:00pm

Front Lines of SEO

The Frontlines of SEO – Google Algorithm Updates, Penalties, and the Implications for Marketers
Explore Google’s war on webspam, learn about key changes and updates occurring in Search right now, and fully understand the implications for digital marketers.

There’s a battle going on every day in Search that many people aren’t aware of. With millions of dollars in revenue on the line, some businesses are pushing the limits of what’s acceptable from an SEO perspective. In other words, gaming Google’s algorithm to gain an advantage in the search results.

Google, with its dominant place in Search, is waging war against tactics that attempt to manipulate its algorithm. From crafting specific algorithm updates that target webspam to applying manual actions to websites, Google has the ability to impact the bottom line of many businesses across the world. And that includes companies ranging from large brands to small local businesses. This session will introduce the various methods Google is using to address webspam in order to keep its search results as pure as possible. Specific examples will be presented, including case studies of companies that have dealt with algorithm updates like Panda and Penguin. Manual penalties will be discussed as well.

Beyond battling webspam, the major search engines have been innovating at an extremely rapid pace. The smartphone and tablet boom has impacted how consumers search for data (and how companies can be found). And now the wearable revolution has begun, which will add yet another challenge for marketers looking to reach targeted audiences. Glenn will introduce several of the key changes taking place and explain how marketers can adapt. Glenn is also a Glass Explorer and will provide key insights into how Google Glass and other wearables could impact marketing and advertising.

Register today to learn more about Google’s war on webspam, to better understand the future of Search, and to prepare your business for what’s coming next.

You can register online by clicking the following link:
https://www.surveymonkey.com/s/N6G5K5B

Panda Report – How To Find Low Quality Content By Comparing Top Landing Pages From Google Organic

December 18, 2013 By Glenn Gabe 13 Comments

Top Landing Pages Report in Google Analytics

Note, this tutorial works in conjunction with my Search Engine Watch column, which explains how to analyze the top landing pages from Google Organic prior to, and then after, Panda arrives. With the amount of confusion circling Panda, I wanted to cover a report webmasters can run today that can help guide them down the right path while on their hunt for low-quality content.

My Search Engine Watch column covers an overview of the situation, why you would want to run the top landing pages report (with comparison), and how to analyze the data. And my tutorial below covers how to actually create the report. The posts together comprise a two-headed monster that can help those hit by Panda get on the right track. In addition, my Search Engine Watch column covers a bonus report from Google Webmaster Tools that can help business owners gather more information about content that was impacted by the mighty Panda.

Why This Report is Important for Panda Victims
The report I’m going to help you create today is important, since it contains the pages that Google was ranking well and driving traffic to prior to a Panda attack. And that’s where Google was receiving a lot of intelligence about content quality and user engagement. By analyzing these pages, you can often find glaring Panda-related problems. For example, thin content, duplicate content, technical problems causing content issues, low-quality affiliate content, hacked content, etc. It’s a great way to get on the right path, and quickly.

There are several ways to run the report in Google Analytics, and I’ll explain one of those methods below. And remember, this should not be the only report you run… A rounded analysis can help you identify a range of problems from a content quality standpoint. In other words, pages not receiving a lot of traffic could also be causing Panda-related problems. But for now, let’s analyze the top landing pages from Google Organic prior to a Panda hit (which were sending Google the most data before Panda arrived).

And remember to visit my Search Engine Watch column after running this report to learn more about why this data is important, how to use it, red flags you can identify, and next steps for websites that were impacted. Let’s get started.

How To Run a Top Landing Pages Report in Google Analytics (with date comparison):

First, log into Google Analytics and click the “All Traffic” tab under “Acquisition”. Then click “Google / Organic” to isolate that traffic source.
Next, set your timeframe to the date after Panda arrived and extend that for a decent amount of time (at least a few weeks if you have the data). If time allows, I like to set the report to 4-6 weeks after Panda hit. If this is right after an algorithm update, then use whatever data you have (but make sure it’s at least one week). I’m using a date range after the Phantom update hit (which was May 8th).
Your next move is to change the primary dimension to “Landing Page” to view all landing pages from Google organic search traffic. Click the “Other” link next to “Primary Dimension” and select “Acquisition”, and then “Landing Page”. Now you will see all landing pages from Google organic during that time period.
Now let’s use some built-in magic from Google Analytics. In the timeframe calendar, you can click a checkbox for “Compare to” and leave “Previous period” selected. Once you click “Apply”, you are going to see all of the metrics for each landing page, but with a comparison of the two timeframes. And you’ll even have a nice trending graph up top to visualize the Panda horror.
As you start going down the list of urls, pay particular attention to the “% Change” column. Warning, profanity may ensue. When you start seeing pages that lost 30%, 40%, 50% or more traffic when comparing timeframes, then it would be wise to check out those urls in greater detail. Again, if Google was sending a lot of traffic to those urls, then it had plenty of user engagement data from those visits. You might just find that those urls are seriously problematic from a content quality standpoint.

Bonus 1: Export to Excel for Deeper Analysis

It’s ok to stay within Google Analytics to analyze the data, but you would be better off exporting this data to Excel for deeper analysis. If you scroll to the top of the Google Analytics interface, you will see the “Export” button. Click that button and then choose “Excel (XLSX)”. Once the export is complete, it should open in Excel. Navigate to the “Dataset” worksheet to see your landing page data (which is typically the second worksheet).
At this point, you should clean up your spreadsheet by deleting columns that aren’t critical for this report. Also, you definitely want to space out each column so you can see the data clearly (and the data headers).
You’ll notice that each url has two rows, one for the current timeframe, and one for the previous timeframe. This enables you to see all of the data for each url during both timeframes (the comparison).
That’s nice, but wouldn’t it be great to create a new column that showed the percentage decrease or increase for visits (like we saw in Google Analytics?) Maybe even with highlighting to show steep decreases in traffic Let’s do it. Create a new column to the right of “Visits” and before “% New Visits”. I would title this column “% Change” or something similar.
Next, let’s create a formula that provides the percentage change based on the two rows of data for each url. Find the “Visits” column and the first landing page url (which will have two rows). Remember, there’s one row for each timeframe. If your visits data is in column C, then the post-Panda data is in row 2, and the pre-Panda data is in row 3 (see screenshot below). You can enter the following formula in the first cell for the new column “% Change”.=(C3-C2)/C3.Again, C3 is the traffic levels from the previous timeframe, C2 is the traffic levels from the current timeframe (after the Panda hit), and you are dividing by the previous traffic levels to come up with the percentage change. For example, if a url dropped from 5,450 visits to 640 visits, then your percentage drop would be 88%. And yes, you would definitely want to investigate that url further!
Don’t worry about the floating decimal point. We’ll tackle that soon. Now we need to copy that formula to the rest of the column (but by twos). Remember, we have two records for each url, so you’ll need to highlight both cells before double clicking the bottom right corner of the second cell to copy the formula to all rows. Once you do, Excel automatically copies the two rows to the rest of the cells in that column. Now you should have percentage drops (or increases) for all the urls you exported. Note, you can also highlight the two cells, copy them, and then highlight the rest of that column, and click paste. That will copy the formula to the right cells in the column as well.
Now, you will see a long, floating decimal point in our new column labeled “% Change”. That’s an easy fix, since we want to see the actual percentage instead. Highlight the column, right click the column, and choose “Format Cells”. Then choose “Percentage” and click “OK”. That’s it. You now have a column containing all top landing pages from Google organic, with the percentage drop after the Panda hit.

Bonus 2: Highlight Cells With A Steep Drop in Red

If you want the data to “pop” a little more, then you can use conditional formatting to highlight cells that exceed a certain percentage drop in traffic. That can easily help you and your team quickly identify problematic landing pages.
To do that, highlight the new column we created (titled “% Change”), and click the “Conditional Formatting” button in your Home tab in Excel (located in the “Styles” group). Then select, “Highlight Cells Rules”, and then select, “Greater Than”. When the dialog box comes up, enter a minimum percentage that you want highlighted. And don’t forget to add the % symbol! Choose the color you want to highlight your data with and click “OK”. Voila, your problematic urls are highlighted for you. Nice.

Summary – Analyzing Panda Data
If you made it through this tutorial, then you should have a killer spreadsheet containing a boatload of important data. Again, this report will contain the percentage increase or decrease for top landing pages from Google Organic (prior to, and then after, a Panda hit). This is where Google gathered the most intelligence based on user engagement. It’s a great place to start your analysis.

Now it’s time to head over to my Search Engine Watch column to take a deeper look at the report, what you should look for, and how to get on the right track with Panda recovery. Between the tutorial and my Search Engine Watch column, I hope to clear up at least some of the confusion about “content quality” surrounding Panda updates. Good luck.

Trackbacks in Google Analytics – How To Analyze Inbound Links in GA’s Social Reports

December 17, 2012 By Glenn Gabe 2 Comments

In May of 2012, Google Analytics introduced trackbacks in its social reporting. If you’re not familiar with trackbacks, they enable you to understand when another website links to your content. So, using Google Analytics, and the new trackbacks reporting, you could start to track inbound links you are building from across the web.

Note, if you want to perform advanced-level analysis of your links, you should still use more robust tools like Open Site Explorer or Majestic SEO. But, trackbacks reporting is a quick and easy way to identify backlinks, and right within Google Analytics. It can definitely supplement your link analysis efforts.

If you’re in charge of content strategy for your company, or if you are publishing content on a regular basis, then checking trackbacks reporting in GA can quickly help you understand the fruits of your labor. But since trackbacks reporting isn’t immediately visible, I’ve written this post to explain how you can find trackbacks, and then what you can do with the data once you access the reporting.

Social Reports and Trackbacks
First, if you’re not familiar with social reporting in Google Analytics, you should check out my post from March where I cover how to use the new social reports to analyze content. Social reports are a great addition to GA, but I still find many marketers either don’t know about them, or don’t know how to use them. And that’s a shame, since they provide some great insights about the traffic coming from social networks, and the conversations going on there (at least for data hub partners).

Below, I’m going to walk you step by step through the process of finding links to your content via trackbacks reporting. Once we find them, I’ll explain what you can do with your newly-found link data.

How To Find Trackbacks (Step by Step)
1. Access your Google Analytics reporting, and click “Traffic Sources”, “Social”, and then “Network Referrals”.

2. Next, click a network referral in the list like Google Plus, Twitter, Facebook, etc. Note, “Network Referral” is new language used by Google Analytics for “Social Network” or “Source”.

3. Once you click through a source, you should click the “Activity Stream” tab located near the top of the screen (right above the trending graph).

4. Once you click the activity stream tab, you’ll need to click the dropdown arrow next to the “Social Network” label at the very top of the screen. Once you do, you’ll see a link in that list for “Trackbacks”. Click that link.

5. Once you click the “Trackbacks” link, you will see the links to your content that Google Analytics picked up.

Congratulations, you found the hidden treasure of trackbacks in Google Analytics! Not the easiest report to find, is it? Now let’s find out what you can do with the data.

What You Can Do Once You Find Trackbacks
First, I’ll quickly cover the data provided in the trackbacks reporting. Google Analytics provides the following information for each trackback it picks up:

The date the trackback was picked up.
The title and URL of the page linking to your content.
The ability to launch and view your content that’s receiving the link.
And a quick way to isolate that content in your social reports (to view all social activity for that specific page).

Next, I’ll cover four ways you can benefit from analyzing trackbacks data in Google Analytics, including a bonus at the end. Let’s jump in.

1. Understand the source of the trackback (Who is linking to you.)
Linkbuilding is hard. So when your content builds links naturally, you definitely want to understand the source of those links. Trackbacks in Google Analytics provides an easy and quick way to identify links to your content. But once you build some links, you shouldn’t stop and have a tropical drink with a fancy umbrella as you admire your results. You should analyze your newly-found inbound links.

For example, you should determine if the links are strong, relevant, and how much will those links help with your SEO efforts. You should also determine which authors decided to link to you, what’s their background, and where else do they write?m

One of the first things you’ll see in trackbacks reporting is the title and URL of the page linking to your content. At this point, you can click the small arrow icon next to the URL to open the referring page in a new window. You can also click the “More” button on the right side of the page, and then click “View Activity” to be taken to the page linking to your content.

At this point, you can check out the article or post linking to you, understand who wrote the content, what they focus on, link to their social accounts, find their contact information, etc. Building relationships with quality authors in your niche is a great way to earn links down the line. Therefore, analyzing the people who already link to your content is low-hanging fruit. Trackbacks in GA make it easy to find them.

2. Understand Your Content That’s Building Links
When I’m working with content teams, I always get the question, “what should we write about?” I’m a big believer that a content generation plan should be based on data, and not intuition. And trackbacks provide another piece of data to analyze. Let’s face it, the proof is in the pudding from a linkbuilding standpoint. Either your content builds links or it doesn’t. If it does, you need to find out why that content built the links it did. And if it didn’t build links, you need to document that and make sure you don’t make the same mistake again.

As I mentioned earlier, there are some outstanding link analysis tools on the market, like Open Site Explorer and Majestic SEO, and I’m not saying that trackbacks in Google Analytics are the end-all. But, you can definitely use the reporting to quickly understand which content is building links.

Once you find trackbacks and identify the content that built those links, you can start to analyze and understand what drove interest. Was it breaking news, evergreen content, how-to’s, industry analysis, etc? Which topics were hot from a linkbuilding standpoint, and were those the topics you expected to build links? If you find a subject that worked well in the past, you can build a plan for expanding on that topic. Also, are the pages linking to you providing ideas for new posts? Do the comments on the page provide ideas, what did the author mention, etc? Trackbacks provide a mechanism for supplementing your analysis.

3. Join the conversation, Engage Influencers
I explained above how you can find the people (and websites) linking to your content. That’s great, but you shouldn’t stop there. If there’s a conversation happening on that referring page, then you should join the conversation. If someone went to the extent to mention and link to your content, the least you can do is thank them, and provide value to the conversation.

Adding value to the conversation and engaging a targeted audience can help you build more credibility and connect with targeted people in your niche. And as I mentioned above, you can connect with the author of the post via email or via their social accounts.

4. Understand Linkbuilding Over Time
Using the trending graph in Google Analytics, you can visually understand linkbuilding over time. The graph at the top of the screen will show you the number of trackbacks earned over the time period you have selected in GA. I’m not saying that it’s better than using other, dedicated link analysis tools, but this is a quick way to find link data right within Google Analytics.

In addition, if you click the “More” button for any specific trackback, and then click “Page Analytics”, you can isolate specific pieces of content receiving links. Note, I’ve been seeing a test in Google Analytics where “Page Analytics” is replaced by “Filter on this Page”. Personally, I like “Filter on this Page” since it’s more intuitive. Regardless, after clicking the link you can trend linkbuilding over time for a specific piece of content.

In addition, you can always compare timeframe to see how links were built during one timeframe versus another. You might find some interesting things, like a piece of content that built more inbound links months later versus when the content was first published. Then you can dig into the links to find out why…

Bonus: Export The Data!
As with any report in Google Analytics, you can easily export trackbacks data. If you are viewing any trackbacks report, you can click “Export” at the top of the screen, and then choose a format to quickly export the data for further analysis in Excel. Then you can slice and dice the data, combine data from other reports, etc. What you do with the data depends on your own Excel skills. :)

Summary – Quick Link Analysis in Google Analytics
I hope after reading this post you’re ready to jump into Google Analytics to hunt down trackbacks. Again, Google didn’t necessarily make it super-easy to find trackbacks, but they are there. Once you do find them, you can analyze those links to glean important insights that can help your future content and linkbuilding efforts. Although there are more robust link analysis solutions on the market, trackbacks reporting is a quick and easy way to identify and then analyze inbound links. I recommend checking out the reporting today. You never know what you’ll find. :)

How Google Analytics Really Handles Referring Traffic Sources [Experiment] – Why Clicks and Visits Might Not Match Up

November 27, 2012 By Glenn Gabe 14 Comments

Google Analytics Referrals

Let me walk you through a common scenario in web marketing. You have a website, and some people visit your site by clicking through links on other websites. In your web analytics reporting, those visits are categorized as referring visits. In Google Analytics specifically, those visits show up in your “Referrals” report under “Traffic Sources”. And when visitors click on an outbound link on your site (a link to another website), your site shows up as a referring source in that website’s referrals report.

That’s pretty straight forward, but what I’m about to cover isn’t. I find many marketers and webmasters don’t understand how Google Analytics handles that referring traffic during future visits to their websites. For example, if someone clicks through to your site from sampledomain.com, leaves your site, and then returns the next day.

Do you know how that visit will be categorized in Google Analytics? There’s a good chance you don’t, and I’m going to cover the topic in detail in this post.

Understanding Referring Visits is Important When Revenue and Cost Are Involved
I believe one of the reasons this topic isn’t understood very well is because it often doesn’t directly impact revenue or cost for many webmasters. Sure, you definitely want to know how many people are coming from each referring site, but for many webmasters, the exact number doesn’t impact revenue, or payments to other webmasters.

But, for websites that need to track the monetary value of inbound visits and outbound clicks, accurately determining referring visits is extremely important. For example, imagine you were charging certain partners for traffic you were sending from your site to theirs, or vice versa. The fact of the matter is that checking referring sources could show different numbers than you think, and could be much different than the outbound clicks you see. And depending on your own situation, the numbers could be way off…

The Core Disconnect – How Google Analytics Calculates Visits from Referring Sources (or any campaign, search visit, etc.)
Here’s the core disconnect. When someone clicks through to your site via a referring source, the utm_z cookie is updated with traffic source information. That cookie will not be overwritten unless another referring source or campaign takes it place. Direct Traffic will not overwrite this value. Let me say that again. Direct Traffic will not overwrite the utm_z cookie value. That means the utm_z value will remain the referring source of traffic when those visitors return to the site.

Google Analytics utm_z Cookie

What This Means To You
I know what you’re thinking. This guy is telling me about utm_z cookies?? What the heck does that mean to me? OK, stick with me for a second. Let’s say you had a partnership set up where another website pays you for traffic. Maybe you’re both in the same niche and want to leverage each other’s traffic for more exposure. You check your stats for the previous month and notice that you sent 500 visits to partner A. Cool, so you contact them to check how the partnership is going and to make sure they are seeing the same number of visits. They come back and say they’ve seen 700 visits from your site and thank you for the traffic. The check will be cut soon.

Google Analytics Clicks and Visits Could Be Off

But that 200 visit discrepancy is bothering you. Why is there a big difference between your partner’s reporting and the numbers you are seeing? And let’s assume you have a solid setup for tracking clicks out of your website. For example, maybe you are running outbound clicks to partners through a redirect that captures a number of important metrics. The redirect then sends the visitor off to the correct URL on the partner site. Basically, you know you are capturing all outbound clicks to the partner website.

This is where the native handling of referring sources in Google Analytics comes into play. Sure, you are tracking clicks off your site, but your partner’s analytics package is capturing those clicks plus any return visits that are direct visits. So, if someone clicks through to your partner’s site, then that’s one visit. If they leave that site, and return directly (by typing the url directly in their browser or via a bookmark), then the visit will show up as a visit from the original referring source (your website). That’s now two visits. And if they do it again, that will be three visits. That’s until another referring source or campaign overwrites the utm_z cookie. In this example, there were 3 visits to your 1 outbound click!

An Example of How Google Analytics Handles Referrals

Based on this simple example, you can easily see how over a month’s time, some people would click through to your partner’s site and then revisit their site directly (and possibly a few times). That would lead to more than one visit per user, and could sway the visit count from your website.

Still confused? Let me clear this up via an experiment below.

Experiment – Calculating Referring Visits in Google Analytics
In the following simple example, I set up a webpage on a second domain that links to a landing page I set up on my website just for this experiment. I didn’t want to skew the reporting by using an existing page on my site that gets a lot of visits. Then I used several computers I have here with clean browsers to first visit the referring page that links to my new landing page, and then I clicked through. The referring source should show up as the domain name of the referring site. That would be visit #1.

Next, I would leave the new landing page on my site and revisit my website later by typing the exact URL into my browser (what most people would think is a Direct Traffic visit). In theory, the referring site should show up as the traffic source, even though I’m entering the site as “Direct Traffic”. Remember, the utm_z cookie will only be overwritten by another referring source or campaign.

Last, I would search for a keyword that my site ranks for, and then click through to the site. And since this visit was from a search engine, the utm_z cookie would be updated with this new value, and my reporting would show Google as the referring source (along with the keyword I entered). Let’s find out the results of the experiment below.

The Results
1. First Visit
First, I visited the second domain and clicked through to my website. Here is the first referring visit showing up in my analytics package:
Referring Sites Experiment - First Visit

2. Second Visit (Directly Visiting the Site)
Next, I left the site and returned via Direct Traffic. Google Analytics shows the referring site as the source for this traffic, even though I entered via “Direct Traffic”. Also notice it accurately categorizes me as a “return visitor”:
Google Analytics Referral Experiment - Second Visit

3. Third Visit (Again Directly Visiting the Site, but the Next Day)
Just to underscore my point, I left and revisited the site the next day (again via Direct Traffic). Google Analytics again shows the visit is from the initial referring source:
Google Analytics Referral Experiment - Third Visit

4. Fourth Visit, This Time From Search
Finally, I searched for my name on Google and visited my website. Now Google Analytics shows the keyword that led to the site (from the traffic source “Google”). Remember, the utm_z cookie will only be updated when another referring source is identified (versus Direct Traffic).

So there you have it. Proof that your visit count by source may not be what you think it is. Now, if you’re reading this post and are either generating revenue from referring visits, or you have to pay partners based on visits, then you might be frantically running to Google Analytics to rerun your reports. Yes, this could impact things quite a bit. I’ll leave it up to you how you handle the situation. :)

What You Can Do – The Importance of Clarity
If you do have a partnership where you are either generating revenue by driving traffic, or you are paying for traffic from other websites, then each party needs to clearly understand the arrangement. Each website involved needs to be clear on the definitions of “traffic”, “clicks”, “visits”, etc. For example, think about AdWords for a second. You pay Google for clicks on ads, but don’t pay Google for direct visits back to your site (even though those visits will show up as campaign visits). And by the way, most partners will not give you access to their reporting anyway… Therefore, you will only know the clicks out from your site.

If you are tracking outbound clicks, you can use event tracking in Google Analytics to track those clicks, including the pages or links where those clicks are originating. If you don’t want to use event tracking, then you can run outbound clicks through a 302 redirect and capture the information you need to accurately track clicks. If you are receiving traffic, then you can make sure the referring links contain querystring parameters so you can understand which partner the traffic is coming from (and that it’s not a standard referral from the site). There are other ways to handle this, and those are just a few ideas.

Summary – Understanding Visits in Google Analytics
I hope you found this post explaining how Google Analytics handles referring visits helpful. I know this topic can be confusing, and experiments always help clear up some of the confusion. So now you know why visits might be higher or lower than you think, and how the utm_z cookie controls what shows up in your reporting. I bet you’ll never look at referring sources the same again.

And let’s hope you’re not on the short end of the stick. :)

Happy Reporting.