Archive for the ‘google-analytics’ Category

Wednesday, November 26th, 2014

Panda Analysis Using Google Analytics Segments – How To Isolate Desktop, Mobile, and Tablet Traffic From Google

Segments in Google Analytics to Isolate Traffic

In previous posts about Panda analysis, I’ve mentioned the importance of understanding the content that users are visiting from Google organic. Since Google is measuring user engagement, hunting down those top landing pages can often reveal serious content quality problems.

In addition, I’ve written about understanding the devices being used to access your site from the search results. For example, what’s the breakdown of users by desktop, mobile, and tablets from Google organic? If 50% of your visits are from smartphones, then you absolutely need to analyze your site through that lens. If not, you can miss important problems that users are experiencing while visiting your website. And if left unfixed, those problems can lead to a boatload of horrible engagement signals being sent to Google. And that can lead to serious Panda problems.

Panda Help Via Segments in Google Analytics
So, if you want to analyze your content by desktop, mobile, and tablet users through a Panda lens, what’s the best way to achieve that? Well, there’s an incredibly powerful feature in Google Analytics that I find many webmasters simply don’t use. It’s called segmentation and enables you slice and dice your traffic based on a number of dimensions or metrics.

Segments are non-destructive, meaning that you can apply them to your data and not affect the source of the data. Yes, that means you can’t screw up your reporting. :) In addition, you can apply new segments to previous traffic (they are backwards compatible). So you can build a new segment today and apply it to traffic from six months ago, or longer.

For our purposes today, I’m going to walk you through how to quickly build three new segments. The segments will isolate Google organic traffic from desktop users, mobile users, and tablet users. Then I’ll explain how to use the new segments while analyzing Panda hits.


How To Create Segments in Google Analytics
When you fire up Google Analytics, the “All Sessions” segment is automatically applied to your reporting. So yes, you’ve already been using segments without even knowing it. If you click the “All Sessions” segment, you’ll see a list of additional segments you can choose.

Google Analytics All Sessions Segment

You might be surprised to see a number of segments have been built for you already. They are located in the “System” category (accessed via the left side links). For example, “Direct Traffic”, “AdWords”, “Organic Traffic”, and more.

Google Analytics System Segments


We are going to build custom segments by copying three system segments and then adding more dimensions. We’ll start by creating a custom segment for mobile traffic from Google organic.

1. Access the system segments by clicking “All Sessions” and then clicking the link labeled “System” (located on the left side of the UI).


Google Analytics System Segments


2. Scroll down and find the “Mobile Traffic” segment. To the far right, click the “Actions” dropdown. Then choose “Copy” from the list.


Copying a System Segment in Google Analytics


3. The segment already has “Device Category”, “exactly matches”, and “mobile” as the condition. We are going to add one more condition to the list, which is Google organic traffic. Click the “And” button on the far right. Then choose “Acquisition” and the “Source/Medium” from the dimensions list. Then choose “exactly matches” and select “google/organic” from the list. Note, autocomplete will list the top sources of traffic once you place your cursor in the text box.


Creating a Segment by Adding Conditions


4. Name your segment “Mobile Google Organic” by using the text box labeled “Segment Name” at the top of the window. It’s easy to miss.


Name a Custom Segment in Google Analytics


5. Then click “Save” at the bottom of the create segment window.


Save a Custom Segment in Google Analytics


Congratulations! You just created a custom segment.


Create The Tablet Traffic Segment
Now repeat the process listed above to create a custom segment for tablet traffic from Google organic.  You will begin with the system segment for “Tablet Traffic” and then copy it. Then you will add a condition for Google organic as the source and medium.


Desktop Traffic (Not a default system segment.)
I held off on explaining the “Desktop Traffic” segment, since there’s an additional step in creating one. For whatever reason, there’s not a system segment for isolating desktop traffic. So, you need to create this segment differently. Don’t worry, it’s still easy to do.

We’ll start with the “Mobile Traffic” segment in the “System” list, copy it, and then refine the condition.

1. Click “All Sessions” and the find “Mobile Traffic” in the “System” list. Click “Actions” to the far right and then click “Copy”.


Copying a System Segment in Google Analytics


2. The current condition is set for “Device Category” exactly matching “mobile”. We’ll simply change mobile to “desktop”. Delete “mobile” and start typing “desktop”. Then just select the word “desktop” as it shows up.

Creating a Desktop Segment in Google Analytics


3. Since we want Desktop traffic from Google Organic, we need to add another condition. You can do this by clicking “And” to the far right, selecting “Acquisition”, and then “Source/Medium” from the dropdown. Then select “exactly matches” and enter “Google/Organic” in the text box. Remember, autocomplete will list the top sources of traffic as you start to type.


Creating a Google Organic Desktop Segment in Google Analytics


4. Name your segment “Desktop Google Organic” and then click “Save” at the bottom of the segment window to save your new custom segment.


Quickly Check Your Segments
OK, at this point you should have three new segments for Google organic traffic from desktop, mobile, and tablets. To ensure you have these segments available, click “All Sessions” at the top of your reporting, and click the “Custom” link on the left. Scroll down and make sure you have all three new segments. Remember, you named them “Desktop Google Organic”, “Mobile Google Organic”, and “Tablet Google Organic”.

If you have them, then you’re good to go. If you don’t, read through the instructions again and create all three segments.


Run Panda Reports by Segment
In the past, I’ve explained the importance of running a Panda report in Google Analytics for identifying problematic content. A Panda report isolates landing pages from Google organic that have dropped substantially after a Panda hit. Well, now that you have segments for desktop, mobile, and tablet traffic from Google organic, you can run Panda reports by segment.

For example, click “All Sessions” at the top of your reporting and select “Mobile Google Organic” from the “All” or “Custom” categories. Then visit your “Landing Pages” report under “Behavior” and “Site Content” in the left side menu in GA. Since you have a specific segment active in Google Analytics, the reporting you see will be directly tied to that segment (and filter out any other traffic).

Creating a Google Panda Report Using Custom Segments


Then follow the directions in my previous post to run and export the Panda report. You’ll end up with an Excel spreadsheet highlighting top landing pages from mobile devices that dropped significantly after the Panda hit. Then you can dig deeper to better understand the content quality (or engagement) problems impacting those pages.

Combine with Adjusted Bounce Rate (ABR)
User engagement matters for Panda. I’ve documented that point many times in my previous posts about Panda analysis, remediation, and recovery. The more poor engagement signals you send Google, the more bamboo you are building up. And it’s only a matter of time before Panda comes knocking.

So, when analyzing user engagement, many people jump to the almighty Bounce Rate metric to see what’s going on. But here’s the problem. Standard Bounce Rate is flawed. Someone could spend five minutes reading a webpage on your site, leave, and it’s considered a bounce. But that’s not how Google sees it. That would be considered a “long click” to Google and would be absolutely fine.

And this is where Adjusted Bounce Rate shines. If you aren’t familiar with ABR, then read my post about it (including how to implement it). Basically, Adjusted Bounce Rate takes time on page into account and can give you a much stronger view of actual bounce rate. Once you implement ABR, you can check bounce rates for each of the segments you created earlier (and by landing page). Then you can find high ABR pages by segment (desktop, mobile, and tablet traffic).

Combining Adjusted Bounce Rate with Custom Segments


Check Devices By Segment (Smartphones and Tablets)
In addition to running a Panda report, you can also check the top devices being used by people searching Google and visiting your website. Then you can analyze that data to see if there are specific problems per device. And if it’s a device that’s heavily used by people visiting your site from Google organic, then you could uncover serious problems that might lie undetected by typical audits.

GA’s mobile reporting is great, but the default reporting is not by traffic source. But using your new segments, you could identify top devices by mobile and tablet traffic from Google organic. And that’s exactly what you need to see when analyzing Panda hits.

Analyzing Devices with Custom Segments in Google Analytics

For example, imagine you saw very high bounce rates (or adjusted bounce rates) for ipad users visiting from Google organic. Or maybe your mobile segment reveals very low engagement from Galaxy S5 users. You could then test your site via those specific devices to uncover rendering problems, usability problems, etc.


Summary – Isolate SEO Problems Via Google Analytics Segments
After reading this post, I hope you are ready to jump into Google Analytics to create segments for desktop, mobile, and tablet traffic from Google organic. Once you do, you can analyze all of your reporting through the lens of each segment. And that can enable you to identify potential problems impacting your site from a Panda standpoint. I recommend setting up those segments today and digging into your reporting. You might just find some amazing nuggets of information. Good luck.



Wednesday, September 17th, 2014

How To Check If Google Analytics Is Firing On Android Devices Using Remote Debugging With Chrome [Tutorial]

How To Debug Google Analytics on Mobile Devices

We all know that having a strong analytics setup is important. Marketing without measurement is a risky proposition for sure. But in a multi-device world, it’s not as easy to make sure your setup is accurately tracking what you need – or tracking at all. And if your analytics code isn’t firing properly across smartphones, tablets, and desktop computers, your data will be messy, incomplete, and inaccurate. And there’s nothing that drives a marketer crazier than flawed data.

A few weeks ago, Annie Cushing tweeted a quick question to her followers asking how everyone was testing their Google Analytics setup via mobile devices. This is something many digital marketers grapple with, especially when you are trying to track down problems. For example, I do a lot of algorithm update work and often dig into the analytics setup for a site to ensure we are seeing the full drop in traffic, conversion, revenue, etc.

My knee-jerk response was to check real-time reporting in Google Analytics while accessing specific pages to ensure those visits were being tracked, in addition to events. That could work, but it’s not as granular or isolated as you would want. I also mentioned to Annie that using a chrome extension like User Agent Switcher could help. That wouldn’t document the firing of analytics code, but would let you see the source code when accessing a webpage via a specific type of smartphone or tablet. But again, you couldn’t see the actual firing of the code or the events being tracked. And that’s obviously an important aspect to debugging analytics problems.

A Solution – Remote Debugging on Android with Chrome
So I did what I typically do when I run into a tricky situation. I find a solution! And for Android devices, I found a solid one. Many of you might be familiar with Chrome Developer Tools (on your desktop computer). It holds some outstanding functionality for debugging websites and web applications. But although it’s extremely helpful for debugging desktop webpages, it didn’t really address the problem at hand (out of the box), since we want to debug mobile devices.

So I started to research the issue and that’s when I came across a nifty technique which would allow you to connect your Android device to your desktop computer and then debug the Chrome tabs running on your mobile device from your desktop computer. And since I could use Chrome Developer Tools to debug the tabs on my desktop computer, I could check to see if Google Analytics was indeed firing when accessing webpages via my Android device. Awesome.

So, I spent some time testing this out and it does work. Sure, I had to jump through some hoops to get it to run properly, but it finally did work. Below I’ll cover what you’ll need to test this out for yourself and how to overcome some of the problems I encountered. Let’s get started.


What You’ll Need
In order to debug GA code running on your mobile device, you’ll need the proper setup both on your desktop computer and on your Android device. In its simplest form, you’ll need:

  • Chrome installed on your desktop (version 32 or later).
  • Android 4.0 or later.
  • A USB Cable to connect your device to your computer.
  • Android SDK {this will not be required for some of you, but others might need to install it. More on that situation below}.

If you run into the problems I ran into, you’ll need the Android SDK installed. I already had it installed since I’ve been testing various Android functionality and code, so it wasn’t a big deal. But you might need to install it on your own. I wouldn’t run to do that just yet, though. If the straight setup works for you, then run with it. If not, then you might need to install the Android SDK.

If you are confident you have the necessary setup listed above, then you can move to the tutorial listed below. I’ll walk you through how to debug Chrome tabs running on your mobile device via Chrome on your desktop computer. And yes, we’ll be isolating Google Analytics code firing on our Android devices to ensure you are tracking what you need.

How To Debug Google Analytics on Your Android Device – Step-By-Step Instructions

  1. Enable USB Debugging on Your Android Device
    Access your settings on your Android device and click Developer Options. On my device, that was located in the more “More” grouping of my settings and under System Manager. If you don’t see Developer Options, then you need to enable it.You can do that by accessing Settings, tapping About Phone or About Device and tapping Build Number seven times. Yes, that sounds extremely cryptic, but that’s what you need to do. Once you do, Developer Options will show up in under System Manager in your phone’s settings.

    Enable USB Debugging on Android Device

    Then you can check the box to enable USB Debugging on your device. You will need to do this in order to debug Google Analytics in Chrome on your device.

  2. Enable USB Discovery in Chrome (on your desktop)
    Next, type chrome:inspect in a new tab in Chrome on your desktop. Ensure “Discover USB devices” is checked on this screen.

    Enable USB Discovery in Chrome Desktop
  3. Connect Your Phone To Your Computer via USB
  4. Allow USB Debugging
    When you connect your phone to your computer, you should see a dialog box on your phone that asks you if you want to allow USB debugging. Click OK. Note, if you don’t see this dialog box, debugging your mobile device from Chrome on your desktop will not work. I provide instructions for getting around this problem later in the tutorial. If you are experiencing this problem, hop down to that section now.

    Allow USB Debugging on Android Device
  5. Fire up Chrome On Your Mobile Device
    Start Chrome on your Android device and access a webpage (any webpage you want to debug).
  6. Inspect With Chrome on your Desktop
    Once you open a webpage in Chrome on your mobile device, access Chrome on your desktop and visit chrome:inspect. Once you do, you should see your device listed and the various tabs that are open in Chrome on your Android device.

    Inspect Chrome Tabs on Desktop Computer
  7. Click Inspect To Debug The Mobile Tab
    When you click “inspect”, you can use Chrome Developer Tools on your desktop to debug the mobile web view. You can use all of the functionality in Chrome Developer Tools to debug the webpage open on your mobile device.
  8. Click the Network Tab in Chrome Developer Tools
    By accessing the Network Tab, you can view all network activity based on the webpage you have loaded in Chrome on your mobile device. That includes any resources that are requested by the webpage. Then reload the webpage on your mobile device to ensure you are seeing all resources.
  9. First Check for GA.js
    When you load a webpage on your mobile device, many resources will be listed in the network tab. But you should look for ga.js to see if the Google Analytics snippet is being loaded.Tip: You can use the search box and enter “ga.js” to filter all resources by that string. It’s an easy way to isolate what you are looking for.

    Check for ga.js in Network Tab in Developer Tools
  10. Next Check for utm.gif
    After checking for ga.js, you should look for the tracking pixel that’s sent to GA named utm.gif. If that is listed in the network panel, then your mobile webpage is tracking properly (at least basic tracking). Again, you can use the search box to filter by utm.gif.

    Check for utm.gif in Network Tab in Developer Tools
  11. Bonus: Advanced Tracking
    If you are firing events from mobile webpages, then you can see them listed here as well. For example, you can see an event being fired when a user stays on the page for more than 30 seconds below. So for this situation, we know that pageviews are accurately being tracked and the time on page event is being tracked via mobile. Nice.

    Check event tracking in Chrome for Android


A Note About Troubleshooting
I mentioned earlier that if you don’t see the “Allow USB Debugging” dialog on your mobile device when you connect your phone to your computer, then this setup won’t work for you. It didn’t initially work for me. After doing some digging around, I found the legacy workflow for remote debugging on Android.

By following the steps listed below, I finally got the prompt to show up on my mobile device. Then I was able to debug open Chrome tabs on my Android device.


  1. Install the Android SDK (if you don’t already have it installed)
    You can learn more about the SDK here and download the necessary files.
  2. Kill the ADB Server
    Use a command prompt to access the “platform-tools” folder in the SDK directory and then issue the following command: adb kill-server. Note, you should use the cd command to change directory to the folder containing adb. That’s the platform-tools folder in your Android SDK directory.

    Kill ADB Server
  3. Revoke USB Debugging on Your Android Device
    Disconnect your phone from your computer. Then go back to Developer Options on your Android phone and tap Revoke USB debugging authorization.

    Revoke USB Debugging
  4. Start the ADB Server
    Now you must restart the adb server. Use a command prompt, access the platform-tools folder again, and enter the following command: adb start-server.

    Start ADB Server
  5. Reconnect Your Device To Your Computer
    Once you reconnect your device, you should see the “Allow USB Debugging” dialog box. Click “OK” and you should be good to go. This will enable you to debug Chrome tabs running on your mobile device via Chrome running on your desktop.
  6. Open Chrome on Your Android Device
    Go ahead and open a webpage that you want to debug in Chrome on your Android phone. Once it’s loaded in Chrome in Android, you can follow the instructions listed earlier for using the network panel to debug the GA setup.


Summary – Know When Google Analytics is Firing on Mobile Devices
So there you have it. There is a way to debug the actual firing of GA code on your Android devices and it works well. Sure, you may need to go the extra mile, use the legacy workflow, and install the Android SDK, but you should be able to get it working. And once you do, you’ll never have to guess if GA is really working on Android devices. You’ll know if it is by debugging your Chrome tabs on Android via Chrome running on your desktop. Good luck.




Monday, May 12th, 2014

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

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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.



Wednesday, December 18th, 2013

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

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.
    Accessing Google Organic Traffic in Google Analytics
  • 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).
    Setting a Timeframe in Google Analytics
  • 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.
    Primary Dimension to Landing Page in Google Analytics
  • 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.
    Comparing Timeframes in Google Analytics
  • 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.
    Viewing The Percent Change in Traffic in Google Analytics


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).
    Exporting A Report In Google Analytics
  • 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).
    Clean Up Google Analytics Export in Excel
  • 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).
    Two Rows For Each URL Based on Timeframe
  • 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.
    Creating a New Column for Percent Change in Excel
  • 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!
    Creating a Formula to Calculate Percent Change in Excel
  • 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.
    Copying a Formula to All Rows in Excel
  • 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.
    Formatting Cells in Excel


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.
    Applying Conditional Formatting in ExcelApplying Conditional Formatting by Percentage in Excel


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.




Sunday, April 14th, 2013

You Might Be Losing Out – How To Make Sure Sitelink Extensions in Bing Ads Are Tracked Properly [Tutorial]

Bing Ads released sitelink extensions in October of 2012, which enables advertisers to provide additional links in their text ads.  Google AdWords has had ad sitelinks for some time, so this was a great addition by our friends at Bing Ads.  For example, if you were an ecommerce website selling sporting goods, you could provide ad sitelinks for your top categories, like football, baseball, basketball, etc. right beneath your standard text ad.  Sitelink extensions are great usability-wise, while they also provide a nice advantage in the SERPs (since they take up more real-estate).

Here are two examples of sitelink extensions in action (2 Formats):
Example of Sitelink Extensions in Bing Ads for Lucky Jeans


Example of Sitelink Extensions in Bing Ads for Adidas

So, let’s say you set up sitelink extensions for some of your campaigns, and you’re basking in the glory of those beautiful ads (and the click through they are getting).  But, maybe your reporting isn’t lining up clicks and visits-wise.  Sure, there are several reasons that could be happening, but maybe it got worse since you launched sitelink extensions.  Well, the reason could very well be the lack of tagging on your ad sitelinks.  If those additional URLs aren’t tagged properly, then your analytics package could very well be reporting that traffic as organic search.  And that would be shame.

In this post, I’m going to walk you through why this could be happening, and how to rectify the situation.  After reading this post, you might just run to Bing Ads today and make changes.  Let’s jump in.

Sitelink Extensions and Tracking Parameters
In Bing Ads, you can include sitelink extensions several ways.  First, you can add them manually via the Bing Ads web UI.  Second, you can use Bing Ads Editor to add them locally, and then upload them to your account.  And third, and possibly the top reason ad sitelinks don’t get tagged, is that you can import them from AdWords via the “Import from Google” functionality.  Note, the import from AdWords functionality is awesome, so don’t get me wrong.  It’s just that it’s easy to import ad sitelinks and not know they are there.  Then you run the risk of uploading untagged sitelink extensions.

How To Create Sitelink Extensions in Bing Ads

So, you need to make sure that your ad sitelinks are tagged properly, based on the analytics package you are using to track campaigns.  For example, if you are using Google Analytics, then you need to make sure that you identify each click coming from your sitelink extensions.  That means you should be appending tracking parameters to your sitelink URLs.  For Google Anlaytics, you can use URL Builder to tag your landing page URLs.

Tagging Sitelink URLs Using URL Builder


How To Tag Your Ad Sitelinks in Bing Ads
Again there are various ways to include sitelink extensions in your campaigns, from using the web UI to using Bing Ads Editor to using the “Import from Google” functionality.  I’ll quickly cover each method below to make sure you know where to apply your tracking parameters.

1.  The Bing Ads Web UI
You can currently apply ad sitelinks at the campaign level in Bing Ads.  When you access a campaign, you can click the “Ad Extensions” tab to include ad sitelinks.  Once there, you can click “Create” to add a new sitelink extension.  If you have other sitelink extensions set up across campaigns, you will see them listed (and you can apply those to your campaign if it makes sense).

Creating Sitelink Extensions Using the Bing Web UI

If you want to add a completely new sitelink extension, then click “Create New”.  When adding the sitelink extension, Bing Ads provide a field for link text and then a field for the destination URL.  When you add the URL, make sure your tracking parameters are added!  If not, your visits will show up as “Bing Organic” versus “Bing CPC”.  Good for the SEO team, but not so good for the paid search team.  :)


Adding Sitelinks Using the Bing Web UI


2. Bing Ads Editor
I love Bing Ads Editor.  It’s an awesome way to manage your campaigns locally and then sync with the Bing Ads web UI.  And as you can guess, there is functionality for adding and editing sitelink extensions in Bing Ads Editor.  You can access your sitelink extensions by clicking the “Ad Extensions” tab for any selected campaign.

Once you click the “Ad Extensions” tab, you can add sitelink extensions by clicking the “Create a Sitelink Extension” button from the top menu.  Then similar to the web UI, you can add the link text and the destination URL.  When adding your destination URLs, make sure your tracking parameters are added.

Adding Sitelinks Using the Bing Ads Editor


3. Import from Google (in Bing Ads Editor)
As I explained earlier, I love having the ability to import campaigns, changes, etc. from AdWords directly into Bing Ads Editor.  It makes managing campaigns across both platforms much more efficient.  But, I’ve seen advertisers import campaigns from AdWords that have sitelink extensions, but they don’t realize it.  Then they upload their campaigns to Bing Ads and don’t understand that prospective customers are clicking their sitelinks, visiting their sites, etc., but those visits aren’t being tracked correctly.  Again, those visits will show up as “Bing Organic” in your analytics reporting.

When you go through the process of importing your campaigns, make sure you double check the “Ad Extensions” tab for the newly-imported campaign.  You just might find sitelink extensions sitting there.  And yes, they very well could be left untagged.  Make sure you add your tracking parameters before uploading them to Bing Ads (from Bing Ads Editor).

You can also uncheck the “Ad Extensions” radio button when importing your campaigns from AdWords.  Then you can add your sitelink extensions directly in Bing Ads Editor (via the second method I covered earlier in this post.

Importing Sitelink Extensions in Bing Ads Editor


Sitelinks Are Powerful, But Only If They Are Tracked
Sitelinks extensions are a great addition to Bing Ads, and they absolutely can yield higher click through rates.  But, you need to make sure those clicks are being tracked and attributed to the right source – your Bing Ads campaigns!  I recommend checking your campaigns today to make sure your sitelink extensions have the proper tracking parameters appended.  If not, you can quickly refine those links to make sure all is ok.   And when everything is being tracked properly, you just might see a boost in visits, orders, and revenue being attributed to Bing Ads.  And that’s always a good thing.




Monday, December 17th, 2012

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

Trackbacks in Google Analytics

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”.

Trackback Reporting in Google Analytics

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”.

Network Referrals in Google Analytics

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).

Activity Stream in Google Analytics Social Reports

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.

Finding Trackbacks in Google Analytics

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

Viewing Trackbacks in Google Analytics Social Reports

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.

Viewing Trackbacks in Google Analytics

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.

Trackbacks Trending in 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.

Viewing Trackbacks for a Specific Page

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.  :)

Exporting Trackback Data in Google Analytics

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.  :)



Tuesday, November 27th, 2012

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

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, 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).
Google Analytics Referral Experiment - Search Visit


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.



Tuesday, October 16th, 2012

How To Find Keywords Triggering Product Listing Ads Using AdWords and Google Analytics [Includes a Custom Report for PLA’s]

If you’re an ecommerce retailer, then you have probably heard of Product Listing Ads in Google AdWords.  Product Listing Ads (PLA’s) are powerful ad units that enable you to display image thumbnails in the search results for products you sell on your site.  As you can imagine, the visual nature of the ads yield more ad real estate and can greatly help with click-through rate (since the ads are hard to overlook.)  And with the holidays quickly approaching, standing out from your competitors is an important aspect to landing new customers.

Here is a screenshot of product listings ads in action:
Interested in a Keurig Coffee Maker? I bet the ads on the right will catch your eye.
Product Listing Ads for Keurig Coffee Makers

Are you looking for a new golf driver? Again, the PLA’s on the right will probably catch your attention:

Product Listing Ads for Golf Drivers

Google Shopping Goes Commercial
This past spring, Google announced that Google Product Search was moving to a full commercial model and would be called Google Shopping.  No longer would you be able to have your product ads show up for free (blended in the organic search results).  Google originally set a target deadline of October 1st, 2012 for the transition so ecommerce retailers could get familiar with product listings ads (which would be the mechanism for displaying products in the search results).  The ads would be cost per click-based (CPC), like PLA’s have always been.

This was a big move for Google, as many ecommerce retailers relied on shopping results to gain free clicks to their sites from prospective customers searching for products.  Now, in order to have similar results, those ecommerce retailers would need to pay.  Therefore, many ecommerce retailers jumped on board the product listing ads bandwagon (as they should).

Google Shopping Transitions to Commercial Model

Optimization is Important
When you run product listing ads, you don’t bid on keywords.  Instead, Google reviews your merchant center feed and then matches your ads with queries that it believes are relevant.  In my experience, there are times I see Google displaying product listing ads for queries that aren’t directly tied to the product at hand, or that are more category-driven.  This can yield untargeted visitors, higher costs, and lower ROI.  And that’s exactly what you don’t want in SEM.  Therefore, it’s important to optimize your product listing ads campaigns over time in order to increase performance.

It’s Hard to Determine Out of the Box
Given what I listed above, where do you find the keywords triggering your product listing ads?  Unfortunately, they aren’t so easy to find out of the box.  In addition, finding the keywords triggering your ads also depends on how you set up and structured your product listing ads campaigns.  For example, are you using product targets to segment your merchant feed, are you lumping all products in one ad group, etc?

Today, I’m here to help.  I’m going to list two ways to find the keywords triggering your product listing ads and I’ll include a bonus custom report at the end of this post that provides even more information for you to analyze.  Let’s get started.

Two Ways to Find Keywords Triggering Your Product Listing Ads
1. The AdWords UI
The first place you can find the keywords triggering your product listing ads is in the AdWords UI (managing your campaign on the web).  First, click the campaign holding your product listing ads (which should be a campaign that’s separate from your other search or display network campaigns).  Then click the “Keywords” tab.

Keywords Tab in Google AdWords

Next, click “Keyword Details”, and finally “All”.  This will reveal all the raw search queries that have triggered your product listing ads and that drove traffic to your site (by ad group).  Then you can adjust the columns in the report and export the report to Excel.

Matched Search Queries for Product Listing Ads in AdWords

2. AdWords Reporting in Google Analytics (Match Search Queries + Second Dimension)
The second way you can find the queries triggering your product listing ads is to access your AdWords reporting in Google Analytics.  You can click the “AdWords” tab, and then the “Matched Search Queries” link to view all matched search queries for your campaigns.  Then, you can add a second dimension for “Ad Group” to view a list of raw search queries by ad group.  This is extremely powerful if you segmented your merchant feed using product targets (as mentioned earlier).  For example, imagine viewing all raw search queries by major brand, product type, etc.

Viewing Matched Search Queries for Product Listing Ads in Google Analytics

Next, you will need to filter this report based on your naming convention for product listings ads in AdWords.  That’s because the report will initially contain all ad groups and matched search queries (and not just queries for your product listing ads).  You can use the filter box in your reporting to filter your ad groups to isolate the ad groups for your product listing ads.  For example, if your ad groups for product listing ads begin with “PLA”, then you can filter the report to select ad groups that contain “PLA” in the title.  When you do this, you will be left with all of your ad groups for product listing ads and the matched search queries that have driven traffic to your site.  Then you can export this report to Excel for further analysis.

Filtering Product Listing Ads in Google Analytics


Bonus: Product Listing Ads Custom Report in Google Analytics
All of what I listed above works well, and can be extremely useful, but there’s a quicker way to drill into this data.  You can use custom reporting in Google Analytics to create a new report that enables you to drill into campaign, ad group, raw search query, and then landing page by query.  Sounds awesome, right?

Well, I’ve built that report and provided a link to it below (so you can use it for your own campaigns).  If you are logged into your Google Analytics account, then clicking the link will launch the report in your account (just the structure, not the data).  Then you will need to tailor the report structure for your own campaigns.  For example, I created the report to isolate a campaign with “PLA” in the name.  You’ll need to identify your own product listing ads campaigns based on your own naming convention.

Once you do, you’ll be able to drill into your campaign, ad groups within that campaign, matched search queries per ad group, and then the landing page from each query.  The report will enable you to quickly identify negatives to use per ad group, and will help you double check landing pages per query.   Note, the landing page (destination URL) is based on your merchant center feed, and depending on the retailer, there can be thousands or tens of thousands of products in a feed.  It’s always good to double check the destination URL’s to make sure the right queries lead to the right product listing ads, which lead to the right product detail pages.  If not, you could be shooting yourself in the foot.

Click the link to access the product listing ads custom report I built.

Summary – Make the Most of Your Product Listing Ads
As I mentioned earlier, product listing ads are a powerful ad unit for ecommerce retailers.  And now with Google Shopping moving to a full commercial model, it’s critically important for retailers to get a handle on their PLA’s.  You can use the methods I provided above to find the search queries triggering your ads and driving prospective customers to your site.  In addition, you can use the custom report I provided to drill into your campaigns, ad groups, keywords, and landing pages.  Then it’s up to you to analyze your newly-found reporting in order to refine your efforts.  And that’s the name of the game in SEM.

Have a killer holiday season.



Thursday, August 23rd, 2012

Adjusted Bounce Rate in Google Analytics – One Step Closer to Actual Bounce Rate

Adjusted Bounce Rate in Google Analytics

I’ve written extensively in the past about Bounce Rate both here on my blog and on Search Engine Journal.  Bounce Rate is an incredibly powerful metric, and can help marketers better understand the quality of their traffic, and the quality of their content.  If you’re not familiar with Bounce Rate, it’s the percentage of visits that view only one page on your site.  They find your site, view one page, and leave.  As you can guess, that’s usually not a good thing.

Traffic-wise, high bounce rates can raise red flags about the quality of traffic from a given source, campaign, or keyword.  For example, imagine spending $1500 in AdWords driving visitors to your site for a certain category of keywords and seeing a 92% bounce rate.  That should raise a red flag that either the visitor quality is poor or that your landing page is not providing what people are looking for. For example, maybe visitors are looking for A, B, and C, but are getting X, Y, and Z from your landing page.  That could very well cause a bounce.

But, that’s not the full story for bounce rate.  And I find many people don’t understand how analytics packages calculate the metric.  Here’s a scenario that shows a serious flaw.  What if someone visits your site, spends 15 minutes reading a blog post, and then leaves?  If there’s that much engagement with your content, it probably shouldn’t be a bounce, right?  But it is.  Since the user viewed just one page, Google Analytics has no way to determine user engagement.  Therefore, it’s counted as a bounce.  Yes, it’s a huge problem, and could be tricking webmasters into making changes when they really don’t need to.

The Problem with Standard Bounce Rate

Actual Bounce Rate
Over the years, there’s been a lot of talk about how Google and Bing use bounce rate as a ranking factor.  For example, if the engines saw a page with a very high bounce rate, maybe they would use that against the page (which could result in lower rankings).  Add the Panda update, which targets low quality content, and you can see why SEO’s became extremely concerned with bounce rate.

In August of last year, I wrote a post on Search Engine Journal about Actual Bounce Rate.  The post explains some of the mechanisms that Google can use to determine actual bounce rate, and not just the standard bounce rate that Google Analytics provides.  For example, dwell time, toolbar data, Chrome data, etc.  The core point of the post is that Google has access to much more data than you think.  Therefore, don’t focus solely on the standard bounce rate presented in Google Analytics, since the actual bounce rate is what Google could be using to impact rankings.

Actual Bounce Rate Factors

Google Introduces “Adjusted Bounce Rate”
So, given what I just explained, is there a way to gain a better view of actual bounce rate in Google Analytics?  Until recently, the answer was no.  But, I’m happy to announce that Google Analytics released an update in July that enables you to view “Adjusted Bounce Rate”.  It’s not perfect, but it’s definitely a step closer to understanding actual bounce rate.

The Definition of Adjusted Bounce Rate
By adding a new line of code to your Google Analytics snippet, you can trigger an event when users stay for a minimum amount of time.  That amount of time is determined by you, based on your specific site and content.  So, adjusted bounce rate will provide the percentage of visits that view only one page on your site and that stay on that page for less than your target timeframe.  For example, you can set the required time to 20 seconds, and that time would be used to calculate the bounce rate numbers used in your reporting.  If users stayed less than 20 seconds, then it’s a bounce.  If they stayed longer than 20 seconds, it’s not a bounce (even if they visited just one page).

Changes Needed
As I mentioned above, you need to add one line of code to your Google Analytics snippet.  Here is the revised snippet (from the Google Analytics blog post about adjusted bounce rate):

Google Analytics Snippet for Adjusted Bounce Rate

Note, that new line needs to be added to your Google Analytics snippet on every page of your site.  Also, the piece of code that includes 15000 represents the time in milliseconds.  15000 equals 15 seconds.  You can adjust that based on your own site and content.  For some sites, you might set that to 30 seconds, 1 minute, or more.  The minimum is 10 seconds.

Impact, and a Real Example
You might be wondering how this actually impacts your reporting.  Does changing that line of code really impact your bounce rate numbers?  Well, it does, and it can radically change your bounce rate numbers.  And that’s a good thing, since it will give you a closer look at actual bounce rate since time on page is now factored in.  Remember my example above about a user spending 15 minutes on a post and it’s counted as a bounce?  Well that wouldn’t be a bounce if you added this code.  Let’s take a look at an example that demonstrates adjusted bounce rate.

Below, I’ve included the metrics for a page that was showing an 76.7% bounce rate.  Clearly, that’s not a great bounce rate, and it could be driving the webmaster to make changes to the content.  But, check out the bounce rate after we started calculating adjusted bounce rate.  Yes, you are seeing that correctly.  It’s now only 28.5%.  That means 71.5% of users are either visiting other pages on the site or staying on the page for longer than 20 seconds (which is the time the company is using to calculate adjusted bounce rate).  And by the way, the new bounce rate is 62.8% lower than the original bounce rate percentage.  That’s a huge swing.

An example of adjusted bounce rate in Google Analytics

What This Means to You & How This Could Be Better
As a marketer, bounce rate is an extremely important metric.  But, you need an accurate number to rely on if you plan to make changes.  That’s why I wrote about actual bounce rate last summer.  Adjusted bounce rate enables you to add another ingredient to the standard bounce rates calculated by Google Analytics.  By understanding the minimum time spent on the page, you can gain intelligence about how users are engaging with your content.  Are they hitting your page and immediately leaving, or are they at least spending 20 or 30 seconds reading the content?  There’s a big difference between the two (especially for SEO).

Understanding adjusted bounce rate can help you:

  • Refine the right pages on your site, and not just ones that show a high standard bounce rates.
  • Better understand the quality of your top landing pages from organic search. Do you have a quality problem, or are users spending a good amount of time with that content?  Adjusted bounce rate can help you understand that.
  • Better understand the quality of campaign traffic.  Seeing a 95% bounce rate is a lot different than 25%.  Sure, you want conversions from campaign traffic, but engagement is important to understand as well.
  • Troubleshoot SEO problems related to Panda.  When a Panda update stomps on your site, you should analyze your content to determine what’s deemed “low quality”.  Adjusted bounce rate is a much stronger metric than standard bounce rate for doing this.

How Adjusted Bounce Rate Could Be Improved – Revealing Dwell Time
I would love to see dwell time added to Google Analytics somehow.  Dwell time is the amount of time someone spends on your page before hitting the back button to the search results.  Duane Forrester from Bing explained that they use dwell time to understand low and high quality content.  As an SEO, imagine you could understand which pages have high dwell time.  That would be incredible intelligence to use when trying to enhance the content on your site.

Summary – Adjusted is Closer to Actual
Again, I believe this is a great addition by the Google Analytics team.  Adjusted bounce rate can absolutely help you better understand the quality of content on your site, and the quality of traffic you are driving to your site.  I recommend adding the line of code to your Google Analytics snippet today, and then analyze how your bounce rates change.  I have a feeling you’ll be surprised.



Wednesday, July 18th, 2012

How To Use Social Reports in Google Analytics To Analyze Specific Blog Posts or Content [Tutorial]

Social Reports in Google Analytics

In March of this year, Google Analytics released a set of new reports for measuring the effectiveness of traffic from social networks.  It was a great addition and provides some valuable information about how social is affecting your business.  For example, you can view social referrers, content that received traffic from social networks, view conversations across certain social networks, view conversion data (including last click and assisted attribution), how social visitors flow through your site, and more.

One question I keep getting from business owners is how to easily analyze a piece of content they are tracking?  For example, let’s say a certain blog post went live recently, was heavily shared across social networks, and ended up driving a lot of traffic.  What if you want to isolate that page and view data via GA’s social reports?  Well, you can absolutely do that, and I’m going to walk you through some of the core insights you can glean from the reporting.  Let’s get started.

Isolating a Blog Post or Piece of Content
For this tutorial, I’m going to use a recent post of mine, which ended up being popular within the search marketing industry.  Last month, I attended the Google Agency Summit and found out that the old Google Wonder Wheel’s engine actually drives the Contextual Targeting Tool.  The Wonder Wheel was a great tool for finding related searches, based on actual Google data, and many in my industry loved using it.  Needless to say, search marketers were thrilled to find out the functionality can still be found in the Contextual Targeting Tool.  The post ended up getting shared quite a bit on Twitter, Facebook, and Google+.  Let’s take a look at the social reporting for this post.

You can isolate a page in two ways via social reporting in Google Analytics.  The first way is from the overview page, and the second way is from the Pages report.  Let’s jump to the Pages report, which will list your top content receiving traffic from social networks.  You can access this report by clicking “Traffic Sources”, “Social”, and then “Pages”.

The Pages Report in Google Analytics Social Reports

At this point, you will see a list of pages from your site, along with key metrics like visits, pageviews, time on site, data hub activities, etc.  I’ll cover what data hub partners are in a second.  For now, find the page you want to analyze and click the URL.  For me, I’m going to click the URL for my Google Wonder Wheel post, which had 1040 visits from social networks from June 20th through June 30th.

After clicking the URL, the Social Referral tab is the default view.  Here, you can view the social networks driving the most traffic to the post, along with viewing trending for all traffic versus trending for social traffic.  In addition, the primary dimension in the report is “Social Network”, which as I mentioned above, will display a list of social networks driving the most traffic to this specific post.  For me, Twitter, Facebook, and Google+ drove the most traffic to this post over the 10 day period.

Social Networks in Social Reports

Social Actions and Data Hub Partners
If you click the “Social Network and Action” dimension, you will see Data Hub Activities for the post. Data Hub partners are social networks that have chosen to share additional information with Google so users of Google Analytics can view that data within Google Analytics reporting.  The activity stream from data hub partners can provide rich information that can be organized and viewed via Social Reports.

Unfortunately, some of the big players in Social are not participating, like Facebook and Twitter.  This means you will only get basic data in your reporting from these networks.  Current Data Hub partners include Google+, Delicious, Blogger, Disqus, Diigo, Pocket, etc.  You can tell which social network is a data hub partner since there will be a data hub icon next to participating networks.  See the icon below.

Data Hub Partners in Social Reports

Back to our example.  If you click the “Social Network and Action” dimension, you can analyze Data Hub activities for specific pieces of content.  For example, you can view Google+ posts, +1’s, reshares, bookmarks from Delicious, Pocket saves, etc.  You can also view a graphical breakdown of the data hub activities to the right.  Again, I wish more social networks were data hub partners, so you could get a full view of activities like tweets, likes, etc. from major networks like Twitter and Facebook.  That said, this is still valuable, and we’ll get more granular next.

Data Hub Activities in Social Reports

Activity Stream and Special Treatment for Data Hub Partners
You can click the Activity Stream tab to view specific data hub activities across social networks.  Sure, it’s cool to see top-level activity like we’ve seen so far, but the activity stream gets much more specific.  When clicking the tab, you will see actual conversations and events from across data hub partners.  The default tab is the Conversations tab, which will display shares and comments from data hub actions. You will see specific users, their shares, what they wrote when sharing the content, resharing, or commenting on a post.   For example, you can view Google+ and Diigo information below for my Wonder Wheel post.

Activity Stream in Social Reports

It’s important to note that while analyzing the activity stream (starting with conversations), you’ll notice some great functionality for Google+ content.  For example, you can click a person’s photo to view their G+ profile and there are icons that let you know if the person shared an update, reshared someone else’s update, or commented on a G+ update.  Then you can click the dropdown arrow on the far right to view additional information, including the Google+ ripple for the piece of content, you can view specific shares on G+, etc.  This is awesome data, as you can find influencers, view their posts about your content, view +1’s from other G+ users, etc.

Viewing additional data for data hub partners.

The Power of Ripples
In particular, viewing the Google+ Ripple for a specific URL reveals incredible data.  I’ve written previously about how to analyze G+ Ripples, and you should definitely check out that post.  Ripples enable you to see how your content was shared across Google+, from user to user.  You can also view influencers, sharing sequences, links to each public Google+ post, view shares over time, etc.  Spend some time with Ripples… you can find some incredible information.

Viewing Google Plus Ripples for Specific URL's

Events in Activity Stream
The second dimension in the Activity Stream report is Events.  By clicking this dimension, you can view additional information beyond just the conversations people are having about your content.  For example, you can view data hub partner events like +1’s, delicious bookmarks, pocket saves, trackbacks, etc.  I’ll cover more about trackbacks shortly, but this was a cool addition by Google recently.

Similar to what we did earlier, using the dropdown arrow on the right side enables you to see the actual activity on each social network.  For example, selecting “View Activity” for a delicious bookmark takes you to the actual bookmark page.  Here, you can view the profile of the person bookmarking your content, view comments, etc.  This is a great way to understand what people are saying about your content, find influencers, connect with similar people, etc.

Events in Social Reports in Google Analytics

Quick Tip:
By clicking the social network logo in the events list for any action, you can link to a page that shows all activity from that specific social network.  For example, clicking the delicious icon in the screenshot below, you will be taken to all delicious events for this specific piece of content.

A Note About Twitter
I mentioned earlier that you can only get advanced level data from Data Hub Partners.  That’s true (and unfortunate), but there is some additional data you can get from Twitter.  If you click the the link for Twitter when viewing social networks in your reporting, you will see a list of links (shortened links from Twitter).  If you move fast enough, you can enter those shortened URL’s in Twitter Search to view the actual tweets.  Then you can check out each Twitter user to find influencers, follow them, engage them, etc.  Twitter Search does not go back very far, so you’ll need to move fast.  You can also use a number of third party tools to mine Twitter data, but that’s for another post. :)

Analyzing Tweets via Social Reports in Google Analytics

If you click back to the Social Referral tab, and click the “Social Referrers” dimension, you might see “Trackbacks” listed in the report.  Note, you might have to use the rows dropdown at the bottom of the report to reveal additional rows to view trackbacks.  If you click the “Trackbacks” link, and then click the Activity Stream tab, you will see inbound links that Google Analytics picked up.  Trackbacks will display links to your content from outside your site (inbound links).

Viewing trackbacks in social reports

From this report, you can view the pages linking to your content by clicking the link icon next to the URL, or by clicking the arrow dropdown and clicking “View Activity”.

Trackbacks are a Great Addition, But Not Perfect
It’s important to understand the links that your content is building on several levels.  First, you can start to understand what people are saying about your content, what types of sites are linking to you, understand the authors of that content, what the comments are saying, etc.  That’s all really useful information.  Second, you want to understand the SEO power of those links. Are they relevant websites, is the content high quality, is it a spammy website, etc?  Third, you can absolutely use this intelligence to connect with influencers, whether that’s the blog author, or people commenting.  And no, this isn’t as robust as using Open Site Explorer, Majestic SEO Tools, Google and Bing Webmaster Tools, etc., but it’s nice having this data in Google Analytics.

Social Conversion Data for Specific Content
GA’s Social Reports include a valuable conversion report that displays the last click and assisted conversions from social networks. This is important data to analyze, since you can understand how social networks impact conversion (by directly impacting conversion and/or assisting conversion).

But, the social conversion report is not broken down by content.  In order to get that data, you would need to create an advanced segment for social traffic, then view top landing pages with that segment active.  Then you can analyze the conversion impact of visits to that piece of content from social networks.  At a top-level view, it’s great to see conversion data from each social network, but if you are laser focused on a specific piece of content, then the standard social reports won’t really help you.

Summary – Get Social with Google Analytics
As you can see, Social Reporting was a great addition for Google Analytics.  It’s ultra-important to understand the impact of social traffic, what’s being shared across social networks, which influencers are sharing your content, who is engaging that content, etc.  It’s also important to analyze specific pieces of content that are being actively shared across social networks.  I hope this post explained more about how to find and analyze data for a specific post.  But like anything else in digital marketing, you need to test it out for yourself!  So target a piece of content, fire up Google Analytics, and hit the social reports.  Good luck.