Google policies mandate that no data be passed to them that could be recognized as personally identifiable. This post aims to provide an easy-to-follow, structured approach to identifying Personally Identifiable Information (PII) that might exist in your or your client’s Google Analytics account, as well as different methods for preventing further collection of such information.  In this post I will outline what constitutes as PII, and how to avoid potentially passing this information to Google when implementing Analytics on a property.

The approaches outlined below aim to help alert you that PII is being captured. Ultimately however, Google requires that:

“You will not and will not assist or permit any third party to, pass information to Google that Google could use or recognize as personally identifiable information.”

This means that if you find PII in your data collection, simply filtering out the data from your Google Analytics property is only half the battle. Ultimately no PII should make it into Google Analytics at all.

What constitutes PII according to Google?

Any name, email address, billing information, social security numbers, or other data which can be reasonably linked to such information by Google, or data that permanently identifies a particular device (such as a mobile phone’s unique device identifier), even in hashed form.

“The Google Analytics terms of service, which all Google Analytics customers must adhere to, prohibits sending personally identifiable information (PII) to Google Analytics … Your Google Analytics account could be terminated and your data destroyed if you use any of this information.”

Possible trouble areas

So you suspect that you might be collecting PII, but are not sure of where to look or what to look for? Then this post is for you! Below are some of the major areas where users can run into trouble with PII within their Google Analytics Data. Oftentimes, the inclusion of PII in any of these different areas is unintentional, which is why performing a PII audit is so important.

Looking for PII during the setup and testing phase of your Google Analytics implementation is recommended as a best practice in order to avoid running into any PII collection issues further down the line.

Places where PII can be found

  • Query string parameters located in URLs
  • Data imports
  • Event parameters (category, action, label)
  • Custom dimensions
  • Social event dimensions
  • Campaign tags

Common PII types (as defined by Google)

  • Email address
  • First name / last name
  • Billing Information
  • Social security number
  • Credit card number
  • IP address
  • Device ID
  • Any other information that would identify a specific individual

Common Regular Expressions

So, now we know where and what to look for in our Google Analytics reporting interface. But before we dive into the various auditing methods, I wanted to take a moment to highlight one of the techniques we will use to assist us in our task. According to Jan Goyvaerts over at

“A regular expression (regex or regexp for short) is a special text string for describing a search pattern. You can think of regular expressions as wildcards on steroids.”

Below you can view an assortment of regular expressions for matching some of the different types of PII. These expressions will allow you to search for some common PII types. There are probably  many other variations of these regular expressions or even regular expression types that would fit in here and essentially do the same thing, but these are some of the more common ones:

*Caveat: not every type of PII can be searched for in this way due to the complexity of the text (e.g.  a physical home address, or first/last name).

PII Type RegEx
Email address ([a-zA-Z0-9_\.-]+)@([\da-zA-Z\.-]+)\.([a-zA-Z\.]{2,6})
Social security number ^\d{3}-?\d{2}-?\d{4}$
IP address ^(?:[0-9]{1,3}\.){3}[0-9]{1,3}$

Auditing Methods

This is an overview of the two main methods you will be using to identify potential PII within the common trouble areas, and their limitations. Here you can use the regular expressions listed above, as well as your own personal sleuthing skills to look for PII. Since regular expressions won’t help you when it comes to things like physical address or first/last name combinations, you will need to manually scan the different reports for those types of PII.

Inline Filter

The inline filter method will be your first, and likely best approach for identifying PII in your data. It will allow you to quickly scan your standard reports for the presence of the most common types of PII.  As previously mentioned, some of the most common places where PII lives include: query string and event parameters. The most common reports where this auditing technique can be used:

  • Reporting > Behavior > Site Content
  • Reporting > Behavior > Events
  • Reporting > Behavior > Site Search

The process is simple, and consists of four easy steps:

  1. Click on the “Advanced” button next to the inline filter input box at the top of your chosen report
  2. From the filter type drop-down, select the “Matching RegExp” option
  3. In the input field, copy and paste your desired regular expression from the table above (or use a custom one designed by you)
  4. Click on ‘Apply’

Your chosen report will now be filtered to only show you data which includes PII according to which regular expression you have chosen. If you don’t see any records this is GREAT NEWS! It means that your data does not contain the type of PII you are searching for. If you do see results, then this means that your data contains PII and you will need to take some action to address the issue (more on this later).



Figure 1.0

Advanced Segment

The advanced segment method is similar to the inline filter method with the major difference being that the segment applies to all reports automatically once it is created. We will be using the Regular Expressions listed above to create a segment which will identify any sessions which contained different types of PII.

The example segment setup below (Figure 2.0) looks for sessions which contained pageviews containing PII in the URL, however this approach could also be applied to event parameters (event category, event action, event label), as well as custom dimensions, site search terms, or social events.

Using this approach also displays the number of users and the number of sessions (Figure 2.1) as a percentage of the total.

As with the inline filter approach, the most common reports where your newly created segment will identify PII are:

  • Reporting > Behavior > Site Content
  • Reporting > Behavior > Events
  • Reporting > Behavior > Site Search


Figure 2.0


Figure 2.1


So now that you’ve gone through and checked for PII and haven’t found anything then congratulations, you can stop reading here!

If you have found some form of PII, don’t panic. You will just need to take the following steps:

  1. Work with your implementation team and stop the collection of PII (simply filtering out PII in the Google Analytics interface will not be sufficient, as Google requires that you stop sending any PII to their servers, even if it is being filtered out)
  2. Once PII collection has ceased, backup your data (Analytics 360 customers can export unsampled reports to an Excel spreadsheet, or Google Sheets. They can also migrate their data into Google BigQuery, a service which does not have PII limitations)
  3. Create copies of the views in which you found PII (copy over all configuration settings: filters, goals, view settings, etc), and start collecting PII free, fresh data.
  4. Work with Google Support and inform them that your web property has been collecting PII.
    1. It is better to be proactive here, as Google Support is much more likely to remove only offending data if they are informed ahead of time.
    2. Should the Google Support team discover PII in your account on their own volition, they are much more likely to purge the entire account of all data.

The Shopping Analysis reports within the Google Analytics Ecommerce section are comprised of the Shopping Behavior and Checkout Behavior reports. In this post, I will go into detail about how to create and interpret them. These reports both include visualizations of the overall funnel activity of users through a site’s Ecommerce experience, from the number of sessions that viewed a product, to the number of sessions that engaged in the checkout process, to the sessions completing transactions. Also included in these reports are metrics on funnel drop offs between any step of the various processes, and funnel entries, within the same session.


These visualizations are generated through information sent to the dataLayer through the Ecommerce object or by commands sent through the ga function (depending on the method of implementation), and in particular data sent with the action object associated with an Ecommerce activity, such as a click, add, checkout, purchase, etc. Be aware that these rich reports are only available with Enhanced Ecommerce, and not with Standard Ecommerce.

Shopping Behavior Report

The Shopping Behavior Report allows you to see your visitors’ flow through the various stages of your site’s shopping experience, beginning with the total number of sessions for a given date range (which is the same as the total number of sessions in the Audience Overview report), and including product views, cart adds, and checkouts.

At each stage, the report shows the number of sessions that entered, the number of sessions that abandoned, and the number of sessions that moved on to the next stage through the normal flow. Clicking on the various elements of the report also reveals the ability to immediately create segments based on the stage that was clicked. For example, if you clicked on the abandonments from the “Sessions with Add to Cart” stage, you will see this segment creation prompt, where you are offered the option of creating a segment based on the cart abandonment stage either for the current GA view or for any other view.




Shopping Stage Name

By clicking on “View Configuration” of any segment creation prompt, you will reveal the Shopping Stage name for this particular step.


The Shopping Stage name is internal to Google Analytics (cannot be changed by the user), and represents the various stages of the shopping experience that users completed in a session. It is internal in the sense that the stage names are provided, by GA, even if a custom name was not provided or configured in the Enhanced Ecommerce configuration interface. Each step of the shopping stage could have several components. For example, the step “Sessions with Add to Cart” could include session with: 1. entries from the previous step (in the normal flow), 2. entries directly into that step, 3. abandonments from that step.


Each component is identified in GA with a fixed, internal name and is mapped like this:

  1. ADD_TO_CART → Direct entries to Add to Cart
  2. ADD_TO_CART_WITHOUT_VIEW → Entries from previous step, Product Views
  3. CART_ABANDONMENT → Abandonments from the cart

The internal shopping stage name corresponds to the shopping step action that is assigned to a particular step in the shopping flow. For example, any shopping step that has the ecommerce action of add would be designated with the “ADD_TO_CART” shopping stage name, or steps with an ecommerce action of checkout would be designated with the “CHECKOUT” shopping stage name. This can be accomplished in the dataLayer or with ga function calls, and will be discussed further below.

The complete mappings for all Shopping Behavior Analysis steps are as follows:


Finally, the report includes a table which allows you to break down the different steps of the funnel, in both the normal flows and drop offs, based on specific segments defined by built-in dimensions like Browser, Country, Campaign, and User Type, or by custom dimensions.

Here is an example of the shopping behavior report which shows the same shopping stages broken down by country:


Checkout Behavior Report

Similar to the Shopping Behavior Report, the Checkout Behavior Report shows the movement through each step of the checkout process, including details of the number of sessions that moved through each step of the normal flow, the number of sessions that dropped off, and the number of sessions with intermediary entries.

8_sessions and abandonments

Clicking into a blue box, anywhere in the drop off section, or between the blue boxes on the transition arrows, for any particular step, allows you to create a segment for those sessions corresponding to the clicked element:


The grey transition arrows (  button) only allow you to create a segment based on checkout options that may have been used in a particular checkout step:

10_clickto apply

Shopping Stage Name

Clicking on “View Configuration” of any segment creation prompt reveals the Shopping Stage name for this particular step.


The internal checkout stage name is again fixed and corresponds to the checkout step number or Ecommerce action that is assigned to a particular step in the checkout flow, through the dataLayer or through a Google Analytics function call. For example, the payment step could be defined by the user as checkout step 4, which has an internal checkout stage name of “CHECKOUT_4”, or the final Thank You step could be defined by the user with the Ecommerce action of purchase, which has an internal checkout stage name of “TRANSACTION”. More about how this is accomplished will be discussed in subsequent sections.
Within the enhanced Ecommerce interface in GA, the user can also rename checkout steps to make the report easier to read, so with respect to the previous examples, “Step 4” could be renamed to “Payment” and “Sessions with Transactions” could be renamed to “Confirmation”. These changes would apply immediately to the Checkout Behavior report.


The complete mapping of the shopping stage names for the Checkout Behavior reports is shown here:


A table below the checkout behavior report allows for limited customization in order to see the breakdown of the different steps of the funnel, in both the normal flows and drop-offs, based on specific segments defined by built-in dimensions or by custom dimensions.

Here is an example of the same checkout behavior report which shows the checkout stages broken down by source/medium:


Technical Implementation

The number of sessions that engage in particular stages of the Shopping Behavior and Checkout Behavior reports is determined by specific data that is sent to GA either as part of the Ecommerce object, if using the dataLayer, or by commands sent with a ga function call (ie. ga(‘require’, ‘ec’);), if using the enhanced Ecommerce plugin method. A brief explanation of each method is included below.

An example for the Shopping Behavior flow is an add object (highlighted in yellow below), which signifies that the action, for which the data is being sent, applies to the add to cart action. The other possible actions of the shopping behavior flow include remove (for removal of product from the cart), detail (for product detail views), checkout (for any checkout stage), and purchase (for completed transactions).

Example: Shopping Behavior Add to Cart via the dataLayer:


Example: Shopping Behavior Add to Cart via the ecommerce plugin:


(More code samples for other object types, for both the dataLayer method and plugin method, are linked to in Appendix [3] and [4].)

For the Checkout Behavior flow, all possible steps are part of the checkout object (as highlighted in yellow below). The step key (highlighted in green below), which is part of the actionField object, signifies the step, in the checkout funnel, for which the data is being sent applies to. There would be any number of steps in the checkout flow, depending on how the flow was set up for the particular website. The checkout flow could also include a checkout_option object that includes supplemental information for the checkout page, like payment option (ie. credit card).

Example: Checkout Behavior Checkout via the dataLayer:



In both the Shopping Behavior and Checkout Behavior reports, the different object types created and passed into GA, eg. add, checkout, remove, etc., are used to determine the shopping stage or checkout stage name. For example, the add object triggers the ADD_TO_CART shopping stage, and the detail object triggers the PRODUCT_VIEW shopping stage. Similarly, the checkout object triggers all the checkout related stages in both the Shopping and Checkout behavior reports.


The Google Analytics Shopping Behavior and Checkout Behavior reports provide a richer and more detailed, visual report of the activities of users that engage with the ecommerce experience. The reports break down both the shopping activities flows and checkout flows to include, at a session level, actions taken throughout various stages, like where users are entering, exiting, or transitioning between stages of a particular flow. Google Analytics determines these actions and stages by processing specific objects and parameters that are passed in when those actions are executed.


  1. Google Analytics support document for Enhanced Ecommerce Shopping Behavior reports
  2. Google Analytics support document for Enhanced Ecommerce Checkout Behavior reports
  3. Google Tag Manager technical developer documentation for Enhanced Ecommerce
  4. Google Analytics technical developer documentation for Enhanced Ecommerce
  5. [Access only available for GA Certified Partners] GACP Forum post on Enhanced Ecommerce

There are both good and bad people in the world of the internet… And, like always, some of the good guys spend a lot of their time trying to figure out how to stop the bad guys from causing trouble. Don’t worry, I’m one of the good guys.

As Google Analytics grows more and more popular for web analytics and online behavior tracking, we see an increasing amount of bots and referral spam hits in GA accounts. Sometimes, this can even amount to 35-40% of total traffic being reported, thereby skewing all metrics ranging from session count and average session duration, to bounce rates and conversion rates.

What is referral spam?
The first step in solving any problem is recognizing that one exists.

One of the biggest misconceptions around referral spam is that it is recorded by the website sending information to the GA account, which gives rise to the notion that the attackers will always target websites of established companies and popular domains. However, referral spam has nothing to do with the website. In fact, in most cases, the attackers do not even (need to) know the website/domain name before they target it.

All they need is the GA property ID, which they generate in huge volumes as random numbers to match the GA pattern of UA-99999999-99 and use custom scripts from their servers to send hits to all these GA properties.

The primary motive of the attackers is to lure you back to their site to sell something. If you look at your GA acquisition reports and find that 20% of your traffic is being referred to your site by, under normal circumstances, you’d want to find out who they are, and thank them. But when you do go to visit their site, you realize they’re a very fishy looking ecommerce site that have nothing to do with your site or business…

How to identify referral spam in Google Analytics?
Acquisition and Behavior Metrics
There are 3 criteria collectively used to identify referral spam sources:

  • Bounce Rate = 100%
  • % New Sessions = 100%
  • Average Session Duration = 00:00:00

The Referrals report is a good place to identify it. Sort your report in decreasing order of Bounce Rate and if you have spam referrals, you should see them. They look something like this:


It’s important to look for all 3 criteria together, since, it’s perfectly normal to have a source with a few sessions and 100% bounce rate or 00:00:00 session duration, or even a brand new source with 100% new sessions.

The last, but not least, most important factor is the name of the source itself. In most cases, the name is a clear give-away. Looking at the image above, and are some obvious candidates, but not You may see a few domains that you recognize on this list that meet all 3 criteria. This means that real traffic from that source really did bounce off your site as soon as they landed.

If you’re unable to identify a source as spam even by looking at it, your last resort would be to visit the domain in question to find out. Please ensure that you have appropriate antivirus and anti-malware software setup on your system before you do so since some of them can harm your system the moment you enter the site.

Technology Metrics

This is a more technical approach to identifying metrics, but can remove large chunks of spam if done correctly. Looking at the Technology reports in GA, you can identify some browser versions which skew your data.

Here’s a good example of this:


The image shows Browser Version 46.0.2490.80, which is an October 2015 version of Google Chrome. As of July 2016, Chrome is running 51.0.2704.106 as the latest stable version on all devices, and ideally, nobody should be using such an old version. The spam qualifying metrics are also extremely close to the mark, thereby skewing 12.83% of the reported data! In this case, it’s safe to conclude that the spam is coming from Browser Version 46.0.2490.80.

You can check the latest versions of Google Chrome here.

The next image shows Flash Version 11.5 also skewing 21.39% of the analytics data with metrics alarmingly close to the spam matching criteria:


The latest versions of Flash Player are 18.0 or 22.0 for most browsers/platforms. You can check them out here!

Bad Hostnames

Although less frequent and lower in volume, this form of data corruption is neither spam nor a referral. In the All Pages report, switch to Hostname as your primary dimension. Sometimes you may see hostnames that are not a part of your business. They may just mean that other websites are inadvertently using your GA Property ID.

In the image below, we see a few hostnames:,, etc. that are not part of the Cardinal Path brand of websites. The numbers show that they constitute a small fraction of the data, but it’s always good to identify any element that corrupts the cleanliness and integrity of your reports and weed it out.


How does referral spam affect my reports?

Let’s look at the data in the first image in this document and assume that 20% of all sessions qualify as spam/bot referrals and bad hostnames. Let’s analyze the impact of excluding these sessions on the behavior metrics:

It’s clear from the analysis that all metrics are not only more accurate, but also significantly better when spam sources are excluded.

How do I keep referral spam out of my reports?

Now that we’ve seen how to identify spammers on your GA account and how it affects your reporting and metrics, the next step is to understand how to stop it from corrupting your data. The solution to cleaning your reports up is multi-fold:

Google Analytics Bot Filter

GA provides a built-in bot filter at View level. Go to the View Settings and check the box for Bot Filtering.


Manual Exclude Filter

While GA’s bot filter is fairly effective, it may not cover all spam source referrals. Any additional spam sources identified in the reports can be debarred from reports using manual filters.

Create an Exclude filter for all Campaign Source, Browser Version or Flash Version values that have been identified as spam to be excluded from reports. The screenshots below show sample filters created for Campaign Source on and, and Flash Version 11.5.


Manual Include Filter

Similar to the exclude filter, it’s good to have an include filter to keep out any traffic that is not in your “white-listed” set of domains. For instance, the filter to keep out all traffic without in the hostname looks like this:



In accordance with GA filter behavior, neither of these methods is retroactive. This means that spam/bot data that already exists in the reports cannot be removed. The filters will simply block spam referrals and other data corruption going forward.

You can learn more about RegEx here or at the link provided on the filter configuration page.

What else do I need to know?

Regular Checks

It is also helpful to regularly update these filters with new spam sources. The cadence for this will depend upon many factors within the organization and we recommend anywhere between a biweekly to monthly check-in for the same.

Referral Exclusion List

Do NOT use the Referral Exclusion list in Property > Tracking Info to block the spam domain. This list is used to preserve sessions from internal cross-domains. Adding spam source domains to this list will only hide the bot traffic from reporting, but won’t actually remove it.


Annotations are a handy GA tool to keep track of significant activity on your GA account / website that may affect traffic trends. Implementing bot and manual filters will result in a noticeable drop in traffic volume being reported and it’s good to keep a note of when the filters were applied.


These are some of the most common sources of spam and data corruption in GA reports. Careful identification and filtering of these elements increases the cleanliness, integrity and reliability of your reports and ultimately, gives you more confidence in your analysis and insights helping you make better decisions for your business.

Whether you’re looking for a vendor-agnostic expert to help you with your analytics implementation, or if you are looking for an analytics audit, we can help. Contact us to speak with an expert about your needs.

Cardinal Path Point of View: Ad BlockingAt the recent 2016 Google Performance Summit, we heard a number of announcements around Google’s advertising and analytics platforms. We’ve honed in on the analytics updates with a summary of what’s new and what’s ahead.


Some of the Google Analytics 360 Suite’s key features and products recently rolled out include:

  • Optimize 360 – A content testing tool that allows for the rapid prototyping of experiments with Google Analytics audiences to deliver personalized web experiences. A paid beta is currently available.
  • Attribution 360 – Built from Google’s acquisition of Adometry, Attribution 360 allows you to give credit where credit is due across the multiple ad interactions a user has with your brand. Achieve a holistic view from across multiple marketing channels – both online and offline. Measure TV like digital in order to understand cross-channel performance. This is publicly available; though a client fit assessment is required.
  • Audience Center 360 – With Google’s long-anticipated data management platform (DMP) offering, you can now bring together data from disparate sources (e.g., CRM, display, search) to provide a deeper understanding of how audiences engage with your brand across all channels. The native integration with DoubleClick offers access to Google’s proprietary data, as well as 3rd-party data providers. A paid beta is currently available with a client fit assessment is required.
  • Data Studio 360 – Build dynamic reports in minutes by bringing in data from Google Analytics, BigQuery, Google Sheets, AdWords, Attribution 360, and YouTube Analytics. Share and edit in real time with built-in collaboration tools. Paid and free versions of the product are available in the U.S. Additional regions coming later this year.
  • Tag Manager 360 – Tag Management Solution with enterprise class tag management features backed by front end and back end service level agreements (SLAs) and services for Google Analytics 360 clients.

Mobile First
The redesign of AdWords to fit the “mobile-first” world has led to ~20% improvement in click-through rates for advertisers who have adopted these changes. This news comes quickly on the heels of the Analytics 360 Suite announcement, showing an investment in broader marketing technology efforts.

Both users and advertisers will benefit from Google’s mobile-first updates to AdWords. Mobile users will benefit from longer, more informative ad headlines, as well as longer descriptions on a single line of text. Bid adjustments for all device types and demographics for search ads (beta) are now available, allowing advertisers to bid by gender and age group. On the display side, advertisers can now simply submit a few basic attributes (ad text, ad image, landing page URL) and let Google dynamically design creative to fit publisher sites, ad slots, and mobile apps.

Knowing that nearly a third of all mobile searches are related to location, Google has strengthened the integration between AdWords and Google Maps to lead the way in measuring the online to offline customer journey with AdWords omnichannel measurement. Users now have the ability to search in-store inventory when drilled into a local business page, while advertisers can measure cost-per-store-visits across campaigns to help build local strategies.

Precision Targeting & Machine Learning
Taking customer targeting a step further, Similar Audiences is now available for Search to help extend your reach and target new prospects. Similar Audiences looks at your existing remarketing audiences and finds new and qualified customers with shared interests. The reach of Google Display Network (GDN) remarketing campaigns is being extended to include additional sites and apps, while continuing to offer the same precise targeting based on users’ past site visitation behavior.

Google is also exploring new ways to bring the power of its Machine Learning practice forward to help companies generate more insights in an intuitive way. The promise: Get instant answers about your marketing performance from Google Assistant by typing in a question or by asking the question directly into your mic. For example: “How much of my web traffic came from paid search last week?”, or, “What were my top selling products last month?”

With the announcement of its Analytics 360 Suite, Google is embracing the idea of providing advertisers with a marketing technology stack that unites several Google-branded platforms — Google Analytics 360 Suite, DoubleClick, AdWords, Google Cloud Platform, and Google Apps — in addition to enabling 3rd-party integrations. The intended result is seamless delivery of insights to guide marketing decisions, and the tools and integrations to act on those decisions.

What’s Next
Google is continuing to update the AdWords experience to better surface insights and take action. Over the next few months, Cardinal Path will be testing the new AdWords experience and providing feedback prior to the public launch to help ensure an optimal experience for our clients. Launch of this new experience is slated for 2017.

By now offering personalization and testing, a Data Management Platform (DMP), data visualizations, online and offline measurement, plus a revamped tag management system to help ensure consistent data and simplify the technical work needed to power the new suite, Google Analytics 360 Suite is covering a lot of bases for marketers.

To learn more about putting Google Analytics 360 Suite to work for you, please contact us.

Where do you go when you want to understand how Google views your site? Whether you are looking for information on how Google crawls and indexes your site, how your landing pages are performing in search results pages or what content is being surfaced on a Google search engine results page, chances are the tool you use is Google Search Console. In addition to a name change (formerly known as Webmaster Tools) there are some other updates and additions you should be aware of. In this article, I’ll walk you through these to ensure you’re set up for success.

 old  new

Access to the new reports are through the Acquisition / Search Console menu. The menu name has been changed from Search Engine Optimization to Search Console.

One of the most important is the Search Analytics reports. Located in Search Console, this feature allows content marketers and publishers to gain a better understanding of how visible their content – whether it be App content, mobile content or desktop content – is across search verticals, devices and even countries. While limited, the reports were integrated with Google Analytics. Savvy search marketers would skip the bare bones Google Analytics Search Engine Optimization reports in favor of the more rich data available in Search Console.  Google Analytics 360 has changed this with a deeper integration within the search analytics reports of Google Search Console.

What Hasn’t Changed

  • We are still limited to data from the previous 90 (Pro tip: Download monthly if you would like to analysis and trend this data over time)
  • The Google Analytics API does not download the Search Console data, and you cannot access it via the Google Analytics worksheet plugin
  • Some keyword data is not shared – Google does not display some queries “… made a small number of times or containing personal or sensitive information
  • Search Console reports cannot be added to the Google Analytics dashboards or custom reports.

This integration makes our job as search optimization practitioners easier. While the data provided isn’t new, having it available in Google Analytics reduces time spent downloading and lets us analyze the data from a native tool. By integrating the analytics metrics and adding reports to compare to such as the Device Type report, the new reports can help web site managers not only better understand how their content is performing in search but be able to answer key questions about their site health and search visibility.

Landing Page Report


landing page report

The new Landing Page report integrates Google Analytics Acquisition, Behavior and Conversion metrics.


The new landing page report has been expanded to include the familiar Acquisition, Behavior and Conversion metrics. Having Sessions, Bounce Rate, Pages/Session and goals in one interface is especially helpful as previously you would have to use a third party paid tool or download the data and merge with Search Console.  Now you can see how Google Analytics data aligns with your Search Analytics data – how impressions and clicks affect engagement and conversion on your site.


Search Console Metrics

  • Impressions: The number of times your landing pages appear in a Google Search result.
  • Clicks: If a user clicks through from an organic search result, it is recorded as a click
  • CTR: Click through rate is the percentage of clicks divided by impressions.
  • Average Position: The average rank your page appears at in Google search results.

More secondary dimensions are available. You can now slice and dice your landing page reports with dimensions including device and by country. While Query data is not available as a secondary dimension from the landing page report, you can drill down into each landing page to get an idea of what queries were used to surface your content in a Google Search.


Unfortunately, the Query data set is not integrated with Google Analytics but you can still access various dimensions.


With the Search Console landing pages report, you can now answer these questions directly from Google Analytics:

  • How visible are my landing pages in Google search results?
  • How do our top pages perform once visitors arrive on the site?
  • What is the average position of my landing page?

And when you are ready to take action:

  • Look for pages that have strong impression numbers and low clicks – a perfect opportunity to beef up page titles and meta descriptions for your top content.
  • Bounce rate high for some key content? Look to improve your on-page content – ensure what is in your page title and meta description aligns with what your visitor expects to find on your landing page.
  • Look for pages that have average positions that could be improved. These may be great pages to optimize further – you just may bump up a few positions to take a more visible spot.



The countries report replaces the Geographical Summary report. Now you can see what countries your content is most visible in, drill down by country to see the top landing pages and then drill down ever further to see what queries surfaced your content in that country. All this and you also get to view the Acquisition, Behavior and Conversion reports in-line with your Search Analytics data.



Secondary dimensions also allow you to view your top countries and landing pages by device category.


The countries report allows you to answer questions such as:

  • How does my content perform in searches in (country) India?
  • Does my (country) India content drive engagement and/or conversions?
  • Is my content competitively positioned in (country) India?



The Device Category report is fantastic. Not only can you see the same metrics as we accessed in all of the other reports, but you can also compare your desktop vs your mobile vs tablet search visibility and analytics metrics in one view. Here you can also drill down and view your top landing pages or top queries per device category.



Easily switch between queries and landing pages to discover what content resonates on what device.

As search engines adapt to increased mobile usage, this data allows marketers to answer questions such as:

  • What content shows up in a mobile search result?
  • How does my content perform in search results pages?
  • How do mobile visitors engage with my content compared to desktop?


Verify All Properties or Variants

  • Be sure to verify all variants of your site – this includes www and nonwww, subdomains, https/http, mobile sites.
  • Google’s Search Console can also be used for Apps. Verify your Android app and gain valuable insights into how often your app content displays in Google’s search results.


The queries report, unlike the previous reports, does not integrate with Google Analytics metrics. Your data is limited to the traditional Impressions, Clicks, CTR and Average Position. Where the report has strengthened is with the secondary dimension. You can now bring in Device Category, Landing Page and Countries to examine where the queries were initiated and what the end result was.


Query user

The queries report is the only report that retained the Metric comparison tool. In the queries report you can compare metrics and visualize the result in the trend chart.


When we speak about Queries, it is important to note that this data is not the keyword reports that Google Analytics used to supply. Google is not bringing these back in the Search Console reports. The query reports are based on keyword and phrases that have been used to surface your content in a Google search result. When your content shows in a result, these are reported as Impressions and, when a visitor clicks through to your site, as a click.

While the traditional keyword data is gone, the queries report can help you answer questions such as:

  • What queries are bringing visitors to my landing page?
  • What position are my top queries showing for?
  • How do our branded terms compare to non-branded? What content resonates with branded or non-branded queries?
  • What queries show up for searches on mobile devices?


If you’ve skipped the Google Analytics Search Optimization reports previously, it’s now safe to return. The reports all work well together to give you a deep view into how your content is performing in Google Search results and when the visitor clicks through to your site. And if you haven’t set up Google Analytics to work with Search Console, it is a very easy, straightforward process.

How to Integrate

  1. Ensure you have a verified web set up in Search Console.
  2. You’ll need owner permissions to associate the site with a Google Analytics property.
  3. In google Analytics go to the Admin tab and look at the property section.
  4. Select All Products. If your property is not linked, the option to add it will be under the “unlinked” section.

The addition of these key Search Analytics reports and related functionality that let you examine and compare the content by country, device, query and landing page are tremendously helpful. Bringing the Search Analytics reports to Google Analytics will help marketers make quick analysis and reporting that they can take actionable insights.


Featured image source: Pixabay

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