Yes, it’s possible to filter GSC data using regex. Like many of you, I’ve been asking for regex support in GSC for a long time. But as we know, the search analytics reporting unfortunately only provides basic filters for queries and landing pages. In addition, you can’t export more than one thousand rows in the GSC UI, which is limiting. And… you can’t export just filtered the data (which is a pain in the neck). It’s maddening.

But I’m here to show you a different way to slice and dice GSC data. And it’s been right under your nose the entire time. In this post, I’ll explain how to use regex to filter GSC data by using Google Analytics. And if you follow my Search Engine Land column, then you know I just wrote a post about filtering crawl data using advanced filters. So, you can consider this the sequel to that post. They say the sequel isn’t as good as the original, so let’s see if I can change that. Let’s rock and roll.

Requirement – Connecting GSC and GA
First, in order to do this, you need to connect Google Search Console to Google Analytics. To be honest, you should have done this already, but if you haven’t yet, better late than never. :)

I won’t cover how to connect the two, since there is solid documentation about how to connect them. Once you do, then you’ll begin seeing historical GSC data soon. Then you can start using regex in GA like I’m about to show you below. Note, you will still only be able to see the past ninety days of data. I’m hoping that changes at some point in the future, but we just have ninety days now.

Using regex in GA, it’s available and ready to go.
Once you connect GSC to GA, you will see the reporting under the Acquisition bucket of reports. There is a section labeled Search Console with a “NEW” tag. Within that set of reports, you will see landing pages, countries, devices, and queries. We’ll focus on queries and landing pages reports for this post.

Let’s start by accessing the queries report. Once you click the link in the left navigation, you’ll see your queries listed in the report. Make sure to adjust the date to show you the past ninety days.

Now we’re ready to slice and dice data via regex. In order to filter the report using a simple text search, you can easily enter some text in the filter box. But don’t stop with a simple text filter. That’s the same type of lame filtering you get in GSC. Let’s take this a step further and use regex. Click the “advanced” link next to the search box to access advanced search filters.

After clicking the “advanced” link, make sure to keep “Include” and “Search Query” for the first two options, but you should change the next dropdown that defaults to “Containing” to “Matching RegExp”. That will enable you to enter a regular expression versus a simple text search.

Simple example: Filter queries using pipes (OR)
One of the easiest, but still powerful, regular expressions can be achieved by using pipe characters. A pipe {|} represents “or” in a regex. So if you were trying to filter the list of queries by three different text searches, you could enter them like this:

queryA|queryB|queryC

And when you do, GA will list only the queries in the report that match either of those conditions.

And as you can guess, you don’t need to limit the queries to simple text. You can use any regular expression for each entry in your statement. For example, let’s filter by any query that starts with query A or ends with query B.

You can go crazy here, and your own regular expressions will be based on your own analysis. The key point is that you can slice and dice GSC data in GA to your heart’s delight.

Layer on average position.
The other benefit you get when using GA to filter GSC data is the ability to layer on dimensions and/or metrics. Under your GA filters, you’ll see an option for “Add a dimension or metric”. When you click that dropdown, you can add other GSC metrics like Average Position, Clicks, CTR, and Position. So you can create more advanced filters quickly and easily.

Below, I’ll add a metric to a pipe-based regex to show us any query that matches the text we enter AND has an average position of less than 5.

Layer on clicks and/or impressions.
And you can do the same with clicks or impressions. This will enable you to filter specific queries that also have a minimum or maximum number of impressions or clicks. For example, how about queries that contain certain keywords plus have at least 500 clicks over the past ninety days:

Add exclude vs. include to create advanced filters.
Previously, we kept “Include” as the first option for including the filters we were applying. But you can switch that to “Exclude” to hone your reporting. The exclude option when filtering by regex is powerful and enables you to drill down by excluding queries or landing pages that match your regex. I also mentioned this technique in my Search Engine Land post when explaining how to filter crawl data using DeepCrawl.

You can also filter landing pages using regex!
You can use a similar approach for filtering landing page data. If you want to find out if certain page types are gaining impressions and clicks, then you can use regex to surface them. For example, imagine you wanted to find all urls that have a certain parameter.

Or how about any landing page url containing certain keywords that have more than 3000 impressions?

You get the picture! The possibilities are endless.

Hunting featured snippets
Let’s go hunting for featured snippets by filtering the report by certain queries and then entering an average position of less than two. Featured snippets will rank #1 in GSC since they hold the top position. That was covered in the help documentation provided by Google about clicks and impressions. But you can enter “Less than 2” versus equaling one since the query will not always rank number one. It simply helps you capture more queries that might be yielding a featured snippet. Here I’m filtering by “how to” or “what is” queries with an average position of less than two.

Export it!
As I mentioned earlier, the GSC UI limits your exports to just one thousand rows. That’s extremely limiting for many sites. In GA, you can export five thousand rows at a time by using the dropdown at the bottom of the report. In addition, you can export in stages by clicking the next page in the pagination and then exporting another five thousand rows. It’s not optimal, but better than an absolute limit of one thousand rows.

Summary – Regex is available for GSC data (with the help of GA)
If you’re like me, then you’ve wanted regex support in GSC for a long time. But by using GA, you can essentially get that regex support, but just not in GSC proper. If you need to drill into your data, slice and dice query and landing page data, etc., then I recommend using the approach I listed above. I think you’ll dig it. Now brush up on your regex skills and dig in. :)

GG

 

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.

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.

 

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).

 

 

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

 

 

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.

 

 

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.

 

 

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

 

 

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”.

 

 

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.

 

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.

 

 

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).

 

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).

 

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.

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.

GG