How to compare hourly sessions in Google Analytics 4 to track the impact from major Google algorithm updates (like broad core updates)

March 15, 2023 By Glenn Gabe Leave a Comment

I was just asked on Twitter if there was an easy way to compare Google organic traffic hourly like you can in Universal Analytics. That’s a great question, and that’s a super useful report to have as major algorithm updates roll out. You can typically start to see the separation over time as the update rolls out (if your site was heavily impacted by a major update like broad core updates, Product Review Updates, etc.)

So I fired up GA4 and created a quick exploration report for analyzing hourly traffic. Here is a short tutorial for creating the report:

1. Fire up GA4 and click the “Explore” tab in the left-side menu.

2. Click the “Free Form” reporting option.

Free form exploration reporting in Google Analytics 4

3. Click the plus sign next to “Segments” to add a new session segment. Then create a segment for Google Organic by adding a new condition, selecting “Session source / medium” and then adding a filter for “google / organic”.

Creating a segment for Google Organic in Google Analytics 4

Selecting session source and medium and then filtering by Google Organic when creating a new segment in GA4

4. Add that segment to your reporting by dragging it to the “Segment Comparisons” section of the report.

Adding a segment to the reporting in Google Analytics 4

5. Set “Granularity” to Hour.

Selecting Hour as the granularity for the reporting in Google Analytics 4

6. Add a new metric and select “Sessions”. And then drag “Sessions” to “Values”.

Adding sessions as a metric in Google Analytics 4

7. Change the visualization to line chart by clicking the line chart icon.

Changing the visualization of the reporting to line graph in Google Analytics 4

8. For timeframe, select “Compare” and choose a day. Then choose the day to compare against. Note, GA4 isn’t letting me choose today (which is a common way to see how the current day compares to a previous day). So, you’ll have to just compare the previous day to another day. Sorry, I didn’t create GA4.

Comparing timeframes in Google Analytics 4

9. Name your report and enjoy comparing hourly sessions.

I hope you found this helpful, especially since the March 2023 broad core update is currently rolling out. Have fun. :)

How to analyze the impact of continuous scroll in Google’s desktop search results using Analytics Edge and the GSC API

December 12, 2022 By Glenn Gabe Leave a Comment

Analyzing the impact of continuous scroll in Google's desktop search results.

Google rolled out continuous scroll in the desktop search results in the U.S. on December 5, 2022, which follows a rollout in the mobile search results in October of 2021. It’s basically like infinite scroll for the search results. When you approach the bottom of page one, the second page of results seamlessly load, enabling users to easily continue their journey to find answers.

Starting today, we’re bringing continuous scrolling to desktop in English in the U.S. so you can continue to see more search results easily. When you reach the bottom of a search results page, you'll now be able to see up to six pages of results. pic.twitter.com/xIuVP24FFm
— Google (@Google) December 5, 2022

For site owners and SEOs, this means hidden treasures ranking on page two and beyond in the search results could see higher visibility (as users load additional pages in the SERPs without having to click the next button at the bottom of each page). I said “could” because that’s in theory and would need to be proven via data. It wasn’t long before I started hearing questions about how to best track the addition of continuous scroll in the desktop search results, and how that’s impact clicks, impressions, and click through rate. That’s when I fired up Analytics Edge in Excel to come up with a solution that could help.

Automating A Solution By Combining The GSC API And Analytics Edge In Excel
If you’ve been following me on Twitter and reading my blog for a while, then you have probably seen some of my tutorials for using Analytics Edge to automate the exporting of data from GSC (and then automatically work with that data via macros). Analytics Edge is an amazing solution created by Mike Sullivan and I often call it a Swiss Army Knife for working with various APIs.

In this tutorial, I’ll explain how to bulk export data from GSC, compare that data to a previous timeframe, filter by position in the search results, and create separate worksheets by Google search result page. When you’re done, you will have separate worksheets for page two, page three, etc., and you’ll be able to see the difference in clicks, impressions, and click through rate based on Google rolling out continuous scroll in the desktop search results in the United States.

Let’s jump into the tutorial. I’m sure you are eager to see the data for your own properties!

Tutorial: How to use Analytics Edge to analyze the impact of continuous scroll in the desktop search results.

1. Set up Analytics Edge in Excel:
I have covered this several times in previous tutorials. Please reference those blog posts to learn how to download and install Analytics Edge. For example, my post about creating Delta Reports explains how to set up Analytics Edge. Also, there is a free trial available for Analytics Edge, and the cost is super economical (it’s just $99 for the year for the core add-in and $50 per year for the Google Search Console add-on). Note, Analytics Edge is up to version 10.9 now (the image below shows a previous version).

2. Export all GSC data for the timeframe AFTER Google rolled out continuous scroll in the desktop results:
Analytics Edge enables you to build a macro with several tasks that work together to accomplish your goal. The first step in our Analytics Edge macro is to export all GSC query data for desktop searches for the timeframe after continuous scroll rolled out in the desktop search results in the U.S. Click the Analytics Edge tab in Excel and click “Google Search”, and then “Search Analytics”.

Using the Search Analytics API in Analytics Edge in Excel

3. Choose your settings for exporting data via the GSC API:
When the dialog box opens, select the account and then GSC property you want to export data from.

4. Choose dimensions and metrics to export:
Then click the Fields tab and click the query dimension in the left side pane labeled “Available Dimensions and Metrics”. Then click the “Add” button to add that dimension to your export. Notice that the selected metrics include clicks, impressions, ctr, and position. Keep all of those as-is.

Select fields to export using Analytics Edge

5. Set a filter for Desktop devices only in the United States:
Next, we don’t want to muddy our data with mobile traffic and non-U.S. traffic, since we are trying to analyze the impact of continuous scroll rolling out in the DESKTOP results in the U.S. only. So, click the “Filters” tab and click the dropdown for “Devices” and select “DESKTOP”. Then for “Country”, select “United States”. Then keep all other settings as-is for this tab.

Select devices as a filter in Analytics Edge to focus on desktop-only

6. Select dates to compare:
Next, we want to analyze the difference in clicks, impressions, and ctr for the timeframe after Google rolled out continuous scroll in the desktop search results to the timeframe before. The rollout began on 12/5, so select “Start” and choose a start date of 12/5. For the end date, I would choose a date with full data (and not partial data). I used 12/9 as the end date.

Make sure you select the “Compare to” checkbox and then enter dates to compare the data with. For the start date, select specific dates that line up for day of the week and number of days. If this isn’t the same number of days, or if it’s a different set of days of the week, your data could be off. I selected 11/28 through 12/2.

Select dates to compare in Analytics Edge in Excel

7. Choose a sort order:
You can tell Analytics Edge to sort the results by a specific metric. For our purposes, you can choose clicks or impressions in descending order (which means it will be highest to lowest amount of clicks or impressions). Just select one metric for this tutorial (I chose clicks). Note, you can easily change the sorting once the data has been exported in Excel. Click OK to export the data.

Choose a sort order in Analytics Edge in Excel

8. Set the table name:
Analytics Edge will export the data and hold it memory. You will see a partial set of data in a worksheet highlighted in green. Before we write the full data to a worksheet, we want to store that data in a virtual table that we can reference later via Analytics Edge (so we can filter the data later on). To add the data to a table, click the “Analytics Edge” tab in Excel and then select “Table Name”. In the dialog box, set the table name to whatever you want. I named it “allpages”. Then click “OK”.

Set a table name to store exported data in memory in Analytics Edge

9. Write the full data to a worksheet (just to have all of the data documented):
Although we are looking to isolate queries where the site ranks on page two and three in the desktop search results, we are going to export all of your query data (just to have a worksheet you can reference if needed). You will notice Analytics Edge is showing you a subset of the data highlighted in green. The full data is in memory. To write that data to a worksheet, click the File menu in Analytics Edge and select “Write to Worksheet”. Name the worksheet something like “Queries All Data” and click “OK”.

Write data to a worksheet in Analytics Edge in Excel

10. Filter the data for just page two results:
OK, so now we have a worksheet containing all of our query data compared to a previous timeframe. Next, we are going to filter the data to only pull results with a position of 11 through 20 (roughly page two results in Google) and write that to a new worksheet. Sure, some pages contain more than 10 results, but overall this should work for us. Click the “Analytics Edge” menu and click “Table”, then “Filter”. In the dialog box, we are going to filter by the column containing position for the time period after continuous scroll rolled out in the desktop results in the U.S.

Select the column in the dropdown box and choose “Greater than” in the criteria filed and enter 10. Then add another rule using the same field, but this time select “Less than” and enter 21. That gives us results with a position of 11-20. And to make sure we are comparing apples to apples, let’s make sure the site ranked in a similar position in the previous timeframe. So add one more filter rule using the field with the previous position and select “Greater than” 10. We are doing this to make sure the position didn’t radically change (and move from page one to two).

Filter data to isolate page two results from Google in Analytics Edge

11. Write to worksheet:
Now that we’ve filtered the results for just page two data, we need to write that data to a new worksheet (so we can analyze the data in Excel). Click the File menu in Analytics Edge and select “Write to Worksheet” like we did before. Name the worksheet something like “Page Two” and click OK. The new worksheet should appear with your data filtered for positions 11-20.

12. Set the table name again before filtering:
In step 10 we set a table name holding all of our exported data and I said we would need that again. Well, now that we exported the second page of results, we also want to isolate the third page of results. So, we’ll need to reference that virtual table again before filtering for positions 21-30. To do that, click the Table menu again and select “Table Name”. In the dialog box, select the radio button for “Switch to a previously named table” and select the “allpages” table we set earlier. If you named it something different, then choose that name. Then click OK.

Switching to a previously named table in order to filter data in Analytics Edge

13. Filter the results for third page rankings:
Just like we filtered the results for page two rankings, we’ll do that now for page three. To do that, click the Table menu in Analytics Edge and then select “Filter”. In the dialog box, select the column for position for the most recent timeframe and select “Greater than” and set the value as 20. Then add a second rule and choose that column again, but this time select “Less than” as the criteria and enter 31. That will limit the queries to ranking between 20 and 30 (roughly page three in the Google search results). Then to make sure we are comparing apples to apples, add one more rule to make sure the previous position was at least 20. So select the column for position for the previous timeframe, select “Greater than” as the criteria, and enter 20. Then click OK.

Filtering by position in Analytics Edge in Excel

14. Write to worksheet to complete the macro:
Now that we are filtering by page three rankings, we need to finalize that step by writing the data to a new worksheet (so we can analyze the data separately). Click the File menu in Analytics Edge and select “Write to Worksheet”. Name the worksheet something like “Page Three” and click OK. The new worksheet will be created with page three data.

Writing the final data to a worksheet in Analytics Edge

Congratulations! You just created a system for analyzing the change in impressions, clicks, and click-through rate based on continuous scroll launching in the desktop search results in the United States! Now it’s time to dig into the data to identify surges or drops across various metrics. Next, I’ll provide some final tips for working with the data so you can begin to identify the change based on continuous scroll rolling out on desktop.

Next steps and final tips for analyzing the data:

I recommend formatting the CTR columns to percentages using Excel’s functionality. It will make it much easier to scan and determine the percentage change for each query. Also, once you run this for a specific property in GSC, the columns will retain their formatting. So if you rerun the query, the CTR columns should stay as percentages, which is great.
I would also format the clicks and impressions columns to be “Number”, with no decimal points, and add a comma for thousands. Again, this is just to help you easily scan the data.
And last, format the position columns to Number with one decimal place. So 11.9125 would become 11.9.
Analysis-wise, look for larger changes in impressions and click through rate when scanning the data. That could mean that continuous scroll is having an impact for those queries. But, make sure position is comparable when checking the previous timeframe. For example, if you see a huge increase in impressions, make sure the position didn’t cause the change versus continuous scroll. If a site ranked on the bottom of page one versus top of page two, that could yield a big difference in impressions.
I would also filter each worksheet so you can slice and dice the data. For example, you could easily sort the data by impressions in descending order (largest to smallest), you could do that by clicks, or CTR change. Playing with the data can help you surface interesting findings quicker. In order to filter, click the Data menu in Excel and click “Filter”, which is a funnel icon.
You can also use color coding in Excel to highlight drops and surges in green and red. This is especially helpful if you are sending the data to a client or someone else in your company that isn’t as familiar with GSC data.
And once you create a template, it can easily be used for other properties in GSC. Just save a new spreadsheet for each property you want to analyze. And again, the formatting for each column should remain (so you don’t have reformat the worksheet each time you export the data).

Summary – Determining the impact of continuous scroll on desktop via Analytics Edge and the GSC API.
With the addition of continuous scroll in the desktop search results in the U.S., users can easily make their way from page one to two (and beyond) without having to click to the next page of results. And that can definitely impact impressions, clicks, and CTR of your listing that are ranking beyond page one. Using the approach I explained in this tutorial, you can use GSC data to analyze the impact. If you have any questions while going through this tutorial, feel free to ping me on Twitter. I think you’ll dig using Analytics Edge for this task! It’s just another powerful way to use one of my favorite SEO tools.

GG

Google Multisearch – Exploring how “Searching outside the box” is being tracked in Google Search Console (GSC) and Google Analytics (GA)

October 5, 2022 By Glenn Gabe Leave a Comment

Google first announced multisearch in April 2022 (right before Search I/O 2022) and it was really the beginning of Lens entering the spotlight. With multisearch, you can use Google Lens to analyze a photo and then add to your search by typing in a query. Then Google will display more results based on the query and photo analysis.

For example, after multisearch was announced I took a photo of a cocktail via Lens and then entered “recipe” via multisearch. Then Google returned recipe results for me based on analyzing the photo.

That’s powerful, but there are challenges. The biggest problem in my opinion is that most people:

Don’t know about Lens at all (or don’t know much about it).
Have no idea how to use Lens.

Then at Search I/O 2022 in May, Google announced Multisearch near me, which will enable you to use Lens to snap a photo, enter “near me” via multisearch, and then Google will help you find local businesses that can provide that product. The example they provided was taking a photo of a dish you enjoyed and finding local businesses that offer that dish. This should be rolling out in the fall of 2022 (which was just announced at Search On 2022 last week).

But with Search ON 2022, there was more focus on Lens and multisearch, pun intended. First, multisearch will be expanded beyond just English to 70 languages in the coming months. Also, Google announced a shop option at the top of the SERPs based on using Lens to analyze photos of products. That will provide more opportunities for people to use Lens, and then multisearch, to shop for products based on the photos they take. So, Google can jumpstart your shopping experience with this new addition.

Tracking Multisearch: It’s complex.
Multisearch is cool and exciting, but as SEOs and site owners, how do we know if people are finding our content via Lens and multisearch? That’s a question that Jamie Indigo asked on Twitter earlier in the week. It’s a great question and I dug in a bit to see how it was working and being tracked.

https://twitter.com/Jammer_Volts/status/1576963080558284800

After Jamie’s tweet, I ended up running several tests to see how multisearch was being tracked in Google Search Console (GSC) and in Google Analytics (GA). Below, I’ll provide the results of my testing to see how multisearch is being tracked, or if it’s being tracked at all.

The Lens and Multisearch SERP: “Visual Matches” versus Google Images.
After using Lens and multisearch, you are presented with a SERP that resembles Google Images, but it’s not really Google Images. Instead, it’s labeled “Visual Matches” and I believe it’s a special treatment based on using Lens. It even says, “Results for this feature are still experimental.”

From that SERP, you can tap a listing which takes you to a Google Images-like SERP where you can view the photo and tap through to the website if you want. But again, it’s not Google Images (at least for now).

So, how is this tracked in Google Search Console (GSC)? You might think the impressions and clicks will show up in the Performance reporting for Image Search. That would make the most sense at this point (until Google provides specific reporting for Lens and multisearch – if they do that at some point).

Well, I wanted to test this out so I ran three tests across clients to see if the GSC reporting would show the activity. I used Lens and multisearch using specific queries while documenting the landing pages. Then I waited for data to arrive in GSC. I’ll cover the results below.

Google Search Console Tracking for Multisearch:
Well, this will be quick. I’m sorry to say that there is currently no tracking at this point in Search Console for multisearch. Again, you would think impressions and clicks would show up in the Performance reporting for Google Images, but nothing shows up based on my testing. I checked both Web Search and Image Search and the activity was not tracked. So the “Visual Matches” SERP isn’t being tracked like Google Images.

Google search console tracking for multisearch (image search)

Google search console tracking for multisearch (web search)

Google Analytics Tracking for Multisearch:
What about visits from multisearch? How do they show up in Google Analytics? Well, I see visits showing up as either Direct Traffic or via Google quicksearchbox (like Discover can shows up as sometimes). So, you basically can’t track multisearch specifically in GA either. Multisearch will be mixed into Direct Traffic or Google quicksearchbox (which again, is what some Discover traffic shows up as).

Multisearch showing up as “GoogleQuickSearchBox” in log files:

Multisearch showing up as GoogleQuickSearchBox in log files

Multisearch showing up as Direct Traffic in GA:

Multisearch visits showing up as Direct Traffic in Google Analytics

How multisearch tracking could look in GSC: A mockup.
I pinged Daniel Waisberg from Google who leads the GSC product team about the tracking issue, along with a mockup of what the reporting could look like. There are several options, but I think the most logical would be a filter for multisearch in the Performance reporting (sitting alongside Web, Image, Video, and News).

Here is the mockup I sent Daniel:

Google Search Console mockup of multisearch reporting.

Summary: Multisearch is powerful, but site owners need reporting to understand usage!
Again, I think multisearch is super-powerful. The ability to search via Lens and then add a query to the analysis to find more information can help on multiple levels (including local, shopping, and more). But site owners need to know how their audience is using multisearch to find their content. And that means tracking in GSC.

We are very early on with multisearch, so I’m hopeful we will see some type of tracking show up in Search Console. But I’m also aware that there needs to be significant usage before that happens. I can’t see Google releasing reporting in GSC that shows little activity for a majority of sites.

Time will tell when multisearch reporting shows up. Until then, you should definitely check out and test Lens and multisearch. And after using it, spread the word. It’s a powerful way to “Search outside the box”, as Google would say.

GG

Smart Delta Reports – How To Automate Exporting, Filtering, and Comparing Google Search Data Across Timeframes Via The Search Console API and Analytics Edge

April 12, 2021 By Glenn Gabe Leave a Comment

How to automate a delta report via Analytics Edge in Excel.

In 2013, I wrote a post explaining how to create what I called a Panda Report, which enabled you to identify landing pages seeing the biggest drop during a major algorithm update. The post explained how to do this based on Google Analytics data, but you can definitely do the same thing with GSC data (and with queries in addition to landing pages).

Well, it’s 2021, the process I use has been enhanced, and I wanted to publish a new post explaining how to automate the process using Analytics Edge. First, since medieval Panda is now missing in the SEO trees, the report needed a new name. For the past several years, I’ve simply called it a Delta Report. That fits much better since you can use this approach to identify the change in impressions, clicks, or rankings based on any event impacting Search (like a broad core update, a site migration, website redesign, or any other situation causing volatility). By identifying the landing pages and/or queries seeing the biggest drop, you can often find glaring issues. It’s a great way to start digging into the data after a big drop or surge in rankings and traffic.

And beyond the name change, there are some great ways to bulk export data via the Search Console API now. Since the GSC UI limits exports to just one thousand rows of data per report, using the Search Console API is critically important for exporting all of your data. For example, I often use the API to mass-export landing pages and queries from GSC (going well beyond the one thousand row limit).

A Delta Report is a Panda Report on Steroids – The Power of Analytics Edge and APIs
I have covered Analytics Edge a number of times before in blog posts about exporting data. It’s an add-on for Microsoft Excel that quickly and efficiently enables you to bulk export data via the Search Console API, Google Analytics API, and more. But that’s not all you can do with Analytics Edge. Beyond just the export, you can create macros that filter and organize the data to create advanced reports (so you can export, compare, filter, etc. and all in one shot).

For our purposes, that means exporting all queries or landing pages, comparing the data based on timeframe, filtering based on your site structure, important query types, etc., and then writing those exports to their own worksheets for further analysis. And this is all done in one shot based on the macro you create (build once, use often).

Excited? Below, I’ll walk you through the process of creating the macro via Analytics Edge. And while you go through this tutorial, you’ll probably think of 100 other things you can use Analytics Edge for while working with data. It’s basically a Swiss Army Knife for working with APIs.

How to export GSC data via the Search Console API, compare data across timeframes, filter the data by page or query type, and create separate worksheets, all in one shot:
First, there are some requirements. Although Analytics Edge isn’t free, it’s extremely cost-effective. The core add-in costs $99 per year and the GSC connector costs $50 per year. Also, the good news is that there is a 30-day free trial so you can walk through this tutorial and use the process for 30 days to see how it works for you.

So, for $150 per year, you can use Analytics Edge to your heart’s delight. If you are helping larger sites where the API is necessary to export all of your data, then Analytics Edge is a great way to go, and it definitely won’t break the bank.

How To Create A Delta Report: Step-by-step instructions

1. Download and Install Analytics Edge (Core Add-in):
You can download and run the Analytics Edge installer to quickly install the add-in. After installing the add-in, click the license button and accept the Terms of Use. Once you do, a 30-day free trial will start for the core add-in.

2. Install the GSC Connector from within Excel:
Now that the Core Add-in is installed, you need to add the GSC connector so you can work with the Search Console API. It’s very easy to install the connectors available in Analytics Edge. Simply click the License button in the Analytics Edge menu and then click the dropdown to add a new connector. Select Google Search and then click Add. Then click Install. Once the connector is installed, a 30-day trial will begin for that connector.

3. Connect Your Google Account:
In order to export data from your GSC properties, you first need to connect your Google Account associated with those properties. Once you set this up, you will not need to do this over and over. And you can add multiple Google Accounts if you have access to GSC properties across various accounts. Click the Google Search dropdown in the Analytics Edge menu and select Accounts. Click Add Account and walk through the process of quickly connecting your Google account. Once you do, you’ll be able to use the API to connect to any property you have access to.

4. Create Your Macro – Exporting All Landing Pages or Queries and Comparing Data Across Timeframes:
Let’s start creating our macro by exporting all landing pages and comparing data across timeframes. In a real-world situation, you would compare the timeframe after a major event (like a broad core update) to the previous timeframe to see the changes per landing page or query. But for this tutorial, we’ll keep it simple. Let’s just pull all landing pages for the last 28 days and compare to the previous timeframe. Once you get the hang of this, you can customize the report for each situation you encounter. Note, if you ever lose the Macro window, just click “Task Pane” in the Analytics Edge menu in Excel. It will show back up on the right side of the spreadsheet. Let’s start creating our macro. Go ahead and click Analytics Edge in the main menu, then Google Search, and then Search Analytics. Name your macro DeltaReport (or whatever you want).

5. Choose The Account and GSC property:
Select the Google account you want to use and then select the GSC property in the site list.

6. Select Fields To Export:
In the available dimensions and metrics list, select page and click “Add” to export all landing pages from Google organic Web Search. Leave the selected metrics as-is (with clicks, impressions, ctr, and position all selected).

7. Leave Filters Tab As-Is, But Review The Settings:
For this tutorial, we’ll leave the filters tab as-is, but note the options you have here while exporting data from GSC. You can filter by page, query, country, device type, search type, and search appearance. You’ll notice the default search type is Web Search. That’s what we want for this specific report, so keep the default settings.

8. Select A Date Range – Comparing Data Across Timeframes Made Easy:
Depending on your situation, select the appropriate timeframe for exporting data. For this tutorial, let’s pull the last 28 days of data and compare to the previous timeframe. Click the Dates tab and simply use the dropdown to select Last 28 Days. To compare timeframes, make sure to select the checkbox for “Compare to” and select a timeframe to compare with. To keep things simple, we’ll just select “Previous period”.

9. Sort By Clicks Or Impressions:
Under the Sort/Count tab, use the dropdown to select a metric to sort the data by. I typically choose clicks in descending order. Make sure to click the button labeled “Descending” to apply the sort preference.

10. Run The Query!
Click OK in the bottom right corner of the wizard to run the query. Depending on how much data needs to be exported, it can take a few seconds (or longer). Once the query has completed, you will see the results highlighted in green. Note, this does NOT show the full results from the export. Analytics Edge just shows a sample of the results and is waiting for more input from you (either to write the full data to a worksheet now or to use the built-in functions to create more advanced reports).

11. Set The Table Name:
Since we’ll be filtering the data we just exported multiple times (by page type), we need to set the table name so we can come back to the full data in future steps. To do this, click the Table dropdown in the Analytics Edge menu and select Table Name. Set the table name to something like “allpages” and click OK. Again, we’ll need this in the future.

12. Write To Worksheet:
Let’s complete the export by writing the data to a new worksheet. In order to do this, click the File dropdown in the Analytics Edge menu and then select Write to Worksheet. Name your worksheet something like “Landing Pages All Data” and then click OK. Analytics Edge will create a new worksheet containing the full export from GSC. Just click the new worksheet to view all of the data. You’ll notice all of the landing pages were exported with columns showing the difference in clicks, impressions, CTR, and position based on comparing the last 28 days to the previous timeframe. Awesome, right? But we’re not done yet. Our macro will be smarter than that. :)

13. Start Filtering Your Data:
Our goal is to create separate worksheets by page type so you can easily analyze each one separately. To keep things simple, let’s say we wanted to break out category pages (/category/), product pages (/products/), and blog posts (/blog/) so we could analyze them separately. Let’s start with category pages. Click the Table dropdown in the Analytics Edge menu and select “Filter”. This menu will enable you to filter the data by any column in the active table. The active table now is “allpages”, which we set up in step 11. Once you click “Filter in the menu”, you can set the filter rules. The column should say “A page”, which will enable you to filter by the column in our active table titled “page”. For Criteria, you have several helpful options, including regex. Yes, you can use regex here if needed, which is awesome. To filter by category pages which contain /category/ in the url, select “Contains” and then enter /category/ in the Value text box. Then click the Add button. Note, you can combine rules here if you want to create more complex filtering options. Click OK to filter the active table. You will see the results again highlighted in green. We’ll write the filtered data to a worksheet in the next step.

14. Write The Filtered Data To A Worksheet:
Just like we did before, let’s write the filtered category data to a new worksheet. Click the File dropdown in the Analytics Edge menu and select “Write Worksheet”. Then name the new worksheet “Landing Pages Category” and click OK. A new worksheet will be created with all of the category page data. At this point, you should have two worksheets, one containing all landing page data and another just containing category page data.

15. Switch The Table Back In Order To Filter Again:
Now we want to filter the full data again for product pages, which contain /product/ in the url. In order to do that, we need to switch the active table back to “allpages”, which contains all of our exported data and not just the filtered category data. If we don’t switch the table name again, then Analytics Edge will use the current active table, which is the category page data. In order to switch the table, click the Table dropdown in the Analytics Edge menu and click Table Name. Click the second radio button to switch to a previously named table and select “allpages”. Now that becomes the active table and we can filter it again.

16. Filter Product Pages:
We’ll use the same approach that we did when filtering the category pages, but this time, we’ll filter by urls containing /products/. After switching the table name, select Table again in the Analytics Edge menu and then Filter. Now enter /products/ in the value field for page. Then click OK. The data will now be filtered by any url with /products/ in it.

17. Write Filtered Data To Worksheet:
Next, we need to write the product filtered data to a new worksheet. Click the File dropdown in the Analytics Edge menu and select “Write Worksheet”. Name the worksheet “Landing Pages Products” and click OK. You will now have a new worksheet with the filtered data. And now you should have three worksheets containing landing page data (full data, category pages, and product pages).

18. Rinse and Repeat For Blog URLs:
Use the same approach to export all data filtered by blog urls (containing /blog/ in the url). First, switch the table name back to “allpages” (see step 14 for how to do this). Then filter the data by any url containing /blog/, and then write to a new worksheet called “Landing Pages Blog”. When you’re done, you should have four worksheets in total with three that contain filtered data (one for category urls, one for product urls, and one for blog urls). And Analytics Edge already took care of comparing data across timeframes and provided difference columns in the worksheets.

Congratulations! You have exported all of your landing pages from GSC, compared data across timeframes, filtered by page type, and then created specific worksheets containing the filtered data. Oh, and now you have a macro using Analytics Edge that you can reuse whenever you want to accomplish a similar task in the future. Just reopen the spreadsheet, save a new file, edit the macro to change the settings like GSC property, click “Refresh All” in the upper left corner of the Analytics Edge menu, and boom, you’re good to go. Time is valuable and this can save you a lot of it in the future…

Beyond The Delta Report: More Functionality = More Advanced Reporting
As I mentioned earlier, Analytics Edge comes with a ton of functionality built-in. You can create advanced reporting by using the various functions available in Analytics Edge when working with data exported from GSC, Google Analytics, and more. So, if you’re feeling ambitious, here are some other things you can try using Analytics Edge and the GSC Connector:

Run the same type of report, but for queries instead of landing pages. Then you can analyze drops or surges by query type instead of page type.
You can segment by search type to analyze drops and surges for Image Search, Video Search, or the News tab.
You can segment by search feature (like AMP, how-to, FAQs, Q&A, reviews, recipes, etc.) Note, you can follow my tutorial for exporting data by search appearance to learn more about that process.
And make sure to review all of the functions available in the Analytics Edge menu within the Multiple, Table, and Column dropdowns. For example, you can filter, group, pivot, sort, append, combine, compare, convert, split, and more. Again, Analytics Edge is like a Swiss Army Knife for APIs.

Summary – Automated Delta Reports are Panda Reports on Steroids
It’s always smart to analyze the top landing pages and/or queries when a site sees a big drop or surge in rankings and traffic from Google (due to an algorithm update, site migration, website redesign, etc.) Since the Performance reporting in the GSC UI limits exports by 1K rows, using a tool like Analytics Edge can help you quickly and efficiently export all of your data via the Search Console API.

In addition, Analytics Edge comes with a number of functions for filtering and working with your data to create advanced reports (including comparing data by timeframe). By following this tutorial, you can create a template for quickly exporting data, comparing data across timeframes, filtering by page or query type, and then writing the results to separate worksheets for further analysis. Once you get the hang of Analytics Edge, the sky’s the limit. I think you’ll dig it.

GG

Exit The Black Hole Of Web Story Tracking – How To Track User Progress In Web Stories Via Event Tracking In Google Analytics

November 2, 2020 By Glenn Gabe Leave a Comment

Google’s Web Stories, previously called AMP Stories, can provide an immersive AMP experience across both desktop and mobile. Google has been pushing them hard recently and stories can rank in Search, Google Images, and in Google Discover. On that front, Google recently rolled out a Web Story carousel in Discover, which can definitely attract a lot of eyeballs in the Discover feed. And those eyeballs can translate into a lot of traffic for publishers.

I’ve covered Web Stories heavily over the past year or so and I’ve written a blog post covering a number of tips for building your own stories. I have also developed several of my own Web Stories covering Google’s Disqus indexing bug and the upcoming Page Experience Signal.

Building those stories by hand was a great way to learn the ins and outs of developing a story, understanding the functionality available to creators, the limitations of stories, and how to best go through the life cycle of developing a story. As I explained in my post covering various tips, it’s definitely a process. Planning, creativity, and some technical know-how go a long way in developing an engaging and powerful Web Story.

From a feedback perspective, analytics can help creators understand how well their story is being received, if users are engaged, and how far they are progressing through a story. Unfortunately, that has been challenging to understand and accomplish for many publishers starting off with Web Stories. And that situation has led me to research a better way to track stories via Google Analytics. That’s what I’ll be covering in this post. By the end, you’ll be tracking Web Stories in a more powerful and granular way. I think you’ll dig it.

Analytics for Web Stories – Confusing For Many Creators
From the start, it seemed like analytics took a back seat for stories. There wasn’t great documentation about how to add analytics tracking and the WordPress plugin originally didn’t even have the option for including tracking codes. That changed recently, which was great to see, but questions still remained about how to best track Web Stories. For example, can you use Google Tag Manager, can you add advanced tracking to understand more about how users are engaging with your story, can you track specific elements in your story, etc.?

Basic page-level tracking in Web Stories.

After looking at basic metrics for my stories in Google Analytics (yawn), I went on a mission to enhance my story tracking setup. Unfortunately, there’s still not a one-stop resource from Google for tracking Web Stories (hint-hint Paul Bakaus), but I was able to dig into various documents and articles and figure out a pretty cool solution that’s easy to set up. I’ll provide that setup below so you can start tracking your own stories in a more powerful and granular way.

Tracking User Progress Through A Web Story: A Simple Goal
If you just add a basic tracking code to your story, you will at least know how many people are viewing the story and gain basic metrics for the page (just like any other page in Google Analytics). But that doesn’t really do Web Stories justice…

Web Stories are a unique combination of pages within a story. In other words, Web Stories string together multiple pages, which make up the larger story. Users can click back and forth to view each page within the story. You can also automatically advance the user to the next page after a specific amount of time. And once a user completes a story, they are presented with a “bookend”, which is a final page that contains information selected by the creator.

With a basic tracking setup, Web Stories are like a black hole. People enter, and you have no idea what’s going on within the story. For example, how many pages have they viewed, how far are users progressing through the story, did they reach the bookend, how long did it take to get to the end, etc.?

Wouldn’t it be awesome to be able to track that information??

The good news is that you can, and it’s pretty easy to set up. Below, I’ll cover how to add the necessary tracking to your Web Stories so you can gain more information about how users are engaging with your stories. And beyond just setting up this level of tracking, I wanted to provide more information about how events and triggers work in stories so you can start testing your own advanced tracking setup. Let’s jump in.

Web Story Tracking: A Top-level View of What We Are Trying To Accomplish
Before I cover the tracking framework you can utilize today in order to better track your Web Stories, let’s cover the basic bullet points of what we are trying to achieve. Here is what we want to achieve:

Track user progress through each Web Story you have published. i.e. Track each page within the story to understand how far users are progressing.
Document the Web Story title and organize each page within the story so they can be tracked properly.
Track when users reach the final page in your Web Story so you can identify how many users actually reach the end.
Track when users enter your bookend, which is a special page at the end of your Web Story that contains social sharing and related links. It’s just another way to understand when users have reached the final part of your story.

For example, wouldn’t it be incredible to see the following? That’s a sample Web Story and data for each page within the story. Yep, this is what we want… let’s go get it:

Event reporting in Google Analytics for Web Stories.

The Inner Workings: Events, Triggers, and Variables
Every Web Story issues events as a user progresses through a story. For example, when a user progresses from one page to another within a story, the “story-page-visible” trigger fires every time a new page is loaded. You can capture an event like that and report it in Google Analytics using event tracking.

When sending those events to Google Analytics from within your story, you can provide the typical event parameters like event_action, event_category, and event_label so you can track your Web Story data in your GA reporting.

List of Web Story Triggers and Variables:
There are several triggers you can capture and a list of them can found on github for AMP Project/AMP HTML. In addition, you can view the variables available to you via AMP by checking out the AMP HTML Variable Substitutions page. Between the two documents, you can learn how to combine triggers and variables to set up advanced tracking.

A Template From The AMP Project!
During my research, I was excited to see that the AMP blog published a post about tracking Web Stories and it contained a skeleton structure for advanced tracking! For example, the post listed a code snippet for firing an event every time a user progresses from one page to another within a web story (to track user progress). We could use this snippet, and expand on it, to customize our tracking.

Here is an image from the AMP project’s blog post about tracking user progress. Again, this is exactly what we are looking to do.

By using the right triggers and variables and then firing events from our Web Story, we can get a much stronger picture of user engagement. Below, I’ll provide the triggers and events we’ll use and then I’ll provide the final code later in the post.

Note, there are three triggers we’ll be capturing in our Web Story and we’ll fire an event when those triggers are captured.

Trigger: story-page-visible. When each new page in the story loads, story-page-visible fires. When that fires, you will send an event to Google Analytics with the following variables.
event_name: You can name this whatever you want. Event tracking in Google Analytics focuses on the following three fields.
event_action: Name this something descriptive. For this example, we’ll use “story-progress” which is what the original blog post used covering this approach.
event_category: For this field, I’m going to use a variable for Web Stories, which is the title of Web Story. The variable is ${title}, which is what’s present in your title tag. I linked to the variables available to you earlier in this post.
event_label: For the final field, we’ll use both the page index value (page number) and ID for the page within the Web Story (which is the descriptive name for the page you provide in your story). This will enable us to see how many times a specific page within the Web Story is loaded by users. The variables are ${storyPageIndex} and ${storyPageID} and you can combine the two in your code. I added “Page: ${storyPageIndex} ID: ${storyPageID}” to combine both in your event reporting. It makes it easier to see the page number and then the ID associated with that page. BTW, thank you to Bernie Torras who pinged me on Twitter about storyPageIndex, which is a great way to capture the page number within your story.

Next, we want to know when users visit the final page of each Web Story. That can help us understand how many people are actually reaching the end. To accomplish that, we can add another trigger:

Trigger: story-last-page-visible. Note, this is not the bookend. Instead, this is the last page in your story before the bookend is displayed. Story-last-page-visible fires when a user reaches that final page in your story before the bookend.
event_name: You can name this whatever you want. Just like earlier, the reporting in Google Analytics focuses on the following fields.
event_action: Name this something descriptive. For this example, we’ll use “story-complete” since the original blog post covering this tracking framework used that action name.
event_category: Make sure to use the same event_category for this trigger as you did earlier to keep the various triggers organized by Web Story. The variable is ${title}. Then you can drill into a specific story in Google Analytics and view the actions and labels associated with that one story.

And finally, let’s add one more trigger to understand when users reach the bookend in your Web Story, which is a special page at the end that contains social sharing and related links. It’s just another way to understand that users made it to the very end of your Web Story. You’ll need to add one more section of code to your tracking script:

Trigger: story-bookend-enter
event_name: You can name this whatever you want. As I mentioned earlier, the reporting in Google Analytics focuses on the following fields.
event_action: You can also name this whatever you want. For this example, let’s use story-bookend-enter.
event_category: Like earlier, I’m going to use a variable for Web Stories, which is the title of Web Story. The variable is ${title}. Remember to keep the category consistent with each trigger so you can view all events within a single web story in your reporting.

By adding this setup, the event tracking reporting in Google Analytics will enable you to drill into specific stories, see the number of “pageviews” for each page within a story, know how many users are reaching the final page in a story, and then how many are viewing the story bookend. It’s a much stronger setup than just seeing a single pageview for your Web Story (AKA, the black hole of Web Stories).

Here is the final code based on what I mapped out above. Make sure you replace the placeholder GA account ID with your own:

<amp-analytics type="gtag" data-credentials="include">
  <script type="application/json">
	{
	  "vars": {
		"gtag_id": "UA-XXXXXX-X",
		"config": {
		  "UA-XXXXXX-X": {
			"groups": "default"
		  }
		}
	  },
	  "triggers": {
		"storyProgress": {
		  "on": "story-page-visible",
		  "vars": {
			"event_name": "custom",
			"event_action": "story_progress",
			"event_category": "${title}",
			"event_label": "Page: ${storyPageIndex} ID: ${storyPageId}",
			"send_to": ["UA-XXXXXX-X"]
		  }
		},
		"storyEnd": {
		  "on": "story-last-page-visible",
		  "vars": {
			"event_name": "custom",
			"event_action": "story_complete",
			"event_category": "${title}",
			"event_label": "${totalEngagedTime}",
			"send_to": ["UA-XXXXXX-X"]
		  }
		},
		"storyBookendStart": {
		  "on": "story-bookend-enter",
		  "vars": {
			"event_name": "custom",
			"event_action": "story_bookend_enter",
			"event_category": "${title}",
			"send_to": ["UA-XXXXXX-X"]
		  }
		}
	  }
	}
  </script>
</amp-analytics>

How To Add Your GA Tracking Script To A Web Story:
Once you have your tracking script ready, you need to add that to your Web Story code. I’ve been hand-coding my stories so it’s easy to have control over where the amp analytics tag is placed. In my stories, I place the amp analytics tag after the final page in my story, but before the bookend tag and the closing <amp-story> tag. If you place the amp analytics tag outside of your <amp-story> tag, the Web Story will not be valid. You can see the placement of my amp analytics tag in the screenshot below.

Amp analytics placement in Web Story code.

Make sure your amp-analytics tag is placed before the closing amp-story tag.

Placing amp analytics tag before bookend and closing amp story tag.

A Note About The Web Stories WordPress Plugin:
Again, I have been hand-coding my stories and haven’t dug too much into the WordPress plugin. I’ve heard good things about it, but I really wanted to learn the ins and outs of building a story, so I stuck with hand-coding my stories.

The WordPress plugin finally added the ability to easily include your Google Analytics tracking ID, but it doesn’t look like you can add advanced-level tracking easily (like what I’m mapping out in this post). I’ll reach out to the Web Story team to see if they will add the ability to accomplish this in the future, but for now I think you’ll be limited to the basic tracking I mentioned earlier.

{Update: WordPress Plugin Automatically Firing Events}
I have very good news for you if you are using the Web Stories WordPress plugin. Brodie Clark pinged me today after going through my post. He is using the Web Stories plugin, checked the Events reporting in Google Analytics, and noticed the plugin is automatically firing those events! That’s amazing news for any plugin users!

Again, I’ve been hand-coding my stories so I haven’t played around too much with the plugin. But that’s outstanding news, since plugin users can view user progress and a host of other events being fired within their stories.

Once you add your GA tracking ID, it seems the plugin is automatically capturing the data and firing events:

Adding a Google Analytics tracking ID to the WordPress Web Story plugin.

Here are the triggers being captured based on what Brodie sent me:

WordPress Web Story Plugin automatically firing events.

And here is what it looks like once you click into story_progress. The plugin is using storyPageIndex versus storyPageID so you can see the page number in the reporting. I’m thinking about combining the two actually.

Tracking user progress through Web Stories via the WordPress plugin.

How To Test Your Tracking Via Google Analytics Real-time Reporting
The easiest way to test your new tracking setup is to upload your story to your site and view real-time reporting in Google Analytics. There’s a tab for Events, where you can see all of the events being triggered. Just visit your story and look for the various events, actions, and labels.

Viewing real-time reporting in Google Analytics for Web Story events.

Viewing Web Story Tracking In Google Analytics:
Once your story is live that contains your new tracking setup, and users are going through your story, you can check the Events reporting within the Behavior tab in Google Analytics to view all of the events that have been captured. This is where the naming convention we used comes in handy. When you click “Top Events”, you will see the event categories listed. We used the story title as the category, so you will see a list of all stories where events were captured.

When you click into a story, you will see each action that was captured (each trigger that was captured as an event).

Viewing Web Story triggers in the Events reporting in Google Analytics.

And by clicking an action, you can see the labels associated with that action. For example, story_progress enables you to see the list of pages that users viewed within your story and how many events were triggered for each page (helping you understand how far users are progressing through each story).

Viewing user progress through a Web Story in Google Analytics.

And there you have it! You can now track your Web Stories in a more powerful and granular way. It’s not perfect, but much stronger than the “black hole” approach of just basic page-level metrics. And remember, you can totally expand on this setup by adding more triggers and using the variables available to you.

Summary – Creep out of the black hole of Web Story tracking.
I hope you are excited to add stronger tracking to your Web Stories. As I documented in this post, you can creep out of the black hole of story tracking and analyze user progress through your story. By doing so, you can better understand user engagement and then refine your stories to increase engagement and user happiness.

So don’t settle for black hole reporting. With a basic level of understanding of event tracking, triggers, and variables, you can set up some very interesting tracking scenarios. Good luck.

GG