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Google Search Console (GSC) reporting for Soft 404s is now more accurate. But where did those Soft 404s go?

February 19, 2021 By Glenn Gabe Leave a Comment

On January 11, 2021 Google announced improvements to GSC’s Index Coverage data. With the update, which is clearly apparent in the reporting, Search Console removed the generic “crawl anomaly” issue (and mapped those to more granular issues), changed how urls are reported that are blocked by robots.txt, added a new issue called “indexed without content” as a warning, and then had one final note about soft 404s. That last bullet said, “Soft 404 reporting is now more accurate.” And based on my work with large-scale sites, I was particularly interested in that final bullet.

A number of the sites I help had tens of thousands, or even hundreds of thousands of soft 404s being reported based on the nature of their content. Starting on January 5, 2021, the number of soft 404s reported in GSC fell off a cliff. I’m talking about a severe drop, like these examples:

In order to understand why alarms were going off in my office, and why I’m particularly interested in those drops, let me quickly cover more about soft 404s. Then I’ll explain what I found once I dug into each situation.

A Deeper Look at Soft 404s:
Google explains that a soft 404 is a url that tells the user the page doesn’t exist, but it still returns a 200 header response code (instead of a hard 404). In practice, the soft 404s that Google has surfaced historically have been urls that span different page types. For example, the most obvious is a product that’s no longer available that still returns a 200 response code. It might have very little content and a message telling users the product is not available on the site anymore.

But, other types of pages have showed up in the soft 404s reporting over time. For example, I’ve seen super-thin pages show up there like articles, slideshows, job listings, pages with images or video with little textual content, and more. And on larger-scale sites, those pages can be problematic quality-wise depending on how Google is handling them.

For example, a soft 404 is treated as a hard 404. Google will not index those urls identified as soft 404s. But, if a site had a big soft 404 problem, I often found that Google wasn’t always handling all of those pages types as soft 404s… And that led to many thinner urls getting indexed.

So, if you had 50K super-thin pages and Google saw them as soft 404s, then technically that shouldn’t be a huge problem quality-wise. Since they are seen as hard 404s, Google will obviously not take them into account when evaluating quality.

But, what if suddenly those urls aren’t seen as soft 404s anymore? Well, those urls could be counted when Google evaluates quality overall for the site. That’s a big reason my “quality antennae” went up when I saw tens of thousands, or even hundreds of thousands, of soft 404s suddenly disappear. Are they indexed now, categorized as another issue in GSC and not being indexed, or maybe not even known to Google anymore? I NEEDED TO KNOW!

In addition, soft 404s can impact crawl budget, which can be a concern for larger-scale sites. Note, most sites do not need to worry about crawl budget. Only very large sites (typically sites with over one million urls) need to worry about crawl budget. For example, this site needs to worry about crawl budget:

Google’s own documentation explains that soft 404s can eat up crawl budget. So it’s another concern when it comes to soft 404s (and understanding where they magically went on 1/5/21). In the past, if you saw them flagged in GSC, then you could identify patterns and handle appropriately. But if they suddenly disappear, it could be harder to surface and handle.

In Search Of: Lost Soft 404s
This all leads me back to one question… where did all of those soft 404s go?? After seeing the number of soft 404s drop off a cliff on 1/5/21, I decided to dig into the data across sites and find out what happened. So, I grabbed my virtual pickaxe and headed into the GSC mine. I was determined to find out how those urls were being treated now.

Now, if you choose to dig into search console after a change is rolled out by the GSC product team, you’ll quickly realize that you cannot access historical data. That sucks and means you will have a hard time finding urls previously categorized as soft 404s unless you documented them somehow. I had this covered since I always dig heavily into GSC data when auditing client sites. I was able to open my vault of recent audit documents and resurface many soft 404s that were reported over the past six to twelve months.

Based on digging into the data, I was sometimes relieved with what I discovered for certain situations, but also alarmed by what I found in other situations. I’ll explain more below.

Where Did Soft 404s Go and How Is Google Currently Handling Those URLs?
Below, I’ll cover the various categories of findings based on digging into a number of large-scale sites that previously reported many soft 404s. On 1/5/21, the number of soft 404s dropped off a cliff and I was determined to find out how they were currently being treated.

Super-interesting: “The URL is unknown to Google”
This was incredibly interesting to me. I noticed many url across sites that were once reported as soft 404s now returning “The URL is unknown to Google” when inspected in GSC. It’s almost like Google completely wiped out the history for these urls.

This isn’t a category that shows up in the Coverage reporting for obvious reasons… since it’s NOT known to Google. But, it was known to Google at some point recently. I know that for sure. Google’s documentation does explain more about this categorization, but I’m not sure it answers why the soft 404s are now “unknown to Google”. Here is what Google’s documentation explains:

“If the index coverage status “URL is unknown to Google” appears, it means that Google hasn’t indexed the URL either because it hasn’t seen the URL before, or because it has found it as a properly marked alternate page, but it can’t be crawled.”

I’m going to reach out to Google to learn more about this. I would imagine that Google handling many urls as this category is helping it improve the efficiency of its own systems, but again, I’ll see if Google can comment about this. But if it’s unknown to Google, it won’t hurt your site quality-wise. That’s good.

“Crawled, not indexed” and “Discovered, not indexed”:
I have covered this category a number of times in my other posts about technical SEO and broad core updates. Both “Crawled, not indexed” and “Discovered, not indexed” can signal quality problems and/or crawl budget issues. With “Crawled, not indexed”, Google discovered and crawled the url, but has chosen to not index it. I have often found low-quality or thin content when analyzing this category across sites (or just parts of a site Google isn’t digging for some reason).

So, if your soft 404s end up as “Crawled, not indexed”, then that’s ok in the short-term (since they aren’t being indexed and can’t be held against the site quality-wise), but I would still heavily dig into the urls and handle them appropriately. This is also one of the reasons I believe we need larger exports from GSC’s Coverage reporting! Exporting this category in bulk would help site owners identify more patterns across the site that could be causing problems.

And for “Discovered, not indexed”, Google knows about the url, but has decided to not even crawl it (and clearly not index it). I have seen quality problems and problematic url patterns while analyzing this category of urls. For example, with some large and complex sites with deep quality issues, it’s like Google knows ahead of time what it’s going to find on many of those urls (based on url structure) and doesn’t even want to crawl them.

So, if you see many soft 404s now being categorized as “Discovered, not indexed”, then that’s also ok in the short-term since they can’t be held against the site quality-wise if they aren’t being indexed. But just like with “Crawled, not indexed”, I would dig in and make sure the urls are being handled properly. If they should 404, then make sure they return hard 404s. If they are thin and low-quality, then boost the content there. And if it’s a problematic area of the site, then work on improving that area overall. Remember, “Discovered, not indexed” means Google knows about the urls, but doesn’t believe it’s worth it to even crawl them (let alone index them).

URLs Now Indexed: Danger Will Robinson! Danger!
Yes, this is a super important category. When digging into urls that were previously categorized as soft 404s, I noticed some of them were now being indexed! That can be a dangerous situation for some sites. For example, if they were thin and low-quality urls that Google is now indexing for some reason, those urls will now be taken into account while Google evaluates quality for the site overall. “Quality indexing” is very important (especially for larger-scale sites), so having an influx of low-quality content get indexed is not a good thing.

If you notice many urls that were once flagged as soft 404s now being indexed, then it’s important to handle those urls appropriately now. For example, you can noindex low-quality content, boost the content there (improve it so it meets or exceeds user expectations), 301 it to a newer version of the content, or 404 the pages if they shouldn’t be on the site anymore. But I would not leave them as-is if they are low-quality or thin.

True soft 404s still seen as soft 404s:
Even though many soft 404s disappeared from the reporting in GSC, there were still urls categorized as true soft 404s. For example, pages that were literally blank that returned 200 header response codes, pages that returned “not found” messages on the page, but returned 200 response codes, etc. Again, true soft 404s. I guess this is the “handling soft 404s more accurately” part from Google. For these urls, I would make sure to handle them appropriately. If they should 404, then have them return hard 404s and not 200 header response codes. And if they shouldn’t be seen as soft 404s, then fix or boost the content there.

Moving Forward: What Site Owners Can Do
If you have seen a big drop in soft 404s starting in early January 2021, then I highly recommend digging into the situation. If you don’t have historical data containing urls that were once categorized as soft 404s, there’s not really much you can do to see how those soft 404s are being reported now. But, if you did document soft 404s for your site, then you can inspect those urls to see how Google is handling them now.

Are they “Unknown to Google” now, are they being categorized as “Crawled, not indexed”, “Discovered, not indexed”, are they still soft 404s, or are they being indexed? And if they are being indexed, then they can be counted when Google evaluates quality for the site overall. That’s why it’s important to handle the urls appropriately.

Summary – Know Your Soft 404s
With the latest data improvements in GSC, Google started better handling soft 404s. That’s great, but many sites saw a big drop in urls reported as soft 404s at that time. Since that category in GSC often yielded thin or low-quality content on a site, it’s important to understand how Google is currently handling the urls. Are they still not being indexed, or are they now being indexed and counting towards quality? I recommend digging into the situation for your site and then handling those urls appropriately.

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 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

Image Packs in Google Web Search – A reason you might be seeing high impressions and rankings in GSC but insanely low click-through rate (CTR)

October 14, 2020 By Glenn Gabe Leave a Comment

Image pack rankings in Google web search.

While analyzing performance in Google Search Console (GSC), some site owners have reported a strange situation with impressions and position (and some even believe it’s a bug). I’ve seen this many times and I often get questions about it from site owners left scratching their heads after witnessing those metrics in their own stats. Although it’s easy to chalk it up to a GSC bug or anomaly, the answer can be hiding in plain sight.

I’m referring to seeing a site ranking very highly in the search results (according to GSC), but when you check the search results, you can’t find the page ranking. And even stranger, you might see that #1 or #2 ranking with insanely low click-through rates compared to other top rankings (which is often a sign of the situation I’m covering today).

So, what can cause this strange situation in the Google search results with high rankings but insanely low click-through rate? Well, it’s sometimes just an image pack ranking in the SERPs or an image ranking in a knowledge panel. Most site owners scan right past those SERP features and look for high rankings in the 10-blue links, when those very features are often the culprit. Let’s explore this situation in greater detail.

A quick reminder about how Google calculates impressions and position in GSC:
Last year I wrote a post demystifying clicks, impressions, and position in Search Console. In my post, I covered how image blocks were tracked and calculated. For example, if Google was ranking an image block #1 in the SERPs and that block contained 10 images, then each of those images take on the rank of the entire block. Therefore, your image, even if ranking seventh within the image block, would rank #1 in GSC.

If you are surprised by that, which is totally understandable since it’s confusing, then you should definitely read my entire post on Search Engine Land. There are several other surprises in the article that can help you better understand tracking performance metrics in GSC.

Google only counts clicks outside of the search results and not query refinement links. But is that really correct??
To register an impression, Google explains that a link must lead out of the Google search results and to your site. If it’s just a query refinement link (which generates a new search result), then that link will not register an impression.

What is a click in Google Search Console (GSC)?

This is where image packs stand out (and maybe not in a good way).

Image packs contain a series of images from various websites. When clicking a thumbnail, you are not taken directly to the site and outside of the search results. Instead, you are taken to Google Images with that image highlighted and expanded. Then, if you click the image again, you are taken to the third-party site. So the click leading outside of Google doesn’t happen from the web search results. It happens from Google Images.

So, if that’s the case, then images within image packs in the web search results should NOT register an impression. But they do. And that’s super confusing for many site owners, and even experienced SEOs.

For example, here is an image pack ranking in Google for “new york photos”. Notice the image pack ranking at the top of the search results (web search).

Image block for New York Photos in the Google web search results that lead to Google Images.

When clicking an image in the image pack, you are taken to Google Images and not the third-party site that contains the image. But… that image will register an impression and position in GSC. More about that soon (with evidence). After clicking the second image in the image pack, I was taken to Google Images with that image expanded:

Image search results for New York Photos.

The reason that urls from publisher sites do gain an impression is that the image tag’s title attribute contains the url for the site. And that url is the one gaining the impression and position in your GSC reporting. Here is the code showing the title attribute containing the site’s url:

Image pack links actually lead to Google Images but the title attribute contains the url from third-party websites.

To clarify, the image doesn’t link to third-party sites. The link is to Google Images and not to your site. But, the image tag’s title attribute does contain your link, so that is what gains an impression and click in GSC.

Confusing? Yep… And can that throw off your reporting? Affirmative.

But it’s not just image packs. It’s knowledge panels as well:
Just a quick note that this also impacts knowledge panels that contain a block of images. So, if you search for “tesla model 3” and see the knowledge panel containing a number of images, those will behave the same way from a tracking standpoint. They will take on the rank of the knowledge panel (the parent search feature). And to make things even more confusing, a knowledge panel will often rank on the right side of the SERP on desktop, which means it can be in position #11, #12, #13, etc. depending on the query and the resulting search results. The knowledge panel below ranks #11 for this query on desktop:

Knowledge panel in Google search that contains images from publisher sites.

And that same knowledge panel on mobile is at the top of the search results (which would show up as position of #1 in GSC). So you really need to filter your GSC reporting by desktop and mobile to see an accurate position for queries that are impacted.

Knowledge panel on mobile that also contains images from publisher sites.

Now, if you have a lot of images ranking in the SERPs, you might have squeezed your coffee mug so hard it splintered into 35 pieces while reading this post. That’s ok, just clean up the coffee, brew another cup, and get ready for some evidence. Below, I’ll cover several different examples of image packs and knowledge panels registering impressions and clicks in GSC based on simply an image ranking in those features (that actually lead to Google Images and not to the third-party website).

Examples of image pack rankings with GSC metrics:
Below, I’ll provide three examples of image packs or knowledge panels yielding high rankings and low CTR in GSC. I’ll provide the SERP, GSC metrics for web search, and then GSC metrics for image search. I think you’ll get the picture, so to speak. :)

Here’s an image pack ranking for the query “rainbow”. The pack ranks #1 in the SERPs, which means the images take on the rank of the pack (so they also rank #1):

Image pack ranking number one for the query "rainbow".

For image search in GSC, you can see the image has yielded many clicks… but that’s image search. Web search is a different story.

Image search metrics yield stronger numbers.

For web search, there are almost 3M impressions, but only 430 clicks (for a CTR of basically 0%). This is exactly what can happen when an image ranks in an image pack in the web search results:

Web search results for image packs in GSC.

For the second example, here’s an image pack ranking #2 for the query “jaguar suv”.

Image pack ranking in Google for "jaguar suv".

In image search, you can see 562 clicks based on 55K impressions for a CTR of 1%:

Image search statistics for "jaguar suv".

But in web search, there have only been 70 clicks based on 43K impressions. The CTR is only .2%. So although GSC shows strong rankings and 43.8K impressions, the reality is that the ranking leads to almost no traffic at all from Google. Again, this can cause a lot of confusion while site owners are analyzing their performance in Search:

Web search stats in GSC for "jaguar suv".

The last example I’ll provide is from a knowledge panel. There are images ranking in the knowledge panel that make for an interesting case. On desktop, the knowledge panel ranks on the right side (in position 11 for this query):

Knowledge panel with images ranking in the web search results.

But on mobile, the knowledge panel ranks #1 at the top of the SERP:

Knowledge panel ranking in the mobile search results.

The images in the knowledge panel for desktop rank #11 and the images in the knowledge panel on mobile rank #1. The following screenshots from GSC show this is the case. Here are the desktop stats. Yep, just 9 clicks with a position of #11 since the knowledge panel ranks on the right side:

GSC metrics for a knowledge panel ranking in the web search results.

And here are the mobile search stats. 210 clicks with a position of #1 (since the knowledge panel ranks at the top of the SERP on mobile). There were 242K impressions and just 210 clicks:

GSC metrics for a mobile knowledge panel ranking in Google.

And compare both of those to the image search stats… 3.3K clicks for the query. A big difference and shows the gap between images ranking in web search (via image packs or knowledge panels) versus ranking in image search:

Image search results versus knowledge panel results.

Image Packs, Knowledge Panel Images, and GSC: Key Takeaways
As you can see from the examples above, images ranking in image packs and knowledge panels can definitely yield some strange metrics in Google Search Console. It’s important to understand this, since it can lead to a lot of confusion while analyzing a site’s performance in Search.

Below, I’ll cover some key takeaways below based on my own research.

First, if you think GSC is wrong, check again, but keep your eyes peeled. There may be an image ranking in an image pack or a knowledge panel that’s throwing off your reporting.
I would fully understand how GSC calculates metrics. There are some very interesting and confusing situations based on various SERP features (especially on mobile). I would read my post about calculating clicks, impressions, and position in GSC and Google’s help document covering the subject. Between the two, you can gain a stronger understanding of how everything is calculated.
High rankings with extremely low click-through rate could be a sign you are experiencing this issue. If you see high rankings, but very low CTR, that could mean an image is ranking well in web search either within an image pack or a knowledge panel.
Although Google explains that only links outside of the search results register an impression, that’s not the case for images in the web search results (within image packs and knowledge panels). The image tag’s title attribute contains the url of the page containing your image, and that’s what is gaining an impression and click.
This is a great example of why it’s tough to look at aggregated statistics in GSC. It takes many different variables into account. The devil is in the details (like everything else in SEO). For example, those images ranking well in web search could absolutely be dragging down CTR overall. Make sure you know that while reviewing your stats.

Summary – Look for image packs and knowledge panels throwing off your reporting:
If you see high rankings with crazy-low click-through rates in GSC and you’re confused about why that’s showing up, you might want to check for images packs or knowledge panels for those queries. You might have images ranking in one of those two search features. It’s a great example of when ranking number one doesn’t really mean very much. Users often scan right by those images in the SERPs, and when that happens, you can end up with a false sense of accomplishment and little traffic to your site. So again, keep your eyes peeled. The answer might be hiding in plain sight.

GG

Web Stories Powered by AMP – 12 Tips and Recommendations For Creating Your First Story

July 15, 2020 By Glenn Gabe Leave a Comment

I’ve been following the progression of Web Stories (formerly called AMP stories) since 2018 when the developer preview launched, and they have been hard to ignore recently. Google has been pushing the Story format pretty hard over the past year and I’ve seen stories show up more and more both in Search and in Discover.

If you aren’t familiar with Web Stories, they are immersive AMP experiences that let publishers cover a topic over multiple pages or screens. It’s similar to the stories format you’re familiar with across social networks like Instagram, Snapchat, and Facebook.

Here are screenshots of the Web Story I created. The first image is the story on desktop and the second image shows the story on mobile:

From a visibility standpoint, Web Stories can rank just like any other web pages and you will also get a nifty story icon in the mobile search results. I’ll cover more about the ranking effect of stories later in the post.

Jumping in and building my first Web Story, powered by AMP:
Based on Google’s recent focus on Web Stories, I decided to learn more about the format and publish one of my own. So, last week I chose a topic I had researched heavily (Google’s Disqus indexing bug) and dove in head-first. I published that story and shared it across social media accounts. It definitely piqued the curiosity of many SEOs and ended up driving quite a bit of traffic (it still is actually). More about that soon.

If you’re interested in seeing what a Web Story looks like now, you can check out my story about the Disqus indexing problem. If you’re already familiar with how Web Stories work, then you can continue below.

After developing and publishing the story, there were some important points, tips, and recommendations I thought could help others looking to get involved with Web Stories. So I compiled a list of 12 tips and recommendations in this post.

The information below spans a number of topics from creative strategy to technical execution to tracking to Search visibility. I do believe we’ll start seeing more and more Web Stories across surfaces and I hope my tips are helpful as you start to explore stories for your own projects!

Web Stories: 12 Tips for building your first story.

1. Story Creation: It’s a process…
When I sat down to map out my first Web Story, it was clear it wouldn’t be a quick process. It reminded me of developing multimedia applications, or a video production, since there was a lot involved during the planning stages. For example, storyboarding the various screens, mapping out visual assets that would be needed, determining audio and video components that would support the story, and more.

I love working on that type of project (based on heavily working in that niche earlier in my career), but it’s important to understand that building a Web Story is not a ten-minute project. It’s a process that will take some time if you’re going to do it right.

Planning is critically important or you can end up spending enormous amounts of time on areas that aren’t impactful. Topic-wise, my recommendation is to cover a very specific topic with each story (and one you are extremely familiar with).

2. Storyboard First: Don’t skip this critically important step.
I mentioned earlier that a storyboard was necessary. If you haven’t storyboarded a video or multimedia application, it’s essentially a plan of what will be covered step-by-step (or page by page for Web Stories). That includes the copy, visuals, animation, audio, and video for each page in the story.

Once you map out a solid storyboard, you can start designing and developing based on that plan. Having a strong storyboard can make it much easier to execute than “riffing” through each page (designing, writing, and building on the fly).

When building a storyboard, you can sketch out the screens by hand or use software to help you map out each page in your story. You can even use PowerPoint or Google Slides to storyboard your Web Story if you want. It’s not about the technology, it’s more about having each page planned before digging in. I find sketching out the screens works well for me.

Here is what a storyboard looks like. And note, my sketches are definitely not as good as what you are seeing in the photo! It’s more about mapping out your production and not being an amazing artist. :)

BTW, Masterclass has a blog post featuring Jodi Foster that covers storyboards (for video). You can check that out for more information and tips about creating them.

3. Photoshop is your friend. No… it’s your core weapon in the game of Web Stories.
The visual nature of Web Stories means you’ll need killer visuals. And that typically means you’ll be working in Photoshop heavily (or some type of image editing application). While developing my story, I found myself combining, masking, layering, and adjusting images to get the right visuals.

In Web Stories, you can use background images and then layer other images (with transparency if needed). You can also use animated gifs to provide more effect. Regardless, you’ll be working with images heavily.

The core point is that you should be comfortable editing images in an application like Photoshop. It will be your best friend throughout the process of building your story. And it can make a huge difference in the impact that your story has with users.

4. Multimedia for supporting your Web Story, including video, audio, and animation.
Web Stories provide an immersive AMP experience and support video, audio, and animation. Multimedia elements like this can add a powerful component to your stories that enable users to experience your topic on another level. You can check the AMP documentation for the various AMP components that are supported.

But, there’s a fine balance between providing supporting multimedia components and overwhelming your users with unnecessary distractions. I recommend testing various multimedia assets locally and in your staging environment. Have other people go through your story while you’re developing it. You might find certain elements are over-the-top, while others really pack an exciting punch.

Remember, multimedia should support your story and not distract users from the story. In my first story, I added a subtle video of typing a comment in Disqus (that questions whether the comment will be indexed):

5. Know Your (HTML) Tags
Web Stories (like other AMP urls) support a subset of html tags. This is important to understand as you plan and build your story. You can review those tags, and more, in the AMP html specification in the developer documentation. Also, some html tags should be replaced with specific amp tags (like image, video, audio, etc.)

Also, you can style your Web Story elements via CSS (up to a 75K limit for your CSS, whether the styles are provided inline or via linked stylesheets).

And from a linking standpoint, I’ve received a lot of questions about adding links to external content from within a story. Yes, you can definitely do that and I included two links in my first story to supporting content.

5a. Deep-linking to specific pages in your Web Story is possible.
Since a Web Story is a collection of pages, there are times you might want to drive users to a specific page in that story. In other words, deep link users to page 5 in your story. Saijo George just pinged me about finding deep linking in the documentation. It’s called Branching and enables you to add a hash to a url and drive users to a specific page based on that page’s ID.

It’s very easy to implement and uses the following format:
https://www.domain.com/story-url.html#page=<page-id>
Just replace <page-id> with the actual page ID you are using in your Web Story and the link will drive users directly to that deeper page.

For example, here is a link that will take you to a deeper page in my Web Story about the Page Experience Signal (the page about speed).

6. Story Execution: Code first, use tools later.
There are several story creation tools on the market, but I recommend hand coding your first Web Story. By coding your story, you can learn a lot about how Web Stories work, the code running each story, how the setup works, and more. This will also help you debug stories in the future that aren’t behaving correctly in production.

After you learn more about how Stories are actually coded, you might check out various story builders. I haven’t tried many third-party tools for Web Stories yet, but there is an official WordPress plugin in beta that looks pretty cool. The AMP team announced that a few weeks ago. If you’re brave, you can install it now and play around. That said, I still recommending hand coding your first Web Story! :)

7. Web Stories Are Device Agnostic: They work on both desktop and mobile.
Since Stories are AMP-based, many people think they are just for mobile. That’s not true. Web Stories run on desktop as well! You can upload Stories to your server just like any other webpage and they can be accessed directly by users and by Google.

You will definitely want to test out your Web Story across both desktop and mobile to make sure everything looks good and works properly. You can’t guarantee that users will just be arriving via mobile, so thoroughly test your story across browsers, operating systems, and devices.

Here is a screenshot of my Disqus Web Story running on desktop:

8. Speed: Web Stories are served from the AMP cache.
Since Stories are AMP-based, you can leverage the AMP cache for lightning-fast delivery. As long as they are valid AMP urls, Google can add your Story to the AMP cache. Once it’s cached, that version of your story will be served via the AMP cache in the mobile search results.

Google’s AMP cache enables your AMP urls and Stories to load near-instantly (an important benefit of running AMP).

9. Tracking Stories: Don’t forget analytics!
You will definitely want to track your Web Stories, so make sure to include the necessary tracking scripts. To start, I would focus on just getting basic Google Analytics tracking implemented and then you can expand from there (to include advanced tracking throughout your Web Story).

For example, I wrote a post explaining how to track user progress through your Web Stories, how to know if users are completing the story, etc. That post covers both hand-coding stories and using the Web Stories WordPress plugin.

Note, I definitely ran into some initial challenges with getting GA tracking to work, but ultimately did get it to work… Also, I used Google Tag Manager and it was a little tricky to set up. But again, it is tracking well now (at least basic tracking). I plan to expand that soon.

10. Rankings: Can Web Stories rank? Yes, they can, and across surfaces.
Over the past year, I’ve seen Web Stories show up more and more in the search results. Also, Google can display a Visual Stories carousel in the search results. So yes, Web Stories can rank.

And when your stories rank, your listing receives an image thumbnail in the mobile SERPs, along with a nifty Web Story icon. Both can help your story gain attention and can help drive stronger click through rates from the mobile SERPs.

Here is my web story ranking in the desktop search results and you can check previous screenshots in this post to see it ranking on mobile.

In Google Search Console (GSC), you can track stories just like any other urls in the performance reporting. You can see queries that users searched for that yielded your story in the SERPs, and you can see impressions, clicks, position, and click through rate. Also, there is an AMP Story filter in GSC where you can isolate your Web Stories.

Also, Web Stories can show up in Google Discover and there is dedicated reporting for Discover in GSC. That’s a good segue to my next tip…

11. Web Stories in Google Discover: A hidden weapon for publishers (at least for now).
Over the past year, I have noticed more Web Stories showing up in Google Discover. Actually, I shared that on Twitter several times and explained it was a huge opportunity for publishers that are early adopters of the format. Here’s my tweet from February after seeing Web Stories show up in Discover:

Interested in Google Discover? Here's an opportunity that many aren't thinking about. I just saw an AMP story in my Discover feed. They can take up a lot of real estate… Def. caught my attention. It didn't show the typical Stories icon for some reason. Worth checking out: pic.twitter.com/lcABOEgLAr
— Glenn Gabe (@glenngabe) February 20, 2020

And here is what a Web Story looks like in the Discover feed. Notice the larger image in the feed (with a 3×4 aspect ratio):

Well, after publishing my own story, I think I was right. My Web Story has gained over 304K impressions since being published last week. So, Google is pushing out stories hard in the Discover feed (for sites that have earned their spot in the feed).

Although the story has driven over 800 clicks from Discover in the last week, the click through rate has been relatively low (compared to Discover stats across publishers I’m helping). I’m not sure if that’s the narrow focus of the story, how it looks in the feed, where it’s placed in the feed, etc. That’s tough to figure out since you can’t see exactly how others are seeing your story in their own Discover feed. For example, the story could be in a carousel of stories, stand-alone in the feed (which can yield larger images), it could be ranking lower in their feed, etc.

I’ll be testing out more stories soon to see how they perform over time. Regardless, if you’re a publisher looking to gain more Discover visibility, you should definitely check out Web Stories.

Note, Google just updated its help document for Google Discover and provided more information about how content appears in the feed. Kenichi Suzuki found that the other day and tweeted the update.

Google's Discover documentation now mentions E-A-T.

As Kenichi pointed out, Google now mentions expertise, authoritativeness, and trust (E-A-T) in that document. For example, “Our automated systems surface content in Discover from sites that have many individual pages that demonstrate expertise, authoritativeness and trustworthiness (E-A-T).” That’s important to understand for Discover (and for Search in general).

So again, if you’re eligible to show in Discover, and you have earned your spot in someone’s feed, then Web Stories can provide a powerful Discover listing. And it seems Google is pushing more stories in the Discover feed based on what I’ve seen over the past year.

In addition, Google recently began testing a Web Stories carousel in Discover. I haven’t seen that yet in my feed, but it shows that Google is experimenting heavily with stories in Discover. This is all important for publishers looking to gain more visibility.

12. Web Story Content Strategy
I could write an entire post on this topic, but I wanted to quickly bring it up now. When planning your Web Stories, it’s important to figure out your content strategy per story. There are several paths you can take. For example, will your Stories be stand-alone projects, will they be combined with another post, will you repurpose other articles into stories, will you be covering events as Web Stories, etc.?

On that note, Web Stories support live stories, which enable you to cover live events. It’s just an example of the various ways that Web Stories can be utilized.

The sky’s the limit creativity-wise, but that can also be overwhelming. I recommend testing out various approaches and see what works best for your situation. Experimentation is key and you might be surprised with what works best.

Summary: Web Stories, a new immersive format powered by AMP
If you are interested in testing the waters with Web Stories, then I hope the various tips and recommendations I provided were helpful. Again, Google seems to be pushing Web Stories hard and they can rank well in both Search and Discover.

In my opinion, it’s definitely worth testing out stories for your own site. But as I explained earlier, it’s not a ten-minute project… it’s definitely a process. I recommend taking a focused approach to building your first Web Story. Map it out, storyboard it, and hand code it. You never know, when it’s all said and done, you might have a good story to tell. :)

GG

Visualizing The SEO Engagement Trap – How To Use Behavior Flow In Google Analytics To View User Frustration [Case Study]

June 15, 2020 By Glenn Gabe Leave a Comment

When thinking about content quality, it’s incredibly important to meet or exceed user expectations based on query. If your content cannot do that, then users can end up extremely frustrated as they search Google, click through your listing in the search results, scan your page, and not find what they were looking for. And that can lead to all sorts of problems SEO-wise.

When you don’t meet or exceed user expectations, one possible result is a user jumping back to the search results to find another solution (low dwell time). That’s clearly not what you want, but beyond the jump back to the SERPs, there are also times users stay on the site and keep digging believing they can ultimately find what they were looking for. That’s a classic engagement trap where users can end up running around a site visiting page after page without finding what they were looking for.

From a broad core algorithm update standpoint, Google is not going to react well over time when sites frustrate users and cannot provide answers that meet or exceed user expectations. We’ve seen that time and time again when broad core ranking updates roll out. In addition, I’ve seen this for a very long time with major algorithm updates overall, including during medieval Panda days.

As I’ve said many times before while writing about major algorithm updates, “Hell hath no fury like a user scorned”. In other words, don’t frustrate your users or bad things can happen during major Google algorithm updates.

This sounds logical, but how you can show a site owner user frustration like I explained above? For example, real data showing that users are frustrated versus just providing your opinion. Well, I surfaced a great example recently and I’m going to share more about that below. It’s something you can do for your own site and you can accomplish this using a tool that’s freely available and right under your nose – Google Analytics.

Backing Up SEO Intuition With Data – Visualizing User Frustration
I’m helping a site now that has been negatively impacted by two broad core updates in a row. I started helping them in April after they reached out to me about being hit by the January 2020 Core Update. Then as I was auditing the site, and they were making changes, the site was hit a second time during the May 2020 Core Update.

Remember, recent changes will typically not be taken into account with broad core updates and the site was just in the beginning stages of implementing changes. You can read my latest post about the May 2020 Core Update to learn more about that point (and other important points that site owners should know about Google’s broad core updates).

During the audit, I surfaced a number of key problems and the site owner has been moving as quickly as possible to implement those changes. One of the issues I surfaced involved user frustration and not meeting expectations based on query.

The site had a number of pages that were ranking well for queries where users had a very specific intent (and I would go as far as saying it was an extremely strong intent). Unfortunately, the pages could not help users achieve that goal. They tangentially covered the topic, but couldn’t provide exactly what users were looking for.

And beyond that, the pages contained information and links that might lead some users to believe they could still find a solution deeper on the site. This could lead to the engagement trap situation I mentioned earlier where users end up digging and digging with no possible way to find gold.

To show the impact during a broad core update, here are two pages like this on the site and their respective drops during the January 2020 core update:

As I was auditing the site, I began to experience user frustration like I mentioned earlier… I was running in circles when I put myself in the shoes of a typical user. It was extremely frustrating… and although I could see many clicks leading to these pages, GSC alone couldn’t provide the full story. So I switched to Google Analytics and a feature that I believe is completely underutilized – Behavior Flow.

Behavior flow enables you to visualize the journey users take through your site based on a dimension you select as a starting point. For example, you can view the path through your site based on traffic source, medium, campaign, landing page, and more. I bolded landing page since that’s exactly what I’m going to show you below for the page type I covered earlier.

Here is what Behavior Flow reporting looks like in GA:

A quick note about sampling:
It’s important to know that Flow Visualization reports in Google Analytics are sampled. Behavior Flow is based on 100K sessions based on the selected timeframe. To get around sampling, you can always shorten the time range to get the number of sessions under the sampling threshold. Just keep this in mind while reviewing the reporting.

When “Engagement” Could Mean Frustration: How To Use Behavior Flow To Visualize Frustrated Users
During the engagement, I ran a traffic drop report (previously called Panda report) to surface the largest drops by landing page after the January and May core updates. Again, the site had a number of key problems across various page types that were problematic. One was the page type I explained earlier, which not only couldn’t meet or exceed user expectations, but it also had the potential to lead users to more pages thinking they could find a solution.

I checked Google Analytics for landing page metrics and noticed one of the problematic urls had a 65% standard bounce rate. That means 35% of users visited another page on the site (which accounts for nearly 20K users during the timeframe). I wanted to see where those users were visiting after the landing page. In other words, were they happy with their visit or were they banging their head against their monitor while endlessly roaming around the site in search for a solution?

So I jumped to Behavior Flow in Google Analytics and set my starting point as that landing page. And once I did, I immediately noticed a problem. And that’s pretty much what I thought I would see!

For this page, 65% dropped off after visiting the landing page during the timeframe I selected (pre-core update). That’s not surprising, but the 35% remaining on the site displayed a path of frustration that was hard to ignore.

The next step for almost all of the users, which is labeled the “first interaction” after the landing page, was to pages that seemed like they could provide a solution, but really couldn’t. So, the landing page couldn’t meet user expectations and then 35% of the users landing on that page ended up visiting another page that couldn’t meet expectations either. Not good, but we’re just getting started.

And then the second interaction was eye-opening. 68% of the users that clicked to a second page went back to the original page they visited from the search results! Yep, I was beginning to see user frustration in the visuals. They visited the landing page from the SERPs, couldn’t find what they were looking for, visited another page thinking they could find a solution, but couldn’t. So they went right back to the original landing page still looking for a solution!

But the fun doesn’t stop there. The third interaction reveals users visiting even more pages from that original landing page believing they could find a solution. 20% of the users visiting the original landing page were now three interactions into their visit, with no hope of finding a solution.

But we’re not done yet. I keep using the word “trap” in this post and the fourth interaction underscores why. To quickly recap, we have users landing on a page (mostly from the SERPs), visiting another page to find a solution, then jumping back to the original landing page, and then desperately visiting more pages to find a solution.

What do you think happened next??

Yep, they went back to the original landing page again! 14% of users were now four interactions into the site (but back at the original landing page). Ugh.

And if they haven’t had enough, some users were still jumping to other pages from the original landing page to find a solution. Oh, those poor people… :)

As you can imagine, this led to a horribly frustrating experience for those users. And over time, hundreds of thousands of users were going through this process. I’m not saying this is the sole reason the site got pummeled by two core updates (remember, there are “millions of baby algorithms” at play with core updates), but it sure doesn’t help matters.

Using behavior flow in Google Analytics enabled me to visualize the frustration that users were experiencing while entering specific landing pages (and most visitors were from Google organic). And that visualization can go a long way when explaining to site owners how certain things need to change on a site. I know this example did.

I’ll end this post with some final tips and recommendations based on going through the process of visualizing user frustration using Behavior Flow in Google Analytics. Again, the great thing is you can go through this process today, and it’s free.

Using Behavior Flow To Visualize Frustration: Tips and Recommendations

Identify high volume pages and dig into the data. Make sure you understand what’s going on user engagement-wise. Don’t assume all visits end with happy users.
You can also run a traffic drop report to identify the biggest drops to landing pages across the site based on a core update, and then dig into those pages.
Don’t just check a metric like standard bounce rate and make decisions. Dig deeper with other user engagement metrics and with additional functionality like Behavior Flow in Google Analytics.
Use specific landing pages as the starting point in Behavior Flow and visualize the journey through your site. This is how you can identify engagement traps like I covered in this post.
If you identify engagement traps, take action before Google takes action. Always try to meet or exceed user expectations based on query. Don’t wait until you drop based on a broad core update to fix user engagement problems, content quality, and more. Identify key problems with those pages and fix them as quickly as you can.
Track Behavior Flow over time for the pages you identify as engagement traps. You should see improvements in the data and through the visualizations in the reporting after you fix those problems.

Summary – “Hell hath no fury like a user scorned”
This case study demonstrated how you can back up SEO intuition by using Behavior Flow in Google Analytics. By using this underutilized feature in GA, you can view the journey users take through your site. And you can use a number of dimensions as the starting point, including landing page.

Behavior Flow can absolutely help you identify problematic situations and then effectively communicate that story to site owners (backed by data). And that can help drive change. Like I said earlier, the amazing part is that it’s freely available in Google Analytics. You can literally start checking this now. So fire up Google Analytics, find those engagement traps, and fix them soon. Take action before Google takes action.

GG