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Continuous Scroll And The GSC Void: Did The Launch Of Continuous Scroll In Google’s Desktop Search Results Impact Impressions And Clicks? [Study]

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Google continuous scroll study based on GSC data.

Google rolled out continuous scroll in the desktop search results for English queries in the United States on December 5, 2022. Continuous scroll enables users to seamlessly continue to page two and beyond without having to click a “next” button at the bottom of the results. This followed Google rolling out continuous scroll in the mobile results in October of 2021 (again, U.S-only for English queries).

Here is Google’s announcement about the rollout to desktop in early December (with a gif of continuous scroll in action):

After the rollout to desktop, many wondered how continuous scroll would impact the visibility of rankings that were beyond page one. For example, for sites with urls ranking on page two, and maybe even the top of page two, the ability for users to easily scroll to additional pages of search results should lead to more impressions, clicks, and conversions. That’s the idea anyway, and something I set out to analyze.

Before continuous scroll rolled out in the SERPs, ranking on page two and beyond meant your listings probably wouldn’t be seen much. Sure, some people would venture to page two and beyond, but most would stick on page one (and just refine their search if they couldn’t find what they needed after scanning the results). But with continuous scroll, users can easily move to the second page of results without having to click a button. The new results just show up as you approach the bottom of the initial set of results.

Analyzing Continuous Scroll in the U.S. Desktop Search Results:
Right after the rollout of continuous scroll on desktop, I published a post explaining how to analyze the change in impressions, clicks, and click through rate based on users being able to seamlessly view more listings in the search results. My tutorial explains how to use the GSC API and Analytics Edge in Excel to bulk export data from GSC, filter by desktop only from the United States, compare timeframes, and then filter by page two and three results. The resulting worksheets quickly provide the changes across metrics when comparing the timeframe before, and after, continuous scroll rolled out.

After publishing that post, I’ve been eagerly waiting for more data to build in order to dig into the reporting across sites. And that’s exactly what I did for a number of sites across verticals. I knew the sites would have a ton of data to analyze, and across verticals, so it should be easier to see differences based on continuous scroll rolling out in the desktop SERPs in the United States.

Below, I’ll cover the methodology I used, the data I analyzed, some interesting (and scary) findings about GSC data, and the impact, or lack thereof, of continuous scroll rolling out in the desktop search results. Let’s jump in.

Methodology:
First, I selected twelve different sites that have a steady and significant amount of traffic from Google organic. Some of the properties are large-scale sites driving a lot of clicks from Google organic, where others were niche sites driving less traffic (but still a good amount of clicks). I made sure the sites were across verticals, and that those verticals weren’t heavily impacted by the holidays (as much as I could). Also, I made sure to focus on just the desktop results from the United States, since continuous scroll did not roll out internationally yet.

Then I used the process I mapped out in my tutorial for analyzing the change in metrics based on exporting data from GSC in bulk, filtering by desktop only from the U.S., comparing timeframes, and then filtering by page two and three results. You can check my tutorial for how to accomplish this using the GSC API, Excel, and Analytics Edge.

When analyzing the data, I made sure to review queries where the average position was about the same before and after continuous scroll rolled out. For example, I wouldn’t review a query that was ranking 24 before the update and then 16 after. That’s a big difference and could obviously impact the data. I looked for queries where the site ranked at about the same position so I could better analyze if continuous scroll was having an impact on visibility and engagement.

Regarding Google organic search traffic for the twelve sites, I have provided the number of clicks over the past three months for each of the sites I analyzed (just so you have a feel for how much traffic they were driving from Google recently).

The sites ranged from 89M clicks to 1.4M clicks over the past three months and spanned a number of verticals:

  • Site 1: 89M clicks
  • Site 2: 31M clicks
  • Site 3: 8.6M clicks
  • Site 4: 4.8M clicks
  • Site 5: 3.8m clicks
  • Site 6: 3.4M clicks
  • Site 7: 2.8M clicks
  • Site 8: 1.4M clicks

The GSC Void: Dark, Murky, and Inconclusive
First, the reality of filtered GSC data hit hard after exporting data based on query. Barry Schwartz covered this in July of 2022 after Patrick Stox wrote a post explaining what he was seeing across sites with GSC filtering data. It was eye-opening to see how much data was filtered for some sites…

When I checked at the time across sites, I also saw massive gaps in data when exporting based on query. For example, the total at the top of the Performance report in GSC can be much greater than what you see after exporting the data by query (and then totaling the clicks and impressions). And I mean WAY OFF. For some sites I analyzed at the time, I was only seeing 20% of the total after exporting the data. Yes, that means 80% of the data was filtered.

The reason is that Google filters queries based on privacy concerns. In its documentation, which it refined after Patrick’s study, Google explains that it filters “anonymized queries” to protect the privacy of users. And for some sites, it can be a ton of data. It’s worth noting that exporting by page will yield the full results (or close), but exporting by query highlights what I call the GSC void.

Back to continuous scroll data… For the twelve sites I analyzed, some GSC properties only provided 20-30% of the total data reported in the Performance reporting due to filtering. You read that correctly. That means 70-80% was filtered for those sites.

On the flip side, I’ve seen as high as 84% of the data showing (so 16% filtered), but that was the most I could find based on reviewing a number of properties in GSC. Don’t get me wrong, that’s much better than 24%!

When I saw the amount of filtering, I knew I had my work cut out for me with trying to analyze the data. I was hoping there was enough to see changes based on continuous scroll rolling out… One thing was clear, the GSC void was dark and murky.

Dark and Murky: Analyzing the impact to impressions, clicks, and click through rate.
First, “Dark and Murky” isn’t the name of a trendy new drink you can order poolside at a resort. It’s just the first thing I said after going through the data across the sites I analyzed. When I got past the first page of results, the numbers across most of the sites plummet. They drop so much that it’s nearly impossible to draw any conclusions about how continuous scroll is impacting clicks and click through rate from the desktop SERPs.

And from an impressions standpoint, I couldn’t see a consistent trend with the increase in impressions. For some queries, impressions did increase. For others, they dropped. And again, clicks and click through rate were very hard to analyze due to the insanely low numbers beyond page one.

For example, as soon as I checked the spreadsheet for page two results across several of the sites, the number of clicks was inconsequential. That shows you how much data is being filtered, by the way… On page one, some queries are yielding tens of thousands of clicks, or more. Then page two drops to almost nothing? So yes, the GSC void is real and it can severely hamper your analysis.

Here are some screenshots from the spreadsheets for page two of the search results. Get ready to be underwhelmed from a clicks standpoint. :)

But, I mentioned one site that only had 16% of its data filtered (which was the best I came across out the twelve). For that site, you would think I would have enough data to make some conclusions… but not really. Clicks were very low once I analyzed page two and beyond. I could see that impressions increased for a number of queries, but clicks didn’t. And since clicks were so low beyond page one, the difference in click through rate was pointless to review.

Here is a screenshot from the site that was only 14% filtered:

For example, for one query the number of impressions jumped by 3,110, but clicks only increased by 18. Average position went from 21.3 to 19.0, which is close, but that could have meant a jump from page three of the results to two. Clearly this isn’t enough data to draw any conclusions. The impressions increase is one thing, but the clicks were so low that it didn’t mean much. And to be honest, who really cares about an increase in impressions if clicks don’t follow. For most site owners, this isn’t really a branding exercise. They want the clicks and subsequent conversions! :)

Here’s another site where there was a nice increase in impressions for some queries, and definitely an increase in clicks for some of the queries. That said, some of the increases were due to the site ranking much stronger in the latest timeframe. There was an increase in impressions for some queries when the site ranked about the same position, but there’s just not enough click data to draw any serious conclusions…

Key takeaways based on analyzing Continuous Scroll in the desktop SERPs:

  • Based on my analysis across sites, there is NOT much data on page two and beyond to analyze… That’s even the case for large-scale sites with a ton of Google organic traffic. That’s based on GSC filtering anonymized queries.
  • I could see an increase in impressions for some queries, but I it was hard to draw any conclusions since there were many that dropped when comparing timeframes as well.
  • Clicks and click through rate were even harder to analyze. There weren’t many clicks to report overall beyond page one, which made it very tough to draw any conclusions.
  • From a GSC data standpoint, I had severely-limited data based on GSC filtering. This has been reported before, and this study underscored how much filtering is going on. For example, some of the exports were only yielding 20-30% of the data reported in GSC in the Performance reporting (when analyzing by query).
  • I do recommend going through this process for your own sites using the tutorial I published (if for no other reason than to see the severe filtering going on with GSC data when exporting by query). Note, you should be able to see the full data if exporting by page, but there are many queries that lead to specific pages (which can muddy waters analysis-wise).  

Summary: The GSC Void Limits Analysis of Continuous Scroll in the SERPs
After continuous scroll rolled out in the desktop search results, I was extremely excited to analyze the impact to impressions and clicks based on users scrolling to page two and beyond. Unfortunately, GSC data filtering hampered my efforts big-time. Some sites were only returning 20-30% of the total data based on GSC’s filtering of anonymized queries.

I’ll be sure to update this post if I come across stronger findings based on analyzing continuous scroll across sites. In the meantime, I do recommend going through this process for your own sites. You never know, GSC might not be filtering as much of your data… Good luck.

GG

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

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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.

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).

Install Analytics Edge in Excel

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.

Select a GSC property in Analytics Edge

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
Assign the table name 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.

Write data to a worksheet in Analytics Edge in Excel

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

Percent Human: A list of tools for detecting lower-quality AI content

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AI content

Updated on 1/10/23: GPTZero was added to the list of AI content detection tools (created by a Princeton University senior).
Updated on 12/29/22: Content at Scale’s AI content detection tool was added.
Updated on 12/14/22: Writer’s AI content detection tool has been updated to detect GPT-3, GPT 3.5, and ChatGPT.
Updated on 12/13/22: Originality.ai was added to the list of AI content detection tools.

———-

As I’ve been sharing examples of sites getting pummeled by the Helpful Content Update (HCU) or the October Spam Update, I’ve also been sharing screenshots from tools that detect AI content (since some sites getting hit are using AI to pump out a lot of lower-quality content – among other things they were doing that could get them in trouble). And based on those screenshots, many people have been asking me which tools I’m using.

So, instead of answering that question a million times (seriously, it might be a million), I figured I would write a quick post listing the top tools I have come across. Then I can just quickly point people to this post versus answering the question over and over.

And note, I’m not saying these tools are foolproof. I have just found them to be pretty darn good at detecting lower-quality AI content. And that’s what we should be trying to detect by the way (not all AI content… but just low-quality AI content that could potentially get a site in trouble SEO-wise).

For example, here is high-quality human content run through a tool:

Detecting human content

And here is an example of lower-quality AI content run through a tool:

Detecting lower quality AI content

Again, it’s not foolproof, but can give you a quick feel for if AI was used to generate the content. Below, I’ll cover my favorite AI content detectors I’ve come across so far. I’ll also keep adding to this list so feel free to ping me on Twitter if you have a tool that’s great at detecting lower-quality AI content!

Here is a list of tools covered in this post for detecting AI content:

  1. Writer’s AI content detector tool.
  2. Huggingface GPT-2 Output Detector Demo.
  3. Giant Language Model Test Room (GLTR).
  4. Originality.ai (AI content and plagiarism detection)
  5. Content at Scale’s AI content detection tool.
  6. GPTZero

1. Writer’s AI content detector tool:
The first tool I’ll cover is from a company that has an AI writing platform (sort of ironic, but does make sense). Also, it seems like the platform is more for assisting writers from what I can see. You can check out their site for more information about the platform. Well, they also have a nifty AI content detector that works very well. You have probably seen my screenshots from the tool several times on Twitter and LinkedIn. :)

Update: 12/14/22 – While I was testing content created via GPT 3.5 and ChatGPT, I noticed that Writer’s detection tool was accurately detecting the content as created by AI. That was a change, since the tool was originally focused on GPT-2, so I quickly reached out to Writer’s CEO for more information. And I was correct! Writer’s AI content detection tool has been updated to detect GPT 3, GPT 3.5, and ChatGPT. So it’s now the second tool on the list that can achieve that.

AI content is progressing, but so are the tools. Below are some examples of using Writer’s AI content detection tool.

Here is Writer’s tool detecting higher-quality human content:

Writer's AI content detection tool measuring high quality human content

And here is Writer’s tool detecting content created via GPT-3.5 (using davinci-003, which is the latest model as of 12/14/22):

Writer's AI content detection tool accurately detecting content created via GPT-3.5 (using davinci-003)

2. Huggingface GPT-2 Output Detector Demo:
If you’re not familiar with Huggingface, it’s one of the top communities and platforms for machine learning. You can check out their site for more information about what they do. Well, they also have a helpful AI content detector tool. Just paste some text and see what it returns. I have found it to be pretty good for detecting lower-quality AI content. 

For example, here is Huggingface’s tool detecting higher quality human content:

Huggingface's AI content detection tool measuring high quality human content

And here is Huggingface’s tool detecting lower-quality AI content:

Writer's AI content detection tool measuring lower quality ai content

3. Giant Language Model Test Room (GLTR.io)
The third tool I’ll cover was actually down recently, but I had heard good things about it from several people (when it was working). It ends up there was a server issue and the tool was hanging. Well, the GLTR is back online now and I’ve been testing it to see how well it detects AI content.

The tool was developed by Hendrik Strobelt, Sebastian Gerhmann, and Alexander Rush from the MIT-IBM Watson AI Lab and Harvard NLP. It’s definitely not as intuitive as the first tools I covered, but once you get the hang of it, it can definitely be helpful.

How it works:
You can paste text into the tool and view a visual representation of the analysis, along with several histograms providing statistics about the text. I think most people will focus on the visual representation to get a feel for how likely each word would be the predicted word based on the word to its left. And that can help you identify if a text was written by AI or by a human. Again, nothing is foolproof, but it can be helpful (and I’ve found the tool does work well). To learn more about GLTR and how it works, you can read the detailed introduction on the site.

For example, if a word is highlighted in green, it’s in the top 10 of most likely predicted words based on the word to its left. Yellow highlighting indicates it’s in the top 100 predictions, red in the top 1,000, and the rest would be highlighted in purple (even less unlikely to be predicted).

The fraction of red and purple words (unlikely predictions) increases when the text was written by a human. If you see a lot of green and yellow highlighting, then it can indicate the text contains many predicted words based on the language model (signaling the text could have been written by AI).

Here are two examples. The first shows AI content (many words highlighted in green and yellow). This text was generated via GPT-2.

Giant Language Model Test Room (GLTR) analysis of AI generated content.

And here is an example from one of my articles about broad core updates. Notice there are many words highlighted in red, and several purple words as well (signaling this is human-written text).

Giant Language Model Test Room (GLTR) analysis of human-written content.

4. Originality.ai (for detecting GPT 3, GPT 3.5, and ChatGPT)
I was able to test Originality.ai recently and I’ve been extremely impressed with their platform. The CEO emailed me and explained they were one of the few tools to be able to detect GPT-3, GPT 3.5 and ChatGPT (as of December 13, 2022). Needless to say, I was excited to jump in and test out its AI content detection tool. Also, it’s worth noting that the tool can detect plagiarism as well (which is an added benefit). They have also released a Chrome extension and they have an API for handling requests in bulk. I’ll cover more about the Chrome extension below.

So, I fired up OpenAI and selected text-davinci-003 (the latest model as of 12/13/22) and started generating essays, short articles, how-tos, and more. I also used ChatGPT to generate a number of examples I could test.

And when testing those examples in Originality.ai’s detection tool, it picked up the work as AI every time. Again, I was extremely impressed with the solution.

For example, here was a short essay based on GPT 3.5:

Originality.ai AI content detection tool

And here was a how-to containing several paragraphs and then a bulleted list of steps. I also checked for plagiarism:

Originality.ai AI and plagiarism detection tool

It’s not a free tool, so you will need to sign up and pay for credits. That said, it’s been a solid solution based on my testing. Note, they are providing a coupon code (BeOriginal) that gets you 50% off your first 2000 credits. One credit scans 100 words according to the site.

Originality.ai Chrome Extension:
I mentioned earlier that Originality.ai has both a Chrome extension and an API. The Chrome extension enables you to highlight text on a page in Chrome and quickly check to see if it was written by AI. You must log in and use the credits you have purchased, so it’s not free. It works very well based on my testing so far.

For example, here is an article created via Automated Insights. By highlighting the article text, right clicking, and selecting Originality.ai in the menu, you can check to see if the content was created by AI.

AI content created via Automated Insights
Originality.ai Chrome extension detecting AI content

5. Content at Scale
Next up is an AI content detector tool from Content at Scale. Like Writer, they provide a platform for AI content generation that uses an interesting approach. You can read more about the platform on their site. But, like Writer, they also have an AI content detection tool. You can include up to 2,500 characters and the tool will analyze the text and determine if it’s AI content or human content. And like Originality.ai and Writer, it can detect GPT-3, 3.5, and ChatGPT.

For example, here is the tool detecting AI content generated by ChatGPT (a short essay):

Content at Scale's AI content detector detecting AI-generated content.

And here is the tool detecting content from one of Barry’s blog posts as human:

Content at Scale's AI content detector detecting human-generated content.

6: GPTZero
Next up is a new AI content detection tool created by a Princeton University student! And it’s causing quite the buzz. I’ve read a number of articles across major publications about Edward Tian and his tool called GPTZero, which works to detect if content was written by ChatGPT.

His approach is interesting, since it uses “perplexity” and “burstiness” in writing to detect if a human or AI wrote the content. “Perplexity” aims to measure the complexity of the content being tested, or what Edward explains as the “randomness of text”. And “burstiness” aims to measure the uniformity of the sentences being tested. For example, Edward explains that “human written language exhibits non-common items appearing in random clusters.” Humans tend to write with more burstiness, while AI tends to be more consistent and uniform.

I’ve been testing the tool over the past few days, and it has worked well (and has been pretty accurate). The site has definitely had some growing pains since launching, since I’m sure Edward didn’t think the tool would become so popular that quickly, but site performance has improved greatly recently. Also, the homepage now explains he is creating a “tailored solution for educators”. I’m eager to hear more about that, but for now, you can add GPTZero as yet another tool in your AI detection arsenal. I think you’ll like it.

For example, here is the tool measuring “perplexity” and “burstiness” of content (based on an essay written by ChatGPT):

And here is the final result accurately detecting AI content written by ChatGPT:

Summary: Although not foolproof, tools can be helpful for detecting AI content.
Again, I’ve received a ton of questions about which tools I’ve been using to detect lower-quality AI content, so I decided to write this quick post versus answering that question over and over. I hope you find these tools helpful in your own projects. And again, if you know of other tools that I should try out, feel free to ping me on Twitter!

GG

True Destination – Demystifying the confusing, but often accurate, true destination url for redirects in Google Search Console’s coverage reporting

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If you are confused when Google reports redirects as other categories, like “blocked by robots.txt”, “soft 404s”, “noindexed”, “404s”, and others, it could be Google silently following the redirect and reporting the status of the true destination url instead. My post covers the situation in detail, and provides examples of this happening in the wild.

While heavily analyzing websites from an SEO standpoint, you will undoubtedly find yourself deep in Google Search Console (GSC) reporting. GSC contains a boatload of data directly from Google and can help site owners and SEOs surface key insights. That said, it’s important to understand the nuances involved with GSC reporting, and how Google determines the information it provides in those reports. Having a clear understanding of what the data is showing is important when taking action to improve SEO.

And there’s no better example of GSC data confusion than the dreaded true destination url for redirects in GSC’s index coverage reporting (and URL inspection tool). I have received so many questions about this from clients that I decided to write this post so I can just point people here versus explaining it again and again.

So, join me on a GSC adventure where we uncover the secrets of the true destination url. Some of you might already know this, but I know some do not. And for those that don’t, this will all make sense very soon. You might not be happy with how this is working, but at least you’ll understand why urls are categorized in certain ways in GSC (and via the URL inspection tool).

What is the dreaded true destination url situation in GSC for redirects?
When viewing the indexing status in GSC of urls that are being redirected, Google reports on the true destination url (even if that url is outside of your own site). For example, if you redirect a url to another url, and that url is not indexable for some reason, GSC will silently follow the redirect and report on the final destination’s status. And that can be super confusing for site owners and SEOs that don’t know this is happening.

Yes, that means you can see urls showing up as “blocked by robots.txt”, “noindexed”, “soft 404”, “404”, and more (when the url you are inspecting is actually redirecting). As you can imagine, many site owners are left confused when they see “blocked by robots.txt” when they know 100% that a url is redirecting.

Google’s John Mueller has been asked about this many times, and he has replied with what I explained above (and does admit it can be a bit confusing). Also, Barry wrote a post covering how this happens with the URL inspection tool based on John’s comments. Even though this has been documented, I find it’s still a very confusing situation for many site owners and SEOs (which is why I’m writing this post).

Here is a tweet of mine with a link to John explaining how Google silently follows redirects (and how that shows up in GSC):

Now that you know this is happening, you might be wondering what this actually looks like in GSC. I’ll cover that next with examples of this happening in the wild.

Examples of Google silently following redirects and reporting the true destination url status in GSC:
Below, I’ll provide examples with screenshots of Google reporting on the true destination urls versus the redirect. Again, this is when the final destination urls are not indexable for some reason.

Blocked by robots.txt:
The url is redirected outside the site to a url that is blocked by robots.txt. Google reports the redirecting url as being “blocked by robots.txt” since the final destination is actually disallowed.

A twist on blocked by robots.txt:
This url redirects first to a tracking url, which is blocked by robots.txt. The final destination is not blocked, but Google can’t follow the first redirect to find the final destination url since it’s disallowed. It just knows that first url in the chain is blocked and reports that in GSC. Below, you can see the second step shows the url is actually blocked by robots.txt (and that’s what is reported in GSC).

Soft 404:
The url redirects to a page that’s a soft 404 (a product is unavailable). Google reports that the redirecting url is a soft 404 (since the true destination url is being seen as a soft 404).

Here is the page the url redirects to (with the product “currently unavailable”). Hence the soft 404:

Noindexed:
Yep, you guessed it. The url redirects to a page that’s noindexed. Google reports the url that is redirecting as noindexed in the coverage reporting:

Crawled, not indexed:
At first glance, you might think the redirect is being reported as “Crawled, not indexed”. Not true! It’s the final destination url that’s not being indexed by Google. Google is reporting “Crawled, not indexed” for the true destination url.

The final destination url is indeed not indexed:

404:
How can Google see a redirect as a 404? It doesn’t. It’s the true destination url that 404s and that’s what is reported in GSC.

404 with domain name change:
This is just a variation on the 404 situation to explain how this works when changing domain names. The url on the old domain redirects to a url on the new domain name, but the url was never migrated (it 404s). So Google reports that the redirecting url is a 404.

Sorry, more confusion with redirects:
When a url redirects to a page that resolves with a 200 header response code, and is indexed, the URL inspection tool reports accurately about the redirect (and says that initial url is a redirect and not indexed), but Google shows the canonical as the true destination url (where the redirect leads to). Talk about confusing, especially based on everything I explained above with the other examples where the redirecting urls are being reported as something different than a redirect…

A possible solution in GSC to clear up the confusion:
So, how can this be more intuitive? I think if GSC actually provided a message that it’s reporting on the true destination url, it could clear up the confusion for site owners and SEOs. Below, I have mocked up what this can look like in GSC. If Daniel Waisberg is reading (and I hope you are), then please add this!

Summary: Clearing up the confusion with redirects and destination url reporting.
I hope this post helped you understand how Google is silently following redirects and reporting on the true destination urls in GSC. I know it’s a confusing topic for many site owners and SEOs and I’m sure it has led to many head-scratching moments. Just keep in mind that as of now, GSC is reporting on the true destination urls when a url redirects. So don’t be surprised when you notice redirects in other categories in GSC’s coverage reporting (or when using the url inspection tool). And who knows, maybe the GSC product team will implement that message I mocked up above…

GG

Google’s September 2022 Broad Core Product Reviews Update (BCPRU) – The complexity and confusion when major algorithm updates overlap

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Google September 2022 broad core product reviews update

Well, SEOs and site owners had a heck of an end to the summer of 2022. It all started with the Helpful Content Update (HCU), which rolled out on August 25, 2022. The rollout of Google’s new site-wide signal took a little more than two weeks to complete (and it only seemed to impact the most egregious sites). I covered that heavily on Twitter while analyzing the update.

But we didn’t have much time to rest after the HCU rollout completed, since Google then rolled out the September 2022 broad core update. That was surprising, especially since I spoke with Google about the HCU and Product Reviews Update prior to the HCU rollout, and didn’t hear anything about a broad core update launch that would follow!

So, now we had a broad core update rolling out that can make the earth shake, and after a new site-wide ranking signal rolled out (the HCU). What else could happen?

In a move that surprised many (due to timing), Google then rolled out the September 2022 Product Reviews Update (PRU). And PRUs can be core update-like for sites that contain products reviews content. Although we knew the next PRU was coming, the timing of the rollout was extremely surprising since Google overlapped the broad core update and the Product Reviews Update.

That’s right, it’s another algo sandwich from Google, which can bring a ton of confusion for site owners and SEOs. To make matters even more confusing, both the broad core update and the PRU completed rolling out on the same exact day (September 26, 2022). Needless to say, many are confused about which update impacted their sites.

Ranking updates page for the September 2022 broad core update and Product Reviews Update

And if you’re an SEO history buff, then you might know that algo sandwiches are not new for Google, just rare (pun intended). Google has rolled out overlapping updates, or updates very close to one another, several times in the past. The one that really sticks out to me was the Panda, Penguin, Panda algorithm sandwich from April of 2012 (where all three updates were rolled out within a 10-day period). It was a triple decker sandwich packed with thin content, spammy links, and topped with Google’s famous hot sauce. As you can guess, many were confused about what hit them at the time… which led to several of my posts about Pandeguin (the combination of Panda and Penguin).

Here’s a graph from my post about Pandeguin showing a site impacted by all three updates:

Pandeguin impact

Covering The Confusion Based On Overlapping Major Algorithm Updates:
In this post, I’m going to cover the two overlapping major algorithm updates that rolled out in September 2022, the confusion that set in for some site owners and SEOs, interesting things I saw during the combined rollout, my recommendations for sites impacted during the algo sandwich, and more.

So strap yourself in, grab your favorite sandwich sauce, and maybe a pickle for good luck. It’s going to be a bumpy ride in Gabe’s Deli for this post. Let’s jump in.

First, here’s a quick table of contents for those that want to jump around the post:

Visualizing the confusion. Was it the broad core update or the Product Reviews Update?
First, let’s get the dates right. The broad core update started rolling out on September 12, 2022. And many sites saw impact very quickly with the broad core update. I started documenting movement about one day into the rollout, which is quick.

For example, here are several sites seeing immediate impact:

Early impact from the September 2022 broad core update
More early impact from the September 2022 broad core update
Another example of early impact from the September 2022 broad core update
Final example of early impact from the September 2022 broad core update

Then the Product Reviews Update started rolling out on September 20, 2022 in the middle of the broad core update rollout. It was eight days into the rollout of the broad core update. And this is where things get interesting, and confusing. Many sites saw a ton of movement starting right at that point. Some were product reviews sites, but some were not. Not even close actually…

Here are some examples of product review sites seeing movement when the September PRU rolled out. This would be clear PRU impact in my opinion (without confusion based on the broad core update):

Example of product review site impacted by September 2022 Product Reviews Update
Another example of product review site impacted by September 2022 Product Reviews Update
Third example of product review site impacted by September 2022 Product Reviews Update
Final example of product review site impacted by September 2022 Product Reviews Update

But not all sites impacted were as clear as those… There were many sites that don’t contain reviews that were impacted heavily starting on 9/20 (right when the PRU started rolling out). And that led to massive confusion about which update actually impacted sites seeing a lot of movement. In other words, was it the broad core update or the Product Reviews Update impacting the site? Only Google knows… or do they? I’ll cover more about that soon.

Here are some examples of sites that don’t contain reviews or affiliate content at all that were heavily impacted starting on 9/20. These were reference sites, news sites (without affiliate content), e-commerce sites, recipe sites (without affiliate content), and more.

Non-review site impacted on the same date the Product Reviews Update rolled out.
Another non-review site impacted on the same date the Product Reviews Update rolled out.
Third example of a non-review site impacted on the same date the Product Reviews Update rolled out.
Final example of a non-review site impacted on the same date the Product Reviews Update rolled out.

Welcome To Google Land – The Overlapping of Major Algorithm Updates
I have said “Welcome to Google Land” many times over the years, and for good reason. It can sometimes feel like you are on a roller coaster, run by AI, in a land of confusion, with no games or stuffed animals at the amusement park to make you feel better.

Google usually tries to keep major algorithm updates separate so site owners can better understand which update actually impacted their sites. Google’s Danny Sullivan has explained that in the past (and even right before this algo sandwich rolled out!)

But, and like I said earlier, there are times that major updates do overlap. This was a great example of that… When the September PRU rolled out during the September broad core update, Google provided some advice to site owners. Google’s advice was basically that if you have product reviews, then it’s probably the PRU impacting your site. If you don’t, then it’s probably not.

And as you can guess, the word “probably” definitely caused some concern. And it wasn’t long before we saw sites that didn’t have product reviews that were impacted heavily when the PRU rolled out.

Here are tweets from Google about the overlapping rollout:

Don’t assume it’s the PRU, it could be the broad core update:
It’s also worth noting that since the core update was still rolling out, site owners could not simply assume it was the PRU impacting their sites on 9/20. Based on what I saw across many sites, I do believe we saw an uptick from the broad core update right on 9/20 when the PRU rolled out. That was either coincidental, or not. But again, many sites without product reviews were heavily impacted on that date.

For example, here is visibility trending from a recipe site without any product reviews or affiliate links that was impacted on 9/20. Some users are leaving a quick review of the recipe in the comments, but that’s not really what the PRU should be targeting. So either the PRU is flawed there or it was the broad core update impacting the site. Personally, I believe it was the core update impacting the site, but I can’t say for sure. The site owner is super-confused. They were impacted by a previous broad core update by the way, which is interesting.

Recipe site without reviews or affiliate links impacted on 9/20 when the Sep 2022 product reviews update rolled out

Here is a quote from the site owner about the situation:

“When the September core update started rolling out my site wasn’t affected at all. But the day the PRU started, it took a relatively big hit (it dropped by about 20%). The site doesn’t have product reviews or affiliate content at all. Unfortunately, the overlapping updates can send you down a rabbit hole trying to fix things that aren’t actually a problem… since you don’t have a clear idea of which update caused the drop.

“For example, if it was the Product Reviews Update, then you would think I should be focused on improving reviews content (but I don’t have any reviews)… Or is it the comments that sometimes have quick reviews of recipes from users? And if it was the core update causing the drop, should I focus on improving the site overall? It’s just very confusing when all I’m trying to do is publish great recipes for my users…”

And if you think that’s a tough situation, then look at this trending (if you dare):
The site contains mostly informational content, but does include some reviews (and affiliate links). Google is clearly having major issues understanding the type of site and whether it should rank well. The site has experienced massive swings in rankings during multiple major algorithm updates (including reversals outside of those updates). Below, you can see impact from a broad core update, Product Reviews Updates, and then random swings outside of major updates.

Insane trending for a site impacted by several major algorithm updates.

Which then led to others sharing their trending on Twitter, which showed similar ups and downs.

Note, I’ll come back to this case shortly… since the next section ties in nicely. Yep, it’s like Inception for SEO. :)

A final September PRU tremor correcting some issues with the July PRU:
So, based on what I explained with the 9/20 impact, could there be a flaw with the September PRU? It’s possible… and I was pretty vocal about some problems I saw with the July PRU. At the time, I said that we could see Google correct those flaws via a tremor or with the next PRU.

Here is my tweet from July explaining we might see a correction:

Thankfully, a change was rolled out! At the very end of the rollout of the September PRU (on 9/25), a number of sites surged back from the dead that were impacted by the July PRU. I tweeted several examples when this happened.

Site surging back from the dead during a late PRU tremor
Another site surging back from the dead during a late PRU tremor
A third site surging back from the dead during a late PRU tremor

Time will tell if some issues with the September PRU get corrected and those sites bounce back like these did on 9/25.

And remember that site from earlier with insane trending? Well, here we go again. That site surged back again on 10/15. Yep, after the broad core update and PRU completed. And after the site dropped heavily during the late PRU tremor on 9/25. Again, Google is having a very hard time understanding where this site fits in, the type of content it contains, etc.

More insane trending for a site impacted by multiple major algorithm updates.

More Negative Impact for Sites Hit By Helpful Content Update (HCU)
After the Helpful Content Update rolled out, there was some confusion about if the HCU could contribute to broad core updates (as another signal). Google’s Danny Sullivan explained the updates are separate, but if you were impacted by the HCU, and if you have broad core update issues, then the combined effect might not be optimal for you… Basically, the effect can be compounded.

Here is a tweet from Danny about this:

And here are some sites that dropped with the Helpful Content Update and then saw more of a drop with the September broad core update:

Site impacted by the Helpful Content Update that saw more of a drop with the Sep 2022 broad core update
Another site impacted by the Helpful Content Update that saw more of a drop with the Sep 2022 broad core update
Third site impacted by the Helpful Content Update that saw more of a drop with the Sep 2022 broad core update

Google on how the PRU evaluates sites. Site-level or url-level?
Based on the varying levels of impact by the Product Reviews Update, and how it impacted some sites that contain a mix of informational content and reviews, Google received some questions about how the PRU evaluates content. For example, is it evaluating at the url-level or site-level? And, how does Google determine that a piece of content is a review in the first place (especially if the site has other types of content)?

Google first responded quickly about the latter question and recommended that sites might want to add structured data to clearly signal to Google that the content was a review. Then when questioned about that point, Google took some more time internally before responding.

Then Danny Sullivan responded and first explained more about how the PRU evaluates websites. He explained that if your site contains a lot of product reviews, then the PRU can be like a site-wide evaluation. i.e. All content can be evaluated… But, if your site contains a mix of content, and reviews don’t make up a large portion of the content, then the PRU evaluates more on the url-level.

Here are Danny’s tweets about that:

This made complete sense to me based on analyzing many sites impacted by Product Reviews Updates. For sites where a majority of the content is comprised of product reviews, the site could be heavily impacted by the PRU (and any content on the site could be negatively impacted).

For example, here is a site that focuses on product reviews that was impacted heavily by the April 2021 PRU. Notice the massive drop when the update rolls out:

Extreme drop for a product reviews site during the April 2021 PRU

And for sites that contain a mix of content, like news sites that also publish some product reviews, then it wouldn’t be like a site-wide evaluation. In other words, not all content would be evaluated by the PRU. For example, here is a news site that contains review content in a specific section. The site overall took a hit when the PRU rolled out, but the section with reviews REALLY took a hit.

First, here is the drop overall for the site:

News site also containing reviews dropping with the Sep 2022 PRU

And here is the drop for the reviews section:

News site with reviews section dropping with the Sep 2022 PRU

This does reinforce the idea that adding all reviews to a section could help Google identify where reviews are on the site. John Mueller explained that the PRU works more broadly on a site or section-level depending on how the site is structured content-wise.

And that dovetails nicely into the next section of my post about structured data and Google identifying reviews…

Structured Data helping Google understand if your content contains reviews. Really?
For sites that contain a mix of content, Danny Sullivan explained that Google can use structured to help it understand if a piece of content is a review. But, it’s not required and it’s just one of several signals they use to understand if content is a review.

And this from earlier in the conversation:

I’ve been vocal that this is pretty ridiculous to me. I mean, Google with all of its unbelievable natural language processing power needs site owners to feed it structured data to understand if it’s a review? That’s crazy. Also, many site owners don’t even know what structured data is and how to use it. That said, if your site contains a mix of content (including reviews), then I would add structured data to signal to Google which pieces of content are indeed reviews.

And if you think that structured data statement from Google caused more confusion, you would be right. I had several site owners reach out to me claiming they were hit by the PRU because of structured data errors, the wrong structured data used, etc. Unfortunately, even though I don’t believe that’s the case, I can’t say it’s NOT the case with 100% certainty.

Google adds examples of product reviews to its documentation.
With each Product Reviews Update, the algorithm continues to evolve. One question I get often is about the types of content that can be impacted by the PRU. For example, is it supposed to just target sites with product reviews or can sites with other types of reviews content be impacted? I’ve seen sites with user-generated content (UGC) reviews get impacted in the past and I’m not sure Google is really targeting that type of review with the PRU. Well, I’ve seen less and less of that with each Product Reviews Update. Google does seem to be focusing just on product reviews with the latest iterations of the PRU.

Also, Google recently refined one of the help documents about the Product Reviews Update. Specifically, Google added three examples of the types of reviews content that can be created by site owners (and I’m assuming Google is saying these are the types of review content that can be evaluated by the PRU – at least for now). And those examples don’t contain UGC reviews.

It’s just worth noting for anyone creating reviews content (or any site owner that has UGC reviews).

Google adds examples of product review pages in their documentation for site owners and SEOs.

Final tips and recommendations for site owners impacted during the September 2022 algorithm sandwich:
If you were impacted during the latest algorithm sandwich, then the tips below could help you get moving in the right direction. I hope the following recommendations help cut through some of the confusion:

  • If you were impacted starting on 9/12, and before 9/20, then you were impacted by the broad core update. You can read my posts about broad core updates to learn more about them, how to identify why you were impacted, and learn the best path forward from a remediation standpoint.
  • If you were impacted starting on 9/20 when the PRU rolled out, and you have a mix of content on your site beyond reviews, do not assume it was the PRU. It could have been the broad core update. I covered this earlier in the post, and I have seen many sites impacted on 9/20 that had no reviews or affiliate content at all.
  • If you were impacted starting on 9/20, and you do have a lot of product reviews content, then you should work to improve your content based on Google’s best practices. You can read my previous posts about Product Reviews Updates to learn more about them.
  • If you have a mix of content on your site, including reviews, then I recommend adding structured data to identify the reviews content (since Google explained it uses structured data as one signal for identifying reviews). I still think it’s crazy that site owners need to do this… but I would probably do it to make sure Google can understand what is reviews content and what’s not. Remember, we’re dealing with machine learning systems and mistakes can definitely be made by Google on this front. Just check the reversals in my post above to see examples of that.
  • If impacted by the September broad core update, run a delta report to understand what dropped, and why. For example (if it was the core update), was it a relevancy adjustment, intent shift, or is it overall site quality problems. Then form a remediation plan based on what you find.
  • If impacted by a major algorithm update, don’t just compare specific urls in the SERPs if that’s the case. With site-level quality algorithms at play, the overall quality evaluation could be dragging rankings down. And for some urls, the content on that page might have little to do with that url specifically dropping. Improve overall… that’s what Google wants to see.
  • For broad core update remediation, using a “kitchen sink” approach is your best path in my opinion. That’s where you surface all potential quality problems and work hard to fix as many as you can. Look to improve overall. That’s all you can do in the age of machine learning-based major algorithm updates.

Summary – Overlapping major algorithm updates can cause confusion for sites owners and SEOs. Try your best to cut through that confusion…
I hope this post helped you better understand the major algorithm updates that rolled out in September of 2022. Unfortunately, two of those major updates overlapped for a week. And when that happens, it can cause massive confusion for site owners and SEOs. By reviewing the points in my post, and understanding the dates you were impacted, you can form a plan of attack remediation-wise.

And like I explained in my post, don’t be surprised if we see some corrections and adjustments based on the latest updates. Again, “Welcome To Google Land”. Good luck.

GG

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