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How To Use ChatGPT For Keyword Research

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How To Use ChatGPT For Keyword Research

Anyone not using ChatGPT for keyword research is missing a trick.

You can save time and understand an entire topic in seconds instead of hours.

In this article, I outline my most effective ChatGPT prompts for keyword research and teach you how I put them together so that you, too, can take, edit, and enhance them even further.

But before we jump into the prompts, I want to emphasize that you shouldn’t replace keyword research tools or disregard traditional keyword research methods.

ChatGPT can make mistakes. It can even create new keywords if you give it the right prompt. For example, I asked it to provide me with a unique keyword for the topic “SEO” that had never been searched before.

Interstellar Internet SEO: Optimizing content for the theoretical concept of an interstellar internet, considering the challenges of space-time and interplanetary communication delays.”

Although I want to jump into my LinkedIn profile and update my title to “Interstellar Internet SEO Consultant,” unfortunately, no one has searched that (and they probably never will)!

You must not blindly rely on the data you get back from ChatGPT.

What you can rely on ChatGPT for is the topic ideation stage of keyword research and inspiration.

ChatGPT is a large language model trained with massive amounts of data to accurately predict what word will come next in a sentence. However, it does not know how to do keyword research yet.

Instead, think of ChatGPT as having an expert on any topic armed with the information if you ask it the right question.

In this guide, that is exactly what I aim to teach you how to do – the most essential prompts you need to know when performing topical keyword research.

Best ChatGPT Keyword Research Prompts

The following ChatGPT keyword research prompts can be used on any niche, even a topic to which you are brand new.

For this demonstration, let’s use the topic of “SEO” to demonstrate these prompts.

Generating Keyword Ideas Based On A Topic

What Are The {X} Most Popular Sub-topics Related To {Topic}?

Screenshot from ChatGPT 4, April 2024

The first prompt is to give you an idea of the niche.

As shown above, ChatGPT did a great job understanding and breaking down SEO into three pillars: on-page, off-page & technical.

The key to the following prompt is to take one of the topics ChatGPT has given and query the sub-topics.

What Are The {X} Most Popular Sub-topics Related To {Sub-topic}?

For this example, let’s query, “What are the most popular sub-topics related to keyword research?”

Having done keyword research for over 10 years, I would expect it to output information related to keyword research metrics, the types of keywords, and intent.

Let’s see.

ChatGPT keyword prompt subtopicScreenshot from ChatGPT 4, April 2024

Again, right on the money.

To get the keywords you want without having ChatGPT describe each answer, use the prompt “list without description.”

Here is an example of that.

List Without Description The Top {X} Most Popular Keywords For The Topic Of {X}chatgpt keyword research prompt for most popular keywords

You can even branch these keywords out further into their long-tail.

Example prompt:

List Without Description The Top {X} Most Popular Long-tail Keywords For The Topic “{X}”

chatgpt keyword research prompt longtail keywordsScreenshot ChatGPT 4,April 2024

List Without Description The Top Semantically Related Keywords And Entities For The Topic {X}

You can even ask ChatGPT what any topic’s semantically related keywords and entities are!

chatgpt keyword research semantic intentScreenshot ChatGPT 4, April 2024

Tip: The Onion Method Of Prompting ChatGPT

When you are happy with a series of prompts, add them all to one prompt. For example, so far in this article, we have asked ChatGPT the following:

  • What are the four most popular sub-topics related to SEO?
  • What are the four most popular sub-topics related to keyword research
  • List without description the top five most popular keywords for “keyword intent”?
  • List without description the top five most popular long-tail keywords for the topic “keyword intent types”?
  • List without description the top semantically related keywords and entities for the topic “types of keyword intent in SEO.”

Combine all five into one prompt by telling ChatGPT to perform a series of steps. Example:

“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4, and Step 5”

Example:

“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4 and Step 5. Step 1 – Generate an answer for the 3 most popular sub-topics related to {Topic}?. Step 2 – Generate 3 of the most popular sub-topics related to each answer. Step 3 – Take those answers and list without description their top 3 most popular keywords. Step 4 – For the answers given of their most popular keywords, provide 3 long-tail keywords. Step 5 – for each long-tail keyword offered in the response, a list without descriptions 3 of their top semantically related keywords and entities.”

Generating Keyword Ideas Based On A Question

Taking the steps approach from above, we can get ChatGPT to help streamline getting keyword ideas based on a question. For example, let’s ask, “What is SEO?

“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, and Step 4. Step 1 Generate 10 questions about “{Question}”?. Step 2 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 3 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 4 – Based on the above Steps 1,2,3 suggest a final list of questions avoiding duplicates or semantically similar questions.”

chatgpt for question keyword researchScreenshot ChatGPT 4, April 2024

Generating Keyword Ideas Using ChatGPT Based On The Alphabet Soup Method

One of my favorite methods, manually, without even using a keyword research tool, is to generate keyword research ideas from Google autocomplete, going from A to Z.

Generating Keyword Ideas using ChatGPT Based on the Alphabet Soup MethodScreenshot from Google autocomplete, April 2024

You can also do this using ChatGPT.

Example prompt:

“give me popular keywords that includes the keyword “SEO”, and the next letter of the word starts with a”

ChatGPT Alphabet keyword research methodScreenshot from ChatGPT 4, April 2024

Tip: Using the onion prompting method above, we can combine all this in one prompt.

“Give me five popular keywords that include “SEO” in the word, and the following letter starts with a. Once the answer has been done, move on to giving five more popular keywords that include “SEO” for each letter of the alphabet b to z.”

Generating Keyword Ideas Based On User Personas

When it comes to keyword research, understanding user personas is essential for understanding your target audience and keeping your keyword research focused and targeted. ChatGPT may help you get an initial understanding of customer personas.

Example prompt:

“For the topic of “{Topic}” list 10 keywords each for the different types of user personas”

ChatGPT and user personasScreenshot from ChatGPT 4, April 2024

You could even go a step further and ask for questions based on those topics that those specific user personas may be searching for:

ChatGPT and keyword research based on personaScreenshot ChatGPT 4, April 2024

As well as get the keywords to target based on those questions:

“For each question listed above for each persona, list the keywords, as well as the long-tail keywords to target, and put them in a table”

question and longtail and user persona using a table for ChatGPT keyword researchScreenshot from ChatGPT 4, April 2024

Generating Keyword Ideas Using ChatGPT Based On Searcher Intent And User Personas

Understanding the keywords your target persona may be searching is the first step to effective keyword research. The next step is to understand the search intent behind those keywords and which content format may work best.

For example, a business owner who is new to SEO or has just heard about it may be searching for “what is SEO.”

However, if they are further down the funnel and in the navigational stage, they may search for “top SEO firms.”

You can query ChatGPT to inspire you here based on any topic and your target user persona.

SEO Example:

“For the topic of “{Topic}” list 10 keywords each for the different types of searcher intent that a {Target Persona} would be searching for”

ChatGPT For Keyword Research Admin

Here is how you can best use ChatGPT for keyword research admin tasks.

Using ChatGPT As A Keyword Categorization Tool

One of the use cases for using ChatGPT is for keyword categorization.

In the past, I would have had to devise spreadsheet formulas to categorize keywords or even spend hours filtering and manually categorizing keywords.

ChatGPT can be a great companion for running a short version of this for you.

Let’s say you have done keyword research in a keyword research tool, have a list of keywords, and want to categorize them.

You could use the following prompt:

“Filter the below list of keywords into categories, target persona, searcher intent, search volume and add information to a six-column table: List of keywords – [LIST OF KEYWORDS], Keyword Search Volume [SEARCH VOLUMES] and Keyword Difficulties [KEYWORD DIFFICUTIES].”

Using Chat GPT as a Keyword Categorization ToolScreenshot from ChatGPT, April 2024

Tip: Add keyword metrics from the keyword research tools, as using the search volumes that a ChatGPT prompt may give you will be wildly inaccurate at best.

Using ChatGPT For Keyword Clustering

Another of ChatGPT’s use cases for keyword research is to help you cluster. Many keywords have the same intent, and by grouping related keywords, you may find that one piece of content can often target multiple keywords at once.

However, be careful not to rely only on LLM data for clustering. What ChatGPT may cluster as a similar keyword, the SERP or the user may not agree with. But it is a good starting point.

The big downside of using ChatGPT for keyword clustering is actually the amount of keyword data you can cluster based on the memory limits.

So, you may find a keyword clustering tool or script that is better for large keyword clustering tasks. But for small amounts of keywords, ChatGPT is actually quite good.

A great use small keyword clustering use case using ChatGPT is for grouping People Also Ask (PAA) questions.

Use the following prompt to group keywords based on their semantic relationships. For example:

“Organize the following keywords into groups based on their semantic relationships, and give a short name to each group: [LIST OF PAA], create a two-column table where each keyword sits on its own row.

Using Chat GPT For Keyword ClusteringScreenshot from ChatGPT, April 2024

Using Chat GPT For Keyword Expansion By Patterns

One of my favorite methods of doing keyword research is pattern spotting.

Most seed keywords have a variable that can expand your target keywords.

Here are a few examples of patterns:

1. Question Patterns

(who, what, where, why, how, are, can, do, does, will)

“Generate [X] keywords for the topic “[Topic]” that contain any or all of the following “who, what, where, why, how, are, can, do, does, will”

question based keywords keyword research ChatGPTScreenshot ChatGPT 4, April 2024

2. Comparison Patterns

Example:

“Generate 50 keywords for the topic “{Topic}” that contain any or all of the following “for, vs, alternative, best, top, review”

chatgpt comparison patterns for keyword researchScreenshot ChatGPT 4, April 2024

3. Brand Patterns

Another one of my favorite modifiers is a keyword by brand.

We are probably all familiar with the most popular SEO brands; however, if you aren’t, you could ask your AI friend to do the heavy lifting.

Example prompt:

“For the top {Topic} brands what are the top “vs” keywords”

ChatGPT brand patterns promptScreenshot ChatGPT 4, April 2024

4. Search Intent Patterns

One of the most common search intent patterns is “best.”

When someone is searching for a “best {topic}” keyword, they are generally searching for a comprehensive list or guide that highlights the top options, products, or services within that specific topic, along with their features, benefits, and potential drawbacks, to make an informed decision.

Example:

“For the topic of “[Topic]” what are the 20 top keywords that include “best”

ChatGPT best based keyword researchScreenshot ChatGPT 4, April 2024

Again, this guide to keyword research using ChatGPT has emphasized the ease of generating keyword research ideas by utilizing ChatGPT throughout the process.

Keyword Research Using ChatGPT Vs. Keyword Research Tools

Free Vs. Paid Keyword Research Tools

Like keyword research tools, ChatGPT has free and paid options.

However, one of the most significant drawbacks of using ChatGPT for keyword research alone is the absence of SEO metrics to help you make smarter decisions.

To improve accuracy, you could take the results it gives you and verify them with your classic keyword research tool – or vice versa, as shown above, uploading accurate data into the tool and then prompting.

However, you must consider how long it takes to type and fine-tune your prompt to get your desired data versus using the filters within popular keyword research tools.

For example, if we use a popular keyword research tool using filters, you could have all of the “best” queries with all of their SEO metrics:

ahrefs screenshot for best seoScreenshot from Ahrefs Keyword Explorer, March 2024

And unlike ChatGPT, generally, there is no token limit; you can extract several hundred, if not thousands, of keywords at a time.

As I have mentioned multiple times throughout this piece, you cannot blindly trust the data or SEO metrics it may attempt to provide you with.

The key is to validate the keyword research with a keyword research tool.

ChatGPT For International SEO Keyword Research

ChatGPT can be a terrific multilingual keyword research assistant.

For example, if you wanted to research keywords in a foreign language such as French. You could ask ChatGPT to translate your English keywords;

translating keywords with ChatGPTScreenshot ChatGPT 4, Apil 2024
The key is to take the data above and paste it into a popular keyword research tool to verify.
As you can see below, many of the keyword translations for the English keywords do not have any search volume for direct translations in French.
verifying the data with ahrefsScreenshot from Ahrefs Keyword Explorer, April 2024

But don’t worry, there is a workaround: If you have access to a competitor keyword research tool, you can see what webpage is ranking for that query – and then identify the top keyword for that page based on the ChatGPT translated keywords that do have search volume.

top keyword from ahrefs keyword explorerScreenshot from Ahrefs Keyword Explorer, April 2024

Or, if you don’t have access to a paid keyword research tool, you could always take the top-performing result, extract the page copy, and then ask ChatGPT what the primary keyword for the page is.

Key Takeaway

ChatGPT can be an expert on any topic and an invaluable keyword research tool. However, it is another tool to add to your toolbox when doing keyword research; it does not replace traditional keyword research tools.

As shown throughout this tutorial, from making up keywords at the beginning to inaccuracies around data and translations, ChatGPT can make mistakes when used for keyword research.

You cannot blindly trust the data you get back from ChatGPT.

However, it can offer a shortcut to understanding any topic for which you need to do keyword research and, as a result, save you countless hours.

But the key is how you prompt.

The prompts I shared with you above will help you understand a topic in minutes instead of hours and allow you to better seed keywords using keyword research tools.

It can even replace mundane keyword clustering tasks that you used to do with formulas in spreadsheets or generate ideas based on keywords you give it.

Paired with traditional keyword research tools, ChatGPT for keyword research can be a powerful tool in your arsenal.

More resources:


Featured Image: Tatiana Shepeleva/Shutterstock

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Google’s Search Algorithm Exposed in Document Leak

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The Search Algorithm Exposed: Inside Google’s Search API Documents Leak

Google’s search algorithm is, essentially, one of the biggest influencers of what gets found on the internet. It decides who gets to be at the top and enjoy the lion’s share of the traffic, and who gets regulated to the dark corners of the web — a.k.a. the 2nd and so on pages of the search results. 

It’s the most consequential system of our digital world. And how that system works has been largely a mystery for years, but no longer. The Google search document leak, just went public just yesterday, drops thousands of pages of purported ranking algorithm factors onto our laps. 

The Leak

There’s some debate as to whether the documentation was “leaked,” or “discovered.” But what we do know is that the API documentation was (likely accidentally) pushed live on GitHub— where it was then found.

The thousands and thousands of pages in these documents, which appear to come from Google’s internal Content API Warehouse, give us an unprecedented look into how Google search and its ranking algorithms work. 

Fast Facts About the Google Search API Documentation

  • Reported to be the internal documentation for Google Search’s Content Warehouse API.
  • The documentation indicates this information is accurate as of March 2024.
  • 2,596 modules are represented in the API documentation with 14,014 attributes. These are what we might call ranking factors or features, but not all attributes may be considered part of the ranking algorithm. 
  • The documentation did not provide how these ranking factors are weighted. 

And here’s the kicker: several factors found on this document were factors that Google has said, on record, they didn’t track and didn’t include in their algorithms. 

That’s invaluable to the SEO industry, and undoubtedly something that will direct how we do SEO for the foreseeable future.

Is The Document Real? 

Another subject of debate is whether these documents are real. On that point, here’s what we know so far:

  • The documentation was on GitHub and was briefly made public from March to May 2024.
  • The documentation contained links to private GitHub repositories and internal pages — these required specific, Google-credentialed logins to access.
  • The documentation uses similar notation styles, formatting, and process/module/feature names and references seen in public Google API documentation.
  • Ex-Googlers say documentation similar to this exists on almost every Google team, i.e., with explanations and definitions for various API attributes and modules.

No doubt Google will deny this is their work (as of writing they refuse to comment on the leak). But all signs, so far, point to this document being the real deal, though I still caution everyone to take everything you learn from it with a grain of salt.

What We Learnt From The Google Search Document Leak

With over 2,500 technical documents to sift through, the insights we have so far are just the tip of the iceberg. I expect that the community will be analyzing this leak for months (possibly years) to gain more SEO-applicable insights.

Other articles have gotten into the nitty-gritty of it already. But if you’re having a hard time understanding all the technical jargon in those breakdowns, here’s a quick and simple summary of the points of interest identified in the leak so far:

  • Google uses something called “Twiddlers.” These are functions that help rerank a page (think boosting or demotion calculations). 
  • Content can be demoted for reasons such as SERP signals (aka user behavior) indicating dissatisfaction, a link not matching the target site, using exact match domains, product reviews, location, or sexual content.
  • Google uses a variety of measurements related to clicks, including “badClicks”, ”goodClicks”, ”lastLongestClicks” and ”unsquashedClicks”.
  • Google keeps a copy of every version of every page it has ever indexed. However, it only uses the last 20 changes of any given URL when analyzing a page.
  • Google uses a domain authority metric, called “siteAuthority
  • Google uses a system called “NavBoost” that uses click data for evaluating pages.
  • Google has a “sandbox” that websites are segregated to, based on age or lack of trust signals. Indicated by an attribute called “hostAge
  • May be related to the last point, but there is an attribute called “smallPersonalSite” in the documentation. Unclear what this is used for.
  • Google does identify entities on a webpage and can sort, rank, and filter them.
  • So far, the only attributes that can be connected to E-E-A-T are author-related attributes.
  • Google uses Chrome data as part of their page quality scoring, with a module featuring a site-level measure of views from Chrome (“chromeInTotal”)
  • The number, diversity, and source of your backlinks matter a lot, even if PageRank has not been mentioned by Google in years.
  • Title tags being keyword-optimized and matching search queries is important.
  • siteFocusScore” attribute measures how much a site is focused on a given topic. 
  • Publish dates and how frequently a page is updated determines content “freshness” — which is also important. 
  • Font size and text weight for links are things that Google notices. It appears that larger links are more positively received by Google.

Author’s Note: This is not the first time a search engine’s ranking algorithm was leaked. I covered the Yandex hack and how it affects SEO in 2023, and you’ll see plenty of similarities in the ranking factors both search engines use.

Action Points for Your SEO

I did my best to review as much of the “ranking features” that were leaked, as well as the original articles by Rand Fishkin and Mike King. From there, I have some insights I want to share with other SEOs and webmasters out there who want to know how to proceed with their SEO.

Links Matter — Link Value Affected by Several Factors 

Links still matter. Shocking? Not really. It’s something I and other SEOs have been saying, even if link-related guidelines barely show up in Google news and updates nowadays.

Still, we need to emphasize link diversity and relevance in our off-page SEO strategies. 

Some insights from the documentation:

  • PageRank of the referring domain’s homepage (also known as Homepage Trust) affects the value of the link.
  • Indexing tier matters. Regularly updated and accessed content is of the highest tier, and provides more value for your rankings.

If you want your off-page SEO to actually do something for your website, then focus on building links from websites that have authority, and from pages that are either fresh or are otherwise featured in the top tier. 

Some PR might help here — news publications tend to drive the best results because of how well they fulfill these factors.

As for guest posts, there’s no clear indication that these will hurt your site, but I definitely would avoid approaching them as a way to game the system. Instead, be discerning about your outreach and treat it as you would if you were networking for new business partners.

Aim for Successful Clicks 

The fact that clicks are a ranking factor should not be a surprise. Despite what Google’s team says, clicks are the clearest indicator of user behavior and how good a page is at fulfilling their search intent.

Google’s whole deal is providing the answers you want, so why wouldn’t they boost pages that seem to do just that?

The core of your strategy should be creating great user experiences. Great content that provides users with the right answers is how you do that. Aiming for qualified traffic is how you do that. Building a great-looking, functioning website is how you do that.

Go beyond just picking clickbait title tags and meta descriptions, and focus on making sure users get what they need from your website.

Author’s Note: If you haven’t been paying attention to page quality since the concepts of E-E-A-T and the HCU were introduced, now is the time to do so. Here’s my guide to ranking for the HCU to help you get started.

Keep Pages Updated

An interesting click-based measurement is the “last good click.” That being in a module related to indexing signals suggests that content decay can affect your rankings. 

Be vigilant about which pages on your website are not driving the expected amount of clicks for its SERP position. Outdated posts should be audited to ensure content has up-to-date and accurate information to help users in their search journey. 

This should revive those posts and drive clicks, preventing content decay. 

It’s especially important to start on this if you have content pillars on your website that aren’t driving the same traffic as they used to.

Establish Expertise & Authority  

Google does notice the entities on a webpage, which include a bunch of things, but what I want to focus on are those related to your authors.

E-E-A-T as a concept is pretty nebulous — because scoring “expertise” and “authority” of a website and its authors is nebulous. So, a lot of SEOs have been skeptical about it.

However, the presence of an “author” attribute combined with the in-depth mapping of entities in the documentation shows there is some weight to having a well-established author on your website.

So, apply author markups, create an author bio page and archive, and showcase your official profiles on your website to prove your expertise. 

Build Your Domain Authority

After countless Q&As and interviews where statements like “we don’t have anything like domain authority,” and “we don’t have website authority score,” were thrown around, we find there does exist an attribute called “siteAuthority”.

Though we don’t know specifically how this measure is computed, and how it weighs in the overall scoring for your website, we know it does matter to your rankings.

So, what do you need to do to improve site authority? It’s simple — keep following best practices and white-hat SEO, and you should be able to grow your authority within your niche. 

Stick to Your Niche

Speaking of niches — I found the “siteFocusScore” attribute interesting. It appears that building more and more content within a specific topic is considered a positive.

It’s something other SEOs have hypothesized before. After all, the more you write about a topic, the more you must be an authority on that topic, right?

But anyone can write tons of blogs on a given topic nowadays with AI, so how do you stand out (and avoid the risk of sounding artificial and spammy?)

That’s where author entities and link-building come in. I do think that great content should be supplemented by link-building efforts, as a sort of way to show that hey, “I’m an authority with these credentials, and these other people think I’m an authority on the topic as well.”

Key Takeaway

Most of the insights from the Google search document leak are things that SEOs have been working on for months (if not years). However, we now have solid evidence behind a lot of our hunches, providing that our theories are in fact best practices. 

The biggest takeaway I have from this leak: Google relies on user behavior (click data and post-click behavior in particular) to find the best content. Other ranking factors supplement that. Optimize to get users to click on and then stay on your page, and you should see benefits to your rankings.

Could Google remove these ranking factors now that they’ve been leaked? They could, but it’s highly unlikely that they’ll remove vital attributes in the algorithm they’ve spent years building. 

So my advice is to follow these now validated SEO practices and be very critical about any Google statements that follow this leak.

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Google Search Leak: Conflicting Signals, Unanswered Questions

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Google Search Leak: Conflicting Signals, Unanswered Questions

An apparent leak of Google Search API documentation has sparked intense debate within the SEO community, with some claiming it proves Google’s dishonesty and others urging caution in interpreting the information.

As the industry grapples with the allegations, a balanced examination of Google’s statements and the perspectives of SEO experts is crucial to understanding the whole picture.

Leaked Documents Vs. Google’s Public Statements

Over the years, Google has consistently maintained that specific ranking signals, such as click data and user engagement metrics, aren’t used directly in its search algorithms.

In public statements and interviews, Google representatives have emphasized the importance of relevance, quality, and user experience while denying the use of specific metrics like click-through rates or bounce rates as ranking-related factors.

However, the leaked API documentation appears to contradict these statements.

It contains references to features like “goodClicks,” “badClicks,” “lastLongestClicks,” impressions, and unicorn clicks, tied to systems called Navboost and Glue, which Google VP Pandu Nayak confirmed in DOJ testimony are parts of Google’s ranking systems.

The documentation also alleges that Google calculates several metrics using Chrome browser data on individual pages and entire domains, suggesting the full clickstream of Chrome users is being leveraged to influence search rankings.

This contradicts past Google statements that Chrome data isn’t used for organic searches.

The Leak’s Origins & Authenticity

Erfan Azimi, CEO of digital marketing agency EA Eagle Digital, alleges he obtained the documents and shared them with Rand Fishkin and Mike King.

Azimi claims to have spoken with ex-Google Search employees who confirmed the authenticity of the information but declined to go on record due to the situation’s sensitivity.

While the leak’s origins remain somewhat ambiguous, several ex-Googlers who reviewed the documents have stated they appear legitimate.

Fishkin states:

“A critical next step in the process was verifying the authenticity of the API Content Warehouse documents. So, I reached out to some ex-Googler friends, shared the leaked docs, and asked for their thoughts.”

Three ex-Googlers responded, with one stating, “It has all the hallmarks of an internal Google API.”

However, without direct confirmation from Google, the authenticity of the leaked information is still debatable. Google has not yet publicly commented on the leak.

It’s important to note that, according to Fishkin’s article, none of the ex-Googlers confirmed that the leaked data was from Google Search. Only that it appears to have originated from within Google.

Industry Perspectives & Analysis

Many in the SEO community have long suspected that Google’s public statements don’t tell the whole story. The leaked API documentation has only fueled these suspicions.

Fishkin and King argue that if the information is accurate, it could have significant implications for SEO strategies and website search optimization.

Key takeaways from their analysis include:

  • Navboost and the use of clicks, CTR, long vs. Short clicks, and user data from Chrome appear to be among Google’s most powerful ranking signals.
  • Google employs safelists for sensitive topics like COVID-19, elections, and travel to control what sites appear.
  • Google uses Quality Rater feedback and ratings in its ranking systems, not just as a training set.
  • Click data influences how Google weights links for ranking purposes.
  • Classic ranking factors like PageRank and anchor text are losing influence compared to more user-centric signals.
  • Building a brand and generating search demand is more critical than ever for SEO success.

However, just because something is mentioned in API documentation doesn’t mean it’s being used to rank search results.

Other industry experts urge caution when interpreting the leaked documents.

They point out that Google may use the information for testing purposes or apply it only to specific search verticals rather than use it as active ranking signals.

There are also open questions about how much weight these signals carry compared to other ranking factors. The leak doesn’t provide the full context or algorithm details.

Unanswered Questions & Future Implications

As the SEO community continues to analyze the leaked documents, many questions still need to be answered.

Without official confirmation from Google, the authenticity and context of the information are still a matter of debate.

Key open questions include:

  • How much of this documented data is actively used to rank search results?
  • What is the relative weighting and importance of these signals compared to other ranking factors?
  • How have Google’s systems and use of this data evolved?
  • Will Google change its public messaging and be more transparent about using behavioral data?

As the debate surrounding the leak continues, it’s wise to approach the information with a balanced, objective mindset.

Unquestioningly accepting the leak as gospel truth or completely dismissing it are both shortsighted reactions. The reality likely lies somewhere in between.

Potential Implications For SEO Strategies and Website Optimization

It would be highly inadvisable to act on information shared from this supposed ‘leak’ without confirming whether it’s an actual Google search document.

Further, even if the content originates from search, the information is a year old and could have changed. Any insights derived from the leaked documentation should not be considered actionable now.

With that in mind, while the full implications remain unknown, here’s what we can glean from the leaked information.

1. Emphasis On User Engagement Metrics

If click data and user engagement metrics are direct ranking factors, as the leaked documents suggest, it could place greater emphasis on optimizing for these metrics.

This means crafting compelling titles and meta descriptions to increase click-through rates, ensuring fast page loads and intuitive navigation to reduce bounces, and strategically linking to keep users engaged on your site.

Driving traffic through other channels like social media and email can also help generate positive engagement signals.

However, it’s important to note that optimizing for user engagement shouldn’t come at the expense of creating reader-focused content. Gaming engagement metrics are unlikely to be a sustainable, long-term strategy.

Google has consistently emphasized the importance of quality and relevance in its public statements, and based on the leaked information, this will likely remain a key focus. Engagement optimization should support and enhance quality content, not replace it.

2. Potential Changes To Link-Building Strategies

The leaked documents contain information about how Google treats different types of links and their impact on search rankings.

This includes details about the use of anchor text, the classification of links into different quality tiers based on traffic to the linking page, and the potential for links to be ignored or demoted based on various spam factors.

If this information is accurate, it could influence how SEO professionals approach link building and the types of links they prioritize.

Links that drive real click-throughs may carry more weight than links on rarely visited pages.

The fundamentals of good link building still apply—create link-worthy content, build genuine relationships, and seek natural, editorially placed links that drive qualified referral traffic.

The leaked information doesn’t change this core approach but offers some additional nuance to be aware of.

3. Increased Focus On Brand Building and Driving Search Demand

The leaked documents suggest that Google uses brand-related signals and offline popularity as ranking factors. This could include metrics like brand mentions, searches for the brand name, and overall brand authority.

As a result, SEO strategies may emphasize building brand awareness and authority through both online and offline channels.

Tactics could include:

  • Securing brand mentions and links from authoritative media sources.
  • Investing in traditional PR, advertising, and sponsorships to increase brand awareness.
  • Encouraging branded searches through other marketing channels.
  • Optimizing for higher search volumes for your brand vs. unbranded keywords.
  • Building engaged social media communities around your brand.
  • Establishing thought leadership through original research, data, and industry contributions.

The idea is to make your brand synonymous with your niche and build an audience that seeks you out directly. The more people search for and engage with your brand, the stronger those brand signals may become in Google’s systems.

4. Adaptation To Vertical-Specific Ranking Factors

Some leaked information suggests that Google may use different ranking factors or algorithms for specific search verticals, such as news, local search, travel, or e-commerce.

If this is the case, SEO strategies may need to adapt to each vertical’s unique ranking signals and user intents.

For example, local search optimization may focus more heavily on factors like Google My Business listings, local reviews, and location-specific content.

Travel SEO could emphasize collecting reviews, optimizing images, and directly providing booking/pricing information on your site.

News SEO requires focusing on timely, newsworthy content and optimized article structure.

While the core principles of search optimization still apply, understanding your particular vertical’s nuances, based on the leaked information and real-world testing, can give you a competitive advantage.

The leaks suggest a vertical-specific approach to SEO could give you an advantage.

Conclusion

The Google API documentation leak has created a vigorous discussion about Google’s ranking systems.

As the SEO community continues to analyze and debate the leaked information, it’s important to remember a few key things:

  1. The information isn’t fully verified and lacks context. Drawing definitive conclusions at this stage is premature.
  2. Google’s ranking algorithms are complex and constantly evolving. Even if entirely accurate, this leak only represents a snapshot in time.
  3. The fundamentals of good SEO – creating high-quality, relevant, user-centric content and promoting it effectively – still apply regardless of the specific ranking factors at play.
  4. Real-world testing and results should always precede theorizing based on incomplete information.

What To Do Next

As an SEO professional, the best course of action is to stay informed about the leak.

Because details about the document remain unknown, it’s not a good idea to consider any takeaways actionable.

Most importantly, remember that chasing algorithms is a losing battle.

The only winning strategy in SEO is to make your website the best result for your message and audience. That’s Google’s endgame, and that’s where your focus should be, regardless of what any particular leaked document suggests.



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Google’s AI Overviews Shake Up Ecommerce Search Visibility

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Google's AI Overviews Shake Up Ecommerce Search Visibility

An analysis of 25,000 ecommerce queries by Bartosz Góralewicz, founder of Onely, reveals the impact of Google’s AI overviews on search visibility for online retailers.

The study found that 16% of eCommerce queries now return an AI overview in search results, accounting for 13% of total search volume in this sector.

Notably, 80% of the sources listed in these AI overviews do not rank organically for the original query.

“Ranking #1-3 gives you only an 8% chance of being a source in AI overviews,” Góralewicz stated.

Shift Toward “Accelerated” Product Experiences

International SEO consultant Aleyda Solis analyzed the disconnect between traditional organic ranking and inclusion in AI overviews.

According to Solis, for product-related queries, Google is prioritizing an “accelerated” approach over summarizing currently ranking pages.

She commented Góralewicz’ findings, stating:

“… rather than providing high level summaries of what’s already ranked organically below, what Google does with e-commerce is “accelerate” the experience by already showcasing what the user would get next.”

Solis explains that for queries where Google previously ranked category pages, reviews, and buying guides, it’s now bypassing this level of results with AI overviews.

Assessing AI Overview Traffic Impact

To help retailers evaluate their exposure, Solis has shared a spreadsheet that analyzes the potential traffic impact of AI overviews.

As Góralewicz notes, this could be an initial rollout, speculating that “Google will expand AI overviews for high-cost queries when enabling ads” based on data showing they are currently excluded for high cost-per-click keywords.

An in-depth report across ecommerce and publishing is expected soon from Góralewicz and Onely, with additional insights into this search trend.

Why SEJ Cares

AI overviews represent a shift in how search visibility is achieved for ecommerce websites.

With most overviews currently pulling product data from non-ranking sources, the traditional connection between organic rankings and search traffic is being disrupted.

Retailers may need to adapt their SEO strategies for this new search environment.

How This Can Benefit You

While unsettling for established brands, AI overviews create new opportunities for retailers to gain visibility without competing for the most commercially valuable keywords.

Ecommerce sites can potentially circumvent traditional ranking barriers by optimizing product data and detail pages for Google’s “accelerated” product displays.

The detailed assessment framework provided by Solis enables merchants to audit their exposure and prioritize optimization needs accordingly.


FAQ

What are the key findings from the analysis of AI overviews & ecommerce queries?

Góralewicz’s analysis of 25,000 ecommerce queries found:

  • 16% of ecommerce queries now return an AI overview in the search results.
  • 80% of the sources listed in these AI overviews do not rank organically for the original query.
  • Ranking positions #1-3 only provides an 8% chance of being a source in AI overviews.

These insights reveal significant shifts in how ecommerce sites need to approach search visibility.

Why are AI overviews pulling product data from non-ranking sources, and what does this mean for retailers?

Google’s AI overviews prioritize “accelerated” experiences over summarizing currently ranked pages for product-related queries.

This shift focuses on showcasing directly what users seek instead of traditional organic results.

For retailers, this means:

  • A need to optimize product pages beyond traditional SEO practices, catering to the data requirements of AI overviews.
  • Opportunities to gain visibility without necessarily holding top organic rankings.
  • Potential to bypass traditional ranking barriers by focusing on enhanced product data integration.

Retailers must adapt quickly to remain competitive in this evolving search environment.

What practical steps can retailers take to evaluate and improve their search visibility in light of AI overview disruptions?

Retailers can take several practical steps to evaluate and improve their search visibility:

  • Utilize the spreadsheet provided by Aleyda Solis to assess the potential traffic impact of AI overviews.
  • Optimize product and detail pages to align with the data and presentation style preferred by AI overviews.
  • Continuously monitor changes and updates to AI overviews, adapting strategies based on new data and trends.

These steps can help retailers navigate the impact of AI overviews and maintain or improve their search visibility.


Featured Image: Marco Lazzarini/Shutterstock



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