Google’s “Branded Search” Patent For Ranking Search Results via @sejournal, @martinibuster

Back in 2012 Google applied for a patent called “Ranking Search Results” that shows how Google can use branded search queries as a ranking factor. The patent is about using branded search queries and navigational queries as ranking factors, plus a count of independent links. Although this patent is from 2012, it’s possible that it may still play a role in ranking.

The patent was misunderstood by the search marketing community in 2012 and the knowledge contained in it was lost.

What Is The Ranking Search Results Patent About? TL/DR

The patent is explicitly about an invention for ranking search results, that’s why the patent is called “Ranking Search Results.” The patent describes an algorithm that uses to ranking factors to re-rank web pages:

Sorting Factor 1: By number of independent inbound links
This is a count of links that are independent from the site being ranked.

Sorting Factor 2: By number of branded search queries & navigational search queries.
The branded and navigational search queries are called “reference queries” and also are referred to as implied links.

The counts of both factors are used to modify the rankings of the web pages.

Why The Patent Was Misunderstood TL/DR

First, I want to say that in 2012, I didn’t understand how to read patents. I was more interested in research papers and left the patent reading to others. When I say that everyone in the search marketing community misunderstood the patent, I include myself in that group.

The “Ranking Search Results” patent was published in 2012, one year after the release of a content quality update called the Panda Update. The Panda update was named after one of the engineers who worked on it, Navneet Panda. Navneet Panda came up with questions that third party quality raters used to rate web pages. Those ratings were used as a test to see if changes to the algorithm were successful at removing “content farm” content.

Navneet Panda is also a co-author of the “Ranking search results” patent. SEOs saw his name on the patent and immediately assumed that this was the Panda patent.

The reason why that assumption is wrong is because the Panda update is an algorithm that uses a “classifier” to classify web pages by content quality. The “Ranking Search Results” patent is about ranking search results, period. The Ranking Search Results patent is not about content quality nor does it feature a content quality classifier.

Nothing in the “Ranking Search Results” patent relates in any way with the Panda update.

Why This Patent Is Not The Panda Update

In 2009 Google released the Caffeine Update which enabled Google to quickly index fresh content but inadvertently created a loophole that allowed content farms to rank millions of web pages on rarely searched topics.

In an interview with Wired, former Google search engineer Matt Cutts described the content farms like this:

“It was like, “What’s the bare minimum that I can do that’s not spam?” It sort of fell between our respective groups. And then we decided, okay, we’ve got to come together and figure out how to address this.”

Google subsequently responded with the Panda Update, named after a search engineer who worked on the algorithm which was specifically designed to filter out content farm content. Google used third party site quality raters to rate websites and the feedback was used to create a new definition of content quality that was used against content farm content.

Matt Cutts described the process:

“There was an engineer who came up with a rigorous set of questions, everything from. “Do you consider this site to be authoritative? Would it be okay if this was in a magazine? Does this site have excessive ads?” Questions along those lines.

…we actually came up with a classifier to say, okay, IRS or Wikipedia or New York Times is over on this side, and the low-quality sites are over on this side. And you can really see mathematical reasons…”

In simple terms, a classifier is an algorithm within a system that categorizes data. In the context of the Panda Update, the classifier categorizes web pages by content quality.

What’s apparent when reading the “Ranking search results” patent is that it’s clearly not about content quality, it’s about ranking search results.

Meaning Of Express Links And Implied Links

The “Ranking Search Results” patent uses two kinds of links to modify ranked search results:

  1. Implied links
  2. Express links

Implied links:
The patent uses branded search queries and navigational queries to calculate a ranking score as if the branded/navigational queries are links, calling them implied links. The implied links are used to create a factor for modifying web pages that are relevant (responsive) to search queries.

Express links:
The patent also uses independent inbound links to the web page as a part of another calculation to come up with a factor for modifying web pages that are responsive to a search query.

Both of those kinds of links (implied and independent express link) are used as factors to modify the rankings of a group of web pages.

Understanding what the patent is about is straightforward because the beginning of the patent explains it in relatively easy to understand English.

This section of the patent uses the following jargon:

  • A resource is a web page or website.
  • A target (target resource) is what is being linked to or referred to.
  • A “source resource” is a resource that makes a citation to the “target resource.”
  • The word “group” means the group of web pages that are relevant to a search query and are being ranked.

The patent talks about “express links” which are just regular links. It also describes “implied links” which are references within search queries, references to a web page (which is called a “target resource”).

I’m going to add bullet points to the original sentences so that they are easier to understand.

Okay, so this is the first important part:

“Links for the group can include express links, implied links, or both.

An express link, e.g., a hyperlink, is a link that is included in a source resource that a user can follow to navigate to a target resource.

An implied link is a reference to a target resource, e.g., a citation to the target resource, which is included in a source resource but is not an express link to the target resource. Thus, a resource in the group can be the target of an implied link without a user being able to navigate to the resource by following the implied link.”

The second important part uses the same jargon to define what implied links are:

  • A resource is a web page or website.
  • The site being linked to or referred to is called a “target resource.”
  • A “group of resources” means a group of web pages.

This is how the patent explains implied links:

“A query can be classified as referring to a particular resource if the query includes a term that is recognized by the system as referring to the particular resource.

For example, a term that refers to a resource may be all of or a portion of a resource identifier, e.g., the URL, for the resource.

For example, the term “example.com” may be a term that is recognized as referring to the home page of that domain, e.g., the resource whose URL is “http://www.example.com”.

Thus, search queries including the term “example.com” can be classified as referring to that home page.

As another example, if the system has data indicating that the terms “example sf” and “esf” are commonly used by users to refer to the resource whose URL is “http://www.sf.example.com,” queries that contain the terms “example sf” or “esf”, e.g., the queries “example sf news” and “esf restaurant reviews,” can be counted as reference queries for the group that includes the resource whose URL is “http://www.sf.example.com.” “

The above explanation defines “reference queries” as the terms that people use to refer to a specific website. So, for example (my example), if people search using “Walmart” with the keyword Air Conditioner within their search query then the query  “Walmart” + Air Conditioner is counted as a “reference query” to Walmart.com, it’s counted as a citation and an implied link.

The Patent Is Not About “Brand Mentions” On Web Pages

Some SEOs believe that a mention of a brand on a web page is counted by Google as if it’s a link. They have misinterpreted this patent to support the belief that an “implied link” is a brand mention on a web page.

As you can see, the patent does not describe the use of “brand mentions” on web pages. It’s crystal clear that the meaning of “implied links” within the context of this patent is about references to brands within search queries, not on a web page.

It also discusses doing the same thing with navigational queries:

“In addition or in the alternative, a query can be categorized as referring to a particular resource when the query has been determined to be a navigational query to the particular resource. From the user point of view, a navigational query is a query that is submitted in order to get to a single, particular web site or web page of a particular entity. The system can determine whether a query is navigational to a resource by accessing data that identifies queries that are classified as navigational to each of a number of resources.”

The takeaway then is that the parent describes the use of “reference queries” (branded/navigational search queries) as a factor similar to links and that’s why they’re called implied links.

Modification Factor

The algorithm generates a “modification factor” which re-ranks (modifies) the a group of web pages that are relevant to a search query based on the “reference queries” (which are branded search queries) and also using a count of independent inbound links.

This is how the modification (or ranking) is done:

  1. A count of inbound links using only “independent” links (links that are not controlled by the site being linked to).
  2. A count is made of the reference queries (branded search queries) (which are given a ranking power like a link).

Reminder: “resources” is a reference to web pages and websites.

Here is how the patent explains the part about the ranking:

“The system generates a modification factor for the group of resources from the count of independent links and the count of reference queries… For example, the modification factor can be a ratio of the number of independent links for the group to the number of reference queries for the group.”

What the patent is doing is it is filtering links in order to use links that are not associated with the website and it is also counting how many branded search queries are made for a webpage or website and using that as a ranking factor (modification factor).

In retrospect it was a mistake for some in the SEO industry to use this patent as “proof” for their idea about brand mentions on websites being a ranking factor.

It’s clear that “implied links” are not about brand mentions in web pages as a ranking factor but rather it’s about brand mentions (and URLs & domains) in search queries that can be used as ranking factors.

Why This Patent Is Important

This patent describes a way to use branded search queries as a signal of popularity and relevance for ranking web pages. It’s a good signal because it’s the users themselves saying that a specific website is relevant for specific search queries. It’s a signal that’s hard to manipulate which may make it a clean non-spam signal.

We don’t know if Google uses what’s described in the patent. But it’s easy to understand why it could still be a relevant signal today.

Read The Patent Within The Entire Context

Patents use specific language and it’s easy to misinterpret the words or overlook the meaning of it by focusing on specific sentences. The biggest mistake I see SEOs do is to remove one or two sentences from their context and then use that to say that Google is doing something or other. This is how SEO misinformation begins.

Read my article about How To Read Google Patents to understand how to read them and avoid misinterpreting them. Even if you don’t read patents, knowing the information is helpful because it’ll make it easier to spot misinformation about patents, which there is a lot of right now.

I limited this article to communicating what the “Ranking Search Results” patent is and what the most important points are. There many granular details about different implementations that I don’t cover because they’re not necessary to understanding the overall patent itself.

If you want the granular details, I strongly encourage first reading my article about how to read patents before reading the patent.

Read the patent here:

Ranking search results

AI Agnostic Optimization: Content For Topical Authority And Citations

The search and AI ecosystem is full of promise, options, and new ways for literally every type of marketer to evolve and grow.

Yes, there is lots of complexity, but there is also commonality: the need for marketers to focus on topical approaches to content creation, build their brand authority for AI citations, and become more predictive in their approach to how consumers interact online.

The introduction of Google AI Overviews and new AI-first platforms like Perplexity AI are making how consumers find answers to their needs a lot more complex.

The advancement of LLMs such as Claude and Google Gemini are also revolutionizing content outputs in visual and video formats. Just recently, Bing introduced GSE and OpenAI SearchGPT.

One thing they all have in common is that they are all fighting for the best authoritative sources for information and citations.

Wikipedia CitationScreenshot from author, July 2024

Today, I will mainly use Google AI Overviews in Search as an example, as they currently offer the most rich insights and best practices that are applicable to future and upcoming engines.

AI And Search Citations, Authority, And Your Brand

Being the cited source is quickly becoming the new form of ranking.

As AI looks to cite trustworthy and relevant content, brands need to be the source. While every engine has a different approach, the reality is that success relies on sources and quality.

They look to answer questions in many ways, and citations are common across the board. They look at authoritative sources to see whether that source answers that question, and then they seem to know whether it’s quotable.

  • Google wants quotable content that is above the fold, not buried. It also likes the question to be directly answered.
  • Perplexity, which has steadily increased traffic referrals (31% in June), focuses on academic and research citations but has had issues with attribution and sources.
  • Bing GSE is engineering its search results to satisfy users in a way that encourages discovery on the websites that originate the content.
  • ChatGPT/search does not need direct answers; it will digest them and express them in its own language. At first glance, it mainly cites and links to sources developed with input from major publishers like the Atlantic and NewsCorp.

So, as marketers, it is the simplest way to start focusing on the commonality and best practices that prepare you for what is ahead. Then, you can pivot and adapt as we learn more about how citations are shown and treated as each AI engine evolves.

For example, Google AI Overviews is beginning to cite more authoritative review publications to help users shop. The removal of user-generated content (UGC) and reviews from Reddit and Quora dropped to near zero in AI Overviews.

  • Reddit citations: 85.71% decrease.
  • Quora citations: 99.69% decrease.

User-generated reviews may not be designed for a broader audience and lack the objectivity that a publication would. BrightEdge Generative Parser™ has recently found:

  • 49% increase in presence from PC Mag.
  • 39% in Forbes increase in presence from Forbes.
Google AIO Screenshot from BrightEdge, July 2024

Sites like Forbes are becoming key players in AI overviews. As well as thought leadership and instructive information, their comparative product reviews define where a product shines and where it falls short against competitors.

Here are three things that marketers can master now to stay ahead in AI and search.

1. Ensure AI Engines Find You: Become The Cited Source

Start by identifying core – and broader, see later in the article –  topics relevant to your audience and aligning with your business objectives. These topics should serve as the foundation for a thematic content strategy.

Schema+: Diversify And Mark-Up Your Content As Much As Possible

The importance of diverse content formats cannot be overstated. To adapt to answer engine models, content must be comprehensive and encompass multiple modalities, including text, video, infographics, and interactive elements.

This approach ensures that content caters to diverse user preferences and provides information in formats that are most accessible and engaging.

Core technical SEO approaches like Schema Markup are essential for content marketers aiming to enhance their visibility and relevance in search results, as they help search engines better understand the content.

This improves the likelihood of content being featured as a direct answer and enhances its overall discoverability.

  • Provide AI engines with hints on who you are.
  • Ensure your teams look at things like Schema so AI entities can see your content.
  • Little formats like these can tell the AI models how to use your content.
  • It ensures that you are more frequently cited as the source in topics where you already have the right to win.

Develop content clusters around these core topics, covering different aspects, subtopics, and related themes. Each piece of content within a cluster should complement and support others, creating a cohesive narrative for multiple users.

Discovery, Engagement, ResultsImage from BrightEdge, July 2024

2. Anticipate Customers’ Next Questions: Focus On The Follow-Up

Build Thematic Content & Focus On Content Clustering

AI-powered search engines like AI Overviews (as explained in The Ultimate Guide to AI Overviews, free, ungated, and updated monthly by my company, BrightEdge) are redefining the criteria for visibility by prioritizing thematically connected content.

This applies even where the content doesn’t rank highly in traditional search results, making intelligent content clustering and thematic coherence essential.

Adopting a strategic approach to thematic content and content clustering means that instead of creating isolated pieces of content, you focus on developing interconnected content clusters that comprehensively explore various aspects of a topic.

  • AI search aims to do more than display a list of products for the keywords.
  • They want to anticipate the following questions that the demographic will likely have: how, what, where, and more.
  • AI models will cite trusted sources to generate these answers before the user even thinks about asking them.
  • Marketers need to create content for all these types of follow-ups in different formats.

Ensure that content within the same cluster is interlinked using relevant anchor text. This helps search engines understand the thematic relationship between different pieces of content and strengthens your website’s authority on the topic.

Understanding what triggers things in AI Overviews will become essential.

For example, in June, there was a 20% increase in “What is” queries showing an AI Overview. For brand-specific queries, there was a 20% decrease.

This could show that Google uses AI for more complex, knowledge-intensive topics while playing it safe with brand queries.

However, expect this to change monthly, as SEJ states and shares more below:

3. Prove Your Expertise: Become The Authority In Your Field Domain

Baking User and Topical Intent Into Every Piece of Content

Traditional SEO focuses on keyword rankings and visibility, but AI-driven search engines prioritize delivering precise, relevant answers based on user queries. This shift means simply ranking highly is no longer enough; you must ensure your content aligns closely with users’ needs and topics of interest.

AI-powered search engines like ChatGPT, Google’s SGE, Perplexity, and now SearchGPT are designed to comprehend the context and nuance behind a user’s query. They aim to provide direct answers and anticipate follow-up questions, creating a more dynamic and personalized search experience.

*A Note of Serving Multiple Intents*

AI-powered search results are evolving to coexist with traditional search. Google is experimenting with blending conventional and AI-enhanced search results. For example, searching for [outdoor lighting solutions].

The traditional search component assumes the user intends to purchase such products and ranks relevant ecommerce sites accordingly. This serves users who know exactly what they’re looking for and need quick access to buy options.

Multiple Intent TypesImage from BrightEdge, July 2024

In contrast, the AI-generated overview caters to users seeking a broader understanding of outdoor lighting. It might provide a conversational explanation covering various aspects, such as:

  • Key considerations when choosing outdoor lights.
  • Various types of outdoor lighting and their characteristics.
  • Available power options for outdoor illumination.
  • Understanding brightness levels and their significance.
  • Best practices for installation and placement.
  • Tips for maintaining outdoor lighting systems.

Anticipating and addressing related queries helps build the site’s credibility and improves the chances of being featured in AI-generated answers.

Since AI-first engines, LLMs, and traditional search engines are designed to recognize and prioritize unique, high-quality content over generic or duplicated material, this increases the chances that your content will surface in response to user queries.

  • Prove your expertise and make it easy for AI models to trust what you say.
  • AI engines need to see that your content is approved (validated) by other experts, as well as user-generated content and reviews.
  • Ensure your content reaches expert influencers and connects to related sources and websites.
  • Gain as much 3rd party validation that your content is trustworthy.
  • Ensure your content workflows consider traditional ranking factors and AI citations, as they rely on some standard but separate signals.

Video And YouTube

We are now seeing (pros and cons) YouTube videos cited in AI Overviews in ways that benefit marketers at the top of the funnel.

If YouTube were not part of Google, it would be the sixth biggest digital platform in the USA. It commands a lot of reach!

Cited Sources for AIO Image from BrightEdge, July 2024

As you can see above, this offers new advantages to marketers targeting early-stage prospects. Visual content can effectively showcase specific offerings and provide tangible reviews, potentially swaying purchasing choices such as buying a washing machine.

They are being shown to help simplify complex topics for users. For example, abstract technological concepts like “blockchain fundamentals” often become clearer through visual demonstrations, accelerating audience understanding.

Ensure that when you identify high-potential topical themes, you pair them with AI’s video citation preferences. Video is on an explosive growth trajectory, so start to build and get creative as part of your more comprehensive marketing strategy and for maximum AI Overview visibility.

This helps offer multiple reference possibilities. A single piece of video content could be cited numerous times, expanding your topical reach, which I mentioned earlier.

Key Takeaways

In an era where AI-driven search and AI-first answer engines or assistants reshape how markers operate, marketers, SEO pros, content creators, and brand marketers must adapt their strategies to optimize for AI answers and multiple types of search engines.

Below are a few end notes and outliers for your consideration also:

  • The core basics of SEO and classic search still matter.
  • AI Overviews are reduced in size to give more concise answers.
  • AI answers more complex questions, but more common questions and queries are also answered in better-served universal or classic formats – balance will be essential.
  • Monitor with cadence new engines; many are so new it will take an informed data-led opinion to form.
  • Going forward, different types of consumers will use engines for various use cases, and each engine will cite some common sources and other specific ones like news academics and publishers. Let’s see how it develops; it is something I am looking into myself now.
  • Always remember that everything varies depending on your vertical and type of business. Experimentation is still very heavy everywhere, including at Google!
  • With new entrants emerging, the news and live experiments every day expect change.
  • What happens in one month can differ from another while engines find equilibrium.

Essential best practices such as focusing on user intent, leveraging structured data markup, and embracing multimedia content aren’t going anywhere. Classic search is here to stay; many skills are transferable to AI.

The future lies in a balance of classic online marketing, adapting to AI, and uncovering new AI engines’ nuances as they grow and establish more of a foothold. It is an exciting time, and I think exercising a little patience will help us all prevail.

As for SearchGPT, I believe its evolution does not diminish SEO; on the contrary, it makes it even more critical!

For now, monitor and use time-based data as your compass, and don’t react to opinions without some substance behind them.

More resources: 


Featured Image from author

Best SEO for Dropshipping

Dropshipping is the entry point for many new ecommerce retail ventures, but selling essentially the same product as hundreds of other online stores makes search engine optimization challenging.

With a payment card, a logo, and a few clicks, entrepreneurs can quickly launch an online store by combining an ecommerce platform such as Shopify, BigCommerce, or WooCommerce with dropshipping apps such as Dsers, Spocket, or SaleHoo.

Advertising

The products for a dropshipping-enabled ecommerce shop could come from multiple sources, including AliExpress, and work on thin margins, effectively practicing retail arbitrage.

The relatively small margins can complicate advertising since just about any dip in ad performance can eliminate profits or worse.

To be sure, there are ways to market an ecommerce dropshipping business successfully, and while ads are the best option for most new stores, traffic from organic search listings and from visitors directly is essential for long-term success. This fact brings us back to dropshipping’s inherent problem: competition.

Almost without exception, whatever product a store chooses to source from a dropshipping service will be available on dozens, if not hundreds, of similar online shops, all vying for prominent search engine rankings.

Hundreds of online stores offer this Star Trek t-shirt from AliExpress.

SEO

SEO is typically iterative — no single procedure guarantees a top ranking on Google.

One SEO strategy for dropshipping is to build a content site that sells products — a content-then-commerce approach. Optimize for articles, videos, podcasts, and related, and then promote the dropshipped products within that editorial content.

Here are five content-then-commerce SEO tactics.

Identify content keyphrases

Classic keyword research is the best place to start for dropshipping SEO. But focus here on content phrases, not transactional or product.

In my research, every dropshipping shop selling the “Live Long and Prosper” licensed adult t-shirt from AliExpress is looking for a long-tail keyphrase. Avoid the crowd and seek keywords for the content.

Content marketing

A content-then-commerce strategy requires creating and distributing articles to target search engines and engage readers.

The articles should be clear and engaging, with proper HTML headers and tags. Repurposed articles make quality social media posts, generating what SEO practitioners call “social signals.” The number of followers, likes, and reposts a shop has on X or Instagram could inform search engines about the business and impact rankings.

Finally, some content marketing efforts, such as customer surveys, could be newsworthy.

Classic link building

Acquiring backlinks for a dropshipping store is perhaps the most difficult and valuable SEO tactic. It is vital for building credibility with search engines, but it demands hard work.

Compelling, original content will likely attract links organically. Otherwise, link building could include writing guest posts or contacting other sites to request links.

Media relations

The aim of media relations is to get links from large news sites.

SEO practitioners were excited to learn that Google’s index of links to global websites resides on three tiers of (massive) computer servers: random access memory, solid-state drives, and hard disk drives. These storage types differ in cost and speed.

The assumption is that Google considers links on the fastest tier (random access memory, or RAM) more valuable. Popular news sites typically reside on that tier and are therefore the best backlinks.

Structured data markup

Structured data markup from the Schema.org vocabulary, JSON-LD, or similar serves at least two purposes.

First, this uniform, structured info tells search engines what a page is about. Structured data could distinguish a site selling “Live Long & Prosper” t-shirts from one offering health tips for prospering over a long life.

Most SEO pros believe that structured markup increases the likelihood that a page will obtain a rich snippet or an AI-generated citation.

The Basics

This list of SEO tactics for dropshipping could have been much longer. Instead,  I’ve focused on content and assumed that ecommerce platforms would provide technical components such as HTML tags, site speed, mobility usability, and more.

Google Confirms 3 Ways To Make Googlebot Crawl More via @sejournal, @martinibuster

Google’s Gary Illyes and Lizzi Sassman discussed three factors that trigger increased Googlebot crawling. While they downplayed the need for constant crawling, they acknowledged there a ways to encourage Googlebot to revisit a website.

1. Impact of High-Quality Content on Crawling Frequency

One of the things they talked about was the quality of a website. A lot of people suffer from the discovered not indexed issue and that’s sometimes caused by certain SEO practices that people have learned and believe are a good practice. I’ve been doing SEO for 25 years and one thing that’s always stayed the same is that industry defined best practices are generally years behind what Google is doing. Yet, it’s hard to see what’s wrong if a person is convinced that they’re doing everything right.

Gary Illyes shared a reason for an elevated crawl frequency at the 4:42 minute mark, explaining that one of triggers for a high level of crawling is signals of high quality that Google’s algorithms detect.

Gary said it at the 4:42 minute mark:

“…generally if the content of a site is of high quality and it’s helpful and people like it in general, then Googlebot–well, Google–tends to crawl more from that site…”

There’s a lot of nuance to the above statement that’s missing, like what are the signals of high quality and helpfulness that will trigger Google to decide to crawl more frequently?

Well, Google never says. But we can speculate and the following are some of my educated guesses.

We know that there are patents about branded search that count branded searches made by users as implied links. Some people think that “implied links” are brand mentions, but “brand mentions” are absolutely not what the patent talks about.

Then there’s the Navboost patent that’s been around since 2004. Some people equate the Navboost patent with clicks but if you read the actual patent from 2004 you’ll see that it never mentions click through rates (CTR). It talks about user interaction signals. Clicks was a topic of intense research in the early 2000s but if you read the research papers and the patents it’s easy to understand what I mean when it’s not so simple as “monkey clicks the website in the SERPs, Google ranks it higher, monkey gets banana.”

In general, I think that signals that indicate people perceive a site as helpful, I think that can help a website rank better. And sometimes that can be giving people what they expect to see, giving people what they expect to see.

Site owners will tell me that Google is ranking garbage and when I take a look I can see what they mean, the sites are kind of garbagey. But on the other hand the content is giving people what they want because they don’t really know how to tell the difference between what they expect to see and actual good quality content (I call that the Froot Loops algorithm).

What’s the Froot Loops algorithm? It’s an effect from Google’s reliance on user satisfaction signals to judge whether their search results are making users happy. Here’s what I previously published about Google’s Froot Loops algorithm:

“Ever walk down a supermarket cereal aisle and note how many sugar-laden kinds of cereal line the shelves? That’s user satisfaction in action. People expect to see sugar bomb cereals in their cereal aisle and supermarkets satisfy that user intent.

I often look at the Froot Loops on the cereal aisle and think, “Who eats that stuff?” Apparently, a lot of people do, that’s why the box is on the supermarket shelf – because people expect to see it there.

Google is doing the same thing as the supermarket. Google is showing the results that are most likely to satisfy users, just like that cereal aisle.”

An example of a garbagey site that satisfies users is a popular recipe site (that I won’t name) that publishes easy to cook recipes that are inauthentic and uses shortcuts like cream of mushroom soup out of the can as an ingredient. I’m fairly experienced in the kitchen and those recipes make me cringe. But people I know love that site because they really don’t know better, they just want an easy recipe.

What the helpfulness conversation is really about is understanding the online audience and giving them what they want, which is different from giving them what they should want. Understanding what people want and giving it to them is, in my opinion, what searchers will find helpful and ring Google’s helpfulness signal bells.

2. Increased Publishing Activity

Another thing that Illyes and Sassman said could trigger Googlebot to crawl more is an increased frequency of publishing, like if a site suddenly increased the amount of pages it is publishing. But Illyes said that in the context of a hacked site that all of a sudden started publishing more web pages. A hacked site that’s publishing a lot of pages would cause Googlebot to crawl more.

If we zoom out to examine that statement from the perspective of the forest then it’s pretty evident that he’s implying that an increase in publication activity may trigger an increase in crawl activity. It’s not that the site was hacked that is causing Googlebot to crawl more, it’s the increase in publishing that’s causing it.

Here is where Gary cites a burst of publishing activity as a Googlebot trigger:

“…but it can also mean that, I don’t know, the site was hacked. And then there’s a bunch of new URLs that Googlebot gets excited about, and then it goes out and then it’s crawling like crazy.”​

A lot of new pages makes Googlebot get excited and crawl a site “like crazy” is the takeaway there. No further elaboration is needed, let’s move on.

3. Consistency Of Content Quality

Gary Illyes goes on to mention that Google may reconsider the overall site quality and that may cause a drop in crawl frequency.

Here’s what Gary said:

“…if we are not crawling much or we are gradually slowing down with crawling, that might be a sign of low-quality content or that we rethought the quality of the site.”

What does Gary mean when he says that Google “rethought the quality of the site?” My take on it is that sometimes the overall site quality of a site can go down if there’s parts of the site that aren’t to the same standard as the original site quality. In my opinion, based on things I’ve seen over the years, at some point the low quality content may begin to outweigh the good content and drag the rest of the site down with it.

When people come to me saying that they have a “content cannibalism” issue, when I take a look at it, what they’re really suffering from is a low quality content issue in another part of the site.

Lizzi Sassman goes on to ask at around the 6 minute mark if there’s an impact if the website content was static, neither improving or getting worse, but simply not changing. Gary resisted giving an answer, simply saying that Googlebot returns to check on the site to see if it has changed and says that “probably” Googlebot might slow down the crawling if there is no changes but qualified that statement by saying that he didn’t know.

Something that went unsaid but is related to the Consistency of Content Quality is that sometimes the topic changes and if the content is static then it may automatically lose relevance and begin to lose rankings. So it’s a good idea to do a regular Content Audit to see if the topic has changed and if so to update the content so that it continues to be relevant to users, readers and consumers when they have conversations about a topic.

Three Ways To Improve Relations With Googlebot

As Gary and Lizzi made clear, it’s not really about poking Googlebot to get it to come around just for the sake of getting it to crawl. The point is to think about your content and its relationship to the users.

1. Is the content high quality?
Does the content address a topic or does it address a keyword? Sites that use a keyword-based content strategy are the ones that I see suffering in the 2024 core algorithm updates. Strategies that are based on topics tend to produce better content and sailed through the algorithm updates.

2. Increased Publishing Activity
An increase in publishing activity can cause Googlebot to come around more often. Regardless of whether it’s because a site is hacked or a site is putting more vigor into their content publishing strategy, a regular content publishing schedule is a good thing and has always been a good thing. There is no “set it and forget it” when it comes to content publishing.

3. Consistency Of Content Quality
Content quality, topicality, and relevance to users over time is an important consideration and will assure that Googlebot will continue to come around to say hello. A drop in any of those factors (quality, topicality, and relevance) could affect Googlebot crawling which itself is a symptom of the more importat factor, which is how Google’s algorithm itself regards the content.

Listen to the Google Search Off The Record Podcast beginning at about the 4 minute mark:

Featured Image by Shutterstock/Cast Of Thousands

The 6 Best AI Content Checkers To Use In 2024 via @sejournal, @annabellenyst

Today, many people see generative AI like ChatGPT, Gemini, and others as indispensable tools that streamline their day-to-day workflows and enhance their productivity.

However, with the proliferation of AI assistants comes an uptick in AI-generated content. AI content detectors can help you prioritize content quality and originality.

These tools can help you discern whether a piece of content was written by a human or AI – a task that’s becoming increasingly difficult – and this can help detect plagiarism, and ensure content is original, unique, and high-quality.

In this article, we’ll look at some of the top AI content checkers available in 2024. Let’s dive in.

The 6 Best AI Content Checkers

1. GPTZero

Screenshot from GPTZero.me, July 2024

Launched in 2022, GPTZero was “the first public open AI detector,” according to its website – and it’s a leading choice among the tools out there today.

GPTZero’s advanced detection model comprises seven different components, including an internet text search to identify whether the content already exists in internet archives, a burstiness analysis to see whether the style and tone reflect that of human writing, end-to-end deep learning, and more.

Its Deep Scan feature gives you a detailed report highlighting sentences likely created by AI and tells you why that is, and GPTZero also offers a user-friendly Detection Dashboard as a source of truth for all your reports.

The tool is straightforward, and the company works with partners and researchers from institutions like Princeton, Penn State, and OpenAI to provide top-tier research and benchmarking.

Cost:

  • The Basic plan is available for free. It includes up to 10,000 words per month.
  • The Essential plan starts at $10 per month, with up to 150,000 words, plagiarism detection, and advanced writing feedback.
  • The Premium plan starts at $16 per month and includes up to 300,000 words, everything in the Essential tier, as well as Deep Scan, AI detection in multiple languages, and downloadable reports.

2. Originality.ai

Screenshot from Originality.ai, July 2024

Originality.ai is designed to detect AI-generated content across various language models, including ChatGPT, GPT-4o, Gemini Pro, Claude 3, Llama 3, and others. It bills itself as the “most accurate AI detector,” and targets publishers, agencies, and writers – but not students.

The latter is relevant because, the company says, by leaving academia, research, and other historical text out of its scope, it’s able to better train its model to hone in on published content across the internet, print, etc.

Originality.ai works across multiple languages and offers a free Chrome extension and API integration. It also has a team that works around the clock, testing out new strategies to create AI content that tools can’t detect. Once it finds one, it trains the tool to sniff it out.

The tool is straightforward; users can just paste content directly into Originality.ai, or upload from a file or even a URL. It will then give you a report that flags AI-detected portions as well as the overall originality of the text. You get three free scans initially, with a 300-word limit.

Cost:

  • Pro membership starts at $12.45 per month and includes 2,000 credits, AI scans, shareable reports, plagiarism and readability scans, and more.
  • Enterprise membership starts at $179 per month and includes 15,000 credits per month, features in the Pro plan, as well as priority support, API, and a 365-day history of your scans.
  • Originality.ai also offers a “pay as you go” tier, which consists of a $30 one-time payment to access 3,000 credits and some of the more limited features listed above.

3. Copyleaks

Screenshot from Copyleaks.com, July 2024

While you’ve probably heard of Copyleaks as a plagiarism detection tool, what you might not know is that it also offers a comprehensive AI-checking solution.

The tool covers 30 languages and detects across AI models including ChatGPT, Gemini, and Claude – and it automatically updates when new language models are released.

According to Copyleaks, its AI detector “has over 99% overall accuracy and a 0.2% false positive rate, the lowest of any platform.”

It works by using its long history of data and learning to spot the pattern of human-generated writing – and thus, flag anything that doesn’t fit common patterns as potentially AI-generated.

Other notable features of Copyleaks’ AI content detector are the ability to detect AI-generated source code, spot content that might have been paraphrased by AI, as well as browser extension and API offerings.

Cost:

  • Users with a Copyleaks account can access a limited number of free scans daily.
  • Paid plans start at $7.99 per month for the AI Detector tool, including up to 1,200 credits, scanning in over 30 languages, two users, and API access.
  • You can also get access to an AI + Plagiarism Detection tier starting at $13.99 per month.

4. Winston AI

Screenshot from GoWinston.ai, July 2024

Another popular AI content detection tool, Winston AI calls itself “the most trusted AI detector,” and claims to be the only such tool with a 99.98% accuracy rate.

Winston AI is designed for users across the education, SEO, and writing industries, and it’s able to identify content generated by LLMs such as ChatGPT, GPT-4, Google Gemini, Claude, and more.

Using Winston AI is easy; paste or upload your documents into the tool, and it will scan the text (including text from scanned pictures or handwriting) and provide a printable report with your results.

Like other tools in this list, Winston AI offers multilingual support, high-grade security, and can also spot content that’s been paraphrased using tools like Quillbot.

One unique feature of Winston AI is its “AI Prediction Map,” a color-coded visualization that highlights which parts of your content sound inauthentic and may be flagged by AI detectors.

Cost

  • Free 7-day trial includes 2,000 credits, AI content checking, AI image and deepfake detection, and more.
  • Paid plans start at $12 per month for 80,000 credits, with additional advanced features based on your membership tier.

5. TraceGPT

Screenshot from plagiarismcheck.org, July 2024

Looking for an extremely accurate AI content detector? Try TraceGPT by PlagiarismCheck.org.

It’s a user-friendly tool that allows you to upload files across a range of formats, including doc, docx, txt, odt, rtf, and pdf. Then, it leverages creativity/predictability ratios and other methods to scan your content for “AI-related breadcrumbs.”

Once it’s done, TraceGPT will provide results that show you what it has flagged as potential AI-generated text, tagging it as “likely” or “highly likely.”

As with many of the options here, TraceGPT offers support in several languages, as well as API and browser extension access. The tool claims to be beneficial for people in academia, SEO, and recruitment.

Cost

  • You can sign up to use TraceGPT and will be given limited free access.
  • Paid plans differ based on the type of membership; for businesses, they start at $69 for 1,000 pages, and for individuals, it starts at $5.99 for 20 pages. Paid plans also give you access to 24/7 support and a grammar checker.

6. Hive Moderation

Screenshot from hivemoderation.com, July 2024

Hive Moderation, a company that specializes in content moderation, offers an AI content detector with a unique differentiator. Unlike most of the other examples listed here, it is capable of checking for AI content across several media formats, including text, audio, and image.

Users can simply input their desired media, and Hive’s models will discern whether they believe them to be AI-generated. You’ll get immediate results with a holistic score and more detailed information, such as whether Hive thinks your image was created by Midjourney, DALL-E, or ChatGPT, for example.

Hive Moderation offers a Chrome extension for its AI detector, as well as several levels of customization so that customers can tweak their usage to fit their needs and industry.

Pricing:

  • You can download the Hive AI Chrome Extension for free, and its browser tool offers at least some free scans.
  • You’ll need to contact the Hive Moderation team for more extensive use of its tools.

What Is An AI Content Checker?

An AI content checker is a tool for detecting whether a piece of content or writing was generated by artificial intelligence.

Using machine learning algorithms and natural language processing, these tools can identify specific patterns and characteristics common in AI-generated content.

An important disclaimer: At this point in time, no AI content detector is perfect. While some are better than others, they all have limitations.

They can make mistakes, from falsely identifying human-written content as AI-generated or failing to spot AI-generated content.

However, they are useful tools for pressure-testing content to spot glaring errors and ensure that it is authentic and not a reproduction or plagiarism.

Why Use An AI Content Detector?

As AI systems become more widespread and sophisticated, it’ll only become harder to tell when AI has produced content – so tools like these could become more important.

Other reasons AI content checkers are beneficial include:

  • They can help you protect your reputation. Say you’re publishing content on a website or blog. You want to make sure your audience can trust that what they’re reading is authentic and original. AI content checkers can help you ensure just that.
  • They can ensure you avoid any plagiarism. Yes, generative AI is only getting better, but it’s still known to reproduce other people’s work without citation in the answers it generates. So, by using an AI content detector, you can steer clear of plagiarism and the many risks associated with it.
  • They can confirm that the content you’re working with is original. Producing unique content isn’t just an SEO best practice – it’s essential to maintaining integrity, whether you’re a business, a content creator, or an academic professional. AI content detectors can help here by weeding out anything that doesn’t meet that standard.

AI content detectors have various use cases, including at the draft stage, during editing, or during the final review of content. They can also be used for ongoing content audits.

AI detectors may produce false positives, so you should scrutinize their results if you’re using them to make a decision. However, false positives can also help identify human-written content that requires a little more work to stand out.

We recommend you use a variety of different tools, cross-check your results, and build trust with your writers. Always remember that these are not a replacement for human editing, fact-checking, or review.

They are merely there as a helping hand and an additional level of scrutiny.

In Summary

While we still have a long way to go before AI detection tools are perfect, they’re useful tools that can help you ensure your content is authentic and of the highest quality.

By making use of AI content checkers, you can maintain trust with your audience and ensure you stay one step ahead of the competition.

Hopefully, this list of the best solutions available today can help you get started. Choose the tool that best fits your resources and requirements, and start integrating AI detection into your content workflow today.

More resources: 


Featured Image: Sammby/Shutterstock

Google Warns: URL Parameters Create Crawl Issues via @sejournal, @MattGSouthern

Gary Illyes, Analyst at Google, has highlighted a major issue for crawlers: URL parameters.

During a recent episode of Google’s Search Off The Record podcast, Illyes explained how parameters can create endless URLs for a single page, causing crawl inefficiencies.

Illyes covered the technical aspects, SEO impact, and potential solutions. He also discussed Google’s past approaches and hinted at future fixes.

This info is especially relevant for large or e-commerce sites.

The Infinite URL Problem

Illyes explained that URL parameters can create what amounts to an infinite number of URLs for a single page.

He explains:

“Technically, you can add that in one almost infinite–well, de facto infinite–number of parameters to any URL, and the server will just ignore those that don’t alter the response.”

This creates a problem for search engine crawlers.

While these variations might lead to the same content, crawlers can’t know this without visiting each URL. This can lead to inefficient use of crawl resources and indexing issues.

E-commerce Sites Most Affected

The problem is prevalent among e-commerce websites, which often use URL parameters to track, filter, and sort products.

For instance, a single product page might have multiple URL variations for different color options, sizes, or referral sources.

Illyes pointed out:

“Because you can just add URL parameters to it… it also means that when you are crawling, and crawling in the proper sense like ‘following links,’ then everything– everything becomes much more complicated.”

Historical Context

Google has grappled with this issue for years. In the past, Google offered a URL Parameters tool in Search Console to help webmasters indicate which parameters were important and which could be ignored.

However, this tool was deprecated in 2022, leaving some SEOs concerned about how to manage this issue.

Potential Solutions

While Illyes didn’t offer a definitive solution, he hinted at potential approaches:

  1. Google is exploring ways to handle URL parameters, potentially by developing algorithms to identify redundant URLs.
  2. Illyes suggested that clearer communication from website owners about their URL structure could help. “We could just tell them that, ‘Okay, use this method to block that URL space,’” he noted.
  3. Illyes mentioned that robots.txt files could potentially be used more to guide crawlers. “With robots.txt, it’s surprisingly flexible what you can do with it,” he said.

Implications For SEO

This discussion has several implications for SEO:

  1. Crawl Budget: For large sites, managing URL parameters can help conserve crawl budget, ensuring that important pages are crawled and indexed.in
  2. Site Architecture: Developers may need to reconsider how they structure URLs, particularly for large e-commerce sites with numerous product variations.
  3. Faceted Navigation: E-commerce sites using faceted navigation should be mindful of how this impacts URL structure and crawlability.
  4. Canonical Tags: Using canonical tags can help Google understand which URL version should be considered primary.

In Summary

URL parameter handling remains tricky for search engines.

Google is working on it, but you should still monitor URL structures and use tools to guide crawlers.

Hear the full discussion in the podcast episode below:

Creating Value And Content Across Multiple City And Area Service Pages via @sejournal, @TaylorDanRW

For enterprise multi-location businesses, the alignment of your SEO strategy and business strategy is crucial for success.

Whether the business is operating a franchise model, a retail chain, or multiple hubs operating as a service area business, your approach to local SEO needs to be tailored to meet your specific goals. It also needs to be scalable and efficient enough to be maintained while returning long-term ROI.

Another key requirement is that your content approach produces enough value for users, and Google, so that it falls above the indexing quality threshold.

This means going beyond the standard best practices for local SEO and creating a local SEO campaign that drives brand visibility and conversions sustainably.

Aligning The SEO & Business Strategies

Multi-location businesses have different objectives.

While the basics of multi-location management are the same, your approach needs to work with the overall strategy and align with the overall business objectives.

For example, the strategy franchise business with multiple operators running service businesses in multiple towns, cities, and states will differ from a big-box store with hundreds of locations in multiple states.

Success metrics also vary. Typically, the KPIs for enterprise local SEO campaigns fall into one of the following categories:

  • To drive visibility and footfall to the individual locations.
  • To funnel local intent searches to the online store for direct delivery, or future interaction with local stores.
  • A combination of the two above.

Depending on what the business determines as “success” will greatly impact your approach to creating a choice architecture for users, and how you report on success.

Approaches To Bulk Local Page Creation

Over the years, our approach to describing and producing multiple area service pages has changed.

A decade ago, we’d describe low-quality versions with small amends and largely the same content as doorway pages, something Google moved to devalue over time.

In more recent years, with the increased popularity of programmatic SEO, or pSEO, this method has become a popular go-to for creating these pages at scale.

Programmatic Content Creation For Local Service Pages

For businesses that operate hundreds or thousands of locations, programmatic or partial-programmatic content creation can be an attractive option.

Programmatic SEO, or pSEO, allows you to scalably generate large volumes of content. This approach has helped a number of businesses scale, but it can also lead to problems if the pages being created don’t create enough of a unique value proposition for Google to invest resources.

If we look at two common website architectures for local service pages, we typically have either a central service page and then local service pages, or a central page that acts as a gateway to the locale service pages – such as a store locator.

Local service page hierarchyImage from author, July 2024

Depending on your business type, you will likely choose one structure over the other by default, but both can come with their challenges.

With a central service page structure you can run into issues with creating unique value propositions and ensuring each page has enough differentiation and falls above Google’s quality thresholds for indexing.

The store locator page approach can cause issues with PageRank distribution and how you internally link to the different locations. Most user-friendly store location applications don’t load HTML links, so while visually linking to all the stores, Google can’t crawl the links.

A common issue with both of these approaches, however, is how you work to capture “wider” searches around the locations.

Local Content Value Propositions

Local pages are at their most helpful when they tailor best to the location.

Historically, I’ve seen companies do this by “bloating” pages with additional information about the area, such as a paragraph or two on local infrastructure, schools, and sports teams – none of which is relevant if you’re trying to get people to visit your hardware store or enquire about your home-visit security fitting services.

It’s also not enough to just change the location name in the URL, H1, Title Tag, and throughout the body copy.

When this happens, Google effectively sees near-duplicate pages with very little differentiation in the value proposition that is relevant to the user query.

A symptom of this is when pages are shown as not indexed in Search Console, and Google is either choosing to override the user-declared canonical, or they’re stuck in either the Discovered or Crawled, not currently indexed phases.

There will always be a level of duplication across local service and location pages. Google is fine with this. Just because something is duplicated on multiple pages doesn’t mean it’s low quality.

Creating Value Proposition Differentiations

This is where I tend to favor the partially programmatic approach.

Programmatic can fulfill 70%(+) of the page’s content; it can cover your service offerings, pricing, and company information for those specific locations.

The remaining percentage of the page is manual but allows you to create the value proposition differentiation against other pages.

Let’s say you’re a multi-state courier service, and you have many routes to market, and your main distribution hubs in Texas are in Austin, San Antonio, and Dallas, and you want to target potential customers in Euless.

The services you offer for Euless are the same as what you offer customers in Pflugerville, Kyle, and Leander – so those parts of each location page will be the same on all of them.

But Euless is served by the Dallas hub and the others by the Austin hub – this is your first content differentiation point to highlight.

You can then use data from within the business, and keyword research, to flesh out these pages with travel time data.

Customers looking for courier services in Euless might be looking for Euless to Austin, or Euless to Houston services – so building this into the local page and having a time estimation to popular locations from the destination shows local specialism and helps customers better understand the service and plan.

Your business data will also help you identify the customer types. For example, many jobs booked in Euless might be for university students moving out to live on campus, so this is again more localized targeting to the customer base that can be included on the page.

Internal Linking

When it comes to internal linking, the use of pseudo-HTML sitemaps can help with this and not only act as clean internal links through the pages, but also be beneficial to users and allow you to create other landing pages to target county or area level searches.

Ten years ago on a property finder page, the team I worked with built out a page structure pattern of County > Town/City whilst pulling through relevant locations into the landing pages along the way.

Search by countyScreenshot from author, July 2024

Visually, this just acted as a more “manual” method for users to filter from the non-location specific pages towards their local areas.

Google Business Profile Linking

Another key component that is often missed is the direct linking of Google Business Profiles (GBPs) to their related location page on the website.

I come across a number of multinationals and nationals who link back to their company homepage, sometimes with a parameter to highlight which GBP the user has clicked through from – but this is both poor web architecture and poor user choice architecture.

If a user is looking for a service/store in XYZ, they don’t want a homepage or generic information page if they click on the website link.

In terms of user-choice architecture, from here a user could navigate to a different store or page and miss key information relevant to them, that otherwise could have driven a sale or enquiry.

Google’s Local Algorithms

In addition to Google’s core algorithm and more general Search ranking signals, Google has released updates specifically targeting local queries. The two main ones are:

  • Pigeon 2014: This update aimed to provide more relevant and accurate local search results by tying local search results more closely to general Search ranking signals. User proximity (as a signal) also received a boost.
  • Possum 2016: This update aimed to enhance the ranking of businesses located just outside city limits, making search results more location-specific to the user’s proximity to the business. Address-based filtering was also introduced to avoid duplicate listings for businesses sharing the same address (such as virtual offices).

These updates make it harder for businesses to spoof being present in a local market, and potentially not offering a value proposition that matches or meets the needs of the searcher.

Anecdotally, Google seems to prioritize ranking businesses that provide the most comprehensive information.

This includes opening dates, onsite dining options (if applicable), special opening hours, business categories, service listings, and defining the service area and service types.

Google Business Profile Importance

Following the guidelines is a must, but even then, you can fall foul of Google’s auto-detection checks.

Working with an international software company, that has multiple offices across Asia, a number are rented floors in shared offices.

We assume that occasionally, Google detects the shared addresses and mistakes them as being a virtual office/fake address, which is something the Possum algorithm update looked to reduce.

When you’re working with an enterprise organization with a large number of physical locations, the approach to Google Business Profile management can become more complex through internal stakeholder management and understanding how GBPs fit into, and contribute, to the overall objectives and ecosystem.

Reporting GBP Data

Depending on your objectives, how you report success will vary between campaigns.

From the Google API, you can access listing-level data for your Impressions, and a breakdown of different user interactions (infer impressions and clicks from GSC mirror metrics).

Atypical Google Business Profile reporting dashboard. (Screenshot from author, July 2024)

In my opinion, any business operating across multiple towns, cities, counties, or states needs to have some form of GBP monitoring and reporting visibility outside of tracking parameterized URLs in Google Search Console and other analytics platforms (assuming you’re using parameters on your GBP website links).

More resources: 


Featured Image: ivector/Shutterstock

Using AI Tools For Global Websites Operation And Management via @sejournal, @motokohunt

Running and maintaining global websites is not an easy task.

The good news is there are new AI tool solutions available that ease some of the work that goes into website management as well as assisting with SEO efforts.

AI technology is advancing rapidly and has been adopted into different work streams and all areas of marketing.

However, AI is not perfect and still needs refinements and human interaction. But that should not stop us from exploring and testing it out.

Here are ways you can benefit from AI to make your work with global websites more efficient and productive in areas including content, SEO, research, and management.

Global Website Content

Creating relevant content and publishing it on multiple websites in different languages requires plenty of resources. This is one of the big challenges and unavoidable tasks with global websites.

Content Translation and Localization

In the past, I always advised against using machine translation to translate original content to other languages. I hadn’t found any translation tools that produced satisfactory quality output, especially for Asian languages.

I’ve been testing different AI-powered translation tools lately and found their quality to be much improved. However, it is still not the same quality as the work of skilled human translators.

My suggestion is to use the AI tools as “go-between” solutions. Because this is one area where a lack of resources (both manpower and budget) holds the entire project back, I think it’s worth a try.

Text Translation And Localization

Let the AI tools handle the initial translation work. It still needs to be edited by humans, especially if the content covers specific industry knowledge, but at least it is in the correct language.

Before you deploy it site-wide, create the prompt based on several tests.

Prioritize the content (by type, category, dates, etc.) for human editing.

Duplicate Content

Use AI to check for duplicate content in the CMS. You can then decide whether to keep or kill reported content.

Having duplicate content is not necessarily a negative issue. Many global websites have content in the same language but each targeting a different country.

In this case, AI tools can help quickly localize the content for each target country by changing the spelling, currency, measurements, addresses, etc.

Image ALT Text Creation And Translation

The image ALT tag has been overlooked for many years. Many websites don’t use it.

Even if the main site uses it, the regional sites don’t have translated text in ALT tags. There are multiple solutions available now with AI tools baked into the image file management systems.

Some use Google’s Vision API to identify the key elements of an image and create appropriate text for the image to be auto-localized.

User Generated Content (UGC) Translation:

Because of the nature of the UGC, it is a huge challenge to translate the content as it is created.

The machine translation with an AI-powered review process is perhaps the best option out there.

Content Creation

Having content that is designed for the target audience in a specific country/region is one of the keys to a successful business.

You sometimes see a small company beat a large corporation in the online realm because a small business has an advantage in its deeper understanding of its local audience.

With the AI-assisted research project, you could quickly identify content gaps and content that satisfies the local audience’s needs.

Content Topic And Opportunity Research

AI tools can help shorten your local audience research process. It can identify the locally unique search demand and different types of information people look for in different countries or regions.

The research may also be used to identify the content gap between your site and competitor sites and give you an idea for locally unique FAQ content. You may also learn that unpopular items on the main site could perform much better in another country.

Other Ways To Improve Content

Localized Images

Images on websites support the understanding of products, corporate messages, and more. You may want to replace some images with more acceptable ones in some countries.

For example, create images with Asian models for websites targeting Asian countries.

Video Transcription And Translation

Transcribing the videos and translating them are other items I often see on the to-do list, but they are always pushed down on the priority list.

International SEO

In addition to content-related work, AI tools can support other international SEO action items.

From the technical SEO standpoint, AI tools can help in many areas, including the below:

  • Hreflang tag URL mapping review.
  • Tags and codes auto-generation review – language tag, title tag, meta description, canonical tag, etc.
  • Schema markup review.
  • Finding broken or unnecessary codes.

Depending on the size of the websites, these tasks could take many resource hours, especially for multinational and multilingual sites. With the help of AI tools, you can focus on improving the sites rather than finding them.

You can also let the AI tool analyze site crawl reports to find patterns in broken links and broken redirects and even suggest where to set redirects based on relevance and other technical SEO issues across the sites.

Data Analysis And Global Website Management

If you manage global websites or international SEO work, you know how important it is to have the same data points, KPIs, report templates, and best practice guidelines across countries.

Strengthen the governance of your global website management with AI tools.

Example Tasks

  • Add visualization of data in the performance reports.
  • Competitors analysis in each country and language.
  • Research local regulations.
  • Create visualization of task process and guidelines.
  • Audience analysis to create local personas.

Conclusion

We should embrace technologies such as AI tools to make our work more efficient and cost-effective. However, remember that AI tools are supporting tools and should not completely replace the work of humans.

As mentioned previously, AI tools are not perfect, and you should not let them auto-run. It is important to test the quality of their output prior to deployment.

Because of its dynamic learning capability, you want to test and improve prompts, requirements, etc., especially at the beginning.

Human reviews should be part of the process, and the settings should be updated or modified as needed.

More resources: 


Featured Image: Fah Studio 27/Shutterstock

Query Refinements: How Google Helps Users Find Products Faster via @sejournal, @Kevin_Indig

Shopping SERPs have been looking more like a feed than ranked results for a while now. In December, I wrote about the integration of Google’s shopping tab into the main results for shopping queries in e-commerce shifts. The result is a marketplace that looks more like Amazon than web search results.

SERP features and query refinements play a big role in this transition. They direct users from unrefined searches to finding products as quickly as possible, having an outsized impact on clicks and revenue.

In this deep dive, I analyzed +28,000 shopping SERPs to understand how query refinements work and how e-commerce sites can use them.

It’s a bit early for shopping season, but I’m writing a lot of e-commerce because most shops need some time to make changes on their site (especially the big ones). So, if you want a fruitful 2024, now is the time to get the work on the roadmap.

This piece builds on two analyses I’ve published previously:

1. Growth Memo

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2. Growth Memo

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What Are Query Refinements

Query Refinements are “pills” at the top of the search results that help users refine their search. In essence, query refinements are product filters.

First announced at Google Search On 2022, Google explained that refinements (and filters on desktop) follow real-time search trends (bolding mine):

Whole page shopping filters on Search are now dynamic and adapt based on real-time Search trends. So if you’re shopping for jeans, you might see filters for “wide leg” and “bootcut” because those are the popular denim styles right now — but those may change over time, depending on what’s trending.1

Examples of Query Refinements. (Image Credit: Kevin Indig)

The purpose of query refinements is to bring users from the “messy middle” to conversion as quickly as possible as they literally refine the query. When you click on a pill, Google sends users to another SERP, just like when users click the product filters on the left.

To understand how query refinements work and how e-commerce sites can use them to their advantage, I dug up some data.

How Query Refinements Work

I analyzed 28,286 shopping keywords (US, desktop) with seoClarity and found that query refinements:

  • follow distinct patterns sites can use for keyword targeting.
  • lead to search queries without search volume.
  • trigger new AI Overviews on mobile.

Common Refinements

I analyzed which refinements come up most often in “position” one, two and three. Think about position one in this context as the first refinement from the left, which is the most visible.

Most refinements specify gender. The term “women” comes up most often in the top 3, but “men” comes up most often in the first refinement. 45% of query refinements mention one gender at least once, 61.4% if you include kids. It makes sense: before diving into product attributes like color or size, you want to make sure a product is “for you.”

The most common query refinements. (Image Credit: Kevin Indig)

The second most common group of refinements is location. Ten percent of the top three refinements include “nearby,” which is much more visible on mobile. Google shows maps by default on mobile devices, as mobile device users are more likely to be on the go.

The third group is attributes around queries that include “for” or “with”, where users try to specify use cases (9.8%), and the fourth is price (9% of refinements include the term “sale”).

Query refinements have a high overlap with product filters on desktops and often feature the first few filters as refinements. Product filters don’t exist on mobile, likely because users might expect the filter sidebar on desktop, but it doesn’t make sense on mobile.

Product filters (desktop) and query refinements tend to have high overlap. (Image Credit: Kevin Indig)

The sorting and visibility of refinements are different on mobile and desktop. Due to the difference in bigger screen size, mobile search results show ~4 refinements on load, while desktop can show over 10.

Since query refinements are based on realtime searches, they also overlap heavily with autosuggest.

(Image Credit: Kevin Indig)

Interesting Findings

Three insights from the data surprised me:

First, Google keeps refinements strictly focused on product attributes but not user intents. I expected searchers to be interested in opinions and reviews on Reddit, but neither “Reddit” nor “reviews” came up as a refinement a single time.

Two, query refinements exactly match the query, meaning you won’t find synonyms or closely related terms in them. As a result, brands don’t appear in refinements, either.

Three, most query refinements don’t have search volume or a CPC. Only 10,696 / 27,262 keywords in the first refinement have search volume (median = 70), and only 6,514 / 27,262 keywords have a CPC. Since query refinements are based on search behavior, we can conclude that search volume and other keyword planner data are very limited metrics.

AI Overview Refinements

Of course, I came across AI Overviews (AIOs) in my research. For the queries I analyzed, mobile results returned AIOs but desktop doesn’t. An example is brown mascara.

Brown mascara on desktop, no showing an AIO. (Image Credit: Kevin Indig)
Brown mascara on mobile, triggering an AIO. (Image Credit: Kevin Indig)

You likely spotted the AIO tabs on the screenshot above, which appear independently of refinements and explain common product attributes.

(Image Credit: Kevin Indig)

Notice how the AIO provides additional guidance and information in tabs (see screenshot below).

(Image Credit: Kevin Indig)

At this point, it’s unclear whether citations in AIO tabs are good because they drive traffic to review articles or bad because they give the answer away.

(Image Credit: Kevin Indig)

For other queries, like “air compressor”, I was able to spot refinements in the AI Overview instead of above it. Clicking an AIO refinement leads to another search with the refinement in the query. For example, on the SERP for “air compressor, one refinement is “for painting cars”. Clicking it leads to another SERP for the query “air compressor for painting cars” (with another AIO and tabs but no refinement). Notice that I was logged into the SGE beta, which means those features might not yet be live for every user.

(Image Credit: Kevin Indig)

5 Lessons

5 key lessons surfaced from my analysis of over 28,000 shopping queries:

  • You should create specific product and category pages for men/women/kids when it matters for products (e.g. fashion).
  • Mine query refinements and autosuggest to find relevant query variations for your keyword research (for example, seoClarity can do this).
  • Monitor ranks by query refinement to drive your decisions around facetted indexing (like Nike or Target). Refinements showing different URLs are an indicator of building specific facets.
  • You need to identify searcher interest beyond search volume. The fact that more than half of queries don’t have search volume, but query refinements are optimized for search behavior shows that you might miss a lot of opportunities by limiting yourself to queries that have search volume. Instead, leverage onsite search data, surveys and qualitative research to enhance keyword targeting.
  • Monitor and compare clicks from desktop and mobile results to understand the impact of product filters (desktop) and AIOs (mobile).