SEO

Is It A Google Ranking Factor?

Published

3 years ago

January 23, 2022

Latent semantic indexing (LSI) is an indexing and information retrieval method used to identify patterns in the relationships between terms and concepts.

With LSI, a mathematical technique is used to find semantically related terms within a collection of text (an index) where those relationships might otherwise be hidden (or latent).

And in that context, this sounds like it could be super important for SEO.

Right?

After all, Google is a massive index of information, and we’re hearing all kinds of things about semantic search and the importance of relevance in the search ranking algorithm.

If you’ve heard rumblings about latent semantic indexing in SEO or been advised to use LSI keywords, you aren’t alone.

But will LSI actually help improve your search rankings? Let’s take a look.

The Claim: Latent Semantic Indexing As A Ranking Factor

The claim is simple: Optimizing web content using LSI keywords helps Google better understand it and you’ll be rewarded with higher rankings.

Backlinko defines LSI keywords in this way:

“LSI (Latent Semantic Indexing) Keywords are conceptually related terms that search engines use to deeply understand content on a webpage.”

By using contextually related terms, you can deepen Google’s understanding of your content. Or so the story goes.

That resource goes on to make some pretty compelling arguments for LSI keywords:

“Google relies on LSI keywords to understand content at such a deep level.”
“LSI Keywords are NOT synonyms. Instead, they’re terms that are closely tied to your target keyword.”
“Google doesn’t ONLY bold terms that exactly match what you just searched for (in search results). They also bold words and phrases that are similar. Needless to say, these are LSI keywords that you want to sprinkle into your content.”

Does this practice of “sprinkling” terms closely related to your target keyword help improve your rankings via LSI?

The Evidence For LSI As A Ranking Factor

Relevance is identified as one of five key factors that help Google determine which result is the best answer for any given query.

As Google explains is its How Search Works resource:

“To return relevant results for your query, we first need to establish what information you’re looking forーthe intent behind your query.”

Once intent has been established:

“…algorithms analyze the content of webpages to assess whether the page contains information that might be relevant to what you are looking for.”

Google goes on to explain that the “most basic signal” of relevance is that the keywords used in the search query appear on the page. That makes sense – if you aren’t using the keywords the searcher is looking for, how could Google tell you’re the best answer?

Now, this is where some believe LSI comes into play.

If using keywords is a signal of relevance, using just the right keywords must be a stronger signal.

There are purpose-build tools dedicated to helping you find these LSI keywords, and believers in this tactic recommend using all kinds of other keyword research tactics to identify them, as well.

The Evidence Against LSI As A Ranking Factor

Google’s John Mueller has been crystal clear on this one:

“…we have no concept of LSI keywords. So that’s something you can completely ignore.”

There’s a healthy skepticism in SEO that Google may say things to lead us astray in order to protect the integrity of the algorithm. So let’s dig in here.

First, it’s important to understand what LSI is and where it came from.

Latent semantic structure emerged as a methodology for retrieving textual objects from files stored in a computer system in the late 1980s. As such, it’s an example of one of the earlier information retrieval (IR) concepts available to programmers.

As computer storage capacity improved and electronically available sets of data grew in size, it became more difficult to locate exactly what one was looking for in that collection.

Researchers described the problem they were trying to solve in a patent application filed September 15, 1988:

“Most systems still require a user or provider of information to specify explicit relationships and links between data objects or text objects, thereby making the systems tedious to use or to apply to large, heterogeneous computer information files whose content may be unfamiliar to the user.”

Keyword matching was being used in IR at the time, but its limitations were evident long before Google came along.

Too often, the words a person used to search for the information they sought were not exact matches for the words used in the indexed information.

There are two reasons for this:

Synonymy: the diverse range of words used to describe a single object or idea results in relevant results being missed.
Polysemy: the different meanings of a single word results in irrelevant results being retrieved.

These are still issues today, and you can imagine what a massive headache it is for Google.

However, the methodologies and technology Google uses to solve for relevance long ago moved on from LSI.

What LSI did was automatically create a “semantic space” for information retrieval.

As the patent explains, LSI treated this unreliability of association data as a statistical problem.

Without getting too into the weeds, these researchers essentially believed that there was a hidden underlying latent semantic structure they could tease out of word usage data.

Doing so would reveal the latent meaning and enable the system to bring back more relevant results – and only the most relevant results – even if there’s no exact keyword match.

Here’s what that LSI process actually looks like:

Image created by author, January 2022

And here’s the most important thing you should note about the above illustration of this methodology from the patent application: there are two separate processes happening.

First, the collection or index undergoes Latent Semantic Analysis.

Second, the query is analyzed and the already-processed index is then searched for similarities.

And that’s where the fundamental problem with LSI as a Google search ranking signal lies.

Google’s index is massive at hundreds of billions of pages, and it’s growing constantly.

Each time a user inputs a query, Google is sorting through its index in a fraction of a second to find the best answer.

Using the above methodology in the algorithm would require that Google:

Recreate that semantic space using LSA across its entire index.
Analyze the semantic meaning of the query.
Find all similarities between the semantic meaning of the query and documents in the semantic space created from analyzing the entire index.
Sort and rank those results.

That’s a gross oversimplification, but the point is that this isn’t a scalable process.

This would be super useful for small collections of information. It was helpful for surfacing relevant reports inside a company’s computerized archive of technical documentation, for example.

The patent application illustrates how LSI works using a collection of nine documents. That’s what it was designed to do. LSI is primitive in terms of computerized information retrieval.

Latent Semantic Indexing As A Ranking Factor: Our Verdict

Latent Semantic Indexing (LSI): Is It A Google Ranking Factor?

While the underlying principles of eliminating noise by determining semantic relevance have surely informed developments in search ranking since LSA/LSI was patented, LSI itself has no useful application in SEO today.

It hasn’t been ruled out completely, but there is no evidence that Google has ever used LSI to rank results. And Google definitely isn’t using LSI or LSI keywords today to rank search results.

Those who recommend using LSI keywords are latching on to a concept they don’t quite understand in an effort to explain why the ways in which words are related (or not) is important in SEO.

Relevance and intent are foundational considerations in Google’s search ranking algorithm.

Those are two of the big questions they’re trying to solve for in surfacing the best answer for any query.

Synonymy and polysemy are still major challenges.

Semantics – that is, our understanding of the various meanings of words and how they’re related – is essential in producing more relevant search results.

But LSI has nothing to do with that.

Featured Image: Paulo Bobita/Search Engine Journal

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SEO

Google Cautions On Blocking GoogleOther Bot

Published

7 hours ago

July 26, 2024

Max

Google cautions about blocking and opting out of getting crawled by the GoogleOther crawler

Google’s Gary Illyes answered a question about the non-search features that the GoogleOther crawler supports, then added a caution about the consequences of blocking GoogleOther.

What Is GoogleOther?

GoogleOther is a generic crawler created by Google for the various purposes that fall outside of those of bots that specialize for Search, Ads, Video, Images, News, Desktop and Mobile. It can be used by internal teams at Google for research and development in relation to various products.

The official description of GoogleOther is:

“GoogleOther is the generic crawler that may be used by various product teams for fetching publicly accessible content from sites. For example, it may be used for one-off crawls for internal research and development.”

Something that may be surprising is that there are actually three kinds of GoogleOther crawlers.

Three Kinds Of GoogleOther Crawlers

GoogleOther
Generic crawler for public URLs
GoogleOther-Image
Optimized to crawl public image URLs
GoogleOther-Video
Optimized to crawl public video URLs

All three GoogleOther crawlers can be used for research and development purposes. That’s just one purpose that Google publicly acknowledges that all three versions of GoogleOther could be used for.

What Non-Search Features Does GoogleOther Support?

Google doesn’t say what specific non-search features GoogleOther supports, probably because it doesn’t really “support” a specific feature. It exists for research and development crawling which could be in support of a new product or an improvement in a current product, it’s a highly open and generic purpose.

This is the question asked that Gary narrated:

“What non-search features does GoogleOther crawling support?”

Gary Illyes answered:

“This is a very topical question, and I think it is a very good question. Besides what’s in the public I don’t have more to share.

GoogleOther is the generic crawler that may be used by various product teams for fetching publicly accessible content from sites. For example, it may be used for one-off crawls for internal research and development.

Historically Googlebot was used for this, but that kind of makes things murky and less transparent, so we launched GoogleOther so you have better controls over what your site is crawled for.

That said GoogleOther is not tied to a single product, so opting out of GoogleOther crawling might affect a wide range of things across the Google universe; alas, not Search, search is only Googlebot.”

It Might Affect A Wide Range Of Things

Gary is clear that blocking GoogleOther wouldn’t have an affect on Google Search because Googlebot is the crawler used for indexing content. So if blocking any of the three versions of GoogleOther is something a site owner wants to do, then it should be okay to do that without a negative effect on search rankings.

But Gary also cautioned about the outcome that blocking GoogleOther, saying that it would have an effect on other products and services across Google. He didn’t state which other products it could affect nor did he elaborate on the pros or cons of blocking GoogleOther.

Pros And Cons Of Blocking GoogleOther

Whether or not to block GoogleOther doesn’t necessarily have a straightforward answer. There are several considerations to whether doing that makes sense.

Pros

Inclusion in research for a future Google product that’s related to search (maps, shopping, images, a new feature in search) could be useful. It might be helpful to have a site included in that kind of research because it might be used for testing something good for a site and be one of the few sites chosen to test a feature that could increase earnings for a site.

Another consideration is that blocking GoogleOther to save on server resources is not necessarily a valid reason because GoogleOther doesn’t seem to crawl so often that it makes a noticeable impact.

If blocking Google from using site content for AI is a concern then blocking GoogleOther will have no impact on that at all. GoogleOther has nothing to do with crawling for Google Gemini apps or Vertex AI, including any future products that will be used for training associated language models. The bot for that specific use case is Google-Extended.

Cons

On the other hand it might not be helpful to allow GoogleOther if it’s being used to test something related to fighting spam and there’s something the site has to hide.

It’s possible that a site owner might not want to participate if GoogleOther comes crawling for market research or for training machine learning models (for internal purposes) that are unrelated to public-facing products like Gemini and Vertex.

Allowing GoogleOther to crawl a site for unknown purposes is like giving Google a blank check to use your site data in any way they see fit outside of training public-facing LLMs or purposes related to named bots like GoogleBot.

Takeaway

Should you block GoogleOther? It’s a coin toss. There are possible potential benefits but in general there isn’t enough information to make an informed decision.

Listen to the Google SEO Office Hours podcast at the 1:30 minute mark:

Featured Image by Shutterstock/Cast Of Thousands

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SEO

AI Search Boosts User Satisfaction

Published

1 day ago

July 26, 2024

Max

AI chat robot on search engine bar. Artificial intelligence bot innovation technology answer question with smart solution. 3D vector created from graphic software.

A new study finds that despite concerns about AI in online services, users are more satisfied with search engines and social media platforms than before.

The American Customer Satisfaction Index (ACSI) conducted its annual survey of search and social media users, finding that satisfaction has either held steady or improved.

This comes at a time when major tech companies are heavily investing in AI to enhance their services.

Search Engine Satisfaction Holds Strong

Google, Bing, and other search engines have rapidly integrated AI features into their platforms over the past year. While critics have raised concerns about potential negative impacts, the ACSI study suggests users are responding positively.

Google maintains its position as the most satisfying search engine with an ACSI score of 81, up 1% from last year. Users particularly appreciate its AI-powered features.

Interestingly, Bing and Yahoo! have seen notable improvements in user satisfaction, notching 3% gains to reach scores of 77 and 76, respectively. These are their highest ACSI scores in over a decade, likely due to their AI enhancements launched in 2023.

The study hints at the potential of new AI-enabled search functionality to drive further improvements in the customer experience. Bing has seen its market share improve by small but notable margins, rising from 6.35% in the first quarter of 2023 to 7.87% in Q1 2024.

Customer Experience Improvements

The ACSI study shows improvements across nearly all benchmarks of the customer experience for search engines. Notable areas of improvement include:

Ease of navigation
Ease of using the site on different devices
Loading speed performance and reliability
Variety of services and information
Freshness of content

These improvements suggest that AI enhancements positively impact various aspects of the search experience.

Social Media Sees Modest Gains

For the third year in a row, user satisfaction with social media platforms is on the rise, increasing 1% to an ACSI score of 74.

TikTok has emerged as the new industry leader among major sites, edging past YouTube with a score of 78. This underscores the platform’s effective use of AI-driven content recommendations.

Meta’s Facebook and Instagram have also seen significant improvements in user satisfaction, showing 3-point gains. While Facebook remains near the bottom of the industry at 69, Instagram’s score of 76 puts it within striking distance of the leaders.

Challenges Remain

Despite improvements, the study highlights ongoing privacy and advertising challenges for search engines and social media platforms. Privacy ratings for search engines remain relatively low but steady at 79, while social media platforms score even lower at 73.

Advertising experiences emerge as a key differentiator between higher- and lower-satisfaction brands, particularly in social media. New ACSI benchmarks reveal user concerns about advertising content’s trustworthiness and personal relevance.

Why This Matters For SEO Professionals

This study provides an independent perspective on how users are responding to the AI push in online services. For SEO professionals, these findings suggest that:

AI-enhanced search features resonate with users, potentially changing search behavior and expectations.
The improving satisfaction with alternative search engines like Bing may lead to a more diverse search landscape.
The continued importance of factors like content freshness and site performance in user satisfaction aligns with long-standing SEO best practices.

As AI becomes more integrated into our online experiences, SEO strategies may need to adapt to changing user preferences.

Featured Image: kate3155/Shutterstock

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SEO

Google To Upgrade All Retailers To New Merchant Center By September

Published

2 days ago

July 25, 2024

Max

Google To Upgrade All Retailers To New Merchant Center By September

Google has announced plans to transition all retailers to its updated Merchant Center platform by September.

This move will affect e-commerce businesses globally and comes ahead of the holiday shopping season.

The Merchant Center is a tool for online retailers to manage how their products appear across Google’s shopping services.

Key Changes & Features

The new Merchant Center includes several significant updates.

Product Studio

An AI-powered tool for content creation. Google reports that 80% of current users view it as improving efficiency.

This feature allows retailers to generate tailored product assets, animate still images, and modify existing product images to match brand aesthetics.

It also simplifies tasks like background removal and image resolution enhancement.

Centralized Analytics

A new tab consolidating various business insights, including pricing data and competitive analysis tools.

Retailers can access pricing recommendations, competitive visibility reports, and retail-specific search trends, enabling them to make data-driven decisions and capitalize on popular product categories.

Redesigned Navigation

Google claims the new interface is more intuitive and cites increased setup success rates for new merchants.

The platform now offers simplified website verification processes and can pre-populate product information during setup.

Initial User Response

According to Google, early adopters have shown increased engagement with the platform.

The company reports a 25% increase in omnichannel merchants adding product offers in the new system. However, these figures have yet to be independently verified.

Jeff Harrell, Google’s Senior Director of Merchant Shopping, states in an announcement:

“We’ve seen a significant increase in retention and engagement among existing online merchants who have moved to the new Merchant Center.”

Potential Challenges and Support

While Google emphasizes the upgrade’s benefits, some retailers, particularly those comfortable with the current version, may face challenges adapting to the new system.

The upgrade’s mandatory nature could raise concerns among users who prefer the existing interface or have integrated workflows based on the current system.

To address these concerns, Google has stated that it will provide resources and support to help with the transition. This includes tutorial videos, detailed documentation, and access to customer support teams for troubleshooting.

Industry Context

This update comes as e-commerce platforms evolve, with major players like Amazon and Shopify enhancing their seller tools. Google’s move is part of broader efforts to maintain competitiveness in the e-commerce services sector.

The upgrade could impact consumers by improving product listings and providing more accurate information across Google’s shopping services.

For the e-commerce industry as a whole, it signals a continued push towards AI-driven tools and data-centric decision-making.