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Google Autocomplete: A Complete SEO Guide

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Google Autocomplete: A Complete SEO Guide


Google Autocomplete is a controversial but powerful search feature.

When you type a word, or even a letter, into Google, it populates a list of search suggestions. That’s what autocomplete is.

SEO professionals, paid search marketers, content marketers, and social media managers can all benefit from using Google Autocomplete to help with different keyword-focused and intent-exploring projects.

On the other hand, Google Autocomplete often makes the news for funny, peculiar, or even offensive habits (often in a negative way).

People use autocomplete constantly, saving thousands of seconds per day, but it has also been blamed for political cover-ups and spoiling movies, TV shows, and video games.

Google Autocomplete can also be a powerful marketing tool. SEO professionals and other digital marketers have used it for years to inform strategy, get keywords, and find the important questions customers are asking.

They can use Autocomplete to better optimize clients’ digital properties and the content and messaging that make them up.

This guide will help you understand the real power this simple but super-helpful feature can do for help with your day-to-day tasks.

What Is Google Autocomplete?

Google’s own words, Google Autocomplete is “designed to make it faster to complete searches that you’re beginning to type.”

It’s integrated across Google Search and other Alphabet properties that use Google, including in the “Omnibox” on Chrome.

Google estimates that, cumulatively, it saves over 200 years of typing every day, and on average reduces typing overall by about 25 percent.

The primary purpose of the Autocomplete dropdown is to cut back on time a user spends typing by offering predictions of what a user may be typing — including for websites using the built-in Google Custom Search Engine feature.

While Autocomplete has been a desktop search feature since late 2004, it has become even more useful as a time-saving feature on mobile devices.

Typing on a mobile device is a bit tougher than doing so on the large keyboards we have grown up with and are accustomed to, so it’s a welcomed tool for providing assistance and saving time for many.

There are several other useful ways that the feature can be used to leverage content ideas, keyword suggestions, intent exploration, online reputation management, and other data-driven tasks.

How Does Autocomplete Work?

Ex-Googler Kevin Gibbs created the project, originally called “Google Suggest” by another former Googler Marissa Mayer.

Google has since moved away from the “Suggest” name since it’s not always offering the most thoughtful, caring, or appropriate predictions.

Google calls the completions it offers “predictions”, not “suggestions.” This is because of how Autocomplete works.

Autocomplete is supposed to help people complete a sentence they were intending – not to suggest a search intent, as with “I’m feeling lucky.” They determine predictions by looking at common searches on Google, including looking at trending searches that might be relevant.

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This allows Autocomplete to quickly update and adapt to new search trends and news stories.

Much of Autocomplete’s behavior is computer-generated, with data collected from millions of other Google searches and their results, including the content on those pages. It also takes information from your search history, location, and other data points.

Google has also put a lot of work in, so as to avoid inappropriate or offensive autocomplete suggestions. This means there are both automated and manual removal procedures that can influence what autocomplete suggestions are left.

Autocomplete is also related to the Knowledge Graph, and especially on mobile, it can bring Knowledge Graph suggestions into the prediction.

It wasn’t until 2008 that Google built Autocomplete into its default search engine (it was previously an opt-in feature).

Best Ways to Use Google Autocomplete

1. Keyword Research

It’s a long, tedious, and laborious task, but it’s also the foundation of all SEO strategies– and has been for a long time.

While we may no longer explicitly target keywords, keywords and their related ideas are always going to be an important part of search marketing.

Keyword research is one of the first tasks tackled at the start of an engagement — and carried on throughout the engagement — to understand not just a brand and the content it creates, but also its potential shortcomings, website strengths and weaknesses, and content gaps.

Autocomplete doesn’t do all the work for you in terms of keyword research, but it’s a great place to start at or to use early and often when developing content calendars and general organic search strategies.

Using it (along with other keyword resource tools like Google Keyword Planner and other third-party keyword databases) to get an idea of the right keywords you want to target by considering monthly search frequency, competition, and even cost-per-click (CPC) pricing will do your search strategy justice.

One of the shining advantages of Google Autocomplete is its ability to uncover quality long-tail phrases that are commonly searched across the web.

Since the primary measure for Autocomplete is popularity — based on real searches by users in real-time — the value of Autocomplete lies in its plethora of keyword-level data that you can dig up if you work at it hard and long enough.

As always, be sure you are signed out of Google to ensure you limit personalization for an unbiased look at predictions.

Long-tail keywords are incredibly useful when fulfilling content gaps but also offer endless possibilities in terms of high-value blog posts and educational content within a brand’s niche.

2. Intent Exploration

Understanding user intent is important because it guides the goal of the page, its messaging, its layout, and even imagery. We know pages perform best when they fully satisfy the user intent of a search query.

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You can use Autocomplete to better understand user intent, but doing so can be involved and laborious. Taking the time to visit many different web pages in the search results tied to specific predictions is going to take some time, focus, and content consumption. But the information you can mine from this method is invaluable.

Keywords overlap various stages of user intent, and without more keyword context, it can be tough to understand the intent.

Autocomplete will help you not just understand different high-value long-tail keywords and the intent surrounding them, but it will also help marketers recognize the volume of content around specific stages of intent, as well as which long-tail phrases and intent stages could be optimized for as a higher priority.

Of course, for high-value keywords — long-tail or traditional one-, two-, and three-word phrases — it’s important to satisfy all stages of intent as they relate to the high-value keywords.

That’s the idea behind an all-encompassing, quality search strategy. And Autocomplete can help get you there.

3. Online Reputation Management

Autocomplete has been significant in the realm of online reputation management, too.

Remember, when a user searches for your name or your brand name, the first thing they see, even before your site on the SERP (search engine results page), is the Autocomplete predictions tied to that name.

If those predictions are negative, or if even just one of them is negative, it can have a real impact on your business’s performance.

Think about it. You search [Dog Washers Inc] and the first prediction finishes with “loses dog,” you probably won’t feel too keen on bringing your dog there for his next bath.

Same for a restaurant; if you search [Ted’s Seaford Spot] and the prediction finishes with “e. Coli,” I have a pretty good idea of what you’re not eating tonight.

Autocomplete makes up an important part of online reputation management (ORM) and cannot be ignored when working to balance all negative connections made with brands.

One must be vigilant, just like most ORM strategies. Several ways brands can work to offset negative Autocomplete predictions are:

  • Take control of your brand’s conversations to ensure the right connections are being made in Google Autocomplete.
  • Social media account optimization reinforces the positive connections that may be overshadowed by negative ones.
  • Social media content, messaging, and engagement are in line with the optimizations above and the brand’s voice and tone.
  • Consistent branding and messaging for profile websites with positive keywords association used elsewhere
  • Starting small and making an impact by searching for positive connections for the brand from different locations. Obviously, the more people, the better. But you’d be surprised at the impact it can have.
  • Building backlinks to Google SERPs for positive keyword associations with your name; things like [sam hollingsworth seo writer] and [sam hollingsworth handicapper] would be great starts for someone like myself. 😊
  • If there are negative autocomplete suggestions, ensure that you have a strategy of how to address them.
See also  Google Search Console Impressions Report and Continuous Scroll SERPs

4. Content Generation and Exploration

You can also now use Autocomplete for content generation and exploring competitor content for your own content ideas. It’s easy, and interesting, to use Autocomplete alongside other online writing tools, to find out what web users are searching for.

FAQs

Just looking at “who”, “what”, “where”, “when”, and “why” with a few brand-related questions can get you a ton of questions for your FAQ– questions people may already be searching for.

Related keywords

You can do this in many ways, for many reasons. An easy one is “brand name vs.”– Google will autofill with competitors. You can also look at “brand name and” and see what autocomplete finds there– finding ways to expand your brand.

Related topics

If you can find Autocomplete suggestions for related topics, that aren’t covered by your main topic, you might have an edge to grow some content in that niche.

Queries like “how * works” can be invaluable, autocomplete filling in the wildcard space with suggestions. You can also do this to find questions about your brand, questions for content marketing, find problems potential customers are looking for, and even find out if users are looking for certain social media accounts.

Screenshot of Google Search, November 2021

Autocomplete Policies

With a history of backlash due to some of its search predictions, Google does manually work to prevent inappropriate Autocomplete predictions when it comes to:

  • Sexually explicit predictions.
  • Hateful predictions against groups and individuals.
  • Violent predictions.
  • Danger and harmful activities in predictions.

It also may remove predictions that could be considered spam, facilitate or advocate piracy, or if given a legal request to do so.

Google makes it clear that it removes predictions that relate to any of the above-mentioned situations unless they contain medical or scientific terms that are not malicious.

Looking for Feedback

To better control inappropriate Autocomplete predictions, Google launched its feedback tool and uses the data to make improvements consistently.

For instance, there doesn’t have to be a particular demographic that is being targeted by something hateful in nature; and feedback helps get that discovered faster and easier.

google-autocomplete-report-inappropriateScreenshot of Google Search, November 2021

Understanding what people are actually searching for is an essential part of your SEO strategy.

See how you can incorporate Google Autocomplete into your research process. You just might be surprised at the specific keywords and search intent it reveals!

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Are Contextual Links A Google Ranking Factor?

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Are Contextual Links A Google Ranking Factor?


Inbound links are a ranking signal that can vary greatly in terms of how they’re weighted by Google.

One of the key attributes that experts say can separate a high value link from a low value link is the context in which it appears.

When a link is placed within relevant content, it’s thought to have a greater impact on rankings than a link randomly inserted within unrelated text.

Is there any bearing to that claim?

Let’s dive deeper into what has been said about contextual links as a ranking factor to see whether there’s any evidence to support those claims.

The Claim: Contextual Links Are A Ranking Factor

A “contextual link” refers to an inbound link pointing to a URL that’s relevant to the content in which the link appears.

When an article links to a source to provide additional context for the reader, for example, that’s a contextual link.

Contextual links add value rather than being a distraction.

They should flow naturally with the content, giving the reader some clues about the page they’re being directed to.

Not to be confused with anchor text, which refers to the clickable part of a link, a contextual link is defined by the surrounding text.

A link’s anchor text could be related to the webpage it’s pointing to, but if it’s surrounded by content that’s otherwise irrelevant then it doesn’t qualify as a contextual link.

Contextual links are said to be a Google ranking factor, with claims that they’re weighted higher by the search engine than other types of links.

One of the reasons why Google might care about context when it comes to links is because of the experience it creates for users.

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When a user clicks a link and lands on a page related to what they were previously looking at, it’s a better experience than getting directed to a webpage they aren’t interested in.

Modern guides to link building all recommend getting links from relevant URLs, as opposed to going out and placing links anywhere that will take them.

There’s now a greater emphasis on quality over quantity when it comes to link building, and a link is considered higher quality when its placement makes sense in context.

One high quality contextual link can, in theory, be worth more than multiple lower quality links.

That’s why experts advise site owners to gain at least a few contextual links, as that will get them further than building dozens of random links.

If Google weights the quality of links higher or lower based on context, it would mean Google’s crawlers can understand webpages and assess how closely they relate to other URLs on the web.

Is there any evidence to support this?

The Evidence For Contextual Links As A Ranking Factor

Evidence in support of contextual links as a ranking factor can be traced back to 2012 with the launch of the Penguin algorithm update.

Google’s original algorithm, PageRank, was built entirely on links. The more links pointing to a website, the more authority it was considered to have.

Websites could catapult their site up to the top of Google’s search results by building as many links as possible. It didn’t matter if the links were contextual or arbitrary.

Google’s PageRank algorithm wasn’t as selective about which links it valued (or devalued) over others until it was augmented with the Penguin update.

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Penguin brought a number of changes to Google’s algorithm that made it more difficult to manipulate search rankings through spammy link building practices.

In Google’s announcement of the launch of Penguin, former search engineer Matt Cutts highlighted a specific example of the link spam it’s designed to target.

This example depicts the exact opposite of a contextual link, with Cutts saying:

“Here’s an example of a site with unusual linking patterns that is also affected by this change. Notice that if you try to read the text aloud you’ll discover that the outgoing links are completely unrelated to the actual content, and in fact, the page text has been “spun” beyond recognition.”

A contextual link, on the other hand, looks like the one a few paragraphs above linking to Google’s blog post.

Links with context share the following characteristics:

  • Placement fits in naturally with the content.
  • Linked URL is relevant to the article.
  • Reader knows where they’re going when they click on it.

All of the documentation Google has published about Penguin over the years is the strongest evidence available in support of contextual links as a ranking factor.

See: A Complete Guide to the Google Penguin Algorithm Update

Google will never outright say “contextual link building is a ranking factor,” however, because the company discourages any deliberate link building at all.

As Cutts adds at the end of his Penguin announcement, Google would prefer to see webpages acquire links organically:

“We want people doing white hat search engine optimization (or even no search engine optimization at all) to be free to focus on creating amazing, compelling web sites.”

Contextual Links Are A Ranking Factor: Our Verdict

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Contextual links are probably a Google ranking factor.

A link is weighted higher when it’s used in context than if it’s randomly placed within unrelated content.

But that doesn’t necessarily mean links without context will negatively impact a site’s rankings.

External links are largely outside a site owner’s control.

If a website links to you out of context it’s not a cause for concern, because Google is capable of ignoring low value links.

On the other hand, if Google detects a pattern of unnatural links, then that could count against a site’s rankings.

If you have actively engaged in non-contextual link building in the past, it may be wise to consider using the disavow tool.


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Is It A Google Ranking Factor?

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Is It A Google Ranking Factor?


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.

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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.”

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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.

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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:

  1. Recreate that semantic space using LSA across its entire index.
  2. Analyze the semantic meaning of the query.
  3. Find all similarities between the semantic meaning of the query and documents in the semantic space created from analyzing the entire index.
  4. 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.


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What Is a Google Broad Core Algorithm Update?

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What Is A Google Broad Core Algorithm Update?


When Google announces a broad core algorithm update, many SEO professionals find themselves asking what exactly changed (besides their rankings).

Google’s acknowledgment of core updates is always vague and doesn’t provide much detail other than to say the update occurred.

The SEO community is typically notified about core updates via the same standard tweets from Google’s Search Liaison.

There’s one announcement from Google when the update begins rolling out, and one on its conclusion, with few additional details in between (if any).

This invariably leaves SEO professionals and site owners asking many questions with respect to how their rankings were impacted by the core update.

To gain insight into what may have caused a site’s rankings to go up, down, or stay the same, it helps to understand what a broad core update is and how it differs from other types of algorithm updates.

After reading this article you’ll have a better idea of what a core update is designed to do, and how to recover from one if your rankings were impacted.

So, What Exactly Is A Core Update?

First, let me get the obligatory “Google makes hundreds of algorithm changes per year, often more than one per day” boilerplate out of the way.

Many of the named updates we hear about (Penguin, Panda, Pigeon, Fred, etc.) are implemented to address specific faults or issues in Google’s algorithms.

In the case of Penguin, it was link spam; in the case of Pigeon, it was local SEO spam.

They all had a specific purpose.

In these cases, Google (sometimes reluctantly) informed us what they were trying to accomplish or prevent with the algorithm update, and we were able to go back and remedy our sites.

A core update is different.

The way I understand it, a core update is a tweak or change to the main search algorithm itself.

You know, the one that has between 200 and 500 ranking factors and signals (depending on which SEO blog you’re reading today).

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What a core update means to me is that Google slightly tweaked the importance, order, weights, or values of these signals.

Because of that, they can’t come right out and tell us what changed without revealing the secret sauce.

The simplest way to visualize this would be to imagine 200 factors listed in order of importance.

Now imagine Google changing the order of 42 of those 200 factors.

Rankings would change, but it would be a combination of many things, not due to one specific factor or cause.

Obviously, it isn’t that simple, but that’s a good way to think about a core update.

Here’s a purely made up, slightly more complicated example of what Google wouldn’t tell us:

“In this core update, we increased the value of keywords in H1 tags by 2%, increased the value of HTTPS by 18%, decreased the value of keyword in title tag by 9%, changed the D value in our PageRank calculation from .85 to .70, and started using a TF-iDUF retrieval method for logged in users instead of the traditional TF-PDF method.”

(I swear these are real things. I just have no idea if they’re real things used by Google.)

For starters, many SEO pros wouldn’t understand it.

Basically, it means Google may have changed the way they calculate term importance on a page, or the weighing of links in PageRank, or both, or a whole bunch of other factors that they can’t talk about (without giving away the algorithm).

Put simply: Google changed the weight and importance of many ranking factors.

That’s the simple explanation.

At its most complex form, Google ran a new training set through their machine learning ranking model and quality raters picked this new set of results as more relevant than the previous set, and the engineers have no idea what weights changed or how they changed because that’s just how machine learning works.

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(We all know Google uses quality raters to rate search results. These ratings are how they choose one algorithm change over another – not how they rate your site. Whether they feed this into machine learning is anybody’s guess. But it’s one possibility.)

It’s likely some random combination of weighting delivered more relevant results for the quality raters, so they tested it more, the test results confirmed it, and they pushed it live.

How Can You Recover From A Core Update?

Unlike a major named update that targeted specific things, a core update may tweak the values of everything.

Because websites are weighted against other websites relevant to your query (engineers call this a corpus) the reason your site dropped could be entirely different than the reason somebody else’s increased or decreased in rankings.

To put it simply, Google isn’t telling you how to “recover” because it’s likely a different answer for every website and query.

It all depends on what everybody else trying to rank for your query is doing.

Does every one of them but you have their keyword in the H1 tag? If so then that could be a contributing factor.

Do you all do that already? Then that probably carries less weight for that corpus of results.

It’s very likely that this algorithm update didn’t “penalize” you for something at all. It most likely just rewarded another site more for something else.

Maybe you were killing it with internal anchor text and they were doing a great job of formatting content to match user intent – and Google shifted the weights so that content formatting was slightly higher and internal anchor text was slightly lower.

(Again, hypothetical examples here.)

In reality, it was probably several minor tweaks that, when combined, tipped the scales slightly in favor of one site or another (think of our reordered list here).

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Finding that “something else” that is helping your competitors isn’t easy – but it’s what keeps SEO professionals in the business.

Next Steps And Action Items

Rankings are down after a core update – now what?

Your next step is to gather intel on the pages that are ranking where your site used to be.

Conduct a SERP analysis to find positive correlations between pages that are ranking higher for queries where your site is now lower.

Try not to overanalyze the technical details, such as how fast each page loads or what their core web vitals scores are.

Pay attention to the content itself. As you go through it, ask yourself questions like:

  • Does it provide a better answer to the query than your article?
  • Does the content contain more recent data and current stats than yours?
  • Are there pictures and videos that help bring the content to life for the reader?

Google aims to serve content that provides the best and most complete answers to searchers’ queries. Relevance is the one ranking factor that will always win out over all others.

Take an honest look at your content to see if it’s as relevant today as it was prior to the core algorithm update.

From there you’ll have an idea of what needs improvement.

The best advice for conquering core updates?

Keep focusing on:

  • User intent.
  • Quality content.
  • Clean architecture.
  • Google’s guidelines.

Finally, don’t stop improving your site once you reach Position 1, because the site in Position 2 isn’t going to stop.

Yeah, I know, it’s not the answer anybody wants and it sounds like Google propaganda. I swear it’s not.

It’s just the reality of what a core update is.

Nobody said SEO was easy.

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