Is GEO the Same as SEO?

“Generative engine optimization” refers to tactics for increasing visibility in and traffic from AI answers. “Answer engine optimization” is synonymous with GEO, as are references to large language models, such as “LLM optimization.”

Whatever the name, optimizing for generative AI is different from traditional search engines. The distinction lies in the underlying technology:

• LLM platforms don’t always perform a search to produce answers. The platforms use training data, which doesn’t typically have sources or URLs. It’s a knowledge base for accessing answers without necessarily knowing the origin.

• Unlike search engines, LLMs don’t have an index or a cache of URLs. When they search, LLMs use external search engines, likely Google for ChatGPT.

• After searching, AI crawlers go to the page, read it, and pull answers from it. AI crawlers are much less advanced than those of search engines and, accordingly, cannot render a page as easily as a Google crawler.

GEO-specific tactics include:

• A brand in AI training data has long-term exposure in answers, but appearing in that data requires an approach beyond SEO. The keys are concise, relevant, problem-solving content on-site, and off-site exposure in reviews, forums, and other reputable mentions.

• Being indexed by Google is more or less essential for AI answers, to a point. Additional optimization steps include (i) ensuring the site is accessible and crawlable by AI bots, (ii) structuring content to enable AI to pull answers easily, and (iii) optimizing for prompts, common needs, and, yes, keywords.

• Keywords remain critical (and evolving) for GEO and SEO, although the former “fans out” to also answer likely follow-up prompts.

SEO

Reliance on Google varies by the genAI platform. ChatGPT, again, taps Google’s index. AI Overviews mostly summarize top-ranking URLs for the initial and fan-out queries. Higher rankings in organic search will likely directly elevate visibility in AI Overviews and Perplexity.

Google Search remains the most powerful discovery and visibility engine. And a brand that ranks high in Google is typically prominent, which drives visibility in AI Answers. As such, organic rankings also drive AI indirectly, through brand signals.

GEO

Thus GEO and SEO overlap. Pages that rank highly in organic search results will almost certainly end up in training data with elevated chances of appearing in AI answers.

Yet for training data, AI platforms continuously crawl sites with their own, limited bots and those of third-party providers, such as Common Crawl.

Hence AI platforms crawl pages via two paths: from links in organic search results and independently with their own (or outsourced) bots.

GEO kicks in when the bots reach a page. The sophistication of AI crawlers is much less than Google’s. GEO requires concise, relevant page content that’s easily accessed, without JavaScript, and succinctly summarizes a need and then answers it directly.

Structured data markup, such as from Schema.org, likely helps, too.

In short, a GEO-ready page has a clear purpose and clear answers, easily crawled.

Is AI Cutting Into Your SEO Conversions? via @sejournal, @Kevin_Indig

Since March 2025 in the U.S. (and May elsewhere), many sites have noticed an uncomfortable pattern: organic conversions slipping.

It’s easy to blame falling traffic from Google’s intensified AI Overviews.

But purchase intent doesn’t just vanish. Does it?

If your conversions are holding steady, congratulations. If they’re not, the reasons may be more layered than you think.

In today’s Memo, I’m breaking down the five biggest forces I see behind SEO conversion declines across industries:

• Loss of top-of-the-funnel (TOFU) traffic (and why it matters more than you thought).
• Platform shifts pulling demand into other ecosystems.
• Channel shifts from organic to paid search.
• Attribution leakage that hides organic’s true impact.
• Macro factors pressuring conversion rates.

I’ll also walk you through the signals to check, how to measure each, and – inside the premium section – the exact process I use to identify which drivers are hitting a site the hardest.

Image Credit: Kevin Indig

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How have your SEO conversions changed since Google intensified AI Overviews?

If they’ve grown – all the power to you!

If not, I’m seeing five underlying reasons that could be contributing to their decline across industry types:

1. Loss of TOFU traffic.
2. Platform shift.
3. Channel shift.
4. Attribution loss.
5. Economic change.

Sites that are noticing an SEO conversion drop have seen it since 2025 (March in the U.S., May in other countries).

It’s logical to assume that the reason is a decline in organic traffic – makes sense – but purchase intent doesn’t just vanish.

Have your conversions gone to other sites, or could there be another explanation behind their decline?

Let’s dig in.

For decades, SEOs have created top-of-the-funnel content (like “what is X” or “why you need X”). This kind of content often has an unclear impact on the bottom line.

Now that organic clicks are dropping, conversions are dropping (to a lower degree) as well.

Was top-of-the-funnel content more impactful than we thought all along?

I raised that theory first in the AI Halftime Report:

AIOs are really mostly TOFU queries. In that case, TOFU content always had more impact on the bottom line than we were able to prove and we can expect the traffic decline to level off.

Or AIOs impact way more than MOFU and BOFU queries as well (which is what I think), and we’re in for a long decline of traffic.

If true, I expect revenue that’s attributed to organic search to decline at a lower rate – or not at all for certain companies – since purchase intent doesn’t just go away. Therefore, revenue results would relate more to our ability to influence purchase intent.

That’s where my concept of “Demand Activation” elasticity comes in.

In economics, price elasticity measures how much demand changes when prices change.

In marketing, Demand Activation elasticity describes how much eventual purchase behavior changes when you influence someone early in their journey.

Think about Demand Activation as how many potential customers you influence to buy from you.

If the “elasticity” is high, being visible at the very top of the funnel creates a disproportionate downstream impact on revenue, even if you can’t directly attribute it in analytics.

If this turns out to be correct, it’s an argument for earning AI visibility.

If Demand Activation has the impact I think it has, being visible in ChatGPT, AI Mode & Co. has stronger downstream effects than we can directly attribute. I’ve certainly seen more high-pipeline deals and purchases come from ChatGPT for some of my clients.

To illustrate the concept, let’s consider an economic example.

I’ve been searching for an excuse to write about the economic impact if Germany were to open stores on Sundays for a long time: Would people buy more if they could, or would purchases simply spread out across more days?

Studies by the EHI, IFO, and IW Köln show that people in Germany would actually buy more if stores were open on Sundays, especially non-food items. ^[1, 2, 3]

Stores in Germany do open a few Sundays a year.

And during those rare occasions, people shop more, especially for impulse buys.

Some research suggests that it’s mainly driven by events and tourism in higher spend areas, but looking at EU neighbors with an open-Sunday policy, like the Netherlands, we see consistently higher incremental retail spend.

To bring it back to Search, exposure early on in the user journey (as in “more open Sundays”) might have a stronger downstream impact (like more top-of-funnel visits) than we thought. Therefore, it could be critical to be broadly visible in LLMs.

Signals To Check:

1. TOFU traffic decline vs. MOFU/BOFU.

• How to measure: In Search Console, filter queries using a TOFU regex (remove branded terms). Compare YOY clicks for TOFU vs. MOFU/BOFU.

2. Branded search volume change.

• How to measure: Use Google Trends or a classic keyword tool (Ahrefs, Semrush) to track branded search volume over time. Correlate drops with TOFU traffic declines and conversions from organic.

3. Assisted conversions drop.

• How to measure: In GA4 or another MTA model, compare YOY assisted conversions from organic search. A sharp drop suggests TOFU content was influencing downstream revenue.

Another explanation is that conversions are happening more on other platforms instead of Google Search.

While Google’s ad market share has grown over the last five years, search behavior has diversified across multiple ecosystems:

• TikTok, YouTube, Reddit, LinkedIn, Instagram, niche forums – all of which have their own “search” layers.
• YouTube has long been the second-largest search engine in the world.
• Reddit is now the second-largest site in the U.S. (only Wikipedia is bigger), and Google is surfacing Reddit content more prominently, except in ecommerce.

The biggest shift, however, may be to LLMs.

ChatGPT alone sees 2.5 billion prompts per day.^[4] While many prompts are additive to Google Search and exploratory in intent, it’s unlikely there’s no overlap with purchase-driven queries.

Why this is happening now:

• Google’s increased integration of Reddit results (high-trust user content) changes click patterns.
• New LLM model releases (ChatGPT o3, Gemini 2.5) improve quality and speed, keeping users inside AI environments longer.
• AI-first platforms are beginning to feel less “experimental” and more like a default research tool.

Image Credit: Kevin Indig

Signals To Check:

1. Referral traffic from non-Google search platforms.

• How to measure: In GA4, track YOY referral traffic from YouTube, Reddit, TikTok, LinkedIn. See if gains coincide with Google organic losses.

2. Share of search activity across platforms.

• How to measure: Use Similarweb, Statcounter, or GWI to compare platform-specific search volumes and market share over time.

3. Self-reported attribution.

• How to measure: Ask users to fill out a short survey about where they first and last saw your brand after signing up or buying.

It is also possible that the clicks that would have gone to organic search are now going to paid search. The logic is simple:

When AI Overviews or zero-click results satisfy most of the informational need, the only prominent offers left are often ads.

If users still want to explore a product or service after reading an AIO, they might be more likely to click the sponsored result than scroll further to organic links.

The timing matches AIO rollout phases. If we see Google reporting strong Search revenue growth while organic traffic declines, it is a sign the demand has not disappeared – it is just being monetized differently.

Image Credit: Kevin Indig

Alphabet’s Q1 2025 10‑Q ^[5] reveals that paid clicks from Google Search either grew or hit 0% growth in the last 10 quarters, but never declined.

Impressions (from Google Network), on the other hand, saw the opposite trend.

Whenever paid impressions drop, paid clicks go up because lower ad inventory means advertisers need to pay more for traffic.

Q2 2025 earnings ^[6] highlighted that Search ad revenue grew 12% year‑over‑year. Industry benchmark data reveals that the average Google CPC in 2025 lands at $5.26 – up approximately 13% year-over-year.^[7]

So, less ad inventory leads to higher CPCs and more paid clicks.

Since we don’t know how many AI Overviews Google shows ads for, we can’t say with certainty that more clicks are going to ads as a direct result, but the data does show that more clicks are going to ads.

Signals To Check:

1. Organic vs. paid search traffic share.

• How to measure: In GA4, compare YOY sessions from organic search vs. paid search. Look for paid’s share increasing as organic drops.

2. Paid search impression and click growth.

• How to measure: Pull impressions and clicks from your Google Ads account (or industry benchmarks) over the last 12 months and compare to pre-AIO periods.

3. CPC and CPA trends.

• How to measure: In Google Ads or industry benchmarks, track YOY CPC and CPA changes in your vertical. Rising CPC with organic decline suggests a mix shift.

One (popular) possibility is that the influence of organic search has not changed much, but the way we measure it has.

Essentially, classic attribution methods are broken.

In the classic model, the path was:

Search → Click → Landing Page → Conversion

Now, the path may look more like:

Search (or AIO) → Brand Recall → Direct Visit → Conversion

AI Overviews answer the user’s question before the click, so when they are ready to buy, they bypass the search click entirely and go straight to the homepage or an app.

In analytics, that conversion shows up as direct traffic, not organic search.

Attribution leakage has always been a challenge for SEO, but AI-driven summaries and brand mentions make it worse.

Because it’s a demand capture channel, consider that SEO takes much more time between first and last touch to convert users than the default 90-day lookback window.

Often, the last touch is prone to paid channels because advertising tips people over the edge.

Also, it’s not uncommon for users to switch devices during a purchase cycle, making attribution way harder. Lastly, most attribution tools are geared towards advertising.

If you only track last-click conversions, you may underestimate the true contribution of search visibility.

Signals To Check:

1. Direct conversions are up while organic conversions are down.

• How to measure: In GA4, compare YOY direct channel conversions vs. organic. Look for inverse movement.

2. Branded search stable or rising.

• How to measure: Use Google Trends or a keyword tool to track branded search queries. Stability with organic session decline suggests clicks are being skipped.

3. Multi-touch attribution still shows search influence.

• How to measure: In GA4 (data-driven model) or a dedicated attribution tool, check if search remains a common first or assist touchpoint even when last-click conversions fall.

Are SEO conversion rates down because people simply have less money?

There is credible evidence that macro conditions in the U.S. are weighing on conversion rates:

1. Price sensitivity and promotion dependence

Adobe reports that shoppers were unusually price elastic during the holiday season of 2024.

A 1% drop in price produced a roughly 1.03% rise in demand, indicating elevated sensitivity to discounts. That effect implies conversions were heavily promotion-led.^[8]

Adobe’s Digital Price Index shows online prices have fallen for 33 straight months through May 2025, suggesting merchants are discounting to stimulate demand.

Sustained discounting typically lifts conversions only when price cuts are material, and it compresses margins.^[9]

2. Consumer caution and mix shift

Salesforce’s Shopping Index commentary notes U.S. shoppers “buying less,” prioritizing essentials, and trading down in 2025.

It also cites 0% U.S. ecommerce sales growth in Q1 2025, consistent with softer sensitivity to purchase.^[10]

Consumer confidence has improved slightly but remains soft relative to 2024, which tends to dampen conversion rates.^[11]

3. Household finance constraints

The New York Fed reports total household debt at a record $18.39 trillion in Q2 2025, with delinquency rates up from earlier periods and credit card balances at $1.21 trillion.

Higher borrowing costs and rising delinquencies constrain checkout conversion, especially for lower-income cohorts.^[12]

4. Observed conversion pressure in digital benchmarks

Contentsquare’s 2025 Digital Experience Benchmark finds online conversion rates fell 6.1% year over year, attributing much to experience friction.

In context with the macro signals above, this supports a broader environment where it is harder to turn visits into orders without heavier incentives.^[13]

But…

Overall, U.S. ecommerce dollars are still growing in many periods, including +5.6% year-over-year in Q1 2025 and strong holiday spend, so demand has not collapsed.

Growth is being “bought” through price cuts and promotions, which can mask weaker underlying conversion propensity.^[14, 15]

Also, you could argue that these economic conditions have been in place for a few years.

Why would they impact SEO conversions so much now?

Signals To Check:

1. Organic conversion rate trend vs. other channels.

• Track monthly SEO conversion rates alongside paid search, direct, and email.
• If all channels decline in parallel, macroeconomic pressure is a likely driver.
• If organic drops disproportionately, AI Overviews are adding to the decline.

2. Correlation with economic indicators.

• Compare organic CR trends to macro metrics like CPI, inflation rate, Consumer Confidence Index, and online price trends (Adobe DPI).
• Look for statistically significant correlations, like CR rising when CPI falls or confidence increases.
• If patterns are similar across Paid Search and Direct, macroeconomic factors are likely influencing purchase readiness.

3. Promotion elasticity

• Measure CR lift during promotions vs. baseline for organic, paid, and direct traffic.
• A bigger lift than in prior years – especially if mirrored across channels – indicates conversions are increasingly discount-driven, a sign of macro pressure.

If you’re experiencing a decline in SEO conversions in 2025, it’s likely not due to one specific reason.

In fact, it’s likely that all five options are playing into SEO conversion drops across the web.

To what degree each option has an impact matters from site to site and industry to industry.

That’s why it’s so important to run the analysis I recommend in each section above for your own data.

AI Mode will intensify the downward trend of SEO conversions.

I don’t think SEO will decline to zero because a small fraction of people will still click, even in AI Mode.

And Google won’t show AI Mode everywhere, because adoption is generational (see the UX study of AIOs for more info).

I think AI Mode will launch at a broader scale (like showing up for more queries overall) when Google figures out monetization.

Plus, ChatGPT is not yet monetizing, so advertisers go to Google and Meta – for now. And that’s my hypothesis as to why Google Search is continuing to grow.

At least for the time being.

It’ll be interesting to see what happens next in the coming months.

Featured Image: Paulo Bobita/Search Engine Journal

Google Web Guide: How It’s Reshaping The SERP And What It Means For Your SEO Strategy via @sejournal, @cyberandy

For decades, the digital world has been defined by hyperlinks, a simple, powerful way to connect documents across a vast, unstructured library. Yet, the foundational vision for the web was always more ambitious.

It was a vision of a Semantic Web, a web where the relationships between concepts are as important as the links between pages, allowing machines to understand the context and meaning of information, not just index its text.

With its latest Search Labs experiment, Web Guide (that got me so excited), Google is taking an important step in this direction.

Google’s Web Guide is designed to make it easier to find the information, not just webpages. It is optimized as an alternative to AI Mode and AI Overview for tackling complex, multi-part questions or to explore a topic from multiple angles.

Built using a customized version of the Gemini AI model, Web Guide organizes search results into helpful, easy-to-browse groups.

This is a pivotal moment. It signals that the core infrastructure of search is now evolving to natively support the principle of semantic understanding.

Web Guide represents a shift away from a web of pages and average rankings and toward a web of understanding and hyper-personalization.

This article will deconstruct the technology behind Web Guide, analyzing its dual impact on publishers and refining a possibly new playbook for the era of SEO or Generative Engine Optimization (GEO) if you like.

I personally don’t see Web Guide as just another feature; I see it as a glimpse into the future of how knowledge shall be discovered and consumed.

How Google’s Web Guide Works: The Technology Behind The Hyper-Personalized SERP

At its surface, Google Web Guide is a visual redesign of the search results page. It replaces the traditional, linear list of “10 blue links” with a structured mosaic of thematic content.

For an exploratory search like [how to solo travel in Japan], a user might see distinct, expandable clusters for “comprehensive guides,” “personal experiences,” and “safety recommendations.”

This allows users to immediately drill down into the facet of their query that is most relevant to them.

But, the real revolution is happening behind the scenes. This curation is powered by a custom version of Google’s Gemini model, but the key to its effectiveness is a technique known as “query fan-out.”

When a user enters a query, the AI doesn’t just search for that exact phrase. Instead, it deconstructs the user’s likely intent into a series of implicit, more specific sub-queries, “fanning out” to search for them in parallel.

For the “solo travel in Japan” query, the fan-out might generate internal searches for “Japan travel safety for solo women,” “best blogs for Japan travel,” and “using the Japan Rail Pass.”

By casting this wider net, the AI gathers a richer, more diverse set of results. It then analyzes and organizes these results into the thematic clusters presented to the user. This is the engine of hyper-personalization.

The SERP is no longer a one-size-fits-all list; it’s a dynamically generated, personalized guide built to match the multiple, often unstated, intents of a specific user’s query. (Here is the early analysis I did by analyzing the network traffic – HAR file – behind a request.)

To visualize how this works in semantic terms, let’s consider the query “things to know about running on the beach,” which the AI breaks down into the following facets:

Screenshot from search for [things to know about running on the beach], Google, August 2025

Image from author, August 2025

The WebGuide UI is composed of several elements designed to provide a comprehensive and personalized experience:

• Main Topic: The central theme or query that the user has entered.
• Branches: The main categories of information generated in response to the user’s query. These branches are derived from various online sources to provide a well-rounded overview.
• Sites: The specific websites from which the information is sourced. Each piece of information within the branches is attributed to its original source, including the entity name and a direct URL.

Let’s review Web Guide in the context of Google’s other AI initiatives.

Web Guide’s unique role is that of an AI-powered curator or librarian.

It adds a layer of AI organization while preserving the fundamental link-clicking experience, making it a strategically distinct and potentially less contentious implementation of AI in search.

The Publisher’s Conundrum: Threat Or Opportunity?

The central concern surrounding any AI-driven search feature is the potential for a severe loss of organic traffic, the economic lifeblood of most content creators. This anxiety is not speculative.

Cloudflare’s CEO has publicly criticized these moves as another step in “breaking publishers’ business models,” a sentiment that reflects deep apprehension across the digital content landscape.

This fear is contextualized by the well-documented impact of Web Guide’s sibling feature, AI Overviews.

A critical study by the Pew Research Center revealed that the presence of an AI summary at the top of a SERP dramatically reduces the likelihood that a user will click on an organic link, a nearly 50% relative drop in click-through rate in its analysis.

Google has mounted a vigorous defense, claiming it has “not observed significant drops in aggregate web traffic” and that the clicks that do come from pages with AI Overviews are of “higher quality.”

Amid this, Web Guide presents a more nuanced picture. There is a credible argument that, by preserving the link-clicking paradigm, it could be a more publisher-friendly application of AI.

Its “query fan-out” technique could benefit high-quality, specialized content that has struggled to rank for broad keywords.

In this optimistic view, Web Guide acts as a helpful librarian, guiding users to the right shelf in the library rather than just reading them a summary at the front desk.

However, even this more “link-friendly” approach cedes immense editorial control to an opaque algorithm, making the ultimate impact on net traffic uncertain to say the least.

The New Playbook: Building For The “Query Fan-Out”

The traditional goal of securing the No. 1 ranking for a specific keyword is rapidly becoming an outdated and insufficient goal.

In this new landscape, visibility is defined by contextual relevance and presence within AI-generated clusters. This requires a new strategic discipline: Generative Engine Optimization (GEO).

GEO expands the focus from optimizing for crawlers to optimizing for discoverability within AI-driven ecosystems.

The key to success in this new paradigm lies in understanding and aligning with the “query fan-out” mechanism.

Pillar 1: Build For The “Query Fan-Out” With Topical Authority

The most effective strategy is to pre-emptively build content that maps directly to the AI’s likely “fan-out” queries.

This means deconstructing your areas of expertise into core topics and constituent subtopics, and then building comprehensive content clusters that cover every facet of a subject.

This involves creating a central “pillar” page for a broad topic, which then links out to a “constellation” of highly detailed, dedicated articles that cover every conceivable sub-topic.

For “things to know about running on the beach,” (the example above) a publisher should create a central guide that links to individual, in-depth articles such as “The Benefits and Risks of Running on Wet vs. Dry Sand,” “What Shoes (If Any) Are Best for Beach Running?,” “Hydration and Sun Protection Tips for Beach Runners,” and “How to Improve Your Technique for Softer Surfaces.”

By creating and intelligently interlinking this content constellation, a publisher signals to the AI that their domain possesses comprehensive authority on the entire topic.

This dramatically increases the probability that when the AI “fans out” its queries, it will find multiple high-quality results from that single domain, making it a prime candidate to be featured across several of Web Guide’s curated clusters.

This strategy must be built upon Google’s established E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) principles, which are amplified in an AI-driven environment.

Pillar 2: Master Technical & Semantic SEO For An AI Audience

While Google states there are no new technical requirements for AI features, the shift to AI curation elevates the importance of existing best practices.

• Structured Data (Schema Markup): This is now more critical than ever. Structured data acts as a direct line of communication to AI models, explicitly defining the entities, properties, and relationships within your content. It makes content “AI-readable,” helping the system understand context with greater precision. This could mean the difference between being correctly identified as a “how-to guide” versus a “personal experience blog,” and thus being placed in the appropriate cluster.
• Foundational Site Health: The AI model needs to see a page the same way a user does. A well-organized site architecture, with clean URL structures that group similar topics into directories, provides strong signals to the AI about your site’s topical structure. Crawlability, a good page experience, and mobile usability are essential prerequisites for competing effectively.
• Write with semiotics in mind: As Gianluca Fiorelli would say, focus on the signals behind the message. AI systems now rely on hybrid chunking; they break content into meaning-rich segments that combine text, structure, visuals, and metadata. The clearer your semiotic signals (headings, entities, structured data, images, and relationships), the easier it is for AI to interpret the purpose and context of your content. In this AI-gated search environment, meaning and context have become your new keywords.

The Unseen Risks: Bias In The Black Box

A significant criticism of AI-driven systems like Web Guide lies in their inherent opacity. These “black boxes” pose a formidable challenge to accountability and fairness.

The criteria by which the Gemini model decides which categories to generate and which pages to include are not public, raising profound questions about the equity of the curation process.

There is a significant risk that the AI will not only reflect but also amplify existing societal and brand biases. A compelling example is to review complex issues to test the fairness of the Web Guide.

Screenshot from search for [Are women more likely to be prescribed antidepressants for physical symptoms?], Google, August 2025

Medical diagnostic queries are complex and can easily reveal biases.

Screenshot from search for [Will AI eliminate most white-collar jobs?], Google, July 2025

Once again, UGC is used and might not always bring the right nuance between doom narratives and overly optimistic positions.

Since the feature is built upon these same core systems of traditional Search, it is highly probable that it will perpetuate existing biases.

Conclusion: The Age Of The Semantic AI-Curated Web

Google’s Web Guide is not a temporary UI update; it is a manifestation of a deeper, irreversible transformation in information discovery.

It represents Google’s attempt to navigate the passage between the old world of the open, link-based web and the new world of generative, answer-based AI.

The “query fan-out” mechanism is the key to understanding its impact and the new strategic direction. For all stakeholders, adaptation is not optional.

The strategies that guaranteed success in the past are no longer sufficient. The core imperatives are clear: Embrace topical authority as a direct response to the AI’s mechanics, master the principles of Semantic SEO, and prioritize the diversification of traffic sources. The era of the 10 blue links is over.

The era of the AI-curated “chunks” has begun, and success will belong to those who build a deep, semantic repository of expertise that AI can reliably understand, trust, and surface.

More Resources:

Featured Image: NicoElNino/Shutterstock

Why Semantic HTML Matters For SEO And AI

I’ve had this post in drafts for a while, mostly as a container for me to drop bits into for when I get time to expand it into a proper newsletter.

Then, my good friend Jono Alderson published his excellent piece on semantic HTML, and for a few weeks, I lost the will to complete mine.

But, I thought I should finish my version anyway, as my focus is slightly different and perhaps a bit more practical than Jono’s.

You should still definitely read Jono’s blog; it says all I want to say and more.

Semantic HTML

Let’s start with a quick overview of what semantic HTML is.

As the language upon which the web is built, HTML is a markup that surrounds text to provide it with structure.

The

tag around a block of content indicates that it is a paragraph of text.

The

tag around a sentence shows that it is the page’s main heading.

The

tag indicates the start of an ordered (usually numbered) list.

The tag indicates you’ll be loading an image onto the webpage. And so forth.

Semantic HTML was used to code every webpage.

Content was surrounded by specific tags that indicated what each bit of content was meant for, and then CSS was applied to make it look good. It wasn’t perfect by any means, but it worked.

It also meant that you could look at the raw HTML source of a webpage and see what the page was trying to deliver, and how. The HTML signposted the structure and meaning of each bit of content on the page. You could see the purpose of the page just by looking at its code.

Then WYSIWYG editors and later JavaScript frameworks arrived on the scene, and HTML took a backseat. Instead of

and

, we got endless nestings of
and tags.

The end result is webpage HTML that lacks structure and has no meaning, until it is completely rendered in the browser and visually painted onto a screen. Only then will the user (and a machine system trying to emulate a user) understand what the page’s purpose is.

It’s why Google goes through the effort of rendering pages as part of its indexing process (even though it really doesn’t want to).

We know Google doesn’t usually have the time to render a news article before it needs to rank it in Top Stories and elsewhere. The raw HTML is therefore immensely important for news publishers.

Good HTML allows Google to effortlessly extract your article content and rank your story where it deserves in Google’s ecosystem.

Semantic HTML is a key factor here. This is the reason why SEOs like me insist that an article’s headline is wrapped in the

heading tag, and that this is the only instance of

on an article page.

The H1 headline indicates a webpage’s primary headline. It signposts where the article begins, so that Google can find the article content easily.

Which HTML Tags Are Semantic?

Beyond the

heading tag, there are many other semantic HTML elements you can implement that allow Google to more easily extract and index your article content.

In no particular order, the elements you should be using are:

• Paragraphs: Don’t use
  and tags to format the article into paragraphs. There’s been a tag for that for as long as HTML has existed, and it’s the

  tag. Use it.

• Subheadings: Use
  

  /

  /

  subheading tags to give your page structure. Use subheadings in an article to preface specific sections of content in your article. Use subheadings for the headers above concrete structural elements, such as recommended articles.

Images: Always use the tag if you want to show an image that you’d like Google to see as well. Google explicitly recommends this.

Clickable Links: When linking to another page, either internal or external, use the tag with an “href” value containing the target URL. It’s the only kind of link that Google will definitely follow.

Relational Links: The tag allows you to create a relationship between the current URL and another URL. This can be a canonical page, a stylesheet, an alternative language version of the current page, etc.

Lists: Bullet lists should use the

tag, and numbered lists should use
tag. You can make them look however you want with CSS, but do use the list tags as the foundation.

Emphasis: When you want to highlight a specific word or phrase, there are semantic HTML tags you should use for that: for italics, and for bold.

All the above tags, with the exception of , are intended for the content of the webpage, providing structure and meaning to the text.

There are additional semantic HTML tags that are intended to provide structure and meaning to the code of the page.

These tags allow Google to identify different elements on the page, such as the navigation vs. a sidebar, and process them accordingly.

Semantic HTML image from W3Schools.com (Image Credit: Barry Adams)

• The  and  tags exist to separate the page’s metadata (in the ) from the actual content (in the ). Every HTML page starts with those two.
• 
  

   can be used to wrap around the head section of the page, where the logo, navigation, and other stylistic elements sit.

• 
  

   should be used for your site’s main navigation. Mega menus, hamburger menus, top navigation links, whatever form your navigation takes, you should wrap it in the

• You can use 
  

  tags to divide your page into multiple sections. One section could be the article; another could be the comments below the article.

• 
  

   is the tag that shows where the page’s actual main article text begins (including the headline). This is a very valuable tag for news publishers.

• With 
  

   you can indicate blocks of content like a sidebar of trending stories, recommended articles, or the latest news.

• 
  

   is used for, you guessed it, the footer of the webpage.

These structural semantic tags help search engines understand the purpose and value of each section of HTML.

It enables Google to rapidly index your content and process the different elements of your pages appropriately.

There are many more semantic HTML tags at your disposal, for various different purposes. Chances are, there’s an HTML element for every imaginable use case.

Rather than cram your code full of

tags to make something happen, first see if there’s a proper HTML element that does the trick.

How Does It Help AI?

We know that LLMs like ChatGPT and Perplexity crawl the open web for training data, as well as for specific user queries that require content from the web.

What some of you may not know is that LLMs do not render JavaScript when they process webpages.

Google is the exception to the rule, as it has devoted a great deal of resources to rendering webpages as part of indexing.

Because Google’s Gemini is the only LLM built on Google’s index, Gemini is the only LLM that uses content from fully rendered webpages.

So, if you want to have any chance of showing up as a cited source in ChatGPT or Perplexity, you’d do well to ensure your complete page content is available in your raw, unrendered HTML.

Using semantic HTML to structure your code and provide meaning also helps these LLMs easily identify your core content.

It’s much simpler for ChatGPT to parse a few dozen semantic HTML tags rather than several hundred (or even thousand) nested

tags to find a webpage’s main content.

If and when the “agentic web” comes to life (I’m skeptical), semantic HTML is likely a crucial aspect of success.

With meaningless

and tags, it’s much easier for an AI agent to misunderstand what actions it should perform.

When you use semantic HTML for things like buttons, links, and forms, the chances of an AI agent failing its task are much lower.

The meaning inherent in proper HTML tags will tell the AI agent where to go and what to do.

What About Structured Data?

You may think that structured data has made semantic HTML obsolete.

After all, with structured data, you can provide machine systems with the necessary information about a page’s content and purpose in a simple machine-readable format.

This is true to an extent. However, structured data was never intended to replace semantic HTML. It serves an entirely different purpose.

Structured data has limitations that semantic HTML doesn’t have.

Structured data won’t tell a machine which button adds a product to a cart, what subheading precedes a critical paragraph of text, and which links the reader should click on for more information.

By all means, use structured data to enrich your pages and help machines understand your content. But you should also use semantic HTML for the same reasons.

Used together, semantic HTML and structured data are an unbeatable combination.

Build Websites, Not Web Apps

I could go off on a 2,500-word rant about why we should be building websites instead of web apps and how the appification of the web is anathema to the principles on which the World Wide Web was founded, but I’ll spare you that particular polemic.

Suffice it to say that web apps for content-delivery websites (like news sites) are almost always inferior to plain old-fashioned websites.

And websites are built, or should be, on HTML. Make use of all that HTML has to offer, and you’re avoiding 90% of the technical SEO pitfalls that web apps tend to faceplant themselves into.

That’s it for another edition. Thanks for reading and subscribing, and I’ll see you at the next one!

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This post was originally published on SEO For Google News.

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Featured Image: N Universe/Shutterstock