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Attribution vs. incrementality: Why you need both

Attribution vs. incrementality: Why the difference matters

Incrementality and attribution are two approaches to measuring marketing performance that are frequently discussed as though they are competing lenses viewing the same data. But they’re actually designed to answer very different questions, using different forms of evidence.

Attribution asks which observed marketing touchpoints should receive credit for a conversion. Incrementality asks whether the marketing activity caused additional conversions that wouldn’t have occurred without it.

A refresher on attribution

Attribution is the favored child of marketing analytics teams everywhere, circa 2015. Marketers discovered that some conversion paths contained multiple touchpoints across the digital landscape, like this:

  • Display → Paid Social → Organic Search → Email → Purchase.

That raised questions about which channel should get what “credit”:

  • Should the display ad get the most credit for the conversion because it was the first exposure?
  • Or should the email, because that’s the touchpoint that finally convinced the user to buy?
  • And what about the social ad and the organic presence in the middle?

That’s where attribution modeling came in. Attribution modeling provided frameworks for deciding how that credit should be distributed. Some models assigned the entire conversion to a single touchpoint. Others divided it among multiple interactions.

So if the final value of the conversion is $100, an attribution model tells marketers that display can take credit for $30, email for $30, and the remaining $40 is split between paid social and organic. 

Then, when you’re evaluating the success of your channels, you have a more nuanced framework for distributing revenue credit. And when you’re deciding on what channels get what budget for the next fiscal year, you have a way to compare and contrast.

Example: How a $100 conversion might be distributed across four marketing touchpoints.

Attribution model Display Paid Social Organic Search Email How credit is assigned
First-touch $100 $0 $0 $0 All credit goes to the first observed interaction.
Last-touch $0 $0 $0  $100  All credit goes to the final observed interaction before purchase.
Linear $25  $25  $25  $25 Credit is divided equally among every observed touchpoint.
Position-
based
$40 $10 $10 $40 The first and last interactions receive the most credit, while the middle interactions split the remainder.
Time-decay $10 $20 $30 $40 Touchpoints receive progressively more credit as they occur closer to the conversion.
Data-driven $30 $20 $20 $30  Credit is distributed according to each touchpoint’s estimated contribution to the conversion.

Note: These are simplified examples. Position-based models can use different weighting rules, time-decay allocations depend on timing, and actual data-driven models vary.

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Incrementality 101

Incrementality began to gain renewed interest from marketers around 2020.

Rather than dole out credit for a sale to different touchpoints and channels based on a mathematical equation, incrementality relies on carefully guardrailed tests of real, live sales data that attempt to prove the “true” impact of a marketing activity rather than its correlation. Incrementality tries to answer the question:

  • How many of these sales were actually caused by this campaign, without counting how many would have happened regardless?

The answer to that question is what marketers call lift. And through tightly controlled tests leaning on the scientific method, marketers were able to isolate the difference in sales between a group exposed to the marketing activity and an equivalent group that wasn’t exposed to marketing materials.

Incrementality is best explained through an example.

Let’s say you want to discover the lift of a given marketing campaign. So you divide your audience into two groups: a control group of folks who won’t be exposed to the campaign and an exposed group that does see the campaign.

You run your campaign for 30 days, then look at the results. While the exposed group completed 1,000 purchases, the control group completed 800 purchases. The incremental lift of the campaign would be 200 purchases.

An attribution model could associate many or all 1,000 purchases with the campaign. It would allocate the value across the platforms and touchpoints involved according to the model you choose.

Incrementality, on the other hand, would conclude that only those 200 additional purchases were actually caused by the campaign.

Dig deeper: Why attribution and impact are no longer the same thing in PPC

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Using attribution and incrementality together

Where marketers go wrong is when they go all in on either framework. The two concepts can play nicely together (provided you’re using the right one to answer the right question). If you’re looking to optimize your campaigns or deep dive into the user journey of your customer, attribution is going to be your best friend, helping you evaluate platforms and touchpoints by giving you a shared success metric with which to compare them.

If, on the other hand, you’re defending your budget from a proposed cut, incrementality is going to be your strongest source of evidence regarding which channels actually create additional business with their budget, rather than capturing business that would have happened anyway.

Attribution Incrementality
Primary question Which observed marketing touchpoints should receive credit for a conversion? How many additional conversions occurred because of the marketing activity?
Best use case Ongoing campaign optimization, understanding customer journeys, and allocating credit across measurable channels. Validating whether an investment creates additional business value and informing higher-level budget decisions.
Main blind spot Correlation is not causation: a touchpoint may receive credit for a conversion it did not actually create. Tests can be expensive, slow, or difficult to design, and results may not explain which individual touchpoints influenced the customer.
Most likely stakeholder Channel managers, performance marketers, platform teams, and marketing analytics teams. Marketing leadership, finance, data science, growth strategy, and budget owners.

Where platforms get confused by attribution and incrementality

If there’s one question that has haunted me throughout my career, it’s this one: “Why don’t these numbers match?”

Most often, it’s asked when a channel platform’s reported revenue or conversions differ from the numbers found in the client’s CRM, web analytics, or other source of truth. They almost never line up perfectly.

What can be tough to explain succinctly to a client is this: The fact that they differ doesn’t necessarily mean either is incorrect. Each system applies its own logic based on the interactions it can observe, which conversions should qualify for credit, and how long after an interaction credit can still be attributed. But neither is “wrong.”

An advertising platform may correctly observe and report that a customer viewed or clicked on an ad before purchasing. But evidence that an ad was seen before a purchase isn’t necessarily proof that the ad caused it, nor is it proof that the ad didn’t cause it.

This becomes particularly important with automated campaigns, especially as platforms continue to push these automated solutions on marketers. Automated systems are designed to maximize performance based on the conversion signals defined inside the platform. They’re simply not designed to maximize performance based on your carefully calculated incremental lift test results.

As a result, automated campaigns target audiences, placements, and queries already associated with users likely to convert, such as existing customers, branded searchers, and remarketing audiences. Those conversions may be entirely valid according to the platform’s attribution model, while creating less additional revenue than the campaign report implies.

In other words, automated campaigns can increase the number of conversions credited to a given campaign without actually causing an equal increase in total sales. 

Dig deeper: Your ROAS looks great — but is it actually driving growth?

A note on in-platform lift studies

It’s true, platforms are increasingly offering lift studies and other incrementality-focused tools. But it’s a mistake to assume that incremental value is automatically incorporated into automated campaign optimization.

After all, “data without insights is meaningless, and insights without action are pointless.” In other words, a lift study will only affect performance if and when someone applies its findings to the campaign’s objectives, inputs, or budget decisions.

A platform like Google Ads may provide a controlled lift experiment, but unless the advertiser applies the test findings — or selects a campaign setting explicitly designed to optimize for incrementality — the measurement system and the delivery system are still going to be working toward two different definitions of success.

Some platforms are starting to address this. Meta, for example, now offers a very promising incremental attribution model intended to optimize delivery toward conversions it predicts were directly caused by advertising. For now, though, that’s a specific optimization choice that, again, requires action by the advertiser and isn’t an inherent feature of every automated campaign.

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Know which question you’re trying to answer

In sum, attribution and incrementality aren’t competing methods for finding one definitive metric. They’re different tools designed to answer different questions. And like most tools, they perform best when they’re doing the job they’re designed for. You wouldn’t try to use your Allen wrench as a hammer, would you?

Attribution helps us marketers understand which touchpoints contributed to a conversion and provides a shared basis for comparing channels. Incrementality helps businesses understand whether their marketing investment generated additional conversions that wouldn’t have occurred otherwise.

The best marketers need both. While attribution provides the ongoing signals needed to optimize campaigns and understand customer journeys, incrementality helps validate whether those optimizations are creating new business value or simply capturing demand that already existed.

As automated campaigns take greater control over targeting, placements, bidding, and budget allocation, understanding both sides will only become more important. A system can become exceptionally efficient at maximizing attributed conversions without becoming equally effective at producing incremental growth.

So the next time a platform report contradicts your CRM, don’t assume either number is wrong — ask which question each number was designed to answer.

Dig deeper: The end of easy PPC attribution — and what to do next

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Why creator content belongs in your AI search strategy

Why creator content belongs in your AI search strategy

Creator content increasingly informs AI answers.

After all, a large language model (LLM) can’t have an opinion. It can’t decide whether your product is the best moisturizer or the water softener that’s the best value.

So when someone asks a subjective question, an LLM borrows a point of view from wherever humans have already shared one. Reviews, community threads, editorial content, retailer pages, and creator content guide these answers.

Various teams own all of those sources. This makes showing up in AI answers less of a content creation issue and more of a coordination problem.

AI needs opinions to build out answers

In Tinuiti’s Q1 2026 AI Citation Trends Report (Disclosure: I’m the senior director of AI SEO innovation at Tinuiti), we found that roughly 82% of AI citations pointed to earned media, not a brand’s own site.

Creators are a fast-rising component of that mix. If you take a look at YouTube’s search engine results page (SERP) presence, inclusion in Google AI Overviews grew from 3.6 million to 36.2 million keywords year over year. That’s likely partially due to the increase in AI Overviews.

Yet YouTube visibility across the entire SERP has been evolving for a long time. Video has now become a format AI uses when it needs to explain or validate something.

Dig deeper: AI search engines cite Reddit, YouTube and LinkedIn most: Study

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Social’s citation share swings hard by category

It’s tempting to invest in creators across industries, but the data doesn’t necessarily support that approach. In our Q2 2026 AI Citation Trends Report, social platforms drove about 13% of AI citations on apparel prompts — but only 3% on over-the-counter health.

Social citation trends move quickly. For example, Perplexity’s share of citations from social media fell from 31% to 13% in a single quarter as it pulled back from Reddit.

The social platforms that matter in each category continue to evolve, too. This requires deeper research into where LLMs source conversations. That’s exactly why a single team watching one social channel can’t see the whole picture.

Dig deeper: Why AI visibility starts before search and ends with citations

Traditional search has been signaling to social for a while

It would be easy to file all of this under AI search. But I’d argue it’s a pattern Google has been building toward as consumer behavior has moved to social.

In 2025, the search engine began automatically adding social media links to Google Business Profiles at scale. This update began surfacing brands’ recent posts right on their profiles.

Google has also started to display a short videos block on SERPs and within the search bar. This block includes vertical clips under five minutes, pulled from sources like Facebook, YouTube, and TikTok.

Based on Semrush organic research data for the past year (U.S.), YouTube’s estimated organic traffic roughly doubled. It’s now Google’s single largest organic domain. Meanwhile, organic traffic climbed by about 60% for both Facebook and Instagram.

Google keeps building surfaces to ingest, display, and now measure social conversations. If we use trends to help inform predictions, I think it’s safe to say there’s high value in social conversations and content, in the spirit of search everywhere optimization strategies.

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The creators winning citations aren’t who you’d expect

The content that wins YouTube citations isn’t the obvious kind. Long-form video accounts for 94% of AI citation, with 40.83% of cited videos having fewer than 1,000 views, OtterlyAI’s YouTube Citation Study 2026 found.

This indicates that popularity doesn’t predict citation value. Instead, structure does.

Mid-size niche creators tend to make well-structured content. These videos typically have clear titles, clean formatting, and a specific focus.

  • Comparison videos.
  • Honest reviews of a category.
  • How-to-style videos.
  • Skincare routines.
  • Tutorials for SaaS.

The sentiment that these videos express also affects AI search. In one client program, when mid-size creators published genuine review content, their language began appearing in citations for branded prompts and in our sentiment theme occurrence rates. A YouTube review and a blog each reached roughly 0.23% citation share on a target term, and it’s kept climbing.

AI search leans on creator content because it’s authentic and not branded. As a result, flooding search with citation bait breaks the very thing that makes it useful. The teams doing this well scale creator content when there’s a real idea or a brand story worth reinforcing.

Dig deeper: How AI-driven shopping discovery changes product page optimization

The real unlock is one shared objective

Most organizations run influencer and SEO on separate budgets, chasing distinct goals with reach and engagement over here and rankings over there. Instead, they should point teams at the same target and encourage them to exchange insights.

The influencer team gets to see its real role in search, while the SEO team brings citation data and measurement. Micro-influencers and repeated sentiment themes can matter as much as any single high-reach post. Shared reporting can show which creator content is influencing search and help both teams make better decisions.

Our tooling is catching up. In July, Google introduced platform properties in Search Console. This feature lets you track how your Instagram, TikTok, X, and YouTube content performs in Google Search, including the queries that send people to your posts. These insights can make creator choices evidence-based instead of instinctive.

Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts

Align your narrative across the content about your brand

Creator content is only one part of what AI engines encounter when forming a picture of your brand. They also draw from content, PR, commerce, social, affiliate, video, and paid.

When those sources reinforce the same narrative, AI models get a clearer picture. Creators are a smart place to start because they’re a growing source of opinions and experiences AI engines draw from.

The opportunity goes beyond any one channel. It’s about giving AI engines a consistent story about your brand wherever they look.

Read more at Read More

How long does it really take to build a website with AI?

Most AI website builders promise you can create a website in minutes, but what does that timeline actually include? While AI can generate a professional-looking website surprisingly quickly, you’ll still need time to choose a domain, review the generated content, make a few edits, and publish your site. In this guide, we’ll break down the entire journey and share a realistic estimate.

Key takeaways

  • AI website builders significantly reduce the time for initial website setup, but require additional time for edits and refinements
  • Creating a basic website typically takes 30 to 90 minutes, depending on preparation, while AI can generate a first draft in about 3 minutes
  • Key stages of website creation include defining purpose, selecting a domain, and adding content, each taking varying amounts of time
  • Post-launch, focus on content updates, SEO improvements, and monitoring performance to maintain a strong online presence
  • AI tools streamline the initial setup, but successful websites still require ongoing personalization and development to truly reflect the brand

A realistic timeline: How long each stage of website creation takes

Every website follows a similar journey before it goes live. Whether you’re creating a portfolio, a business website, or an online store, you’ll need to plan your website, register a domain, set up hosting, create pages, add content, and test everything before publishing.

The time required for each stage depends on your website’s complexity, the resources you already have, and how prepared you are before you begin. The table below provides a realistic estimate of how long each stage typically takes.

Stage What it involves Typical time
Define your website’s purpose Decide what your website should achieve, identify your target audience, and outline the pages and features you’ll need. The clearer your goals are, the easier the rest of the process becomes. 30 minutes–1 day
Research your market
(Recommended)
Review similar websites to understand industry trends, gather inspiration, and identify opportunities to differentiate your website. 1-3 days
Choose a domain name Brainstorm a memorable domain name, check its availability, and register the one that best represents your brand. 30 minutes–1 day
Set up web hosting Choose a hosting provider, create your hosting account, and configure the essentials needed to make your website accessible online.

Tip: Bluehost Web Hosting includes reliable hosting, a free SSL certificate, domain management, and website management tools in one place, helping you get online without juggling multiple services.

30 minutes–1 day
Create your website Build your website’s structure by creating pages, organizing navigation, choosing layouts, and adding the functionality your visitors will use. 1-7 days
Add your content Write and review your page copy, upload images and videos, add contact information, and make sure every page accurately represents your brand. 1-7 days
Review and test your website Test your website on different devices, verify links and forms, check loading speed, review mobile responsiveness, and fix any issues before launch. 2-8 hours
Publish your website Connect your domain, perform final checks, and make your website publicly available for visitors. 30 minutes–2 hours

Additional factors that may extend your timeline

The timeline above reflects the steps involved in building a typical website. However, certain features and functionality can add additional time depending on your requirements. For instance, if you’re building an eCommerce website or a multilingual website, the following will be added to your timeline:

Requirement Additional time
Setting up an online store 1-5 days
Adding products and categories Several hours to multiple days
Configuring product variants (size, color, etc.) A couple of hours to 2 days
Setting up payment gateways 1-2 days
Configuring shipping and tax settings 1-3 days
Configuring shipping and tax settings 2-8 hours
Connecting third-party tools (CRM, email marketing, analytics, booking systems, etc.) A couple of hours to 2 days
Creating multilingual pages Several days to weeks

Keep in mind that the estimates above focus solely on the website creation process. Some projects may require additional planning before you even begin building. For example, businesses entering a competitive market often spend extra time researching competitors, refining their brand positioning, defining their target audience, or planning their content strategy. While these activities can extend your overall timeline, they often result in a more focused website that better serves your visitors and supports your business goals.

How AI website builders have changed the website creation timeline

Traditionally, creating a website involves much more than writing content. Before you can even start customizing your site, you have to make a series of decisions that shape its overall structure and design.

The manual work AI helps you skip

Building a website from scratch typically involves tasks such as:

  • rowsing and selecting a template
  • Choosing colors and typography
  • Deciding which pages to include
  • Creating a navigation menu
  • Arranging sections on each page
  • Drafting placeholder content for your homepage and key pages

None of these tasks is particularly difficult on its own, but together they can easily add hours to the website creation process.

AI website builders shorten this setup phase by turning it into a guided onboarding experience. Instead of making every decision manually, you answer a few questions about your business, and the AI generates a website structure, page layouts, navigation, design elements, and draft content for you.

Rather than starting with a blank canvas, you start with a complete first draft that’s ready for review.

Putting AI website creation into practice

To see how this works in practice, I used the Bluehost AI Website Builder while building a website with AI from scratch.

Bluehost website builder home page.

The process was straightforward:

  • Describe the website in a single sentence
  • Create an account and verify your email address
  • Answer a few questions about your business, website goals, preferred design style, and overall tone
  • Let the AI generate the website

Once onboarding was complete, the AI produced a complete first draft in about 3 minutes. The generated website already includes:

  • A homepage
  • A navigation menu
  • Pre-designed page sections
  • AI-generated copy
  • Placeholder images based on the information provided

It’s still WordPress underneath

One feature that stood out was that the generated website wasn’t locked inside a simplified editor. Because Bluehost’s AI Website Builder is built on WordPress, I could immediately access the complete WordPress dashboard to:

  • Edit pages
  • Preview posts
  • Install plugins
  • Customize themes
  • Manage site settings

In other words, the AI accelerated the initial website setup without limiting what I could do afterward.

Where you’ll probably spend the most time

Although AI significantly reduces the time required to create the first version of a website, there are still several areas where you’ll likely invest additional time.

Refining your content

AI-generated copy provides a strong starting point, but it should reflect your products, services, brand personality, and unique value proposition. Reviewing and personalizing the content is one of the most important steps before publishing.

Choosing the right images

The first draft may include placeholders or stock images. Replacing these with your own photography, product images, or branded visuals helps create a more authentic and trustworthy website.

Expanding your website

Depending on your business, you may want to create additional pages beyond the initial draft, such as an About page, service pages, FAQs, testimonials, pricing information, or a blog. AI accelerates the initial launch, but your website can continue to grow over time.

Optimizing for search engines

Publishing your website doesn’t mean your work is finished. Optimizing page titles, meta descriptions, internal links, headings, and on-page content, as well as creating valuable blog posts, is what helps people discover your website through search engines over the long term.

While AI can dramatically reduce the time it takes to create a website, the biggest improvements usually come after the first draft. Personalizing your content, using original visuals, expanding your website with valuable pages, and continuously improving your SEO are what transform an AI-generated starting point into a website that genuinely represents your brand and stands out from the competition.

Final verdict: So, how long does it really take to build a website with AI?

If you’re wondering whether AI website builders really save time, the answer is yes, but perhaps not in the way most marketing claims suggest.

The website generation itself is remarkably quick. In my experience, the Bluehost AI Website Builder produced a complete first draft in around three minutes after the onboarding process. However, creating an account, verifying your email, answering the AI’s questions, reviewing the generated website, and making your own edits naturally add to the overall timeline.

For most people starting from scratch, it’s reasonable to expect a basic website to be online within 30 to 90 minutes, depending on how prepared you are. If you already have your domain, branding, and content ready, you could realistically publish in under 15 minutes. On the other hand, if you’re building a more polished website with custom copy, additional pages, and SEO improvements, you should expect to spend a few hours or even a full day refining it.

The biggest impact of AI isn’t that it builds a perfect website in minutes. It’s that it compresses what used to be the most time-consuming part of website creation, setting up the structure, layouts, navigation, and first draft into a guided process that takes only a few minutes. Instead of spending hours building a website from an empty canvas, you can dedicate your time to improving the content, design, and user experience, which are ultimately the elements that make a website successful.

So, are AI-generated websites any good?

For most individuals and small businesses, yes, they’re a strong starting point. AI website builders can generate a professional-looking first draft in minutes, but you’ll still get the best results by personalizing the content, visuals, and SEO to reflect your brand and goals.

AI builds websites quickly, but growing one still takes time

Once your website is live, keeping it useful, discoverable, and up to date still requires ongoing effort. AI website builders can help you create and publish a website much faster than traditional methods, but launching your website is only the beginning.

Here are a few areas to focus on after launch.

Publish helpful content regularly

Whether you’re running a business website or an online store, regularly publishing helpful content keeps your website fresh and gives people more reasons to visit. If you’re not sure where to start, check out our guide on writing an SEO-friendly blog post.

Keep your website up to date

Your website should evolve alongside your business. As your products, services, pricing, or contact information change, make sure your website reflects those updates. You can also expand your website over time by adding new landing pages, service pages, FAQs, or case studies as your business grows.

If you are using Yoast SEO, creating FAQ pages becomes very easy. The plugin’s FAQ content block converts your questions into structured data, making them easier for search engines and AI crawlers to digest.

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Continue improving your SEO

Publishing your website is only the first step. To help people find your website, you’ll need to continue optimizing both your content and your website’s technical foundation.

That includes creating helpful content around the topics your audience is searching for, improving internal linking, and regularly reviewing your pages to keep them accurate and relevant. It’s also worth checking that search engines can easily crawl and index your website, that your XML sitemap is up to date, that your pages load quickly, and that there are no broken links or indexing issues affecting your visibility.

If you’re using WordPress, our definitive guide to WordPress SEO covers both on-page and technical SEO best practices to help you build a strong foundation for long-term growth.

Monitor your website’s performance

Understanding how people discover and interact with your website helps you make better decisions.

Start by connecting your website to Google Search Console to monitor search performance, identify indexing issues, and see which queries drive traffic to your site. Pair it with Google Analytics to better understand user behavior and measure the impact of your SEO efforts over time.

Helpful resources:

Build your AI presence

As AI-powered search experiences continue to evolve, it’s becoming increasingly important to understand how your brand is represented beyond traditional search results.

If you’re using Yoast SEO AI+, Yoast AI Brand Insights helps you understand how AI platforms perceive your brand and identify opportunities to strengthen your online presence as AI search continues to grow.

How much faster is building a website with AI?

AI website builders don’t eliminate every step of website creation, but they dramatically shorten the time it takes to go from an idea to a working website. Instead of spending hours manually setting up the foundation, you start with a complete first draft that you can immediately personalize.

Here’s how the experience typically compares.

Traditional website creation Building a website with AI
Days or weeks to create the initial website A working first draft in minutes
Browse and compare templates manually AI recommends a design based on your requirements
Build page layouts section by section AI generates complete page layouts
Create navigation menus manually Navigation is generated automatically
Write placeholder copy from scratch AI creates an initial content draft
Start with a blank page Start with a complete website structure
Spend time assembling the website Spend time refining and personalizing it

The biggest difference isn’t that AI removes the need to edit your website; it changes where your time is spent. Rather than investing hours creating the foundation, you can focus on improving the parts that have the greatest impact on your visitors, such as your messaging, branding, content, and SEO.

AI gives you a head start, not the finish line

Creating a website no longer has to be a weeks-long project. AI website builders have transformed what was once the most time-consuming part of the process, setting up layouts, pages, navigation, and initial content, into something that can be completed in minutes.

That doesn’t mean AI builds a perfect website on its own. A successful website still depends on thoughtful content, strong branding, regular updates, and ongoing SEO. What AI does provide is a significant head start, allowing you to skip the blank page and spend your time refining a website instead of building one from scratch.

If you’ve been putting off creating a website because it felt too technical or time-consuming, AI has made getting started easier than ever. The sooner you launch, the sooner you can begin growing your online presence.

The post How long does it really take to build a website with AI? appeared first on Yoast.

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Ubersuggest Is Now Inside ChatGPT. Here’s What It Does

If you use ChatGPT for your marketing (content ideas, outlines, copy, strategy), you’ve probably run into the same wall: the AI doesn’t know what’s actually ranking. It doesn’t know how competitive a keyword is. It doesn’t know whether 500 monthly searches is high or low for your industry. It doesn’t know if your competitors are already dominating a topic.

You could paste data in manually. But that means switching to Ubersuggest, pulling a report, copying the numbers, and going back. Every time. For every question.

That gap is now closed.

The Ubersuggest homepage.

Ubersuggest is now an official app inside ChatGPT. When you use it, ChatGPT calls Ubersuggest in real time to fetch live SEO data (keyword volumes, difficulty scores, related keywords, SERP snapshots) and weaves the results directly into the conversation. You ask. ChatGPT fetches. You get an answer built on actual data.

What the Ubersuggest ChatGPT App Does

The Ubersuggest ChatGPT app is a native integration built using OpenAI’s App platform and Model Context Protocol (MCP). Not a browser extension. Not a workaround. A first-party connection that lets ChatGPT call Ubersuggest’s APIs directly during your conversation.

Ubersuggest in ChatGPT.

What’s Live Right Now: Keyword Research

The first capability is live keyword research. When you ask ChatGPT a question that requires keyword data, the app fetches it in real time from Ubersuggest’s database of over 100 million keywords. This includes:

  • Search volume: actual monthly search data, not estimates based on training data
  • Keyword difficulty: a 0-100 score showing how competitive a keyword is to rank for
  • Related keywords: semantically related terms you might be missing
  • Keyword suggestions: variations and long-tail opportunities based on your seed keyword
  • SERP data: who’s currently ranking for a term and what types of content dominate the results

What’s Coming Next

The Ubersuggest ChatGPT app is launching in phases. Keyword research is live now. Coming soon:

  • Domain analysis: pull traffic estimates, top pages, and organic keyword counts for any domain
  • Backlink analysis: check referring domains, link authority, and new/lost link history
  • Site audit: surface technical SEO issues directly in your ChatGPT conversation
  • Content research: get content ideas and SEO scores without leaving the chat
  • Projects: access your rank tracking data and keyword position history
  • Utilities: SERP data, intent classification, and keyword expansion tools

Why This Is Different from Just Asking ChatGPT for Keywords

ChatGPT is excellent at reasoning, pattern recognition, and language. But it has a fundamental limitation when it comes to SEO data: it’s working from training data, not live search data.

When you ask ChatGPT what keywords to target, it’s generating plausible answers based on patterns in its training corpus. It’s not querying an actual keyword database. It doesn’t know current search volumes. It doesn’t know what’s ranking right now.

The Ubersuggest ChatGPT app solves this by making ChatGPT’s SEO answers data-grounded. ChatGPT still does the reasoning, strategy, and language. Ubersuggest provides the live facts it’s reasoning from. The combination is more useful than either alone.

A Real Example

Without the Ubersuggest app: You ask for long-tail keywords for your home cleaning service. ChatGPT gives you a list of logical-sounding phrases. No volume, no difficulty, no way to know if any have real traffic worth pursuing.

With the Ubersuggest app: ChatGPT queries Ubersuggest and presents real data in context: “‘house cleaning service Austin’ has 880 monthly searches with difficulty 32, achievable for a newer site. ‘One-time cleaning Austin’ has 480 searches with difficulty 19, a lower-competition entry point. Here’s the full breakdown…”

Same question. Completely different answer.

Asking a query using Ubersuggest in ChatGPT.

How Ubersuggest Compares to Semrush’s ChatGPT Integration

Semrush launched their ChatGPT integration in December 2025, available to Business, Pro, Plus, Guru, and Enterprise plan subscribers, at prices that put it out of reach for most solo operators and small teams.

The Ubersuggest ChatGPT app takes a different approach: available to any Ubersuggest user, on any plan, including free. You don’t need an enterprise license. You don’t need to upgrade. If you have a ChatGPT account and an Ubersuggest account, you’re set.

How to Set Up the Ubersuggest ChatGPT App

Setup takes under a minute:

  1. Open the Ubersuggest app page at app.neilpatel.com/en/chatgpt-app
  2. Sign in to ChatGPT. Free or paid account, any tier works.
  3. Authorize the connection with your Ubersuggest credentials via OAuth.
ChatGPT being linked to Ubersuggest.
  1. Start your conversation. No special commands needed.

10 Prompts to Run Immediately After Setup

  • “What are the best long-tail keywords for [your topic] with under 40 difficulty and over 300 monthly searches?”
  • “What’s the search volume trend for [keyword] over the last 12 months? Growing or declining?”
  • “Compare the keyword difficulty for these 5 keywords. Which is the best entry point for a new blog?”
  • “Give me 20 related keywords for [seed keyword], sorted by lowest difficulty first.”
  • “What types of content are ranking on page 1 for [keyword]? Blog posts, product pages, or something else?”
  • “I’m writing a content plan for a [niche] site. Suggest 10 keywords with volume 200-2,000 and difficulty under 35.”
  • “What question-based keywords exist around [topic] that I could answer in a FAQ section?”
  • “I want to rank for [keyword]. How competitive is it, and what does my content need to beat the top results?”
  • “Give me a keyword cluster around [main topic]: one pillar keyword and 8 supporting long-tail variations.”
  • “What’s the CPC for [keyword]? Is there paid demand here that signals commercial intent?”

Frequently Asked Questions

Is the Ubersuggest ChatGPT app free?

Yes. Connect it with a free Ubersuggest account and a free ChatGPT account. Data depth may vary by Ubersuggest plan tier.

Does this work on mobile ChatGPT?

The app works on web, mobile, and desktop ChatGPT, anywhere you access ChatGPT with your account.

Is my data secure?

Yes. The connection uses OAuth 2.0. No passwords are shared. Ubersuggest only receives the queries your session generates. No account data or personal information is exposed beyond what’s needed to answer your query.

When are domain analysis and backlink features launching?

The team is rolling out additional capabilities in phases. Check app.neilpatel.com/en/chatgpt-app for the latest.

Looking to take advantage of this functionality? You can add Ubersuggest to ChatGPT in under a minute on any plan, completely free to start. Follow the link here to add Ubersuggest to ChatGPT,

Read more at Read More

Google Enhances AI Link Attribution: What It Means for SEO

Key Takeaways

  • Google is rolling out five updates to AI Mode and AI Overviews that make source attribution more visible and informative.
  • New features like inline citations, hover previews, and enhanced publisher attribution are designed to encourage more users to visit external websites.
  • The updates address long-standing concerns from publishers about declining referral traffic from AI-generated search experiences.
  • As AI search evolves, creating authoritative, citation-worthy content is essential for maintaining organic visibility.
  • Marketers should continue monitoring traffic patterns and adapt their SEO strategies as Google’s AI experiences mature.

AI search and AI Link Attribution is changing how people find information, and how publishers reach them. Google’s AI Overviews and AI Mode now deliver direct answers inside the search results page, which has raised a real question: if users never need to click, what happens to your traffic?

Google’s latest update is a direct response to that concern. The company announced five improvements to how AI Mode and AI Overviews credit sources, making citations more visible and giving users more paths to explore beyond the AI-generated answer.

Here’s what changed and what it means for your SEO strategy.

What Changed in Google’s AI Link Attribution?

Google’s latest update goes beyond adding more links. The focus is on making AI link attribution more useful.

Previously, one of the biggest criticisms of AI Overviews was that source attribution often felt secondary. Users could receive an answer without clearly understanding where the information came from or why a particular website had been cited.

The latest changes make attribution much more visible throughout the AI experience.

Each update is worth a closer look.

An example AI Overview.

Inline Citations Appear Next to AI-Generated Text

One of the most noticeable changes is the placement of citations directly alongside the content they support. Instead of grouping sources at the end of an AI-generated response, Google now places links next to the relevant text, making it easier for users to identify where specific information originated.

Before-and-after comparison showing citation placement within AI Overviews.

Source

Before-and-after comparison showing citation placement within AI Overviews.

Source

Hover Previews Give Users More Context

On desktop, users can now hover over many citations to preview information about the linked webpage before clicking. These previews provide additional context about the source, helping users decide whether they want to explore the website further. 

Desktop hover card displaying a website preview from an AI Overview.

Source

Better Attribution for News Publishers

Google is also giving news publishers greater visibility by displaying clearer publisher branding and, in some cases, subscription information alongside AI-generated responses. Providing more context about where reporting originated helps users identify and access the original source. 

Screenshot showing publisher branding within an AI Overview.

Source

More Descriptive Social Media Citations

Google is also improving how it references social content.

AI Mode now provides richer attribution for social media content by displaying creator names or profile handles alongside links, giving users enough context to recognize who created the content before they click.

Suggested Research Angles Encourage Deeper Exploration

The final update may have the biggest long-term impact.

Rather than ending with a single AI-generated answer, Google now suggests additional questions and related topics users may want to explore. These prompts push users to keep researching instead of treating the AI response as the final word.

Screenshot highlighting suggested follow-up questions beneath an AI Overview.

Source

Why Google Is Improving AI Attribution

Since AI Overviews launched, one concern has dominated conversations among publishers and SEO professionals: will AI answers reduce website traffic? It’s a reasonable one. If users receive complete answers directly in search results, fewer people may feel the need to visit the websites that originally produced the information.

Google has consistently argued that AI search should help users discover high-quality content, not replace it. These latest attribution updates reinforce that message.

Citations now sit closer to the relevant text, and hover previews add context before a click. Publisher identities are easier to spot too. Together, these changes make it easier and more appealing for users to continue their journey beyond Google’s interface.

These updates are a positive step. They show Google listening to publisher feedback and working to balance the user experience with the health of the open web as generative search matures.

Whether these improvements lead to higher click-through rates remains to be seen, though they demonstrate that Google recognizes the importance of supporting the broader web ecosystem as AI search evolves.

What These Updates Mean for SEO

Google’s attribution updates don’t change the fundamentals of SEO, but they do reinforce what Google values as AI-powered search continues to evolve. Rather than introducing a new ranking system, these changes make it easier for users to discover and engage with the sources behind AI-generated answers. For marketers, that means authoritative, user-focused content is still the clearest path to visibility.

AI Visibility Creates New Opportunities

As AI Overviews and AI Mode become a larger part of the search experience, earning a citation within an AI-generated response becomes another opportunity to reach potential visitors. Google’s latest updates make those citations more prominent, giving brands another way to stand out beyond traditional organic rankings.

Authority Drives AI Citations

AI-generated answers rely on trustworthy sources. That raises the value of original research and firsthand expertise. Content that demonstrates real knowledge and offers something users can’t find elsewhere is more likely to earn both traditional rankings and AI citations.

Richer Attribution Improves Click-Through Rates

Google’s new attribution features are designed to help users better understand where information comes from before they click. Richer source information and hover previews create a more transparent search experience that could encourage users to visit the original content instead of stopping at the AI-generated summary.

How to Optimize Your Content for AI Attribution

Google’s latest attribution updates don’t require a completely new SEO strategy. Instead, they reinforce what has always contributed to search visibility: content that’s original, built on real expertise, and useful to the people reading it. As AI search continues to evolve, these qualities remain the foundation of search visibility.

Publish Original Insights

Google’s AI systems need reliable information to reference. Original research, proprietary data, or genuine firsthand experience gives your content value that AI-generated answers can’t easily replicate.

Strengthen Your EEAT Signals

Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) remain key indicators of content quality. Clear author credentials and reputable sourcing help establish credibility, and regularly updated content reinforces it further for both users and Google’s AI systems.

Create Citation-Worthy Content

Well-organized content is easier to understand and easier to cite. Use descriptive headings, answer questions directly, and support key points with statistics or concise explanations that can stand on their own.

Monitor AI Search Performance

As Google continues refining AI Overviews and AI Mode, keep a close eye on your analytics. Monitoring changes in impressions, click-through rates, and referral traffic can help you understand how new AI features affect your organic visibility and where your strategy may need to adapt.

AI Search Is Becoming a Gateway Instead of a Destination

One of the biggest concerns surrounding AI search has been that users might get all the information they need without ever leaving Google’s results page.

These attribution updates suggest Google is moving in a different direction.

By making citations more visible and giving users richer context around sources, Google is positioning AI search as a gateway to high-quality content rather than a replacement for it.

For marketers and publishers, that’s an encouraging signal. While AI search will continue to evolve, success will still depend on creating original, trustworthy content that’s valuable enough for users and Google’s AI systems to reference.

FAQs

What are Google’s AI link attribution updates?

Google introduced five enhancements to AI Overviews and AI Mode that improve how sources are displayed, covering inline citations, hover previews, publisher attribution, social media context, and suggested follow-up topics. Together, they’re designed to make it easier for users to find and visit the content behind AI-generated answers.

Why is Google improving AI attribution?

The updates appear to address concerns from publishers and content creators about declining referral traffic from AI search experiences. By making sources more visible and informative, Google aims to create a better balance between providing instant answers and driving users to the original content.

How can I optimize my content for AI citations?

Focus on publishing original, authoritative content that demonstrates expertise and provides unique value. Strengthening your EEAT signals, organizing information clearly, and supporting claims with credible sources can improve your chances of being referenced by Google’s AI systems.

Will AI Overviews replace traditional SEO?

No. Traditional SEO remains essential, but AI search introduces another layer of visibility. Rather than replacing organic rankings, AI Overviews create additional opportunities for authoritative content to reach users through AI-generated responses.

Conclusion

Google’s latest AI link attribution updates may seem like small interface improvements, but they represent a meaningful shift in how AI-powered search connects users with the broader web.

By making citations more prominent and adding richer context around publishers and creators, Google is acknowledging that high-quality content remains the foundation of its search experience, even in the era of generative AI.

For marketers, the takeaway is clear. The best way to earn visibility in AI search isn’t to chase every new feature. It’s to continue producing original, authoritative content that users and Google’s AI systems can trust.

As these experiences continue to evolve, keep a close eye on your analytics and be prepared to adapt your strategy. The fundamentals of SEO haven’t changed, but the ways users discover your content certainly have.

If you want to stay ahead of Google’s latest search developments, explore our resources on AI SEO and content strategy, or reach out to the NP Digital team to learn how emerging search experiences can fit into your long-term marketing strategy.

Read more at Read More

The new SEO rules for bloggers in 2026: Why clarity matters in AI search

The new SEO rules for bloggers in 2026: Why clarity matters in AI search

For years, I told bloggers through audits, podcasts, and live trainings to write their content for “toddlers and drunk adults.”

That was simple advice, and it worked. If a 5-year-old and someone half-paying attention could follow the page content and still find what they needed, the content was probably clear enough for users and Google alike.

But in 2026, that rule needs an update.

Bloggers are no longer writing just for readers skimming on phones, distracted parents trying to make dinner, or travelers planning from an airport terminal.

They’re also writing for large language models, AI Overviews, AI Mode, and search systems that are scanning, summarizing, and deciding whether their content is clear enough to retrieve, cite, or ignore.

That’s the real shift. And as such, my advice to bloggers has evolved to “write for toddlers, drunk adults, and LLMs.”

Google is no longer just asking, “What is the best page to rank for this query?”

Increasingly, it’s asking, “What answer can we construct, what sources support that answer, and what might the user want to do next?”

That changes the job for bloggers.

The old SEO playbook was built around keywords, titles, H2s, and rankings. Those things still matter, but they’re no longer enough by themselves.

The new SEO playbook is built around clarity.

  • Can Google understand who you are?
  • Can users understand what your site is about?
  • Can AI systems understand how your articles connect?
  • Can your content answer the question quickly without making readers dig through clutter, filler, popups, and five nearly identical photos of the same finished dish?

For food, lifestyle, and travel bloggers, this isn’t a small adjustment. It’s a business survival issue.

The bloggers who continue to win won’t be the ones chasing every new acronym, every plugin score, or every AI optimization gimmick.

They’ll be the ones building clearer sites, stronger brands, better internal connections, and more useful content experiences for real people.

In 2026, clarity isn’t just good UX. It’s your SEO strategy.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

See your AI visibility

Why search changed (and why it matters)

For years, most bloggers understood SEO through a pretty simple model.

A user searched for something. Google returned a list of blue links. The blogger’s job was to create the best page for that query and earn the click.

That model still exists. But it’s no longer the only model that matters.

Traditional search mostly asked: “What are the best documents to rank for this query?”

AI search increasingly asks: “What answer can we construct, what sources support that answer, what else might the user need, and what can we help them do next?”

That’s a very different environment.

In classic search, a food blogger trying to rank for “easy chicken enchiladas” mostly had to think about the visible query, the competing pages, the title, the headings, the recipe card, the photos, the intro, the internal links, and the overall usefulness of the page.

Again, those things still matter. But AI search adds another layer.

Google’s AI experiences can use what it calls query fan-out, where a single user query may trigger multiple related searches across subtopics and data sources to build a more complete response.

A search for “easy chicken enchiladas” may also involve questions around cook time, substitutions, whether to use corn or flour tortillas, how to avoid soggy enchiladas, what sauce works best, what to serve with them, how to make them ahead, and whether they freeze well.

In other words, Google may not just be matching one page to one keyword. It may be trying to understand an entire task. That’s the shift bloggers need to internalize.

The old SEO model looked something like this: Keyword → title → H2s → rank.

The modern SEO model looks more like this: Topic clarity → answer clarity → entity clarity → internal links → technical crawlability → structured data accuracy → firsthand experience → brand trust → direct audience.

That’s not as catchy. But it’s a lot closer to how search works now.

For bloggers, this means Google needs to understand more than the fact that you published a recipe, tutorial, itinerary, or guide. It needs to understand:

  • Who you are and what your site is about.
  • What topics, cuisines, destinations, or lifestyle categories you specialize in.
  • Whether your content is based on real experience.
  • How your articles connect to one another.
  • Whether the page is easy to crawl, read, summarize, and trust.
  • Why your version deserves to be surfaced instead of the dozens, hundreds, or thousands of similar options already online.

This is where many bloggers get uncomfortable. For a long time, blogging SEO rewarded a certain kind of mechanical execution.

  • Find a keyword.
  • Write a long post.
  • Put the keyword in the right places.
  • Add the recipe card.
  • Insert the photos.
  • Answer a few FAQs.
  • Hit publish.

That approach was never as foolproof as people wanted it to be, but it worked often enough that bloggers kept doing it.

AI search makes that weaker because keywords alone are a poor substitute for clarity.

A food blogger who publishes another generic chocolate chip cookie recipe with the same tips, photos, substitutions, and “why you’ll love this” section as everyone else is making Google work too hard.

The same goes for a travel article without firsthand details, original recommendations, strong structure, or clear audience focus, or broad lifestyle advice that could have been produced by any website, freelancer, or AI tool.

That kind of content was already vulnerable. Now it actively works against you for algorithmic consideration.

Google’s own guidance around AI search continues to point bloggers back to the fundamentals: helpful content, crawlability, internal links, page experience, visible textual content, high-quality images, and structured data that matches what users can actually see on the page.

There’s no secret AI SEO template, magic schema type, or plugin setting that suddenly makes unclear content useful.

AI search doesn’t remove the need for good SEO. It raises the cost of bad SEO.

If your content is confusing, your site structure is messy, your internal links are generic, or your experience and author signals are weak, you’re giving Google fewer reasons to understand and trust your content.

And if the only reason a page exists is because a keyword tool said it had search volume, that’s not a strategy. That’s content roulette.

For food, lifestyle, and travel bloggers, the opportunity in 2026 is to become easier to understand – for users, Google, and AI systems.

In an AI-shaped search environment, the winning content isn’t necessarily the longest, loudest, or most aggressively optimized. It’s the content that makes the answer obvious, the expertise clear, and the next step easy.

That’s the foundation for the new SEO rules that actually move the needle.

Dig deeper: AI isn’t the enemy: How bloggers can thrive in a generative search world

The new SEO rules that actually move the needle

When search changes quickly, bad advice gets louder.

Every week, bloggers are being told they need a new AI plugin, a new content template, a new schema trick, a new prompt strategy, a new content score, or a new way to “optimize for AI.” Most of it is noise.

Just last week, a blogger told me, “I was told not to add notes to a recipe card because it’s duplicate content.” And I literally fell out of my chair laughing.

First, that’s not how duplicate content works. Second, “notes” are incredibly important in recipe cards because users often print the card and never return to the full article.

Removing “notes” removes a valuable user-first feature that ensures your users can actually make your recipe perfectly the first time by implementing your “best” advice.

Instead of giving nonsense like this validation, bloggers should focus on making real progress by embracing the boring, obvious, high-impact things better than everyone else.

That starts with three priorities.

1. Improve high-value existing content

Most bloggers don’t need to publish more random content. They need to improve the content that already has signals.

This is one of the biggest differences between mature SEO strategy and “I saw someone say this in a Facebook group” SEO strategy. A mature strategy starts with data.

Go into Google Search Console and identify articles with traffic history, strong impressions, revenue potential, seasonal upside, rankings sitting just outside meaningful visibility, clear signs of decay, or content that hasn’t been meaningfully improved in 12 to 24 months.

That’s where you start.

Not with a random list of articles you personally feel like updating. Not with every recipe that has an old date. Not with whatever your SEO tool says is missing 14 semantically related phrases.

Use Search Console. Look for opportunity. Then make the page better.

Tip: Sign up for an account with SEO Gets. It has a fantastic Content Decay Reporting option, which makes finding and updating your weakest content within GSC a snap.

A real update should improve the experience for the user. It should make the recipe, guide, tutorial, or article clearer, more accurate, more complete, or more useful.

For a food blogger, that may mean adding better testing notes, clearer process shots, more useful substitution guidance, storage information, make-ahead tips, or troubleshooting details.

For a travel blogger, it may mean updating prices, hours, logistics, neighborhood recommendations, transportation details, seasonal considerations, or firsthand observations.

For a lifestyle blogger, it may mean tightening the structure, removing generic filler, adding examples, clarifying steps, or improving the connection between related articles.

What it shouldn’t mean is changing the date and pretending the content is fresh.

A good content update answers one question: “Is this page now more useful than it was before?”

If the answer is no, you didn’t improve the content. You just touched it.

2. Build direct audience channels

Google traffic is rented land, and bloggers need to start treating it that way.

That doesn’t mean Google traffic is bad. For many publishers, organic search is still the largest traffic source, the biggest revenue driver, and the reason their businesses exist in the first place.

But you don’t own that traffic. A ranking can disappear. A featured snippet can vanish. An AI Overview can satisfy more of the query directly in the search results. A SERP layout can shift. A core update can rewrite the competitive landscape overnight.

None of that means bloggers should abandon SEO, but it does mean they need to stop pretending Google is a stable business model by itself. It’s not.

The better approach is to use Google as one discovery channel among many, not the entire foundation of the business.

Search should introduce new people to your brand, but once those people arrive, the goal should be to give them a reason to come back without needing Google as the middleman every time.

That means bloggers need to get more serious about email, branded search, repeat visitors, Pinterest, YouTube, short-form video, Facebook groups, communities, direct partnerships, products, courses, cookbooks, memberships, or whatever audience channel actually makes sense for their niche and bandwidth.

Not every blogger needs to do all of those things. In fact, most shouldn’t.

But every blogger should be asking the same basic question: “If Google sent me fewer clicks tomorrow, how would my audience still find me?”

That question is uncomfortable, but it’s also necessary.

An email list isn’t perfect. Deliverability is annoying. Open rates fluctuate. People unsubscribe. Platforms change. But an email subscriber is still closer to an owned audience than a ranking you rent from Google.

The best question for bloggers in 2026 isn’t just, “How do I get more Google traffic?”

It’s, “How do I make sure the people who find me once can find me again?”

That mindset changes how you write, how you structure your site, how you promote content, and how you think about brand.

A blogger with a recognizable point of view, repeat readers, and a direct audience is in a much stronger position than a blogger whose entire business depends on Google continuing to send traffic at the same rate it did three years ago.

SEO still matters. But it should feed the business. It shouldn’t be the entire business.

3. Make content easier for humans and machines to understand

This is where clarity becomes the strategy.

Google’s guidance around AI search continues to emphasize many of the same fundamentals it has emphasized for years: helpful content, crawlability, internal links, page experience, visible text, images, video, and structured data that matches what users can actually see on the page.

That should tell bloggers something important. There’s no separate magic version of SEO for AI search.

There’s no special “AI search” schema that suddenly makes a mediocre article useful. There’s no plugin setting that turns unclear content into trusted content. There’s just less tolerance for bloated, confusing, poorly structured pages.

For bloggers, making content easier to understand usually means improving the basics that should have mattered all along.

A recipe article should tell readers what they’re making, why the recipe works, what ingredients matter, what substitutions are realistic, what can go wrong, how to store it, and why they should trust your version.

A travel article should make it clear who the itinerary is for, when the advice applies, what has been personally tested, what to book in advance, and what mistakes readers can avoid.

A lifestyle article should be organized enough that a reader can quickly understand the problem, the recommendation, and the next step.

This isn’t about dumbing down content. It’s about removing friction.

A tired parent trying to make dinner doesn’t want to scroll through clutter to find the answer. A traveler standing in an airport doesn’t want to dig through vague storytelling to find the train information. A reader looking for a home project doesn’t want 1,500 words of generic filler before the actual instructions.

And an AI system trying to understand your content doesn’t benefit from bloated intros, weak headings, generic anchor text, mismatched schema, or buried answers either.

This is why I keep telling bloggers to write for toddlers, drunk adults, and LLMs.

The toddler needs the page to be simple enough to follow. The drunk adult needs it to be forgiving enough to skim. The LLM needs structure, context, entities, and relationships that are clear enough to interpret accurately.

If your content works for all three, you’re probably moving in the right direction.

That means using clear top-of-article summaries when they help, headings that guide the reader instead of stuffing keywords, internal links with descriptive anchor text, schema that accurately reflects visible content, and FAQs that answer real questions.

Add to that stronger about me and author signals, original images or process shots where they add value, and a page experience that doesn’t actively fight the reader. None of that is flashy. But it’s effective.

The bloggers who win in 2026 won’t be the ones who publish the most content, install the most AI tools, or chase the newest acronym. They’ll be the ones who make their content easier to understand, easier to trust, and easier to remember.

That’s what moves the needle now.

Dig deeper: Blogging, AI, and the SEO road ahead: Why clarity now decides who survives

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The outdated advice bloggers should ignore

Part of succeeding in 2026 is also knowing what to stop doing.

That may be harder than it sounds because a lot of outdated SEO advice did work at some point. Or at least, it appeared to work well enough that bloggers kept repeating it.

The problem is that many of those tactics were built for an older version of search. They were built for a world where ranking often felt like a checklist: find the keyword, put it in the title, repeat it in the headings, write more words than the competitor, add a few FAQs, hit publish, and hope Google approved.

That kind of thinking isn’t completely useless, but it’s painfully incomplete. And in an AI-shaped search environment, incomplete strategies become expensive.

The first thing bloggers need to stop doing is chasing word count.

A 3,000-word recipe article isn’t automatically better than a 900-word recipe article. A 5,000-word travel guide isn’t automatically more helpful than a tighter, better-organized 2,000-word guide. Length isn’t the goal. Usefulness is the goal.

Sometimes a topic needs depth. Sometimes a reader needs context, troubleshooting, photos, variations, and a complete explanation. But sometimes the reader just needs to know whether they can use Greek yogurt instead of sour cream or how long to bake chicken thighs at 400 degrees.

Adding more words doesn’t make a page more helpful if those words don’t solve the user’s problem.

The same goes for stuffing awkward keyword variations into H2s. Bloggers don’t need headings like “Best Easy Moist Homemade Banana Bread Recipe With Ripe Bananas” because a tool suggested combining every phrase into one linguistic crime scene.

Headings should guide the reader. They should make the page easier to scan, easier to understand, and easier to navigate. If a heading sounds like it was written for a spreadsheet instead of a human being, it probably needs to be rewritten.

Bloggers also need to be more skeptical of publishing generic content just because a keyword tool says there is search volume. This is especially important in crowded niches like recipes, travel, and lifestyle, where the web already has more than enough generic versions of almost everything.

Another “chocolate chip cookie recipe” isn’t a strategy unless there is a reason your version deserves to exist.

Another “best things to do in Paris” article isn’t a strategy unless it reflects real experience, clear audience fit, original insight, or a better way of helping the reader make decisions.

Another “how to organize your pantry” article isn’t a strategy if it says the same thing every other article says with different stock photos.

Search volume tells you there is demand. It doesn’t tell you that you deserve to rank. That part is still on you.

Food bloggers especially need to reconsider the way they structure recipe articles visually. Somewhere along the way, many articles turned into an obstacle course of nearly identical finished-dish photos before the reader gets to the actual recipe.

One hero shot is great. A useful process shot is great. A labeled ingredients photo, awesome. Photos that show texture, doneness, technique, or troubleshooting can all absolutely improve the content.

But five beauty shots of the same casserole from slightly different angles before the recipe card aren’t helping a tired person make dinner.

This doesn’t mean bloggers should strip personality, visuals, or storytelling from their sites. On the contrary, it means every element on the page should earn its place.

A photo should help, inspire, or clarify. A paragraph should answer a question, build trust, or move the reader forward. A section should exist because it improves the experience, not because everyone else’s template includes it.

Bloggers also need to stop hiding the answer.

This is one of the simplest and most common problems I see in daily audits. The user arrives with a specific need, and the page makes them work too hard to satisfy that need.

The answer is buried under a long intro, vague personal story, oversized ad units, popups, duplicate photos, or a “jump to recipe” button doing the emotional labor for the entire page.

If the page is about whether you can freeze banana pudding, say so clearly.

If the page is about the best time to visit Sedona, answer it clearly.

If the page is about how to keep meatballs from falling apart, don’t make the reader scroll through 11 paragraphs before you explain the binder.

This isn’t just an SEO issue. It’s a respect-the-reader issue.

FAQs are another place where bloggers have been led astray. A helpful FAQ can be great when it answers real questions users actually have. FAQs also allow the average blogger to optimize for People Also Ask and Things to Know search accordions.

But too many FAQ sections are still being treated as a dumping ground for keyword variations, thin answers, or filler content added because a tool, course, or template said every article needs them.

Every FAQ should pass a basic test: Would a real reader ask this, and does the answer help them do something? If not, cut it.

Updating dates without meaningful updates is another tactic that needs to die.

Changing “2024” to “2026” in the title, or adjusting the publish date and calling it refreshed, doesn’t make the content better. It just makes it newer-looking.

A real update improves accuracy, usefulness, structure, media, internal links, testing notes, examples, recommendations, or user experience.

A fake update changes the timestamp and hopes nobody notices. Readers notice. Google notices.

Bloggers also need to stop writing for SEO scores instead of users.

I understand why this happens. Tools give clear feedback. Green lights feel good. Scores feel objective. It’s comforting to believe that if a tool says the page is optimized, the page must be good.

But tools aren’t users.

Tools don’t make dinner. Tools don’t plan vacations. Tools don’t troubleshoot a broken frosting, choose a hotel near a train station, or decide whether a slow cooker recipe is safe to leave on all day.

Tools can support judgment. They can’t replace it. The same goes for panic-based SEO.

Every Google announcement doesn’t require a sitewide overhaul. Every AI update doesn’t require installing a new plugin. Every ranking drop doesn’t mean your site is broken. Every Facebook thread isn’t an emergency.

One of the worst things bloggers can do right now is make extreme technical or content changes based on fear, screenshots, rumors, or one person’s traffic chart.

The answer to uncertainty isn’t panic. It’s better analysis.

It’s Search Console. It’s content audits. It’s user behavior. It’s competitive review. It’s better internal linking. It’s clearer content. It’s understanding what actually changed before deciding what to change next.

The blunt version is this: Stop producing content that looks like it was made to satisfy a plugin, a keyword tool, or an ad network.

Start producing content that makes a tired person at 5:30 p.m. say, “Thank God, this actually helps.” That’s the standard. 

Not word count. Not tool scores. Not awkward keyword repetition. Not fake freshness. Not more filler.

The standard is whether the content helps a real person solve a real problem faster, better, or with more confidence than the alternatives.

That’s the advice bloggers should carry into 2026. Ignore anything that pulls you further away from that.

Dig deeper: The implosion of the blogging-for-dollars revenue model

Why clarity beats complexity in an AI world

When search gets more complicated, the natural reaction is to make the strategy more complicated as well.

That’s how bloggers end up chasing every new newsletter strategy, every new tool, every new AI feature, every new schema recommendation, and every new “future of SEO” thread that shows up in a Facebook group.

I understand the impulse. When traffic is volatile and Google keeps changing the interface, doing something feels better than doing nothing. The problem is that a lot of the “something” being recommended right now is either unnecessary, unproven, or a distraction from the work that would actually help.

If a blogger asked me what to focus on in 2026, my answer wouldn’t be to make their site more complicated. It would be to make their site easier to understand. That’s the through-line.

AI search doesn’t reward confusion. Users don’t reward confusion. Google doesn’t need more clutter to crawl, interpret, and summarize.

What everyone needs is clarity. That starts with the page itself.

When someone lands on a recipe article, they should quickly understand what the recipe is, why it works, what makes it different, how long it takes, what ingredients matter, and whether it solves the problem they came with.

When someone lands on a travel article, they should quickly understand who the advice is for, when the information applies, whether the writer has firsthand experience, and what decisions the reader needs to make next.

When someone lands on a lifestyle article, they should quickly understand the problem, the recommendation, the steps involved, and why the advice is trustworthy.

That doesn’t mean every article has to be short. It means every article has to be organized.

There’s a big difference between depth and bloat.

  • Depth helps the reader make a better decision. Bloat makes them scroll.
  • Depth answers follow-up questions. Bloat repeats the same point three different ways.
  • Depth shows experience, testing, judgment, and specificity. Bloat fills space because a tool, template, or competitor word count made the blogger nervous.

In an AI world, that difference matters even more.

Large language models, AI Overviews, and AI-assisted search experiences are trying to understand relationships. They’re looking for entities, context, steps, attributes, comparisons, caveats, and source quality signals.

If your content is buried under vague headings, generic filler, weak internal links, and unclear structure, you’re making that job harder.

And making Google work harder is rarely a winning SEO strategy.

This is why “clarity beats complexity” isn’t just a nice phrase. It’s a practical publishing standard.

  • Before adding another section, ask whether it makes the page easier to use.
  • Before adding another FAQ, ask whether a real reader would ask that question.
  • Before adding another image, ask whether it clarifies a step, shows texture, demonstrates technique, or builds confidence.
  • Before adding another internal link, ask whether it helps the reader move naturally to a related answer.
  • Before adding another AI tool, ask whether it improves the user experience or just makes the blogger feel like they’re keeping up.

That last one is important. AI tools aren’t automatically bad. AI summaries, AI buttons, chat features, personalization tools, and recipe modification features can all be worth testing in the right context.

But they should be treated like UX experiments, not magic ranking levers.

If an AI button helps a reader scale a recipe, find a substitution, adjust serving sizes, or understand a process faster, great. Test it. Measure it. See how users respond.

But don’t install something because everyone else is panicking. Don’t assume “AI” in the feature name means it improves SEO. And definitely don’t let the tool become more important than the content.

For most bloggers, the highest-impact AI strategy is still not very sexy: make the page easier to read, easier to crawl, easier to summarize, and easier to trust.

That means clear introductions, useful summaries, descriptive headings, original experience, accurate schema, strong internal links, and visible text that actually says something.

It also means removing the things that get in the way.

  • Over-aggressive ads get in the way.
  • Intrusive popups get in the way.
  • Repetitive photos get in the way.
  • Generic “why you’ll love this” sections get in the way when they’re not personalized to the article.
  • Filler FAQs get in the way.
  • Weak author signals get in the way.
  • Internal links with anchor text like “click here” get in the way.
  • Redundant equipment lists added purely for affiliate reasons get in the way.

A blogger doesn’t need a 47-point AI optimization checklist to understand this.

They need to look at the page and ask a simpler question: “Is this easier to understand than what already ranks?”

Does your page help the reader faster, better, and with more confidence than the alternatives?

That’s what clarity does. It removes friction, makes expertise easier to see, helps Google and AI systems understand your content, builds trust, and makes your brand more memorable.

For food, lifestyle, and travel bloggers, clarity works across traditional organic search, AI Overviews, AI Mode, direct traffic, email, social, and conversion.

That’s why I keep coming back to the same point: clarity isn’t dumbing down your content. It’s respecting the reader’s time.

The bloggers who win in 2026 won’t be the ones who make their content more complicated to look sophisticated. They’ll be the ones who make complicated decisions feel easier for the reader.

  • What should I cook tonight?
  • Can I make this ahead?
  • Is this destination worth three days?
  • Will this work for my family?
  • Can I trust this recommendation?
  • What do I do next?

Answer those questions clearly, and you’re already doing more than most of the web.

That’s why clarity beats complexity. And that’s why clarity is no longer just a writing principle. It’s an SEO strategy.

Your 2026 blogging SEO action plan

The good news is that bloggers don’t need to rebuild their entire sites overnight.

They don’t need to chase every AI feature, rewrite every article, install every new tool, or turn their content strategy into a science experiment.

In most cases, the best next step is much simpler: 

  • Identify the pages that matter most.
  • Make them clearer.
  • Strengthen the signals around them.
  • Stop doing the things that create friction for users.

Start with your highest-value content

These are the articles that already have traffic history, strong impressions, seasonal value, affiliate or ad revenue potential, or rankings that are close enough to improve.

Don’t begin with the random article that annoys you personally or the recipe you happen to have new photos for. Start where the opportunity is visible.

Use Search Console. Look at what Google is already showing. Find the articles where better clarity, stronger internal links, improved structure, fresher details, or more useful supporting content could reasonably move the needle.

Then work through the page like a real user.

Add a clear top-of-article summary

For most bloggers, the first improvement I’d recommend is a clear top-of-article summary. This doesn’t need to be long, and it definitely shouldn’t be a generic AI-written blob pasted across the site. It should be specific to the article and useful for the reader.

  • For a recipe, that summary might explain what the dish is, why it works, how long it takes, what makes it different, which ingredients matter most, and any major make-ahead, storage, or substitution notes.
  • For a travel article, it might clarify who the guide is for, when the information applies, how much time the reader needs, and what decisions they should make first.
  • For a lifestyle article, it might define the problem, preview the solution, and make the next step obvious.

The goal isn’t to repeat the whole article at the top. It’s to reassure the reader that they’re in the right place while giving Google and AI systems a clearer understanding of the page.

Again, this isn’t magic. It’s just good communication.

Strengthen internal links

Improve internal linking with descriptive anchor text.

This is still one of the most underused SEO opportunities in blogging, especially on older sites with hundreds or thousands of articles. Too many bloggers rely on related article widgets, sidebar links, or generic anchor text and assume that’s enough.

In-content links are stronger when they’re natural, useful, and descriptive.

A reader making a slow cooker chicken dinner may also need a related side dish, a sauce, a storage guide, or another easy weeknight meal. A reader planning a weekend in Charleston may need a hotel guide, a restaurant list, a packing guide, or a nearby day trip. A reader working through a furniture troubleshooting article may need links to paint, sanding, and woodworking guides and tutorials.

The anchor text should tell the reader where they’re going.

“Click here” doesn’t help much. “Easy slow cooker chicken dinners for busy weeknights” does.

“Read more” doesn’t help much. “Beginner furniture restoration guide” does.

Clear internal links make topical relationships easier for users, Google, and AI systems to understand. They also keep readers moving through the site instead of sending them back to Google.

Make headings useful

Headings shouldn’t be keyword dumpsters. They should be signposts that help readers and search systems understand the structure of the page.

For recipes, that usually means headings like “Why this recipe works,” “Key ingredients,” “How to make X recipe,” “Recipe testing notes,” “Substitutions and variations,” “Storage and reheating,” “What to serve with X recipe,” and “X recipe FAQs.”

For travel content, it might mean “Best time to visit,” “How many days you need,” “Where to stay in X,” “How to get around X,” “What to book in advance,” “Mistakes to avoid,” and “Sample itinerary in X.”

For lifestyle content, it might mean “What you’ll need,” “How to get started with X,” “Common mistakes with X,” “What worked for me,” “When to avoid this,” and “Next steps.”

The clearer the structure, the easier the page is to use and interpret.

Review your schema

Bloggers should also review schema, but they should do so with restraint.

Structured data isn’t a place to invent things, exaggerate claims, or mark up content that users can’t see. It should accurately reflect what’s visible on the page.

If the recipe says the total time is 45 minutes, the schema shouldn’t say 30 minutes because that looks better in search results. If the author, date, images, ratings, or recipe details are inaccurate, fix them.

Schema should clarify. It shouldn’t cosplay as content quality.

Reduce content friction

Focus on reducing content friction. Ads, popups, sticky units, email captures, video players, and interstitials may all have business value, but they also create user experience costs.

If a user can’t read the recipe because the screen is covered in interruptions, or a travel guide is buried under popups and sticky elements before the reader can find the itinerary, that’s a problem.

This doesn’t mean bloggers should remove every ad or stop monetizing their work. But the page still has to be usable.

If monetization makes the content harder to trust, read, or act on, the short-term revenue win may be creating a long-term visibility problem.

Test AI features carefully

AI features, such as AI buttons, can be useful in the right context. A button that helps users scale a recipe, find substitutions, modify ingredients, summarize key steps, or troubleshoot a process may improve the user experience.

But bloggers should measure before assuming.

  • Do users click the buttons?
  • Do they stay longer?
  • Do they convert better?
  • Do they subscribe?
  • Do they view more pages?
  • Do they actually use the feature, or does it just make the site look more “AI-ready”?

AI tools should support the reader. They shouldn’t distract from the content, replace firsthand experience, or become the strategy by themselves.

Dig deeper: How to design content that AI systems prefer and promote

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

See your AI visibility

Clarity is what wins in 2026

Improve the articles that already have opportunity, add clearer summaries, strengthen internal links, clean up headings, verify schema, reduce friction, build direct audience channels, and test AI features only when they clearly help users.

That’s not flashy. But most durable SEO isn’t flashy. It’s a consistent, thoughtful improvement applied to the pages that matter most.

For the average blogger, that’s the plan I’d recommend. Do fewer random things. Do more useful things. Make the best content easier to understand, easier to trust, and easier to revisit.

That’s how you build a site that can compete in traditional search, AI search, and whatever version of AI Mode Google throws at us next.

Read more at Read More

6 Digital PR Strategies to Boost AI Visibility

Digital PR is the single most important strategy to win in AI search.

A study by Muck Rack found that 84% of AI citations come from earned media, third-party sources like editorial coverage, reviews, and forums.

Where AI gets its information

In AI search, third-party validation beats self-promotion.

Publishing onsite content is still important, no doubt. But your AI visibility is mostly shaped by what happens outside your website.

The more frequently your brand name appears across the web, the more likely LLMs are to recognize, trust, and eventually recommend you.

In this guide, I’ll show you six proven digital PR strategies to earn more backlinks and brand mentions, strengthen your offsite authority, and increase your visibility in AI-generated answers.

Why Digital PR Now Shapes AI Visibility

AI systems go well beyond reading your website.

They pull information from dozens of sources across the web, then combine it into a single answer.

How AI systems discover and cite content

The brands that win in AI search are those that appear consistently across those sources.

That’s where digital PR comes in.

It’s how you get featured in the right publications that AI systems already read and reference.

The more places you show up, the more familiar and trustworthy your brand looks to those models.

Here are six digital PR strategies to start building that presence.

Strategy 1: Data-Led PR

Data-led PR revolves around publishing original research and statistical roundups, and distributing the data to relevant publications.

This is one of the most effective ways to build high-quality backlinks.

Why?

Because journalists, bloggers, and marketers are constantly looking for credible data to support their content.

How It Boosts AI Visibility

Backlinks matter a lot for SEO. And they still matter for AI visibility.

Semrush’s study of 1,000 domains found that brands with stronger backlink authority are more likely to appear in AI-generated answers.

Correlation between backlink metrics and AI visibility

Seer Interactive’s study backs this up:

The top two metrics that impact AI visibility are domain authority and high-quality backlinks (from DA 60+ sites).

Data-led content is how you earn those quality backlinks. People love citing fresh numbers.

I helped one of my clients, Resource Guru, put together the “Agency Overworking Report 2025.”

ResourceGuru – Agency overworking report

Since it was published, this report has generated 21 backlinks naturally, including coverage in Forbes.

Agency overworking report – Backlinks

It’s also consistently being cited in AI answers.

ChatGPT – Agency overworking data

How to Do It

First, publish your data-led content.

You have several options:

  • Conduct new research nobody has done before: This is an excellent choice if you’re in a growing field like GEO, where data is scarce, and any credible study will attract attention.
  • Update existing studies: Find stats that haven’t been refreshed in over two years. Outdated data is everywhere, and writers actively look for newer numbers to replace them. If you can be the source that fills that gap, you’ll earn the citation.
  • Compile existing statistics: You don’t always need to generate new data. Aggregating hard-to-find stats into one well-organized resource can be just as link-worthy.

Once your data is live, pitch it proactively.

Find articles in your niche that reference outdated statistics or cite sources that no longer exist. Reach out to the authors and offer your fresher data as a replacement.

Also, pitch your new data to existing statistical roundups in your niche. These pages are usually updated frequently, so the author is more open to new additions.

Here’s an example of a good pitch that I received for my AI SEO stat roundup:

Research outreach pitch example

Strategy 2: AI Citation Outreach

AI citation outreach is the practice of securing placements in the specific sources AI models already pull from.

In my experience, it’s the quickest way to start earning brand mentions in AI responses.

How It Boosts AI Visibility

If you can get into the content AI cites, you can influence the answers AI gives.

For example, my agency Position Digital was included in Exposure Ninja’s listicle on “The Best AI Search Optimisation Agencies in 2026.”

Exposure Ninja – Best AI search optimization agencies

And since that listicle was cited by ChatGPT, my agency ended up being recommended in one of the answers.

ChatGPT – Best AI search optimization agency

How to Do It

LLMs cite content differently. Each model has its own preferences, tendencies, and patterns.

For example, I asked AI Mode and ChatGPT to recommend the best SEO tools.

Here’s what ChatGPT said:

ChatGPT – Best SEO tools

And here’s the AI Mode answer:

Google AI Mode – Best SEO tools

Similar answers. But totally different citations.

So, you need to tailor your strategy to each AI model you want to target.

1. Research user prompts

Start by mapping out the prompts your target audience is likely to type into AI tools.

AI companies haven’t given us prompt data yet, so we’ll have to settle for educated guesses.

Here are some good places to start:

Source Tips
User questions in sales calls Note the exact language prospects use when describing their problem
Queries in Google Search Console Look for ultra-long queries in your performance report
People Also Ask in SERPs Use a free People Also Ask tool to find common questions about a topic
Keyword data in your SEO tool Filter for question keywords starting with who, what, how, why, which

Alternatively, you can use Semrush’s AI Visibility Toolkit to find out exactly what your audience is typing into LLMs.

Visibility Overview – Position Digital – Topics & Sources

2. Monitor the cited pages

Once you have your prompt list, run each prompt through each AI model and note which pages are cited.

If you don’t want to do it manually, Semrush’s AI Visibility Toolkit shows you the sources for each prompt.

You can also use ListBrew to surface “best X” listicles and comparison pages that already get cited in AI answers for your target prompts.

List Brew – AI citations

3. Find the contacts

Find the contact information for each opportunity using tools like Hunter and Apollo.

Tip: Prioritize someone who has editorial control over the content, like the author or content editor, rather than generic email addresses like hello@domain.com.


4. Send personalized pitches

Reach out to each prospect and ask to be included in the content.

Keep the pitch short and specific to their piece. To increase your chances, offer something in return, such as:

  • A free trial or demo of your product
  • A reciprocal mention in one of your own high-traffic pieces

Also, prepare a short blurb that the author can easily copy and paste into the content. It seems simple, but it makes a big difference.

Here’s an example of a pitch I sent during one of my listicle outreach campaigns:

Listicle outreach email pitch example

Strategy 3: Reactive PR

Most digital PR campaigns take weeks or even months to prepare and execute.

Reactive PR is about speed — being the first one to respond to breaking news, viral trends, and emerging topics.

How It Boosts AI Visibility

LLM training data has a cutoff date.

When someone asks about a current event, the model can’t rely on what it already knows. It has to search the web for fresh sources.

That’s your window.

When a story first breaks, there are usually only a few credible sources covering it.

If your brand is among the first to publish useful commentary, analysis, or original reporting, your content has a much higher chance of being surfaced and cited by AI systems.

Here’s a solid example:

Search Engine Journal was among the first media outlets to report on Google’s new SEO guidelines.

Search Engine Journal – Google's new guidance

The piece has since gained over 16,000 readers, 500 backlinks, and several AI citations.

ChatGPT – SEO news – Search Engine Journal citation

How to Do It

The biggest challenge with reactive PR is spotting trending stories before they become a trend.

By the time a topic is dominating headlines, you’re already too late.

Success comes from identifying emerging conversations while they’re still gaining traction.

A few ways to do that:

  • Join online communities: Many trends gain momentum on Reddit, X, and Slack before they receive mainstream coverage.
  • Follow industry leaders on LinkedIn: Experts often share breaking news, observations, and predictions here before publishing full articles or reports.
  • Follow the major players in your industry: Product launches, partnerships, acquisitions, funding rounds, and policy changes often create PR opportunities. The sooner you spot these developments, the faster you can react.
  • Track journalist requests: Platforms like Qwoted, Featured, and JournoRequests can reveal stories that journalists are actively working on before they’re published.
  • Use Google Trends and Exploding Topics: These tools help you find emerging topics that your audience is searching for right now.

LinkedIn – Kevin Indig post

A secondary layer of this process is monitoring how stories spread across publications.

Many trends don’t appear out of nowhere in major media — they first circulate through smaller blogs and niche industry sites.

Tracking this ripple effect helps you identify narratives early and position your response while the topic is still evolving.

  • Set up Google Alerts: Create alerts for important keywords, competitors, and industry topics so you get notified as soon as new stories are published.
  • Monitor industry newsletters: Curated newsletters are often one of the fastest ways to discover emerging trends and conversations.

The SEOFOMO newsletter

Once you spot an opportunity, move quickly.

Write a report or a commentary piece on your website and social media, and pitch the story to major news outlets in your field.

Strategy 4: Ego Bait

Ego bait involves creating content that features industry experts and influencers.

The goal is to stroke their ego, making them more likely to share your content, mention your brand, or link back to your site.

Expert quote example

How It Boosts AI Visibility

Featuring recognized experts can improve AI visibility in two ways.

First, it increases the credibility of your content.

An AI SEO study found that pages containing expert quotes receive 4.1 citations in ChatGPT on average, compared to 2.4 for pages without them.

And we’ve seen it firsthand.

Our article on SEO competitor analysis features insights from 20 experts, and it’s cited by both Google’s AI Overviews and AI Mode.

Google AI Mode – SEO competitor analysis checklist – Position Digital citation

Expert commentary acts as a trust signal, making the content more authoritative and cite-worthy.

Second, it creates a distribution channel.

When experts are featured in your content, many will share it with their audience, mention it on social media, link to it from their websites, or include it in newsletters.

As those mentions and backlinks accumulate, your brand becomes more visible across the web.

Stronger brand visibility = stronger LLM visibility.

How to Do It

There are three types of ego-bait content you can create:

1. Expert roundups

Use journalist outreach platforms like MentionMatch, Featured.com, and Qwoted to gather expert insights for your content.

Choose the best quotes and feature them on your blog post.

Once it’s live, tag every contributor in your LinkedIn post.

Most will reshare it, comment, or at minimum engage with it, which extends your reach well beyond your own following.

LinkedIn – Sean Begg Flint post

2. Case studies and success stories

Highlight real results from customers, partners, or collaborators.

A well-written case study flatters the subject, gives them something worth sharing, and adds a layer of credibility that generic content can’t replicate.

3. Top experts or influencers lists

Curate lists of respected people, companies, or voices in your category.

Examples include:

  • Top SEO Experts in 2026
  • Most Influential AI Search Voices
  • Leading Growth Marketers in SaaS

Being included in a curated list is often incentive enough for people to share it, especially if the selection feels credible and relevant.

This type of structured content is also highly citable by AI systems.

Google SERP – Top SEO influencers – AI Overview

Strategy 5: Thought Leadership

SEO content is built to rank.

Thought leadership content is designed to establish you as a recognizable voice — both for people and LLMs.

And when you already have an established presence, it’s much easier to market your business.

How It Boosts AI Visibility

Most content gets ignored because it fails to give people a reason to engage.

Thought-provoking content, on the other hand, is designed to challenge assumptions, introduce new perspectives, and spark discussion.

Just look at this “controversial take” from Khanh Linh Le.

This post generated an unusually high number of comments relative to its likes because it challenged conventional thinking and encouraged debate.

LinkedIn – Khanh Linh Le post

Some people agreed. Others pushed back. But everyone had something to say.

That’s the goal: to drive engagement.

Engagement drives shares. Shares drive mentions across blogs, forums, social platforms, and publications.

And when a lot of people talk about you, AI pays attention.

How to Do It

1. Build a LinkedIn presence

The first step is to start posting on LinkedIn.

Why?

Because at the time of Semrush’s study, LinkedIn was the second-most-cited domain in ChatGPT, AI Mode, and Perplexity.

Some tips:

  • Publish consistently: AI citations reward relevance and consistency more than virality. The Semrush study found that 75% of cited authors post at least one post per week.
  • Take a stance: Share opinions, challenge assumptions, and weigh in on debates happening in your industry. Posts that take a clear stance generate far more engagement than informational updates.
  • Use your personal and company pages: Some LLMs like Perplexity gravitate more towards company pages, while others like ChatGPT and AI Mode like citing personal pages.

Anatomy of LinkedIn content in AI search

2. Write guest posts for major publications

Guest blogging is a great way to distribute your ideas and share your expertise in high-authority publications.

When multiple authoritative sources cover the same topic and all point back to you as the originating voice, AI models start to recognize you as the go-to authority on that subject.

At Position Digital, I’ve developed a framework called the Guest Post (GP) Engine.

Here’s how it works:

I publish a blog post on my website.

I then publish variations of that post on other publications, ideally those with a high website authority.

As an example, I wrote about “Content Refreshes” on the Position Digital blog.

I then wrote guest posts on the same topic on Sitebulb and Surfer SEO.

All three articles are now cited by AI Overviews.

Google SERP – Content refresh guide – AI Overview

3. Appear as podcast guests

Podcasts are one of the most underutilized thought leadership channels.

A single appearance can generate:

  • Mentions on the host’s website
  • Show notes and transcripts
  • Social media clips
  • Newsletter features
  • Citations from future content creators

If you’re just getting started, don’t chase after big, established shows.

Start small. Build relationships with newer podcasts in your niche.

Paid Forward Podcast featuring Sean Begg Flint

Smaller shows are easier to get on, their hosts are often more engaged, and the content still gets indexed and cited.

As your reputation grows, the bigger opportunities follow naturally.

Strategy 6: Community Building

Community building is about establishing your brand’s presence in third-party forums, review sites, and customer rating platforms that AI models treat as trusted, independent sources.

This way, your brand is validated not just by what you say about yourself, but by what neutral platforms say about you.

Reddit – AEO – Best AI visibility tools

How It Boosts AI Visibility

AI models trust neutral, community-driven sources over brand-owned marketing.

It’s not an opinion. It’s backed by actual data.

According to another AI visibility study by Semrush, LLMs cite Reddit threads about Microsoft products much more frequently than Microsoft’s own blog.

Let that sink in.

And it’s not just forums like Reddit. Review platforms like Trustpilot and G2, where users can share authentic reviews and ratings, carry significant weight too.

Seer Interactive also did a study of 800k AI responses and found that brands with no Trustpilot profile have a median AI citation rate of just 1%.

Brands with even a minimal profile, as few as 1 to 13 reviews, jump to 53.5%.

The implication is clear:

If AI has to choose between what a brand says about itself and what hundreds of independent users say about that brand, it will often favor the latter.

How to Do It

1. Contribute insights on Q&A platforms like Reddit and Quora

These communities are scraped heavily by AI models and frequently cited in responses.

ChatGPT – Reddit – Sources

But be careful, Reddit and Quora communities have a low tolerance for overt self-promotion.

Accounts that exist purely to promote a product get flagged, downvoted, or banned.

The key is to participate as a real contributor, not as a marketer.

Be transparent about who you are and what company you represent, but focus primarily on being helpful.

A good approach is to contribute meaningfully to 3–5 relevant threads per week.

Only mention your brand when it is genuinely relevant to the question or adds value to the discussion.

You can also host an AMA (Ask Me Anything) session to share your knowledge while building brand awareness.

Reddit – Sweaty Startup – SEO AMA

2. Create optimized profiles in review sites

Depending on your niche, set up and optimize your profiles on review platforms like:

  • G2, GetApp, and Software Advice for B2B SaaS
  • Trustpilot and Clutch for agencies and service providers
  • Tripadvisor and Google Reviews for hospitality and local businesses

After that, make sure to ask your existing customers to leave positive reviews and ratings.

3. Write content on Medium

Medium is another platform that AI frequently cites, according to the Semrush study above.

Use it to republish condensed versions of your best content or share original perspectives that complement your main blog.

Publishing the same ideas on Medium that you’d put on your own site gives those ideas a better chance of being cited.

Show Up Where AI is Already Looking

The quickest path to AI citations is getting into the pages AI already trusts.

Start by using Semrush’s AI Visibility Toolkit to uncover the prompts your audience is typing into AI chatbots.

Then, review the sources each AI platform cites for those prompts and find a way to earn a placement there.

When you’re ready to go deeper into how to optimize for citations and influence AI answers, read this guide on LLM seeding.

The post 6 Digital PR Strategies to Boost AI Visibility appeared first on Backlinko.

Read more at Read More

Hosting guide: How to pick the right host for your site

Do you want to start a blog or online store? Then you’ll need a website. But did you know you’ll also need web hosting to get your website online? There are many aspects to web hosting, and you might not be sure what it entails. You have to choose a host, a hosting type, and many other things. That’s why we wrote this guide, to help you understand what it is and pick the right host for your site!

Key takeaways

  • Web hosting makes your website accessible online, requiring you to choose a host and type of hosting.
  • Decide between fully-hosted and self-hosted options based on your control and data ownership preferences.
  • When picking a host, consider server location, storage space, audience size, and additional services offered.
  • Understand the different types of hosting: shared, dedicated, and VPS hosting to find what suits your needs.
  • Specialized hosting plans exist for WordPress.org sites, which come optimized and may include support for security and updates.

What is web hosting?

Let’s start with the basics. Web hosting is a service that makes sure your website is accessible through the internet. The pages and files that your website consists of live on a server. Whenever someone visits your website, that server sends over the necessary ‘files’ to display your site to the site visitors. This server is owned by a web hosting company, and you can rent space to store your website files on a server that meets your needs. Without web hosting, your website simply can not exist.

It’s also good to know that, although it doesn’t have a direct effect, your choice of hosting impacts your SEO. Reliable hosting improves the security, speed and uptime of your website. All factors that tell search engines that your website is trustworthy and a good option to show to their users. Upgrading your hosting plan or switching to a new hosting company can therefore improve your site performance, just make sure to choose reliability and your website’s needs over low prices.

So, where do you start? By looking at web hosts or hosting companies. These companies provide servers and internet connectivity in a data center. In other words, they provide space for your website’s data, so it can exist and other people can find it. 

Fully-hosted versus self-hosted

The first choice you have to make when picking the right host for your site is whether you want to create a fully-hosted or self-hosted website. The difference is pretty self-explanatory. Fully hosted means every site on the platform is hosted on its own servers. These companies take care of all the hosting stuff for you, such as Shopify and WordPress.com. 

Self-hosted, on the other hand, means you’ll have to arrange hosting yourself. A good example is WordPress.org. Now, you might be thinking: why would I want to do everything myself? And while it’s true that self-hosting is extra work, it also means you own all the data yourself. Plus, you get access to the full codebase and can choose your own hosting company.

How to choose a hosting company

These days, there are thousands of companies to choose from. They all have their own benefits and hosting plans. So, how do you pick the right host for your site? What should you focus on? First, it’s important to take a couple of things into account.

Server location

As those real estate TV shows always say: location is everything. It’s not necessarily everything when it comes to hosting, but it is important. Where your host’s servers are located is an important factor in your site’s speed. Why? The smaller the distance between your audience and your server, the faster your website is. For example, if your audience is located in the US but your host is based on the other side of the world, that could slow down your site for visitors, which is something you want to avoid.

Storage space

Second, consider what kind of website you want to create. This impacts how much storage space you need to acquire. For example, a small blog needs far fewer resources than a big online shop with thousands of products. Consider how many pages your site will have and how much content it will have. If you already know you want to build a large website with many large images and videos, you’ll need to look at hosting plans with more storage space.

How big is your audience?

We know this is a hard one to answer. But you might already have an idea of the number of page views you want to reach. Take this number into account when deciding on a hosting company and plan. Because every time someone views a page on your site, it adds to the amount of bandwidth you use. In other words, all the texts and images on that page need to be downloaded onto each visitor’s computer before they can be displayed in their web browser. 

Other services offered

A final consideration is the need for other services related to your website. Many hosting companies offer additional services, such as email hosting, SSL certificates, automated website backups, and website builders. The website plan you choose should provide the services you need to get you up and running, preferably beyond basic hosting. For example, choosing a hosting plan that comes with a website builder helps you create your website right away.

screenshot of the Bluehost AI Website Builder
The Bluehost AI Website Builder

Bluehost has recently launched an AI Website Builder. You answer a few questions, and it generates your website for you. It lets you make changes through chat, and you see changes in real time, no coding or design skills needed. Just describe what you want. Hosting and a domain are included, and human support is available if needed. Ideal if you’re setting up a website for your small business and want it to go live quickly. If your website is a bit more complex, for example, when you want to create a membership portal, this might not be the best option.

Extra questions to ask

In addition to server location, storage space, and the number of visitors, there are tons of other factors that determine your ideal hosting company. That’s why we compiled a couple of additional questions you could ask yourself (and a hosting company) when picking the right host for your site. 

  • Do I need help or support setting things up? 
  • Does the company offer live chat and phone support, and do they respond quickly? 
  • What happens if my site gets hacked or goes down in the middle of the night? Will they help? 
  • What is my budget? 
  • Do I need the ability to add more websites? 
  • Does this hosting company have good reviews? 
  • And, for higher-end packages, what type of hardware, like SSD storage, CPUs, and memory chips, do I need for my site? 

Different types of hosting

Finally, let’s look at the different types of hosting. In this guide, we’ll focus on the three most common ones: shared hosting, dedicated hosting, and VPS hosting. But there are lots of other options out there.

Shared hosting

Shared hosting means you share the same server resources with multiple customers. These server resources determine how much space, bandwidth, mailboxes, etc., you have available. It also means there will be resource limits on each hosting package. Don’t worry, sharing resources doesn’t mean other customers can see or access your data. Shared hosting is probably the cheapest option, but it might not be the best choice if you expect to need a lot of server space and bandwidth.

Dedicated hosting

Dedicated hosting does exactly what it says on the tin. You’ll get a dedicated, fully allocated hardware server for your use only. This means you’re not sharing the server with other customers. Instead, you’ll have your own personal server with its own processors, hard disks, and memory. This makes dedicated hosting an interesting choice for bigger sites, such as large online shops or corporate sites.

VPS hosting

VPS hosting stands for Virtual Private Server. A VPS is a virtual machine on a physical server that is divided into different sections. You get your own space on this server, with access to a certain amount of resources, like memory and processors. Unlike with shared hosting, these resources are for your use only. Additionally, you can optimize your VPS server to your own preferences. For example, you can determine which software runs on it and adjust the settings to your liking, just like with dedicated hosting.

Hosting plans for WordPress.org sites

If you’re planning to create a WordPress.org website, then we’ve got good news for you. There are tons of hosting companies that offer hosting plans specifically for WordPress.org sites. The benefit? The plans are completely optimized for WordPress-based websites. Usually, they already have WordPress installed, so you don’t have to take care of that anymore. Sometimes they also offer security and update services. This means you don’t have to worry about your site’s security or updating WordPress and the plugins you have installed. 

To help you out, here is a list of WordPress hosting companies.

Conclusion

Now you know all the basics about hosting companies, hosting plans, and the different types of hosting. Still, it’s good to consider which factors are important to you before choosing a hosting company and plan. Because your site’s hosting can impact factors like site speed and the amount of content you can host. 

If you want more information about hosting, we recommend checking out our Technical SEO course. The course dives into considerations when choosing a host, as well as practical tips for making your site easy for search engines to find. And much more!

FAQ

After reading this post, I realized that I might need a better host. Is it possible to switch hosts?

It’s possible, but it’s going to depend on your setup and where you’re moving to. Luckily, most hosts offer great support. Typically, they’ll guide you through all the processes you need to go through. These processes are, for example, moving your files, your database, and maybe some configuration info.

When you’re choosing your host, it’s worth getting in touch with their support team to see what kinds of services they offer and how much they can help you with that.

Okay, but there are many hosting options out there. Is there a tool that lets me enter my data and help me choose?

Oh, that’s tricky! There are lots of tools on the internet to help you pick a hosting provider, but it’s a bit of a minefield. We recommend checking out yoast.com/hosting.

But really, do your own research. Get a feel for what your budget is, what kind of features you might need, and talk to the support people. Many of these companies and websites also offer live chat.

I’m just starting out with my site, and I don’t know how many visits I’ll get. I’m hoping for a lot, of course, but I might get only a few. So, when picking a host, what should I look for in terms of bandwidth?

It’s good that you’re thinking about bandwidth beforehand, but don’t let it dictate your choice. Instead, research how much different hosting companies charge for their different packages, and how the prices change as you need to scale. Maybe you can start off small and upgrade later. Again, shop around, review pricing and packages, and talk to support staff.

The post Hosting guide: How to pick the right host for your site appeared first on Yoast.

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Visual semantics: The missing piece of topical authority

Visual semantics- The missing piece of topical authority

SEO has long focused on what a page says. Increasingly, it also needs to account for how that information is presented.

As Google gets better at understanding page layout, structure, and functionality, visual semantics is becoming an important part of how search engines interpret webpages.

What is visual semantics?

Visual semantics is a meaning model for segmenting, classifying, and understanding documents by working alongside textual semantics.

Google is changing how it interprets web documents, shifting from “web text” to “web layout” to better identify real expertise, uniqueness, and originality by giving more weight to the functional components of a webpage.

Google’s Quality Rater Guidelines cite “human effort and involvement” as one of the most important quality principles, with “design effort” identified as one aspect of that evaluation.

Webpage layout has always been an important part of SEO, dating back to Google’s Page Layout algorithms. Those early algorithms focused primarily on ad placement and simple document-ranking signals, unlike today’s more sophisticated approaches to understanding webpages.

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Why Google is paying more attention to page layout

Google has introduced newer inventions and patents that highlight the importance of understanding webpage layout. Most webpages are no longer built with only prose or simple text-over-text layouts. Instead, they contain much denser information. 

Every 10 to 20 pixels can introduce a new interaction point, engagement element, clickable module, comparison unit, or dynamic component designed to help users.

That’s why some of Google’s leading engineers, including those who have worked on Gemini and AI Mode, are also associated with newer inventions such as Structured Information Cards and layout-aware multimodal document understanding.

Below is a direct citation from Google’s work on structured information cards and layout-aware multimodal document understanding. Google often finds important information within interactive card structures rather than ordinary paragraphs. 

As a result, it needs systems that can understand how different card types are structured, including product cards, hotel cards, real estate cards, trip cards, credit card cards, and other information cards.

In other words, modern search engines must understand not only the text on a page but also the layout, hierarchy, visual relationships, annotations, and functional meaning of each structured information block.

A citation from Google’s “Layout-aware Multimodal Document Understanding” patent
A citation from Google’s “Layout-aware Multimodal Document Understanding” patent

Why layout matters for search engines

Understanding structured information cards and layout-aware document interpretation requires neural networks, and possibly a new type of LLM, that can “verbalize” web documents with annotations and high-confidence citations.

Google can’t reliably rank a flight booking website, a credit card application aggregator, or similar platforms without understanding the data embedded in these documents. 

Much of that data is presented through uniquely designed card structures, comparison modules, tables, and interactive layouts rather than plain text.

Below is an early example of document layout understanding from Microsoft called ViPS, which Google has also cited.

Later, Google patented an alternative approach based on HTML-heavy segmentation.

Both approaches are closely related and rely heavily on HTML to determine which text belongs to each section, component, entity, or visual block on a page.

With the rise of embedding-based algorithms, concepts such as “chunking” have become widely discussed in the SEO industry. 

However, many discussions about text or document chunking miss a critical point: Chunking isn’t only a linguistic process. It’s also a layout-aware and structure-aware process.

If a document isn’t visually segmented and structurally understandable to search engines, the content itself becomes harder to interpret. In that case, it doesn’t matter how many entities, predicates, triples, or entity relationships you include, or how accurate they are. 

Search engines still need to understand where each piece of information belongs, how it relates to the surrounding elements, and which visual or functional component gives it meaning.

Dig deeper: Image SEO for multimodal AI

How centerpiece annotation affects rankings

In modern search, information quality alone isn’t enough. Information also needs to be presented within a layout that helps machines understand its boundaries, hierarchy, context, and purpose.

Google explained this concept through “centerpiece annotation,” describing visual annotations that help its systems better understand a document.

Martin Splitt from Google said the “centerpiece annotation” represents the “primary content” of a webpage. 

Later, documents disclosed during Google’s antitrust case showed that centerpiece annotation was also used to classify and rank news documents. 

The centerpiece annotation was primarily limited to about 400 characters, though those documents also reveal several other noteworthy details.

For example, below you can see how Google extracts the centerpiece annotation from HTML. The sentence is interrupted by unnecessary HTML elements, such as Facebook, email, Twitter (X), and Google+ share buttons.

HTML elements

In the next example from Google’s DOJ documents, proper HTML structure prevents share-button boilerplate from interrupting the centerpiece annotation, allowing Google to extract the content correctly.

What visual semantics looks like in practice

Below is a simple SEO case study. Although it involved 19 changes, the biggest ranking improvement came from one simple adjustment: moving a calculator component from the bottom of the page to the top, making it the centerpiece annotation.

The results of that change are shown below.

Metric Previous Current Increase / Change Success %
Total clicks 3.47 million 4.53 million +1.06 million clicks +30.5%
Total impressions 84.1 million 167 million +82.9M impressions +98.6%
Average CTR 4.1% 2.7% -1.4 percentage points -34.1%
Average position 8.9 8.5 Improved by 0.4 positions +4.5% improvement

This project closely connects visual semantics and textual semantics because it’s a programmatic SEO case study involving more than 100,000 pages.

At that scale, even a small sentence edit, component update, or layout adjustment is multiplied across every URL. That’s why Google re-crawled the entire website after the layout changes and why impressions and clicks increased afterward.

The project is a converter website that ranks for queries such as “2m to cm” and millions of similar numeric and metric variations. In this type of search environment, more than 10,000 competing websites provide essentially the same data and the same answer.

These websites have the same topical coverage and factual accuracy. The competitive advantage doesn’t come from providing a better answer because “1 meter to cm” has the same value everywhere.

It comes from retrieval cost, document understanding efficiency, internal PageRank distribution, and how clearly the answer is presented for Google’s initial ranking systems.

Google's Content Warehouse API leak includes similar semantic labels and annotations for webpages and PDF documents 
Google’s Content Warehouse API leak includes similar semantic labels and annotations for webpages and PDF documents 

In these types of queries, you can’t differentiate yourself by changing the answer. You differentiate yourself by changing how the answer is structured, annotated, prioritized, and visually presented.

That’s why changing the centerpiece annotation caused Google to reprocess the layout, rerank the pages, and further improve the site’s rankings.

Dig deeper: How to make products machine-readable for multimodal AI search

What is the cost of retrieval, and how does it relate to visual semantics?

“The cost of ranking a document” can’t be higher than the “cost of not ranking a document.” I introduced this concept years ago in one of my conference presentations. Google cares about search quality, but its systems also weigh quality against cost. If a website costs more to process than its quality justifies, Google will look for an alternative.

Google reduced the HTML file size limit to 2 MB and carried out large-scale deindexing following the December 2025 core update.

At the same time, it sent a clear signal to websites that scale AI-generated content without meaningful human effort. Google appears less tolerant of practices it accepted for years, and its indexing decisions are likely to become even more selective.

Retrieval costs increase when a webpage doesn’t clearly explain itself or fails to demonstrate sufficient relevance and responsiveness, especially around the “centerpiece annotation.” Google’s Content Warehouse API leak suggests the company truncates documents and predicts quality based on initial signals. If a document doesn’t meet relevance and responsiveness thresholds during those early evaluations, it won’t be considered a candidate.

During Google’s antitrust trial, Pandu Nayak, then Google’s vice president of Search, explained that Google doesn’t run its most computationally expensive algorithms on every webpage because it lacks sufficient click data. Instead, it first evaluates core topicality signals to determine whether a page is worth indexing and keeping as a candidate.

Nayak also explained that RankBrain-like algorithms are expensive to run, so Google reserves them for results that have at least one click, demonstrate strong topicality, and include annotations that justify the investment in crawling, rendering, evaluation, and further processing.

In other words, classifying documents by their layout, components, and structured information cards is a more efficient way to reduce retrieval costs while improving search quality.

Today, most large-scale content publishers rely on AI to generate more text. Far fewer invest in front-end and back-end systems that improve user engagement, interaction, and document understanding.

That distinction increasingly separates low-quality and high-quality sources. Low-quality sources primarily scale text. High-quality sources scale systems, layouts, components, structured information cards, and user interactions that help both users and search engines understand content more efficiently.

Below is Google’s concept of website representation vectors.

Google classifies websites using visual and layout-related embeddings and features to determine whether they resemble expert, apprentice, or amateur sources.

  • “For instance, the website classifications may include a first category of websites authored by experts in the knowledge domain (for example, doctors), a second category authored by apprentices (for example, medical students), and a third category authored by laypersons…”

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How does Google’s helpful content system relate to visual semantics?

The helpful content system is a classifier that identifies which websites genuinely provide helpful information or meaningful engagement and which only imitate usefulness without fulfilling the searcher’s underlying intent.

Much of the SEO industry’s analysis of the helpful content system has focused on textual features. Early discussions centered on keyword stuffing, gibberish content, or adding “unique information” to improve information gain. However, many of the system’s algorithms appear to focus on the function and type of a source.

Google first classifies websites by their type rather than their content quality. That means the same content can rank differently on an affiliate website than it does on an ecommerce website. 

So how does Google distinguish among affiliate sites, aggregators, service providers, ecommerce sites, and SaaS platforms? The answer is visual semantics. What a page can do, or can’t do, is largely determined by its layout and page components.

The biggest distinction between relevance and responsiveness comes from engagement, not understanding.

Classifying search results by their page elements helps Google understand what type of document it's evaluating
Classifying search results by their page elements helps Google understand what type of document it’s evaluating

Google created systems such as neural matching to align the entity type and entity ID in a query with the most relevant documents. In simple terms, if the entity in the query doesn’t match the entity in the document, that page becomes less likely to rank. This is primarily about relevance.

Relevance alone isn’t enough. A document may rank because it’s relevant, but if it doesn’t support meaningful user actions, such as purchasing, comparing, ordering, reviewing, filtering, or watching, it isn’t responsive to the user’s actual task.

That’s why the helpful content system shouldn’t be viewed only as a system that evaluates page text. It also evaluates page function. A helpful page isn’t simply one that contains relevant words. It’s one that helps users complete the action, decision, or information-seeking task behind the query.

Google reinforced this idea by adding “misleading functionality” to its spam policies after the Helpful Content updates. A page can appear helpful by imitating a function without actually providing it.

For example, a page may suggest users can compare, filter, calculate, book, review, or purchase something even though those functions don’t genuinely exist. In those cases, the page may appear functional to both users and algorithms, but it isn’t truly responsive to the user’s task.

Google doesn’t classify websites only by page layout and design. It also appears to apply result-type constraints within the SERP. For example, a query such as “best women’s glasses” may return listicles, ecommerce category pages, product grids, videos, and commercial guides in the same results page.

To satisfy multiple search intents, Google can apply diversity constraints that limit how many ecommerce pages, listicles, videos, or other result types appear together.

Google’s DOJ documents include functions such as “max_total” and “BlogCategorizer,” which show how Twiddlers can classify results and limit the number of pages from the same cluster, category, or source type.

A similar annotation appears in the Google Content Warehouse API leak through the “WebrefFatcatCategory” module, which assigns categorical weight to a result.

In other words, Google doesn’t simply rank documents individually. It also classifies, clusters, and constrains results based on page type, source category, and categorical diversity. As a result, a page may be relevant enough to rank but still be limited by the overall composition of the SERP.

Even when a generated ranked entity list, such as a “best products” page, ranks successfully, it doesn’t rank simply because it’s a blog article. It ranks because it functions as a commercial resource. It helps users compare, evaluate, filter, review, and move closer to a decision. In that sense, Google can rank nonfunctional content when it effectively serves a functional category.

Viewed through this lens, “helpful” in the context of the helpful content system is closely aligned with “functional.”

The following case study demonstrates this principle. We moved identical content from an affiliate website to an ecommerce website, supported it with an integrated topical map, and saw rankings improve almost immediately.

The content itself didn't change. What changed was the function, context, and source type surrounding it. By placing the same information within a more functional, commercial, and task-oriented environment, Google interpreted the document as more useful for the user's search activity.
The content itself didn’t change. What changed was the function, context, and source type surrounding it. By placing the same information within a more functional, commercial, and task-oriented environment, Google interpreted the document as more useful for the user’s search activity.

How is click data used to rerank search results through visual semantics?

Google increasingly understands the purpose of a webpage through its layout, not just its text. As a result, click data is aggregated according to the type of source. Many SEOs assume that long clicks, or longer dwell times, signal quality. 

However, that’s not always true, according to Google’s research. Depending on the category, shorter dwell times can indicate a successful experience, while longer sessions may signal an “engagement trap.”

Below is Google’s reranking model, which applies different ranking and rank-modification models based on user behavior captured by its tracking components.

Another example comes from Google’s “Merging Search Engine Results” patent, alongside the “Twiddler’s anatomy” diagram revealed in the DOJ documents.

“Merging search engine results” is the name of the patent, which aligns with the “Twiddler” functionality above
“Merging search engine results” is the name of the patent, which aligns with the “Twiddler” functionality above

Google also uses the concept of the “Life of a Click” to help engineers understand how search ranking algorithms interpret user behavior.

Taken together, these systems suggest that click data becomes a more meaningful classification signal when interpreted alongside a webpage’s design rather than through text alone.

Classifying documents by their visual structure can be more efficient than analyzing millions of documents, billions of word tokens, co-occurrences, named entity resolutions, attribute extractions, and value corrections.

If certain document layouts consistently generate stronger user satisfaction, Google can classify those pages as more helpful or functional. It can then use those signals to identify other documents with similar layout patterns, component structures, and interaction models.

This means topical authority doesn’t come only from a topical map that defines which topics to cover. It also comes from understanding which page layouts, component structures, information cards, comparison modules, and functional designs best match each topic, query, and search activity.

A proper topical map shouldn’t define only entities, attributes, predicates, and contextual relationships. It should also define the page type and functional layout needed to satisfy both relevance and responsiveness.

This leads to the concepts of coverage and domain-level classification. The following three examples illustrate this approach.

The first example is AudioToText.com, a sub-brand built around a single topic.

GSC Metrics of Audiototext.com. The third-party Semrush data is shown below.

Despite covering only one topic across 12 languages, or 13 pages in total, the site continues to grow in search visibility for three reasons:

  • Its exact-match domain reinforces relevance.
  • Its visual semantics improve responsiveness.
  • It earns its first clicks quickly, allowing Google to run more computationally expensive ranking systems sooner.

Click satisfaction from the other language versions may also reinforce the English version through cross-lingual information retrieval. 

Google can use webpage layout understanding and chain-of-reasoning to classify AudioToText.com as a “no-signup transcription tool” and rank it in AI Overviews. This suggests Google isn’t only reading the text. It’s also interpreting the page’s function, visual annotations, and interaction model.

In other words, Google can use agentic retrieval based on visual signals to understand what a page does and determine whether it deserves to rank for a specific query.

The Audiototext.com’s single-page topical map representation with the fundamentals are below.

The webpage was designed with minimal text while placing its primary conversion element, the content upload component, above the fold.

If that component were moved lower on the page or made smaller, rankings would likely decline, and text changes alone wouldn’t be enough to recover them.

Another example is attorneys.lexinter.net, which ranks primarily through a subdomain because its core content was moved there together with a filtering engagement component.

The primary domain didn’t meet the required thresholds, but moving the content to a subdomain with additional functional elements produced better results.

The same subdomain testing approach also worked for Pricelisto.com. Although most of the design and content remained the same, we added functions and annotations related to purchasing, comparing, examining, and reviewing.

Those functional additions made the pages behave less like passive content and more like task-completing commercial resources. As a result, the site avoided filters associated with the Helpful Content System.

The improvement didn’t come from changing the text. It came from changing how the document functioned, how users interacted with it, and how clearly Google understood the purpose of each page component.

Search engines try to reduce retrieval costs by avoiding computationally expensive algorithms whenever possible. As a result, domains affected by historical or domain-level signals may not receive a completely fresh evaluation immediately.

Testing on a subdomain can give Google a clearer reason to reprocess documents, reevaluate their layouts, and run more advanced evaluation systems. That makes it easier to determine whether improvements come from new designs, functionality, annotations, or document structures rather than from the historical state of the primary domain.

How is visual semantics related to the future of search?

Google is experimenting with fundamental changes to search results, including replacing the traditional search bar with new interfaces.

One example is its Jan. 29 patent, “AI-generated content page tailored to a specific user.” The patent describes generating a landing page that uses visual segmentation, annotations, and generative AI to satisfy a user’s query.

The patent places significant emphasis on "landing page score," using click data and explicit user feedback signals
The patent places significant emphasis on “landing page score,” using click data and explicit user feedback signals

In other words, Google can use visual semantics not only to rank web documents but also to construct new types of search results.

Dig deeper: Google patent hints it could replace your landing pages with AI versions

Google’s patent work is often complemented by its research. For example, the paper “Neural Design Network: Graphic Layout Generation with Constraints” explores how systems can understand, classify, and even generate webpage layouts to improve search performance.

This suggests that layout isn’t only a design consideration. It can also serve as a retrieval, classification, and ranking signal.

Google’s multimodal document understanding also connects to its latest announcement, Google Embedding 2, which uses generative neural networks to understand and vectorize text, images, videos, audio, and documents.

This matters because different versions of the same web document can be compared through their vector representations. Doing so makes it possible to evaluate how well Google understands layout differences, visual structure, and document-level meaning.

In other words, layout changes aren’t merely visual. They can also produce different vector representations, which may affect how a document is understood, classified, and retrieved.

Below is Google’s example of the neural network process for understanding page layouts. The centerpiece annotation that helps classify a webpage as an ecommerce category page, product page, or SaaS page comes from these types of labeling systems.

In the future, Google could apply these same principles to construct its own landing pages from multiple search results.

The patent shown below also illustrates how Google could adjust SERP features based on an entity’s primary attributes. That suggests search results aren’t simply ranked and displayed. They can also be reorganized, redesigned, and presented as dynamic interfaces based on the entity, query intent, and available document structures.

Centerpiece annotation and query processing

Google classifies and augments queries differently from how people naturally think about them. That means one of the most important parts of creating a topical map is understanding search terms the way Google’s systems do and augmenting them accordingly. This process is called query semantics. Below is an example of query augmentation from ChatGPT.

In this example, we searched for “best search engine optimization information sources,” and GPT expanded the query as follows:

  • Best SEO information sources: search engine optimization resources Google research, patents, SEO blogs

If you perform a search in ChatGPT, open the Network tab in Chrome DevTools, filter for XHR requests, and inspect the JSON file associated with the https://chatgpt.com/backend-api/conversation/6a* path. Look for search_model_queries, which shows what the system actually searches for.

Google also has a patent called query augmentation, shown below.

The patent is attributed to engineers, including Krishna Bharat and Anand Shukla. These names are significant because they also appear on patents and systems related to AI Overviews and AI Mode.

For example, the “Search with Stateful Chat” patent includes query augmentation as one of its steps, and its terminology and inventors overlap with this system.

The centerpiece annotation is the primary visual annotation that reflects a webpage’s purpose, function, and context. The context created through the augmented query needs to align with that centerpiece annotation.

The following case study shows how I classified query variations and their contexts across different document types, each with a distinct purpose, function, and visual structure, for a local service directory.

Let’s use “air conditioner” queries as an example. Each query variation should be matched with the appropriate page type, layout, and function.

  • Experience queries require a forum-style layout. For a query such as “How do I repair my AC?” the intent is experience-based. A forum structure works best because users expect real problems, answers, troubleshooting paths, and personal experiences. This content can also live on a subdomain to separate experiential content from the main commercial website.
  • Local service queries require a directory page. For “Air conditioner installation in [City],” the intent is local and service-oriented. The best page type is a local directory or listing page with providers, service areas, ratings, contact options, and conversion elements.
  • Price queries require a hybrid layout. For “air conditioner installation prices,” the intent is both informational and commercial. The page should provide an immediate answer with average prices, cost factors, and price ranges while also presenting local providers, comparisons, and quote-related elements.
  • Instructional queries require an informational layout. For “How to install an air conditioner,” the intent is instructional. The page should minimize local service elements and instead focus on a step-by-step guide, required tools, safety considerations, visuals, and practical instructions.

In short, a topical map should define not only which topics to cover but also the appropriate layout, components, and page function for each search activity. The following example shows some of the early results from this project after classifying query augmentation models for different query variations.

Early GSC results for the same brand.
Early GSC results for the same brand.

If there’s no need for a separate page for the [Local], [Service], [Forum], or [Instructional List] intent, we simply prune it. If other pages are too similar, we merge them.

As a result, the number of pages decreases along with retrieval costs, while PageRank concentration and relevance per document increase. Below are four closely connected components:

  • Mock-up design in draw.io.
  • Production design in Figma.
  • Topical map for different query types.
  • Content brief aligned with the Figma and draw.io designs.

Early on, we defined the topical authority formula as:

  • Historical data x Topical coverage

Later, we expanded it to:

  • Historical data x Topical coverage ÷ Cost of retrieval

Today, I’d extend the formula with one additional factor:

  • ((Historical data x Topical coverage) ÷ Cost of retrieval) x Right visual annotations

Even if you have the lowest retrieval cost, the highest topical relevance, the broadest topical coverage, strong accuracy, the longest duration of satisfied click data, and positive historical performance, none of it matters if the centerpiece annotation is wrong or the page isn’t functional.

Google’s ranking system largely functions as a decision tree. If the first decision-making layer rejects a website, the later evaluations, tests, and reranking processes won’t occur.

To maximize your chances of ranking from the start, visual annotations should be optimized just as carefully as the page’s text, images, and links.

Below is a conceptual model of this system.

A website consists of “letters, pixels, and bytes.” Data2Website is the process of turning a dataset that Google’s algorithms favor into a website by combining textual and visual semantics through those letters, pixels, and bytes.

The example above shows how a local law firm benefited from a topical map, semantically optimized content briefs, specific sentence structures, and visual design decisions.

The Semrush results below show the impact on the firm’s local rankings.

We previously applied the same principles to another ecommerce website.

If you examine the screenshots closely, you’ll see that the same principles carry over from an ecommerce design to a local service provider.

For every attribute within an entity-seeking query, such as “best law firm in Houston” or “birth test kit prices,” you can classify those attributes within the query network and organize them according to their importance.

Some attributes require review components, while others require directly commercial components.

Below are two design examples from the sibling websites Morethanpanel.com and StreamingMafia.com. Their above-the-fold and below-the-fold sections are structured similarly, covering different types of user engagement and functionality.

The above-the-fold area is often referred to as the macro-context because it contains the main content. Google’s Quality Rater Guidelines use the concept of main content to emphasize the importance of relevance, accuracy, and completeness in this section.

The below-the-fold area corresponds to what Google’s Quality Rater Guidelines describe as supplementary content, which we refer to as the micro-context. This section typically contains less important attributes and most internal links.

The next example shows the mock-up design and the distribution of factual content, opinionated content, structured content, and unstructured content.

Google doesn’t always prioritize factual or opinionated content, or structured versus unstructured content. Instead, it evaluates these characteristics based on how the search query is augmented. To improve language relevance, we distribute different types and formats of content using different visualization, verbalization, commercialization, and contextualization techniques.

The following example applies the same approach to the second website in the same industry, together with its topical map, content briefs, and authorship rules.

Algorithmic authorship can be explained through the research paper “Are LLMs Reliable Rankers?” It means writing content according to predefined sentence structures and rules. For example, the research shows that the “Rank anything first” framework increased rankings by 20% to 60%.

The system evaluates which words should follow one another to determine how relevance changes. It performs retrieval within a generative retrieval system and identifies the entity-attribute-value triples that best improve relevance. In the example above, “material” is selected as the attribute and “steel” as the value because they strengthen relevance within that context.

  • Factual content: Supports expertise-focused queries.
  • Opinionated content: Supports experience-focused queries.
  • Structured content: Supports attributes such as symptoms, advantages, and benefits.
  • Unstructured content: Supports concepts such as definitions, processes, and importance.
  • Visualization: Presents content using the appropriate semantic attributes.
  • Commercialization: Adds functional components that help users complete their tasks.
  • Contextualization: Maintains relevance by aligning content with the query.
  • Verbalization: Converts visually important information into text that LLMs and search engine crawlers can understand.

Depending on the query, Google may prefer opinionated and unstructured content, factual and structured content, or other combinations supported by different visualization, commercialization, contextualization, and verbalization techniques.

The following example from the online dating industry shows how different webpage components can improve relevance and responsiveness at the same time.

The next examples illustrate different ways to visualize content.

Comparing these two sections, you’ll see that one answer is highly factual, while the other, distinguished by a different background color, is more conversational and opinion-based.

We can create a Q&A component and add opinion-based content as forum-style discussions at the bottom of the page.

We can also ask users questions and let them contribute answers through voting, allowing those responses to be verbalized into content that is continuously updated.

Below is what we call the preceding question component. It reframes the original question using a semantically similar concept and gradually shifts the content from factual to more opinion-based.

The next example shows a horizontal tab component that distributes internal links to related headings, increasing contextual coverage.

The following Semrush data shows the early and later results for the URLs we modified.

The patents and research behind visual semantics

At this point, we’ve introduced the key concepts, definitions, and website examples needed to explain visual semantics.

We could explore these examples, processes, and implementation details in much greater depth, but every conceptual discussion begins with understanding where Google is heading.

Many of Google’s advances in query semantics, visual semantics, Gemini, and AI Search are driven by two influential engineers: Dr. Marc Najork and Michael Bendersky. They are among Google’s most frequently cited researchers in recent years and have played major roles in shaping the company’s AI-related direction.

They are also listed as inventors on the Layout-Aware Document Understanding and Structured Information Cards patents.

Another important contributor is Alexander Grushetsky, who identifies himself as the founder of RankLab, Google’s internal end-to-end ranking platform.

He’s worth mentioning because he’s frequently cited alongside Bendersky and Najork in foundational patents and research papers.

Grushetsky also worked with Bendersky and other Google engineers on item-ranking models based on item types, attribute sets, and attribute values. We’ll explore what RankLab represents in more detail another time.

Today’s search engines and large language models increasingly rely on visual semantics as part of their vectorization and embedding-based ranking systems.

Even the original Transformer research described extending these ideas to web documents and their layouts.

Years later, that vision became reality through WebRef, Google’s Web Page Transformer.

WebRef vectorizes webpages using not only their text but also their visual layout, page components, HTML structure, and overall document context.

Whether your rankings depend primarily on external PageRank, branded search demand, or internal signals such as semantics, a page’s visual context still carries ranking weight alongside its textual relevance.

Read more at Read More

Why standard SEO advice fails for travel websites

Why standard SEO advice fails for travel websites

If you apply standard SEO playbooks to a travel website, you’ll likely exhaust your budget with little to show for it.

Most SEO advice is written for sectors operating in a search ecosystem where organic text listings still dominate most user journeys.

In travel, the rules of search are entirely different because Google isn’t just a search engine. It’s a direct transactional competitor, a visual aggregator, and a gatekeeper to visibility.

Winning in this space requires accepting that what works elsewhere will fail here. To build a search strategy that actually drives bookings, travel brands must abandon standard organic best practices and instead master unique challenges: 

  • Extreme search engine results page (SERP) dominance.
  • Complex regulatory constraints.
  • Intermittent, non-linear user journeys and highly fragmented user intent.

Travel search is driven by data, not webpages

In almost every other industry, the ultimate goal of SEO is to secure a high-ranking organic text listing.

In the travel sector, particularly for high-intent queries involving hotels, flights, and activities, the traditional “blue link” is functionally dead (and becoming even less relevant as AI, AI Overviews, and other LLMs compete for user attention).

If a user searches for “flights from London to Rome” or “boutique hotels in Edinburgh,” the top of the search page is entirely occupied by Google’s own interactive search tools.

Below these tools sit local map packs, sponsored advertisements, and, in the UK and Europe, the massive “Find results on” directory boxes mandated by antitrust regulations.

Boutique hotels in Edinburgh - SERP features

Instead of just writing content, successful travel SEOs focus on feed management and entity optimization.

For accommodations and hotels, this means treating Google Hotel Center with the same priority an ecommerce specialist gives their primary website. You must ensure that your real-time pricing feeds, inventory levels, and tax calculations are integrated directly with Google’s API.

Travel entity optimization requires meticulous calibration of your Google Business Profile. Google’s local algorithms categorize hotels and attractions based on physical attributes rather than editorial text.

Whether your property appears in a filtered search for “dog-friendly hotels with free parking” depends entirely on your structured attributes, customer review sentiment analysis, and precise location coordinates.

In travel, the search engine is a database, and your primary job is to format your data so the database can display it without friction.

Dig deeper: AI referrals to travel sites surge 194% as engagement rises: Adobe

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How social content bypasses your website

Traditional search campaigns operate under the assumption that organic traffic must land directly on a domain you own to have any measurable value.

In the travel sector, this insular approach ignores how modern searchers, especially younger audiences seeking visual reassurance before booking, actually behave.

Google has adapted to this shift by turning its results pages into visual aggregators that pull content directly from external social networks.

Boutique hotels in Edinburgh - SERP feature short videos

For queries driven by discovery or curation, such as “best rooftop bars in Soho” or “hidden beaches in Cornwall,” Google often serves dedicated “Short Videos” carousels and “Perspectives” feeds directly within the main search results.

These features extract short-form video content from TikTok, Instagram Reels, and YouTube Shorts, allowing users to watch authentic, crowdsourced footage without ever visiting a traditional website. With search engines actively indexing public business posts from platforms like Meta, social media updates are now ingested into search layouts and AI Overviews.

Travel SEO is no longer confined to the boundaries of your content management system. To capture this visual search real estate, digital teams must treat social media profiles as distributed landing pages.

This is further reinforced by Google’s introduction of social and video content analytics within Google Search Console.

Dig deeper: How travel brands can earn AI recommendations

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Fragmented and intermittent intent

The standard content marketing advice for B2B or consumer services is to build comprehensive, long-form informational guides. The theory is that if you write a 4,000-word article covering every possible detail of a destination, you’ll build topical authority and guide the reader smoothly down a conversion funnel.

In travel SEO, this approach often yields high bounce rates and very few conversions. Travelers don’t plan trips in a linear fashion, nor do they want to read extensive blocks of text on mobile devices while walking down a busy street.

Travel planning is highly visual, emotional, and fragmented across different devices and moments.

The Non-Linear Travel Search Journey

A user searching for “things to do in Cornwall” is usually looking for quick inspiration, geography-based grouping, and immediate utility. If they land on a page with lengthy introductory paragraphs and dense blocks of prose, they’ll return to the search results to find a simpler resource.

Travel content must be built for rapid utility rather than high word counts. Instead of writing long essays, your content layouts should rely on interactive, modular components, such as:

  • Tabbed interfaces that allow users to toggle between “Itinerary,” “Cost,” and “Best Time to Visit.”
  • Embedded, interactive maps that show the physical proximity of recommended locations.
  • Bite-sized, structured lists that clearly state opening hours, entry prices, and booking links.

Structuring content this way also makes it much easier for Google to extract your data for its AI Overviews and visual carousels.

Instead of trying to keep users on a long page of text, the goal should be to provide structured, high-value answers that Google can easily parse.

When your site behaves like a functional tool rather than an online magazine, users are much more likely to bookmark your pages and trust your brand when they’re finally ready to book.

Dig deeper: Why Tripadvisor still matters for local SEO in 2026

A different definition of success

Winning at travel SEO requires a fundamental shift in perspective. Success can’t be measured solely by standard keyword rankings or overall organic traffic levels.

It must be evaluated by how effectively your site appears across Google’s SERP features, how reliably your data feeds communicate real-time pricing, and how quickly your landing pages answer highly specific, visual questions.

By stepping away from generic SEO advice and focusing on technical data feeds, regulatory price compliance, and modular user experiences, travel brands can secure a distinct competitive advantage in one of the most crowded landscapes on the web.

Read more at Read More