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How to Use Marketing Attribution to Take Your Business to the Next Level

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Marketing today is more complex than ever. With so many channels, touchpoints, and customer behaviors to track, figuring out what actually drives conversions can feel impossible.

That’s where digital marketing attribution comes in. It shows you which marketing efforts are working and which ones are wasting your budget.

Without attribution, you’re guessing. With it, you can make data-backed decisions that improve return on investment (ROI) and help you grow faster.

This guide breaks down what attribution is, how different models work, and how to choose the right approach for your business.

Key Takeaways

  • Digital marketing attribution tracks which channels and touchpoints drive conversions, so you know where to invest your time and budget.
  • There’s no universal “best” model. Each attribution approach has strengths and tradeoffs based on your goals and customer journey.
  • Single-touch models (like first-touch or last-touch) are simple but miss most of the buyer journey.
  • Multi-touch models give you a fuller picture but require more setup and analysis.
  • The right model depends on your business goals, sales cycle length, and how customers interact with your brand.

What is Marketing Attribution?

Marketing attribution is how you figure out which marketing efforts actually drive results.

It assigns credit to the touchpoints (ads, blog posts, emails, social posts, webinars) that influence someone to convert.

Think of it as connecting the dots between your marketing spend and your revenue.

When someone makes a purchase or fills out a form, attribution helps you trace the path they took to get there. That insight helps you optimize campaigns, improve ROI, and stop pouring budget into channels that don’t work.

But here’s the problem: most marketers either don’t track attribution at all, or they oversimplify it. Only 28% of marketing professionals say their attribution strategies are very successful at achieving strategic objectives. The stakes of misattribution are high as well, potentially costing companies money and time:

A graphic showing ad spend wasted due to poor attribution.

Attribution models set the rules for how credit gets assigned across different touchpoints.

Some give all the credit to the first interaction. Others focus on the last. More advanced models weigh every step of the journey.

Understanding how these models work is the first step to using them effectively.

Why Marketing Attribution is Important

Marketing attribution matters because without it, you’re not measuring performance. You’re guessing.

It connects campaigns to conversions, showing you which efforts drive real impact and which ones drain your budget. The thing about it is it’s also getting harder. Less cookies to rely on and the presence of AI are notable factors.

On top of that, today’s buyer journey isn’t linear. People bounce between search, email, ads, and social, often across multiple devices. Without attribution, you miss the big picture.

That’s especially true if you’re running multi-channel marketing strategies. You might be getting results, but you can’t tie them back to the right touchpoints.

Take a look at what channels marketers are the most confident in when it comes to attribution:

A graphic showing confidence in attribution accuracy by channel.

Email and paid top the list. But here’s the thing: without proper attribution, you can’t tell if any channel is actually driving growth for your business, or if you’re just following what everyone else is doing.

Attribution also improves ROI. When you know what works (and what doesn’t), you can reallocate spend with confidence.

It gives marketing teams clarity, sales teams better leads, and leadership the data they need to make informed decisions.

Bottom line: attribution turns marketing from a cost center into a strategic growth engine.

Types of Marketing Attribution Models

There’s no one-size-fits-all approach to marketing attribution. Only what fits your business best.

Attribution models fall into two categories: single-touch and multi-touch.

Single-touch models give full credit to one touchpoint, like the first click or final conversion. They’re simple to track but miss most of the customer journey.

Multi-touch models spread credit across multiple interactions. They take more effort to set up but give you a clearer picture of what drives revenue. 

Let’s break down each model so you can find the right fit for your goals.

Option #1. First-touch attribution

The first-touch attribution model applies all the ‘credit’ to touch points that lead a visitor to your website for the very first time.

A graphic that says how first-click attribution definition works.

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That holds true even if they don’t make a purchase, subscribe to your email list, or complete any other converting action.

This model is all about the very first part of the customer journey. It’s the first few steps someone takes to visit your site for the very first time.

That’s why it works best for marketers who are focused on demand generation and lead forms. You want to see which actions are driving that very first connection with your brand.

A good thing about this model is that it’s pretty simple to put into effect with Google Analytics.

But, since this model only really focuses on one single touch point, it tends to over-prioritize a channel that might not be the most important.

In other words, the initial social ad used to drive traffic is important to an advertiser or brand marketer. However, it’s not all that helpful to people who are analyzing bottom-of-the-funnel conversions, that generally lead right to a sale or conversion.

The first-touch attribution model also doesn’t actually uncover what made a customer buy, so it doesn’t really allow for a whole lot of optimization.

Option #2. Last-touch attribution

The last-touch attribution model is the exact opposite of the first-touch attribution model, hence the name.

It’s often the “default,” go-to model for most marketers. It gives all the credit to the final touch point before someone buys.

For example, if a customer clicks a retargeting ad and buys, last-touch attribution credits that final ad, even if they interacted with your brand five times before that.

A graphic showing how last-touch attribution works.

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This model puts all the attention on the very end of the customer journey. The items are “in their carts,” so to speak.

This model is great for short sales cycles or conversion-focused teams.

But it ignores all of the factors that influence a customer’s journey to purchase by putting all of the attention on the final interaction.

If you’re using Google Analytics, try looking at Last Non-Direct Click instead. It skips direct visits (like people typing your URL) and highlights the last true channel that drove them in.

A graphic showing how non-direct click attribution works.

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Option #3. Lead-conversion touch attribution

The lead-conversion touch attribution model assigns 100% of the credit to the interaction that generated a lead.

A graphic that shows how the lead-conversion touch attribution model works.

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It’s a popular option in B2B and lead-gen-focused businesses because it gives a clear signal: which campaign, offer, or page got someone to convert.

This is helpful when you’re trying to understand what sparks initial interest, especially if you’re optimizing for marketing qualified leads (MQLs)  or sales-qualified leads.

But like other single-touch models, it only highlights one moment in a longer journey.

That means it misses the role of earlier awareness-building and any post-lead nurturing that helps close the deal.

If you’re using this model, be careful not to over-prioritize top-performing lead channels at the expense of brand-building or retention tactics.

It works best when used alongside other models that measure pipeline movement or final conversions, not as a standalone view.

Option #4. Linear attribution

The linear-attribution model splits credit up evenly across every touch point of the customer journey.

A graphic that shows how the linear attribution model works.

So, if there are five touch points, every touch point gets 20% of the credit. For ten touch points, each touch points gets 10%, and so on.

This model lets marketers make the best of the customer journey as a whole and optimize the entire picture, rather than just focusing on one touch point.

But, since it gives credit to all touch points evenly, some high-performing points will get less credit than they deserve, and some low-performing ones will get more.

Still, it’s a good starting point for teams who want a more balanced look at what’s working across their funnel, without needing complex analytics setups.

It can also serve as a baseline model for comparison when testing more advanced multi-touch approaches.

Option #5. Time-decay attribution

The time-decay attribution model gives more credit to touchpoints that occur closer to the final conversion.

In this setup, the last few interactions (like an email click or retargeting ad) carry more weight than earlier touchpoints.

A graphc showing how the Time Decay Attribution Model works.

This model makes sense for longer journeys, where timing and momentum are critical to pushing someone across the finish line.

It also reflects how user behavior changes closer to conversion. Someone may browse casually at first, but act with more intent later.

However, time-decay can undervalue the early-stage marketing that sparked interest in the first place. That means awareness efforts like content or top-of-funnel ads may look less effective than they really are.

If you’re running nurturing campaigns or have a long sales cycle, time-decay can give you insight into what’s accelerating purchase decisions, even if it doesn’t tell the full story.

Option #6. U-shaped (position-based) attribution

The U-shaped attribution model, also known as the position-based attribution model, gives 40% of the credit to the first and last touch points.

Then it splits up the remaining 20% among each of the touch points in between.

A graphic that show show U-shaped attribution works.

This setup recognizes the importance of both the entry point and the final push, while still accounting for the journey in between.

For example, if someone finds you through a blog post, returns via email, then converts after clicking a retargeting ad, both the blog and the ad would receive the highest share of credit.

This model is a popular middle ground. It highlights the two most critical steps without ignoring everything else.

This model might give inaccurate credit to the first and last touch points in the customer journey, though.

They receive a large, fixed percentage. So you might still see some over-reporting on both ends of the journey.

Still, for many teams, U-shaped attribution offers a practical balance of simplicity and nuance.

Option #7. Custom or algorithmic attribution

Custom, or algorithmic, attribution starts to get technical.

A data scientist creates and builds a model for attribution that matches the customer journey of a certain business in a precise way.

These models analyze your actual conversion paths and weigh each touchpoint’s impact accordingly.

That means your attribution is specific to your business, your audience, and how they buy.

It’s by far the most accurate model, but also the most complex to build.

You’ll usually need a data science team or an advanced analytics platform to get started. That makes it tough for lean teams or smaller organizations to implement.

Still, some platforms now offer algorithmic models out-of-the-box, giving you smarter attribution without having to build it from scratch.

If your marketing is already scaled and data-driven, this model can reveal deep insights you’ll never get from basic reporting.

Option #8. Rules-based attribution

Rules-based attribution lets you define how credit is assigned across the customer journey based on your own logic, not a fixed formula.

For example, you might assign 20% of the credit to first-touch, 20% to last-touch, and distribute the remaining 60% based on engagement or funnel stage.

A graphic showing how fractional attribution works.

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This approach gives you more control and customization without requiring advanced AI or machine learning.

It’s especially useful when you have a clear understanding of your sales cycle and buyer behavior, or when you need to align attribution with internal KPIs.

The downside? It’s still built on human assumptions. If your weighting is off, your data might mislead you.

Rules-based attribution works best for marketing teams that want more flexibility than single-touch or rigid multi-touch models but don’t have the resources for full algorithmic setups.

Option #9. W-shaped attribution

W-shaped attribution is a multi-touch model that assigns credit to three key moments: the first interaction, the lead conversion, and the opportunity creation.

Each of these gets 30% of the credit, with the remaining 10% spread across other touchpoints.

A graphic showing how w-shaped atrribution works.

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This model is particularly useful for B2B marketers who track leads through a defined sales funnel. It focuses on the moments that signal serious interest, not just casual engagement.

For example, a user might find your blog via search (first-touch), download a gated guide (lead conversion), and attend a webinar (opportunity creation).

W-shaped attribution highlights these hand-raising moments while still acknowledging the rest of the journey.

Downside? It assumes every journey fits that mold. Not every customer goes through clear-cut milestones, especially in shorter or less structured funnels.

If you’re managing long, complex buyer journeys, this model gives you more granularity than U-shaped without requiring full customization.

Option #10. Data-driven attribution

Data-driven attribution uses machine learning to assign credit based on how different touchpoints actually contribute to conversions, not predefined rules.

Unlike linear or position-based models, it adapts over time based on real behavior.

Platforms like Google Analytics and certain CRMs offer this as a built-in model, making it more accessible than full-blown custom attribution.

Data-driven attribution in action.

The system looks at all conversion paths and analyzes what works best, distributing credit accordingly.

This gives you a more objective view of what’s really influencing performance, without the bias of manual weighting.

Of course, the quality of your attribution is only as good as your data. Inaccurate tracking, broken events, or missing conversions will lead to flawed insights.

How To Choose the Right Attribution Model for Your Business

There’s no single “best” attribution model. The right choice depends on your funnel, goals, and how much data you have access to. Here’s how to approach it:

Map the Customer Journey

Start by understanding how people discover, engage with, and convert on your site.

Look at your customer journey mapping or analytics tools to spot patterns in behavior. If most users follow a simple path, single-touch might work. If they interact across multiple channels, you’ll want a multi-touch model.

Define Actionable Goals

Your attribution model should help you make better decisions, not just report on past performance.

Are you trying to lower acquisition costs? Improve lead quality? Shift budget to better-performing channels?

Pick a model that aligns with your strategic focus.

Prioritize Lead Quality

Don’t just track what drives volume. Focus on what drives high-quality leads or customers.

Website traffic and leads are common examples, but those are vanity metrics if they don’t convert into revenue.

Attribution tied to lifetime value (LTV), conversions, or revenue will give you far more insight than clicks or impressions.

The best attribution models connect marketing activity to actual business outcomes, not just top-of-funnel metrics.

Test and Adjust Over Time

No model should be static. As your campaigns evolve, revisit your attribution model regularly.

Consider running model comparisons inside tools like Google Analytics or your CRM to see how attribution shifts under different assumptions.

Common Digital Marketing Attribution Challenges

Even with the right model, marketing attribution isn’t always easy to get right. Here are some of the most common roadblocks teams run into:

  • Incomplete or inaccurate tracking: If events aren’t firing properly or conversions aren’t tagged, your data will be flawed, no matter what model you use.
  • Cross-device behavior: A user might research on mobile but convert on desktop. Without unified tracking, you’re missing part of the journey.
  • Platform silos: CRMs, ad platforms, and analytics tools don’t always talk to each other. That can lead to duplicate or fragmented data.
  • Lack of internal resources: Attribution often requires analysts or at least someone who can set up and maintain tracking, and not every team has that bandwidth.
  • Misaligned KPIs: When sales, marketing, and leadership define “success” differently, attribution insights can get lost or misused.

Solving attribution challenges often means improving operations, not just picking a better model.

Attribution Model Reports in Google Analytics

Google Analytics 4 (GA4) includes built-in attribution model reports that help you compare how different models assign credit to your conversions.

This is a powerful way to explore which marketing channels contribute most to your results and how your view of performance changes depending on the model you choose.

You can find attribution reports in GA4 by navigating to:

Reports → Advertising → Model Comparison

How to look at attribution in GA4.

There, you can select multiple models (like last-click, first-click, linear, or data-driven) and view side-by-side results.

This helps you spot where credit might be over- or under-assigned based on your current model.

For example, your email channel might perform better in a linear model than a last-click one, revealing a need to rebalance budget or expectations.

Even if you’re not ready to commit to a new attribution approach, GA4’s model comparison is a low-risk way to experiment and build attribution literacy.

Additional Attribution Software Options

Not every team needs a custom attribution setup, but the right software can make a huge difference.

Platforms like SEMrush, HubSpot, Google Analytics 4, and Wicked Reports offer built-in attribution tools to help you get started without hiring a data science team.

SEMrush and HubSpot are especially helpful for combining attribution with broader campaign management and reporting.

Atrribution in HubSpot

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For more advanced needs, tools like Dreamdata or Funnel.io can integrate data across multiple platforms to give you a unified view of the buyer journey.

Attribution in DreamData

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The key is making sure your tools match your actual marketing complexity. If you’re not tracking conversions accurately or aligning on KPIs, no tool will magically solve that.

Use software to simplify attribution workflows, not replace strategy.

FAQs

Attribution in marketing refers to how credit is assigned to different touchpoints that lead to a conversion.

Whether it’s a first-click blog visit or a final retargeting ad, attribution shows you which parts of your funnel are influencing behavior and how to optimize for more impact.

What attribution model approach is mainly used in marketing?

Last-touch attribution is still the most commonly used model, mostly because it’s simple and built into most ad platforms and CRMs.

But that doesn’t mean it’s the best option. Many teams are now moving toward multi-touch or data-driven models as campaigns get more complex.

Why is attribution important in digital marketing?

Attribution gives you the visibility to connect marketing efforts to actual business outcomes.

Without it, you’re just guessing what works. With it, you can prioritize the right channels, improve ROI, and cut spend where it’s not performing.

What is an example of attribution in marketing?

Let’s say a customer first finds your site through organic search, then clicks a retargeting ad, and finally converts from an email offer.

Depending on your attribution model, credit could go to the search, the email, or all three.

That model determines how you report success and where you double down in future campaigns.

Conclusion

Now that you understand how marketing attribution works, you can focus on the right touchpoints without all the guesswork.

This means no more wasted spend on channels that aren’t moving the needle.

Choose between first-touch, last-touch, lead-conversion, linear, time-decay, position-based, or custom attribution models to determine how your efforts contribute to conversions.

Just remember: no single model works for every business. The right choice depends on your campaign goals, customer journey, and how you define success.

Start with a basic model, then build from there. Use tools like Google Analytics or customer journey mapping to improve visibility across your funnel.

Test often, stay flexible, and evolve your strategy as your data improves.

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Stop Wasting Ad Spend: 8 Step SEO Checklist for Maximizing Google PMax and AI Max ROI

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For years, the talk of ‘synergy’ between paid media and organic search teams was merely talk. But with the rise of Performance Max (PMax) and the new AI Max for Search Campaigns (Google’s latest suite of AI-driven optimizations for standard Search campaigns), that separation is no longer viable.

What are Google PMAX and AI Max? Performance Max is a single, AI-driven campaign that finds customers across all Google surfaces like Search, YouTube, Display, Discover, Gmail, and Maps. AI Max is an opt-in boost inside standard Search that broadens query matching and adapts your ad assets while retaining your classic keyword structure.

How do Google Performance Max and AI Max campaigns work? PMax and AI Max rely entirely on the quality and structure of your website’s content to create ads, determine relevance, and choose landing pages. If your website is a mess, the AI creates messy, low-performing ads. One of the biggest levers for improving PMax and AI Max performance and ROAS is not a budget tweak; it’s strategic website optimization guided by your SEO team.

This guide provides an actionable, 8-step blueprint for turning traditional SEO tasks into direct, high-impact improvements for your paid AI campaigns by ensuring your website is optimized as the AI’s core asset source. Crucially, I also outline the common, costly mistakes to avoid in each step so you can stop wasting budget and start converting.

Key Takeaways

  • Your Website is the Asset Source: For PMax and AI Max, your website is not just a destination; it’s the source material for Google’s AI to create ads. Poorly written, thin, or technically inaccessible pages will lead to lower quality, generic ads.
  • Focus on Content Intent and Depth: Move beyond traditional keyword optimization. AI excels at matching user intent. SEO content must be comprehensive, answer every facet of a topic, and map clearly to a point in the user journey.
  • Prioritize UX and Technical Health: Since both platforms use automated URL Expansion (sending users to the best fit page), an SEO audit that focuses on Core Web Vitals, mobile-friendliness, and simple conversion pathways directly translates into better ad ROI.
  • Embrace Structured Data and Rich Content: Make it easy for AI to understand what your page is about and what the call to action is by implementing relevant schema and providing high-quality, diverse visual assets.

The 8 Step SEO Blueprint for Conversion Value

Core Web Vitals for NeilPatel.com

1. Technical Health and UX: A poor landing page experience directly impairs the Smart Bidding algorithm’s most critical signal: Conversion Rate (CVR). Speed issues cause users to abandon the funnel, wasting every ad dollar spent on that click.

  • Mistake: Only fixing high-priority technical errors like crawl blocks (e.g., accidental Disallow rules in robots.txt or misapplied noindex tags) and broken links.
  • Recommendation: Max out Core Web Vitals: Aggressively optimize for page speed, mobile usability, and aim for a 1–2 second load time. While Server-Side Rendering (SSR) is the ideal for speed, if full SSR is not feasible, implement robust site-wide caching and leverage optimization services to ensure near-instantaneous content display.
  • PMAX Benefit: A high-speed, flawless landing page improves the conversion rate, which is the Smart Bidding algorithm’s key performance signal.
  • AI Max Benefit: Ensures the AI’s Final URL Expansion feature doesn’t route traffic to a page with a poor user experience, preventing wasted ad spend on bounce-inducing pages.
An embedded video on a Neil Patel blog.

2. Multimodal Assets and Rich Media: Asset quantity and quality are fundamental to PMax’s ability to run across all Google channels (YouTube, Display, Search). Missing video assets severely limits PMax reach and forces the AI to create low-quality, automated videos.

  • Mistake: Using generic stock images or not having any video assets on key landing pages.
  • Recommendation: Provide Diverse, High-Res Visuals: Upload high-quality, correctly-sized images (1:1, 1.91:1, 4:5) and embed high-quality vertical videos (15–30 seconds).
  • PMAX Benefit: Prevents the AI from auto-generating low-quality videos and ensures the PMax ad can run across the entire Google ecosystem (YouTube, Display, Discover) effectively.
  • AI Max Benefit: Future-proofs the site for new multimodal searches and gives the AI quality visuals to use in image extensions and richer search formats.

3. E-Commerce/Feed Data (Retail): For any retail client, the product feed is the single most important data source for PMax. Without a rich, accurate feed, Shopping Ads—a key component of PMax—will not function or perform efficiently.

  • Mistake: Writing product descriptions primarily for the organic search page copy.
  • Recommendation: Enrich Merchant Center Feed: Collaborate with the retail team to enhance product titles with attributes (brand, color, size) and fill out all descriptive fields (GTIN, MPN, custom labels).
  • PMAX Benefit: The retail feed is the foundation of Shopping Ads within PMax. Rich data drastically improves ad relevance and Quality Score.
  • AI Max Benefit: Allows the AI to match hyper-specific, long-tail product queries to the correct landing page and generate highly accurate ad details.
An NP Digital landing page.

4. Ad Asset Readiness (Text & Copy): This practice provides the direct, conversion-focused text the AI uses to build dynamic ads. High-quality copy is essential for improving Ad Strength and improving click-through rates.

  • Mistake: Writing vague, keyword-stuffed title tags and H1s that may not be conversion-focused.
  • Recommendation: Isolate USPs & Benefits: Ensure key value propositions, clear pricing, and strong, concise benefit statements are instantly visible and scannable.
  • PMAX Benefit: Feeds the PMax Asset Group with high-quality, on-brand text that the AI uses to automatically generate headlines and descriptions.
  • AI Max Benefit: Gives the AI’s Text Customization feature direct source material to dynamically write ad copy tailored perfectly to the user’s real-time search intent.
Structured data implementation in Google Search Console.

5. Structured Data Implementation: Structured data provides machine-readable signals that directly improve the appearance and information quality of the final ad unit, boosting Click-Through Rate (CTR) and providing richer ad formats.

  • Mistake: Ignoring Schema Markup or using basic site-wide types.
  • Recommendation: Implement Granular Schema: Add specific and accurate schema for Product, Service, FAQ, HowTo, and Review on key conversion pages.
  • PMAX Benefit: The AI extracts this machine-readable data to generate richer, more compelling Ad Extensions (sitelinks, star ratings, prices) which boost CTR.
  • AI Max Benefit: Provides explicit signals about the intent and structure of the page, ensuring the AI confidently selects the right URL and generates accurate, fact-based ad copy.
A Topical Authority model in a graphic.

6. Content Structure and Topical Authority: This shift is crucial for improving long-term content relevance and the accuracy of the Final URL Expansion. It ensures Google’s AI can quickly find the single most authoritative page for a broad search intent.

  • Mistake: Focusing on creating many separate pages for hyper-specific, long-tail keyword variations.
  • Recommendation: Build Content Pillars/Hubs: Create a single, comprehensive “pillar” page for a core service/product with clearly defined sub-sections and use a Table of Contents.
  • PMAX Benefit: Ensures Final URL Expansion can confidently map broad ad intent to the best, most authoritative landing page across all Google channels.
  • AI Max Benefit: Provides the AI with a deep topical map, allowing Search Term Matching to expand reach to complex, “keywordless” queries with high relevance.
An author page on NeilPatel.com

7. Credibility & Authority: E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals are an essential factor for overall quality, trust, and long-term organic success, which implicitly benefits ad quality by building Brand Trust Signals that influence user decision-making.

  • Mistake: Focusing only on acquiring basic backlinks from any domain.
  • Recommendation: Reinforce E-E-A-T Signals: Prominently display author bios, expertise statements, customer reviews, testimonials, and clear contact/policy pages. Ensure all key personnel have detailed, well-linked “About Us” or “Author” pages that establish their qualifications and credibility.
  • PMAX Benefit: Builds implicit Brand Trust Signals that the AI incorporates into its decision-making, leading to higher ad quality and better conversions.
  • AI Max Benefit: Ensures the AI is more likely to cite and leverage your content for dynamic ad copy, as AI models prioritize information from authoritative and trustworthy sources.

8. Cross-Team Collaboration: This is the operational foundation that enables the seven other factors to be consistently implemented and optimized. It turns one-off fixes into a scalable, self-improving marketing machine.

  • Mistake: SEO only looking at Google Search Console and organic rankings.
  • Recommendation: Adopt a Shared Insights Loop: Work with the paid team to review the PMax/AI Max search term reports and asset performance ratings at least monthly.
  • PMAX Benefit: Informs Content Gaps: PMax insights reveal high-converting search queries that the SEO team should create new pages for, feeding the PMax campaign with better landing pages.
  • AI Max Benefit: Allows the SEO team to identify negative/irrelevant AI Max search terms for the paid team to exclude, reducing wasted spend on traffic that won’t convert.

Conclusion

The future of high-performance digital advertising is not about manually writing better ads. It’s about building a better website to fuel the AI. When an SEO team shifts its focus from passively chasing organic rankings to actively structuring content, optimizing technical health, and providing rich assets, they become the most valuable partner to the paid media team.

This strategic collaboration ensures that PMax and AI Max campaigns stop operating on generic guesswork and start running on quality, conversion ready data, ultimately maximizing ROI for the client. The AI is only as smart as the website it crawls, so the key to success is making that website as intelligent as possible. Want to have a quick reference for all these practices? Feel free to use the table below.

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LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

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LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? 

LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today. 

For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.

The discussion comes down to two core areas – and the timeline and work required to act on them:

  • Tracking and monitoring your brand’s presence in LLMs.
  • Improving visibility and performance within them.

Tracking: The foundation of LLM optimization

Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable. 

We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs. 

Tracking is the foundation of identifying what truly works and building strategies that drive brand growth. 

Without it, everyone is shooting in the dark, hoping great content alone will deliver results.

The core challenges are threefold:

  • LLMs don’t publish query frequency or “search volume” equivalents.
  • Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
  • They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.

Why LLM queries are different

Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable. 

People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale. 

These structural differences explain why LLM visibility demands a different measurement model.

This variability requires a different tracking approach than traditional SEO or marketing analytics.

The leading method uses a polling-based model inspired by election forecasting.

The polling-based model for measuring visibility

A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy. 

These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.

Competitive mentions and citations metrics

Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors. 

Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.

Early tools providing this capability include:

  • Profound.
  • Conductor.
  • OpenForge.
Early tools for LLM visibility tracking

Consistent sampling at scale transforms apparent randomness into interpretable signals. 

Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.

Building a multi-faceted tracking framework

While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story. 

Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement. 

Brands need to understand how people interact with their content to build a compelling business case.

Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:

  • Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
  • Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
  • Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.

Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.

Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.

Understanding these limitations is just as important as implementing the tracking itself.

Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.

Why mentions matter more than citations

Dig deeper: In GEO, brand mentions do what links alone can’t

Estimating LLM ‘search volume’

Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.

Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.

The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics. 

The real question becomes: which areas is your site missing, and where should your content strategy focus?

To approximate relative volume, consider three approaches:

Correlate with SEO search volume

Start with your top-performing SEO keywords. 

If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.

Layer in industry adoption of AI

Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:

  • High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
  • Slower-moving industries: Start with 5-10%.

Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.

Using emerging inferential tools

New platforms are beginning to track query data through API-level monitoring and machine learning models. 

Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.

Get the newsletter search marketers rely on.

See terms.

Optimizing for LLM visibility

The technologies that help companies identify what to improve are evolving quickly. 

While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.

Optimization breaks down into two main questions:

  • What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
  • How should you align these insights with broader brand and SEO strategies?

Identify what content to create or update

One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.

These same tools can help answer your optimization questions:

  • Track who is being cited or mentioned for each query, revealing competitive positioning.
  • Identify which queries your competitors appear for that you don’t, highlighting content gaps.
  • Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.

From this data, several key insights emerge:

  • Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content. 
  • Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies. 
  • Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.

Understand the overlap between SEO and LLM optimization

LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.

Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.

That correlation isn’t accidental. 

Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context. 

The more often your content appears in those results, the more likely it is to be cited by LLMs.

Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first. 

Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.

What this means for marketers:

  • Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
  • Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
  • Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.

Redefine on-page and off-page strategies for LLMs

Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.

Off-page: The new link building

Most industries show a consistent pattern in the types of resources LLMs cite:

  • Wikipedia is a frequent reference point, making a verified presence there valuable.
  • Reddit often appears as a trusted source of user discussion.
  • Review websites and “best-of” guides are commonly used to inform LLM outputs.

Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.

This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve. 

Instead of chasing links anywhere, brands should increasingly target:

  • Pages already being cited by LLMs in their category.
  • Reviews or guides that evaluate their product category.
  • Articles where branded mentions reinforce entity associations.

The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.

On-page: What your own content reveals

The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs. 

This provides valuable insight into what type of content performs well in your space.

For example, these tools can identify:

  • What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
  • Where your competitors show up but you don’t.
  • Which of your own pages exist but are not being cited.

From there, three key opportunities emerge:

  • Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
  • Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
  • Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.

Leverage emerging technologies to turn insights into action

The next major evolution in LLM optimization will likely come from tools that connect insight to action.

Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:

  • Detect where your coverage is weak.
  • See how well your content semantically aligns with real LLM answers.
  • Identify where small adjustments could yield large visibility gains.

Current tools mostly generate outlines or recommendations.

The next frontier is automation – systems that turn data into actionable content aligned with business goals.

Timeline and expected results

While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO. 

The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles. 

However, the fundamentals remain unchanged.

Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources. 

Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.

From SEO foundations to LLM visibility

LLM traffic remains small compared to traditional search, but it’s growing fast.

A major shift in resources would be premature, but ignoring LLMs would be shortsighted. 

The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.

Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity. 

Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.

In short:

  • Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
  • Map competitor visibility for key LLM queries using tracking tools.
  • Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
  • Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.

Approach LLM optimization as both research and brand-building.

Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.

Read more at Read More

How to balance speed and credibility in AI-assisted content creation

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How to balance speed and credibility in AI-assisted content creation

AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Source- “Marketers and GenAI- Diving Into the Shallow End,” SAS
Source- “Marketers and GenAI- Diving Into the Shallow End,” SAS

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

  • “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

  • What the review process for AI-created content looks like. 
  • When and how to disclose AI involvement in content creation. 
  • How to protect proprietary information (not uploading confidential or client information into AI tools).
  • Which AI tools are approved for use, and how to request access to new ones.
  • How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

  • “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 
Source: U.S. v. Google LLC court documentation
Source: U.S. v. Google LLC court documentation

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

  • Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
  • Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
  • Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
  • Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
  • Create detailed author bios: Include:
    • Relevant qualifications, certifications, awards, and experience.
    • Links to social media, academic papers (if relevant), or other authoritative works.
  • Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
  • Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.
Source: Creating helpful, reliable, people-first content,” Google Search Central
Source: Creating helpful, reliable, people-first content,” Google Search Central

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

  • Audience personas.
  • Voice traits that matter.
  • Reading level, if applicable.
  • The do’s and don’ts of phrases and language to use. 
  • Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

  • The style guide
    • This covers everything from the audience personas to the voice style and formatting.
    • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
  • A content brief template
    • This can be an editable document that’s filled in for each content project and includes things like:
      • The goal of the content.
      • The specific audience.
      • The style of the content (news, listicle, feature article, how-to).
      • The role (who the LLM is writing as).
      • The desired action or outcome.
  • Content examples
    • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
    • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
  • Sources
    • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
    • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
    • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
  • SEO prompts
    • Consider building SEO into the structure of the content from the outset.  
    • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

  • Crafting a direct answer in the first one to two sentences, then expanding with context.
  • Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
  • Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
  • Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
  • Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

Create a custom GPT in ChatGPT
Create a custom GPT in ChatGPT

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

  • “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
  • “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Microsoft 2025 Annual Work Trend Index
Source: 2025 Microsoft Work Trend Index Annual Report

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

  • Identifying the parts of the content creation workflow that are best suited for LLM assistance.
  • Conducting an editorial meeting to sign off on topics and outlines. 
  • Drafting the content.
  • Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
  • Getting sign-off from stakeholders.  
AI editorial process
AI editorial process

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

  • Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
  • All facts are traceable to credible, approved sources.
  • Outdated statistics (more than two years) are replaced with fresh insights. 
  • Draft meets the style guide’s voice guidelines and tone definitions. 
  • Content adds valuable, expert insights rather than being vague or generic.
  • For thought leadership, ensure the author’s perspective is woven throughout.
  • Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
  • Draft aligns with brand values and meets internal publication standards.
  • Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

Read more at Read More

Structured data with schema for search and AI

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Structured data helps search engines, Large Language Models (LLMs), AI assistants, and other tools understand your website. Using Schema.org and JSON-LD, you make your content clearer and easier to use across platforms. This guide explains what structured data is, why it matters today, and how you can set it up the right way.

Table of contents

Key takeaways

  • Structured data helps search engines and AI better understand your website, enhancing visibility and eligibility for rich results.
  • Using Schema.org and JSON-LD improves content clarity and connects different pieces of information graphically.
  • Implementing structured data today prepares your content for future technologies and AI applications.
  • Yoast SEO simplifies structured data implementation by automatically generating schema for various content types.
  • Focus on key elements like business details and products to maximize the impact of your structured data.

What is structured data?

Structured data is a way to tell computers exactly what’s on your web page. Using a standard set of tags from Schema.org, you can identify important details, like whether a page is about a product, a review, an article, an event, or something else.

This structured format helps search engines, AI assistants, LLMs, and other tools understand your content quickly and accurately. As a result, your site may qualify for special features in search results and can be recognized more easily by digital assistants or new AI applications.

Structured data is written in code, with JSON-LD being the most common format. Adding it to your pages gives your content a better chance to be found and understood, both now and as new technologies develop.

Read more: Schema, and why you need Yoast SEO to do it right »

A simple example of structured data

Below is a simple example of structured data using Schema.org in JSON-LD format. This is a basic schema for a product with review properties. This code tells search engines that the page is a product (Product). It provides the name and description of the product, pricing information, the URL, plus product ratings and reviews. This allows search engines to understand your products and present your content in search results.

<!DOCTYPE html>
<html lang="en">
<head>
    <title>Product Title</title>
    <meta name="description" content="Brief description of the product">
    <script type="application/ld+json">
    {
      "@context": "https://schema.org",
      "@type": "Product",
      "name": "Sample Product",
      "image": "https://www.example.com/product-image.jpg",
      "description": "Product description",
      "brand": {
        "@type": "Brand",
        "name": "Brand Name"
      },
      "sku": "12345",
      "offers": {
        "@type": "Offer",
        "url": "https://www.example.com/product-page",
        "priceCurrency": "USD",
        "price": "99.99",
        "availability": "https://schema.org/InStock"
      },
      "aggregateRating": {
        "@type": "AggregateRating",
        "ratingValue": "4.5",
        "reviewCount": "11"
      },
      "review": [{
        "@type": "Review",
        "reviewRating": {
          "@type": "Rating",
          "ratingValue": "4",
          "bestRating": "5"
        },
        "author": {
          "@type": "Person",
          "name": "Jane Smith"
        },
        "reviewBody": "Review text goes here"
      }]
    }
    </script>
</head>
<body>
    <!-- Your webpage content goes here -->
</body>
</html>

Why do you need structured data?

Structured data gives computers a clear map of what’s on your website. It spells out details about your products, reviews, events, and much more in a format that’s easy for search engines and other systems to process.

This clarity leads to better visibility in search, including features like star ratings, images, or additional links. But the impact reaches further now. Structured data also helps AI assistants, voice search tools, and new web platforms like chatbots powered by Large Language Models understand and represent your content with greater accuracy.

New standards, such as NLWeb (Natural Language Web) and MCP (Model Context Protocol), are emerging to help different systems share and interpret web content consistently. Adding structured data today not only gives your site an advantage in search but also prepares it for a future where your content will flow across more platforms and digital experiences.

The effort you put into structured data now sets up your content to be found, used, and displayed in many places where people search and explore online.

Is structured data important for SEO?

Structured data plays a key role in how your website appears in search results. It helps search engines understand and present your content with extra features, such as review stars, images, and additional links. These enhanced listings can catch attention and drive more clicks to your site.

While using structured data doesn’t directly increase your rankings, it does make your site eligible for these rich results. That alone can set you apart from competitors. As search engines evolve and adopt new standards, well-structured data ensures your content stays visible and accessible in the latest search features.

For SEO, structured data is about making your site stand out, improving user experience, and giving your content the best shot at being discovered, both now and as search technology changes.

Structured data can lead to rich results

By describing your site for search engines, you allow them to do exciting things with your content. Schema.org and its support are constantly developing, improving, and expanding. As structured data forms the basis for many new developments in the SEO world, there will be more shortly. Below is an overview of the rich search results available; examples are in Google’s Search Gallery.

Structured data type Example use/description
Article News, blog, or sports article
Breadcrumb Navigation showing page position
Carousel Gallery/list from one site (with Recipe, Course, Movie, Restaurant)
Course list Lists of educational courses
Dataset Large datasets (Google Dataset Search)
Discussion forum User-generated forum content
Education Q&A Education flashcard Q&As
Employer aggregate rating Ratings about employers in job search results
Event Concerts, festivals, and other events
FAQ Frequently asked questions pages
Image metadata Image creator, credit, and license details
Job posting Listings for job openings
Local business Business details: hours, directions, ratings
Math solver Structured data for math problems
Movie Lists of movies, movie details
Organization About your company: name, logo, contact, etc.
Practice problem Education practice problems for students
Product Product listings with price, reviews, and more
Profile page Info on a single person or organization
Q&A Pages with a single question and answers
Recipe Cooking recipes, steps, and ingredients
Review snippet Short review/rating summaries
Software app Ratings and details on apps or software
Speakable Content for text-to-speech on Google Assistant
Subscription and paywalled content Mark articles/content behind a paywall
Vacation rental Details about vacation property listings
Video Video info, segments, and live content

The rich results formerly known as rich snippets

You might have heard the term “rich snippets” before. Google now calls these enhancements “rich results.” Rich results are improved search listings that use structured data to show extra information, like images, reviews, product details, or FAQs, directly in search.

For example, a product page marked up with structured data can show its price, whether it’s in stock, and customer ratings right below the search listing, even before someone clicks. Here’s what that might look like:

Some listings offer extra information, like star ratings or product details

With rich results, users see helpful details up front—such as a product’s price, star ratings, or stock status. This can make your listing stand out and attract more clicks.

Keep in mind, valid structured data increases your chances of getting rich results, but display is controlled by Google’s systems and is never guaranteed.

Keep reading: Rich snippets everywhere »

Mobile rich results

Tasty, right?

Results like this often appear more prominently on mobile devices. Search listings with structured data can display key information, like product prices, ratings, recipes, or booking options, in a mobile-friendly format. Carousels, images, and quick actions are designed for tapping and swiping with your finger.

For example, searching for a recipe on your phone might bring up a swipeable carousel showing photos, cooking times, and ratings for each dish. Product searches can highlight prices, availability, and reviews right in the results, helping users make decisions faster.

Many people now use mobile search as their default search method. Well-implemented structured data not only improves your visibility on mobile but can also make your content easier for users to explore and act on from their phones. To stay visible and competitive, regularly check your markup and make sure it works smoothly on mobile devices.

Knowledge Graph Panel

A knowledge panel

The Knowledge Graph Panel shows key facts about businesses, organizations, or people beside search results on desktop and at the top on mobile. It can include your logo, business description, location, contact details, and social profiles.

Using structured data, especially Organization, LocalBusiness, or Person markup with current details, helps Google recognize and display your entity accurately. Include recommended fields like your official name, logo, social links (using sameAs), and contact info.

Entity verification is becoming more important. Claim your Knowledge Panel through Google, and make sure your information is consistent across your website, social media, and trusted directories. Major search engines and AI assistants use this entity data for results, summaries, and answers, not just in search but also in AI-powered interfaces and smart devices.

While Google decides who appears in the Knowledge Panel and what details are shown, reliable structured data, verified identity, and a clear online presence give you the best chance of being featured.

Different kinds of structured data

Schema.org includes many types of structured data. You don’t need to use them all, just focus on what matches your site’s content. For example:

  • If you sell products, use product schema
  • For restaurant or local business sites, use local business schema
  • Recipe sites should add recipe schema

Before adding structured data, decide which parts of your site you want to highlight. Check Google’s or other search engines’ documentation to see which types are supported and what details they require. This helps ensure you are using the markup that will actually make your content stand out in search and other platforms.

How Yoast SEO helps with structured data

Yoast SEO automatically adds structured data to your site using smart defaults, making it easier for search engines and platforms to understand your content. The plugin supports a wide range of content types, like articles, products, local businesses, and FAQs, without the need for manual schema coding.

With Yoast SEO, you can:

  • With a few clicks, set the right content type for each page (such as ContactPage, Product, or Article)
  • Use built-in WordPress blocks for FAQs and How-tos, which generate valid schema automatically
  • Link related entities across your site, such as authors, brands, and organizations, to help search engines see the big picture
  • Adjust schema details per page or post through the plugin’s settings

Yoast SEO also offers an extensible structured data platform. Developers can build on top of Yoast’s schema framework, add custom schema types, or connect other plugins. This helps advanced users or larger sites tailor their structured data for specific content, integrations, or new standards.

Yoast keeps pace with updates to structured data guidelines, so your markup stays aligned with what Google and other platforms support. This makes it easier to earn rich results and other search enhancements.

Yoast SEO helps you fine-tune your schema structured data settings per page

Which structured data types matter most?

When adding structured data, focus first on the types that have the biggest impact on visibility and features in Google Search. These forms of schema are widely supported, trigger rich results, and apply to most kinds of sites:

Most important structured data types

  • Article: For news sites, blogs, and sports publishers. Adding Article schema can enable rich results like Top Stories, article carousels, and visual enhancements
  • Product: Essential for ecommerce. Product schema helps show price, stock status, ratings, and reviews right in search. This type is key for online stores and retailers
  • Event: For concerts, webinars, exhibitions, or any scheduled events. Event schema can display dates, times, and locations directly in search results, making it easier for people to find and attend
  • Recipe: This is for food blogs and cooking sites. The recipe schema supports images, cooking times, ratings, and step-by-step instructions as rich results, giving your recipes extra prominence in search
  • FAQPage: For any page with frequently asked questions. This markup can expand your search listing with Q&A drop-downs, helping users get answers fast
  • QAPage: For online communities, forums, or support sites. QAPage schema helps surface full question-and-answer threads in search
  • ReviewSnippet: This markup is for feedback on products, books, businesses, or services. It can display star ratings and short excerpts, adding trust signals to your listings
  • LocalBusiness is vital for local shops, restaurants, and service providers. It supplies address, hours, and contact info, supporting your visibility in the map pack and Knowledge Panel
  • Organization: Use this to describe your brand or company with a logo, contact details, and social profiles. Organization schema feeds into Google’s Knowledge Panel and builds your online presence
  • Video: Mark up video content to enable video previews, structured timestamps (key moments), and improved video visibility
  • Breadcrumb: This feature shows your site’s structure within Google’s results, making navigation easier and your site look more reputable

Other valuable or sector-specific types:

  • Course: Highlight educational course listings and details for training providers or schools
  • JobPosting: Share open roles in job boards or company careers pages, making jobs discoverable in Google’s job search features
  • SoftwareApp: For software and app details, including ratings and download links
  • Movie: Used for movies and film listings, supporting carousels in entertainment searches and extra movie details
  • Dataset: Makes large sets of research or open data discoverable in Google Dataset Search
  • DiscussionForum: Surfaces user-generated threads in dedicated “Forums” search features
  • ProfilePage: Used for pages focused on an individual (author profiles, biographies) or organization
  • EmployerAggregateRating: Displays company ratings and reviews in job search results
  • PracticeProblem: For educational sites offering practice questions or test prep
  • VacationRental: Displays vacation property listings and details in travel results

Special or supporting types:

  • Person: This helps Google recognize and understand individual people for entity and Knowledge Panel purposes (it does not create a direct rich result)
  • Book: Can improve book search features, usually through review or product snippets
  • Speakable: Reserved for news sites and voice assistant features; limited support
  • Image metadata, Math Solver, Subscription/Paywalled content: Niche markups that help Google properly display, credit, or flag special content
  • Carousel: Used in combination with other types (like Recipe or Movie) to display a list or gallery format in results

When choosing which schema to add, always select types that match your site’s actual content. Refer to Google’s Search Gallery for the latest guidance and requirements for each type.

Adding the right structured data makes your pages eligible for rich results, enhances your visibility, and prepares your content for the next generation of search features and AI-powered platforms.

Read on: Local business listings with Schema.org and JSON-LD »

Structured data for voice assistants

Voice search remains important, with a significant share of online queries now coming from voice-enabled devices. Structured data helps content be understood and, in some cases, selected as an answer for voice results.

The Speakable schema (for marking up sections meant to be read aloud by voice assistants) is still officially supported, but adoption is mostly limited to news content. Google and other assistants also use a broader mix of signals, like content clarity, authority, E-E-A-T, and traditional structured data, to power their spoken answers.

If you publish news or regularly answer concise, fact-based questions, consider using Speakable markup. For other content types, focus on structured data and well-organized, user-focused pages to improve your chances of being chosen by voice assistants. Voice search and voice assistants continue to draw on featured snippets, clear Q&A, and trusted sources.

Google Search Console

If you need to check how your structured data is performing in Google, check your Search Console. Find the structured data insights under the Enhancement tab and you’ll see all the pages that have structured data, plus an overview of pages that give errors, if any. Read our Beginner’s guide for Search Console for more info.

The technical details

Structured data uses Schema.org’s hierarchy. This vocabulary starts with broad types like Thing and narrows down to specific ones, such as Product, Movie, or LocalBusiness. Every type has its own properties, and more specific types inherit from their ancestors. For example, a Movie is a type of CreativeWork, which is a type of Thing.

When adding structured data, select the most specific type that fits your content. For a movie, this means using the Movie schema. For a local company, choose the type of business that best matches your offering under LocalBusiness.

Properties

Every Schema.org type includes a range of properties. While you can add many details, focus on the properties that Google or other search engines require or recommend for rich results. For example, a LocalBusiness should include your name, address, phone number, and, if possible, details such as opening hours, geo-coordinates, website, and reviews. You’ll find our Local SEO plugin (available in Yoast SEO Premium) very helpful if you need help with your local business markup.

Here are two examples of structures:

Movie hierarchy

  • Thing
  • CreativeWork
    • Movie
    • Properties: name, description, director, actor, image, genre, duration

Local business hierarchy

  • Thing
  • Organization/Place
    • LocalBusiness
    • Properties: name, address, phone, email, openingHours, geo, review, logo

The more complete and accurate your markup, the greater your chances of being displayed with enhanced features like Knowledge Panels or map results. For details on recommended properties, always check Google’s up-to-date structured data documentation.

In the local business example, you’ll see that Google lists several required properties, like your business’s NAP (Name and Phone) details. There are also recommended properties, like URLs, geo-coordinates, opening hours, etc. Try to fill out as many of these as possible because search engines will only give you the whole presentation you want.

Structured data should be a graph

When you add structured data to your site, you’re not just identifying individual items, but you’re building a data graph. A graph in this context is a web of connections between all the different elements on your site, such as articles, authors, organizations, products, and events. Each entity is linked to others with clear relationships. For instance, an article can be marked as written by a certain author, published by your organization, and referencing a specific product. These connections help search engines and AI systems see the bigger picture of how everything on your site fits together.

Creating a fully connected data graph removes ambiguity. It allows search engines to understand exactly who created content, what brand a product belongs to, or where and when an event takes place, rather than making assumptions based on scattered information. This detailed understanding increases the chances that your site will qualify for rich results, Knowledge Panels, and other enhanced features in search. As your website grows, a well-connected graph also makes it easier to add new content or expand into new areas, since everything slots into place in a way that search engines can quickly process and understand.

Yoast SEO builds a graph

With Yoast SEO, many of the key connections are generated automatically, giving your site a solid foundation. Still, understanding the importance of building a connected data graph helps you make better decisions when structuring your own content or customizing advanced schema. A thoughtful, well-linked graph sets your site up for today’s search features, while making it more adaptable for the future.

Your schema should be a well-formed graph for easier understanding by search engines and AI

Beyond search: AI, assistants, and interoperability

Structured data isn’t just about search results. It’s a map that helps AI assistants, knowledge graphs, and cross‑platform apps understand your content. It’s not just about showing a richer listing; it’s about enabling reliable AI interpretation and reuse across contexts.

Today, the primary payoff is still better search experiences. Tomorrow, AI systems and interoperable platforms will rely on clean, well‑defined data to summarize, reason about, and reuse your content. That shift makes data quality more important than ever.

Practical steps for today

Keep your structured data clean with a few simple habits. Use the same names for people, organizations, and products every time they appear across your site. Connect related information so search engines can see the links. For example, tie each article to its author or a product to its brand. Fill in all the key details for your main schema types and make sure nothing is missing. After making changes or adding new content, run your markup through a validation tool. If you add any custom fields or special schema, write down what they do so others can follow along later. Doing quick checks now and then keeps your data accurate and ready for both search engines and AI.

Interoperability, MCP, and the role of structured data

More and more, AI systems and search tools are looking for websites that are easy to understand, not just for people but also for machines. The Model Context Protocol (MCP) is gaining ground as a way for language models like Google Gemini and ChatGPT to use the structured data already present on your website. MCP draws on formats like Schema.org and JSON-LD to help AI match up the connections between things such as products, authors, and organizations.

Another project, the Natural Language Web (NLWeb), an open project developed by Microsoft, aims to make web content easier for AI to use in conversation and summaries. NLWeb builds on concepts like MCP, but hasn’t become a standard yet. For now, most progress and adoption are happening with MCP, and large language models are focusing their efforts on this area.

Using Schema.org and JSON-LD to keep your structured data clean (no duplicate entities), complete (all indexable content included), and connected (relationships preserved) will prepare you for search engines and new AI-driven features appearing across the web.

Schema.org and JSON-LD: the foundation you can trust

Schema.org and JSON-LD remain the foundation for structured data on the web. They enable today’s rich results in search and form the basis for how AI systems will interpret web content in the future. JSON-LD should be your default format for new markup, allowing you to build structured data graphs that are clean, accurate, and easy to maintain. Focus on accuracy in your markup rather than unnecessary complexity.

To future-proof your data, prioritize stable identifiers such as @id and use clear types to reduce ambiguity. Maintain strong connections between related entities across your pages. If you develop custom extensions to your structured data, document them thoroughly so both your team and automated tools can understand their purpose.

Design your schema so that components can be added or removed without disrupting the entire graph. Make a habit of running validations and audits after you change your site’s structure or content.

Finally, stay current by following guidance and news from official sources, including updates about standards such as NLWeb and MCP, to ensure your site remains compatible with both current search features and new interoperability initiatives.

What do you need to describe for search engines?

To get the most value from structured data, focus first on the most important elements of your site. Describe the details that matter most for users and for search, such as your business information, your main products or services, reviews, events, or original articles. These core pieces of information are what search engines look for to understand your site and display enhanced results.

Rather than trying to mark up everything, start with the essentials that best match your content. As your experience grows, you can build on this foundation by adding more detail and creating links between related entities. Accurate, well-prioritized markup is both easier to maintain and more effective in helping your site stand out in search results and across new AI-driven features.

How to implement structured data

We’d like to remind you that Yoast SEO comes with an excellent structured data implementation. It’ll automatically handle most sites’ most pressing structured data needs. Of course, as mentioned below, you can extend our structured data framework as your needs become bigger.

Do the Yoast SEO configuration and get your site’s structured data set up in a few clicks! The configuration is available for all Yoast SEO users to help you get your plugin configured correctly. It’s quick, it’s easy, and doing it will pay off. Plus, if you’re using the new block editor in WordPress you can also add structured data to your FAQ pages and how-to articles using our structured data content blocks.

Thanks to JSON-LD, there’s nothing scary about adding the data to your pages anymore. This JavaScript-based data format makes it much easier to add structured data since it forms a block of code and is no longer embedded in the HTML of your page. This makes it easier to write and maintain, plus both humans and machines better understand it. If you need help implementing JSON-LD structured data, you can enroll in our free Structured Data for Beginners course, our Understanding Structured Data course, or read Google’s introduction to structured data.

Structured data with JSON-LD

JSON-LD is the recommended way to add structured data to your site. All major search engines, including Google and Bing, now fully support this format. JSON-LD is easy to implement and maintain, as it keeps your structured data separate from the main HTML.

Yoast SEO automatically creates a structured data graph for every page, connecting key elements like articles, authors, products, and organizations. This approach helps search engines and AI systems understand your site’s structure. Our developer resources include detailed Schema documentation and example graphs, making it straightforward to extend or customize your markup as your site grows.

Tools for working with structured data

Yoast SEO automatically handles much of the structured data in the background. You could extend our Schema framework, of course — see the next chapter –, but if adding code by hand seems scary, you could try some of the tools listed below. If you need help with how to proceed, ask your web developer for help. They will fix this for you in a couple of minutes.

The Yoast SEO Schema structured data framework

Implementing structured data has always been challenging. Also, the results of most of those implementations often needed improvement. At Yoast, we set out to enhance the Schema output for millions of sites. For this, we built a Schema framework, which can be adapted and extended by anyone. We combined all those loose bits and pieces of structured data that appear on many sites, improved these, and put them in a graph. By interconnecting all these bits, we offer search engines all your connections on a silver platter.

See this video for more background on the schema graph.

Of course, there’s a lot more to it. We can also extend Yoast SEO output by adding specific Schema pieces, like how-tos or FAQs. We built structured data content blocks for use in the WordPress block editor. We’ve also enabled other WordPress plugins to integrate with our structured data framework, like Easy Digital Downloads, The Events Calendar, Seriously Simple Podcasting, and WP Recipe Maker, with more to come. Together, these help you remove barriers for search engines and users, as it has always been challenging to work with structured data.

Expanding your structured data implementation

A structured and focused approach is key to successful Schema.org markup on your website. Start by understanding Schema.org and how structured data can influence your site’s presence in search and beyond. Resources like Yoast’s developer portal offer useful insights into building flexible and future-proof markup.

Always use JSON-LD as recommended by Google, Bing, and Yoast. This format is easy to maintain and works well with modern websites. To maximize your implementation, use tools and frameworks that allow you to add, customize, and connect Schema.org data efficiently. Yoast SEO’s structured data framework, for example, enables seamless schema integration and extensibility across your site.

Validate your structured data regularly with tools like the Rich Results Test or Schema Markup Validator and monitor Google Search Console’s Enhancements reports for live feedback. Reviewing your markup helps you fix issues early and spot opportunities for richer results as search guidelines change. Periodically revisiting your strategy keeps your markup accurate and effective as new types and standards emerge.

Read up

By following the guidelines and adopting a comprehensive approach, you can successfully get structured data on your pages and enhance the effectiveness of your schema.org markup implementation for a robust SEO performance. Read the Yoast SEO Schema documentation to learn how Yoast SEO works with structured data, how you can extend it via an API, and how you can integrate it into your work.

Several WordPress plugins already integrate their structured data into the Yoast SEO graph

Keep on reading: Open-source software, open Schema protocol! »

Conclusions about structured data

Structured data has become an essential part of building a visible, findable, and adaptable website. Using Schema.org and JSON-LD not only helps search engines understand your content but also sets your site up for better performance in new AI-driven features, rich results, and across platforms.

Start by focusing on the most important parts of your site, like business information, products, articles, or events, and grow your structured data as your needs evolve. Connected, well-maintained markup now prepares your site for search, AI, and whatever comes next in digital content.

Explore our documentation and training resources to learn more about best practices, advanced integrations, or how Yoast SEO can simplify structured data. Investing the time in good markup today will help your content stand out wherever people (or algorithms) find it.

Read more: How to check the performance of your rich results in Google Search Console »

The post Structured data with schema for search and AI appeared first on Yoast.

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Build Brand Awareness: Strategies to Boost Visibility

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If your target audience doesn’t know you exist, they won’t buy from you. Simple as that.

That’s why you need to build brand awareness the right way. Not just through paid ads or ranking for keywords. Real brand awareness is how people remember you, talk about you, and choose you when they’re ready to buy. 

Here’s something most marketers miss: AI tools like ChatGPT and Google’s AI Overviews are now major discovery channels. These platforms cite recognizable brands more than unknown ones. If your brand isn’t mentioned across the web, you’re invisible in AI search results too. 

This guide focuses on organic growth. We’ll cover consistent messaging, smart partnerships, and making the most of platforms you already use. If you want to show up, stand out, and stick in people’s minds, here’s how to do it.

Key Takeaways

  • Brand awareness drives visibility in both traditional search and AI-powered searches
  • Consistent branding across platforms builds familiarity faster than sporadic campaigns. 
  • Thought leadership and strategic partnerships amplify reach without ad spend. 
  • You can build strong brand awareness organically with a focused, persistent plan.

Why Brand Awareness Matters More Now Than Ever

Familiarity breeds trust. The more people recognize your brand through brand mentions, the more likely they are to choose you over competitors.

Studies back this up. According to Invesp, 59% of customers prefer to buy from brands familiar to them. The more people recognize your brand, the more likely they are to choose you over competitors. Familiar brands feel safer. That trust shows up in clicks, conversions, and customer loyalty.

But there’s a new wrinkle: AI visibility.

Platforms like ChatGPT, Perplexity, and Google’s AI Overviews pull from recognizable brands when generating responses. If your brand isn’t mentioned in high-quality content, forum discussions, or authoritative sources, AI tools skip over you. That means potential customers never see your name.

Take a look at a Google AI Overview result for “best project management tools.” You’ll see names like Asana, Monday.com, and Trello cited repeatedly. Those brands didn’t get there by accident. They earned consistent mentions through strong branding, thought leadership, and organic content.

AI overviews for "Best project management tools."

Brand awareness also builds equity. The more recognizable you are, the easier it becomes to launch new products and charge preferred prices. Recognition compounds over time.

Elements of a Brand Awareness Strategy

Before you jump into tactics, you need a foundation. Brand awareness doesn’t happen from random acts of marketing, but a formal strategy.

Start with a clearly defined brand identity. That means locking in your tone of voice, visual style, core values, and key messaging. These elements should carry through your website, social profiles, email campaigns, and any other channel you use. Ideally, put this together in a guide that your team can reference when needed.

Next, understand your audience. You can’t build awareness if you don’t know who you’re targeting. Create detailed buyer personas and perform customer journey mapping so you know what platforms they use, what content they consume, and what problems they’re trying to solve.

You also need a clear content distribution plan. Will you focus on LinkedIn and YouTube? Or prioritize SEO and email marketing? The best strategies start narrow and expand once you’ve mastered one or two channels.

Organic Strategies to Increase Brand Awareness

Here’s where we get tactical. These strategies don’t require ad budgets, but they do require consistency.

Refine and Define Your Brand Identity

Let’s get into a little more detail about brand identities. After all, if you can’t clearly describe your brand’s personality, your audience won’t be able to either.

A real identity goes beyond logos and color palettes. It’s about consistent voice, values, and visuals across every touchpoint. Look at Slack: their playful tone and clean design are instantly recognizable whether you see a billboard or a tweet.

A Slack billboard.

Buffer does this exceptionally well. Check out their homepage and Instagram side by side. The fonts, colors, photography style, and tone are completely aligned. That consistency makes the brand easier to recognize and harder to forget.

The Buffer website.
Buffer's Instagram.

This is what you’re aiming for. Unified branding builds memory and trust.

Here’s your action plan:

  • Document your brand guidelines (tone, colors, fonts, logo usage)
  • Train your team on how to apply those guidelines
  • Audit your current channels to spot inconsistencies
  • Fix the gaps before launching new campaigns

Optimize Profiles on Search Engines and Social

Your digital storefronts often make the first impression, not your website.

Google Business Profiles, LinkedIn, Facebook, Instagram, and even TikTok bios are discovery points. If those profiles are incomplete or outdated, you’re wasting opportunities to build awareness.

Take this optimized Google Business Profile for a local coffee shop. They’ve included high-quality photos, accurate hours, keywords in the business description, customer reviews, and direct links to their website and menu. This kind of completeness signals credibility to both users and search algorithms.

The Google Business profile for the Black Pearl Coffee shop.

The same logic applies to social platforms. A half-finished LinkedIn profile or an Instagram bio with no link hurts your brand more than it helps. Fill out every field. Use keywords naturally. Link to your site.

Pro tip: Claim your brand name on every major platform, even if you’re not active there yet. You don’t want someone else grabbing your handle or creating confusion.

Consider Influencer/Other Brand Partnerships

You don’t need to go viral to reach more people. You can start by tapping into someone else’s audience.

Influencer marketing and strategic brand collaborations amplify your visibility organically. But follower count isn’t everything. Look for:

  • Alignment in audience demographics and values
  • Authentic content that matches your brand tone
  • A track record of real engagement, not just vanity metrics

Gymshark is a perfect example. They partnered with micro-influencers who created TikTok workout videos while wearing their gear. The content looked native to the platform and felt genuine because it was. That authenticity drove massive brand awareness without traditional advertising.

Influencers that partner with Gymshark on TikTok.

Another route: collaborate with complementary brands. If you sell coffee, partner with a local bakery for a co-branded event. Cross-promote on social. Share each other’s audiences. Both brands win.

Find Engagement Opportunities With Your Audience

Conversations spark memory. The more your audience interacts with you, the more likely they are to remember you.

Engagement doesn’t have to be complicated. It can be as simple as replying to comments on Instagram or as involved as hosting live Q&A sessions on LinkedIn. Spotify Wrapped is a masterclass here. Users eagerly share their personalized results every year, generating millions of organic impressions.

Spotify Wrapped

Duolingo takes a different approach with humor. Their social team replies to comments with witty, on-brand responses that often get more engagement than the original post. That two-way interaction builds presence faster than broadcasting alone.

A social media interaction with Duolingo.

Here are practical ways to boost engagement:

  • Respond to every comment on your posts (yes, every one)
  • Ask questions in your captions to spark replies
  • Run polls and surveys to gather feedback
  • Host AMAs (Ask Me Anything) on Reddit or Instagram Live
  • Create shareable content that encourages tagging and reposting

 The more people interact with your brand, the more familiar you become.

Use A/B Testing

Guessing what resonates with your audience is a waste of time. Test it.

A/B testing helps you figure out what messaging, visuals, and formats drive the most engagement. More engagement means more brand recognition.

Start simple. Test two email subject lines to see which gets more opens. Try two different Instagram captions to see which gets more comments. Experiment with video thumbnails on YouTube.

Tools like Google Optimize, Optimizely, or even native platform analytics can help you run these tests. The insights you gain will help you refine your brand messaging over time.

Practice an Omnichannel Strategy

Your audience isn’t glued to one platform. They move between email, social media, search engines, podcasts, and even voice assistants.

Omnichannel marketing means showing up across all of them with consistency. Not copy-pasting the same content everywhere, but adapting your core message to fit each channel’s format and audience expectations.

Canva nails this. Their email campaigns, LinkedIn posts, and TikTok videos all maintain the same visual identity and helpful tone. The messaging shifts slightly to match each platform, but the brand feels cohesive.

An email from Canva.
Canva's Linkedin Page.
Canva's Instagram page.

That cohesion makes the brand easier to remember and trust. People see you everywhere, and repetition builds familiarity.

Here’s how to execute an omnichannel strategy:

  • Identify the three to five platforms your audience uses most
  • Develop content formats that work on each (blog posts, videos, infographics, podcasts)
  • Use scheduling tools to maintain a consistent presence
  • Track performance to see where you’re gaining traction

 You don’t need to be everywhere. Just be consistent where you want to show up.

Provide Value (Without Asking For Something Back)

Not every piece of content needs a CTA or a sales pitch.

Free value builds goodwill and gives people a reason to remember you. Think templates, tutorials, calculators, and guides. No gates. No hard pitch. Just useful content.

HubSpot mastered this years ago. Their free CRM, blog templates, and educational resources turned them into a go-to source for marketers. People associate HubSpot with helpfulness, not just software.

Reports from HubSpot.

You can do the same on a smaller scale:

  • Publish how-to guides that solve real problems
  • Create free tools or templates your audience can download
  • Share behind-the-scenes insights into your processes
  • Offer free consultations or audits (if it fits your business model)

When you consistently give without asking, people remember. And when they’re ready to buy, you’re top of mind.

Build Out A Thought Leadership Plan

Thought leadership isn’t about ego. It’s strategic positioning.

People trust brands that demonstrate expertise. That trust leads to mentions, shares, backlinks, and citations in AI tools. All of these feed into organic brand awareness.

Effective thought leadership formats include:

  • Guest posts on authoritative industry blogs
  • Original research or data studies published on your site
  • Speaking opportunities at conferences or webinars
  • Contributions to expert roundups and interviews
  • Regular insights shared on LinkedIn or Twitter

The key is consistency. One viral post won’t make you a thought leader. Publishing valuable insights month after month will.

And here’s the bonus: thought leadership directly impacts E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which Google uses to evaluate content quality. The more you establish your expertise, the better your content performs in search and AI results.

Generate Social Proof

People trust people more than they trust brands.

That’s why social proof (testimonials, reviews, user-generated content) is one of the most effective ways to build credibility and awareness.

Feature happy customers in your marketing. Encourage product photos and reviews. Highlight tweets or Instagram posts tagging your brand. Showcase case studies that demonstrate real results.

This example from Glossier does it perfectly. They regularly feature customer photos and testimonials across their social channels and website. Real people using real products. That authenticity drives trust and recognition.

Social proof from Glossier.

Here’s how to generate social proof:

  • Ask satisfied customers for testimonials and reviews
  • Create a branded hashtag and encourage customers to use it
  • Run contests that incentivize user-generated content
  • Feature customer stories in your email campaigns and blog posts
  • Display review ratings prominently on your website

The more your customers talk about you, the more awareness you build.

How To Measure Brand Awareness Strategy Success

Not everything that matters can be measured, but a lot of it can.

Here are the key signals that your brand awareness strategy is working:

  • Search traffic for branded keywords: Track how many people search for your brand name or variations in Google Search Console. Rising branded searches indicate growing awareness.
  • Brand mentions: Use tools like Brand24, Mention, or Google Alerts to monitor how often your brand gets mentioned across the web and social media. More mentions mean more visibility.
  • Social engagement: Look beyond follower counts. Are people commenting, sharing, and tagging your brand? High engagement signals strong awareness.
  • Direct traffic: Check your analytics for direct traffic (people typing your URL directly into their browser). This suggests they already know who you are.
  • Survey responses: Run simple brand awareness surveys asking, “Have you heard of [Your Brand]?” Track the percentage over time.
  • AI visibility: Search for industry-related queries in ChatGPT or Google’s AI Overviews. Does your brand get mentioned? This is becoming increasingly important for brand mentions and overall visibility. Dedicated tools like Profound also specifically focus on AI visibility.

Here’s a snapshot of brand tracking in Mention:

How brand mentions are tracked in Mention.

Review these metrics monthly. Trends matter more than one-off spikes. A consistent upward trajectory means your strategy is working.

FAQs

How to build brand awareness?

Start with a clear brand identity and consistent messaging. Optimize your profiles across search and social platforms. Publish valuable content regularly. Engage with your audience. Partner with influencers or complementary brands. Focus on providing value without always asking for something in return.

Why build brand awareness?

Because people buy from brands they recognize and trust. Brand awareness drives customer loyalty, makes new product launches easier, and increases your visibility in both traditional search and AI-powered tools. Without awareness, you’re invisible to potential customers.

How long does it take to build brand awareness?

Typically, three to six months to see initial traction, but long-term brand awareness builds over years. Consistency matters more than speed. Stick with your strategy, measure your progress, and refine based on what’s working.

<h2>Conclusion</h2>

Conclusion

Brand awareness isn’t a vanity metric. It’s the foundation of every sale you’ll make tomorrow.

If people don’t remember you, they can’t choose you. That’s why consistent branding, smart engagement, and value-driven content matter so much. These strategies don’t require massive budgets. They require focus and persistence.

Start with one or two tactics from this guide. Master those before expanding. Track your metrics to see what’s working. Improve your visibility step by step.

Want help building a brand people actually remember? NP Digital can help you develop a full-funnel strategy that drives awareness and growth.

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The future of SEO teams is human-led and agent-powered

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The conversation around artificial intelligence (AI) has been dominated by “replacement theory” headlines. From front-line service roles to white-collar knowledge work, there’s a growing narrative that human capital is under threat.

Economic anxiety has fueled research and debate, but many of the arguments remain narrow in scope.

  • Stanford’s Digital Economy Lab found that since generative AI became widespread, early-career workers in the most exposed jobs have seen a 13% decline in employment.
  • This fear has spread into higher-paid sectors as well, with hedge fund managers and CEOs predicting large-scale restructuring of white-collar roles over the next decade.

However, much of this narrative is steeped in speculation rather than the fundamental, evolving dynamics of skilled work.

Yes, we’ve seen layoffs, hiring slowdowns, and stories of AI automating tasks. But this is happening against the backdrop of high interest rates, shifts in global trade, and post-pandemic over-hiring.

As the global talent thought-leader Josh Bersin argues, claims of mass job destruction are “vastly over-hyped.” Many roles will transform, not vanish. 

What this means for SEO

For the SEO discipline, the familiar refrain “SEO is dead” is just as overstated.

Yes, the nature of the SEO specialist is changing. We’ve seen fewer leadership roles, a contraction in content and technical positions, and cautious hiring. But the function itself is far from disappearing.

In fact, SEO job listings remain resilient in 2025 and mid-level roles still comprise nearly 60% of open positions. Rather than declining, the field is being reshaped by new skill demands.

Don’t ask, “Will AI replace me?” Ask instead, “How can I use AI to multiply my impact?”

Think of AI not as the jackhammer replacing the hammer but as the jackhammer amplifying its effect. SEOs who can harness AI through agents, automation, and intelligent systems will deliver faster, more impactful results than ever before.

  • “AI is a tool. We can make it or teach it to do whatever we want…Life will go on, economies will continue to be driven by emotion, and our businesses will continue to be fueled by human ideas, emotion, grit, and hard work,” Bersin said.

Rewriting the SEO narrative

As an industry, it’s time to change the language we use to describe SEO’s evolution.

Too much of our conversation still revolves around loss. We focus on lost clicks, lost visibility, lost control, and loss of num=100.

That narrative doesn’t serve us anymore.

We should be speaking the language of amplification and revenue generation. SEO has evolved from “optimizing for rankings” to driving measurable business growth through organic discovery, whether that happens through traditional search, AI Overviews, or the emerging layer of Generative Engine Optimization (GEO).

AI isn’t the villain of SEO; it’s the force multiplier.

When harnessed effectively, AI scales insight, accelerates experimentation, and ties our work more directly to outcomes that matter:

  • Pipeline.
  • Conversions.
  • Revenue.

We don’t need to fight the dystopian idea that AI will replace us. We need to prove that AI-empowered SEOs can help businesses grow faster than ever before.

The new language of SEO isn’t about survival, it’s about impact.

The team landscape has already shifted

For years, marketing and SEO teams grew headcount to scale output.

Today, the opposite is true. Hiring freezes, leaner budgets, and uncertainty around the role of SEO in an AI-driven world have forced leaders to rethink team design.

A recent Search Engine Land report noted that remote SEO roles dropped to 34% of listings in early 2025, while content-focused SEO positions declined by 28%. A separate LinkedIn survey found a 37% drop in SEO job postings in Q1 compared to the previous year.

This signals two key shifts:

  • Specialized roles are disappearing. “SEO writers” and “link builders” are being replaced by versatile strategists who blend technical, analytical, and creative skill sets.
  • Leadership is demanding higher ROI per role. Headcount is no longer the metric of success – capability is.

What it means for SEO leadership

If your org chart still looks like a pyramid, you’re behind. 

The new landscape demands flexibility, speed, and cross-functional integration with analytics, UX, paid media, and content.

It’s time to design teams around capabilities, not titles.

Rethinking SEO Talent

The best SEO leaders aren’t hiring specialists, they’re hiring aptitude. Modern SEO organizations value people who can think across disciplines, not just operate within one.

The strongest hires we’re seeing aren’t traditional technical SEOs focused on crawl analysis or schema. They’re problem solvers – marketers who understand how search connects to the broader growth engine and who have experience scaling impact across content, data, and product.

Progressive leaders are also rethinking resourcing. The old model of a technical SEO paired with engineering support is giving way to tech SEOs working alongside AI product managers and, in many cases, vibe coding solutions. This model moves faster, tests bolder, and builds systems that drive real results.

For SEO leaders, rethinking team architecture is critical. The right question isn’t “Who should I hire next?” It’s “What critical capability must we master to stay competitive?”

Once that’s clear, structure your people and your agents around that need. The companies that get this right during the AI transition will be the ones writing the playbook for the next generation of search leadership.

The new human-led, agent-empowered team

The future of SEO teams will be defined by collaboration between humans and agents.

  • These agents are AI-enabled systems like automated content refreshers, site-health bots, or citation-validation agents that work alongside human experts.
  • The human role? To define, train, monitor, and QA their output.

Why this matters

  • Agents handle high-volume, repeatable tasks (e.g., content generation, basic auditing, link-score filtering) so humans can focus on strategy, insight, and business impact.
  • The cost of building AI agents can range from $20,000 to $150,000, depending on the complexity of the system, integrations, and the specialized work required across data science, engineering, and human QA teams, according to RTS Labs.
  • A single human manager might oversee 10-20 agents, shifting the traditional pyramid and echoing the “short pyramid” or “rocket ship” structure explored by Tomasz Tunguz.

The future: teams built around agents and empowered humans.

Real-world archetypes

  • SaaS companies: Develop a bespoke “onboarding agent” that reads product data, builds landing pages, and runs first-pass SEO audits, human strategist refines output.
  • Marketplace brands (e.g., upcoming seasonal trend): Use an “Audience Discovery Agent” that taps customer and marketplace data, but the human team writes the narrative and guides the vertical direction.
  • Enterprise content hubs: deploy “Content Refresh Agents” that identify high-value pages, suggest optimizations, and push drafts that editors review and finalise.

Integration is key

These new teams succeed when they don’t live in silos. The SEO/GEO squad must partner with paid search, analytics, revenue ops, and UX – not just serve them.

Agents create capacity; humans create alignment and amplification.

A call to SEO practitioners

Building the SEO community of the future will require change.

The pace of transformation has never been faster and it’s created a dangerous dependence on third-party “AI tools” as the answer to what is unknown.

But the true AI story doesn’t begin with a subscription. It begins inside your team.

If the only AI in your workflow is someone else’s product, you’re giving up your competitive edge. The future belongs to teams that build, not just buy.

Here’s how to start:

  • Build your own agent frameworks, designed with human-in-the-loop oversight to ensure accuracy, adaptability, and brand alignment.
  • Partner with experts who co-create, not just deliver. The most successful collaborations help your team learn how to manage and scale agents themselves.
  • Evolve your team structure, move beyond the pyramid mentality, and embrace a “rocket ship” model where humans and agents work in tandem to multiply output, insights, and results.

The future of SEO starts with building smarter teams. It’s humans working with agents. It’s capability uplift. And if you lead that charge, you’ll not only adapt to the next generation of search, you’ll be the ones designing it.

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Google Search Console adds Query groups

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Screenshot of Google Search Console

Google added Query groups to the Search Console Insights report. Query groups groups similar search queries together so you can quickly see the main topics your audience searches for.

What Google said. Google wrote, “We are excited to announce Query groups, a powerful Search Console Insights feature that groups similar search queries.”

“Query groups solve this problem by grouping similar queries. Instead of a long, cluttered list of individual queries, you will now see lists of queries representing the main groups that interest your audience. The groups are computed using AI; they may evolve and change over time. They are designed for providing a better high level perspective of your queries and don’t affect ranking,” Google added.

What it looks like. Here is a sample screenshot of this new Query groups report:

You can see that Google is lumping together “search engine optimization, seo optimization, seo website, seo optimierung, search engine optimization (seo), search …” into the “seo” query group in the second line. This shows the site overall is getting 9% fewer clicks on SEO related queries than it did previously.

Availability. Google said query groups will be rolling out gradually over the coming weeks. It is a new card in the Search Console Insights report. Plus, query groups are available only to properties that have a large volume of queries, as the need to group queries is less relevant for sites with fewer queries.

Why we care. Many SEOs have been grouping these queries into these clusters manually or through their own tools. Now, Google will do it for you, making it easier for more novie SEOs and beginner SEOs to understand.

More details will be posted in this help document soon.

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Search Engine Land Awards 2025: And the winners are…

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Search Engine Land 2025 Awards

Every year, Search Engine Land is delighted to celebrate the best of search marketing by rewarding the agencies, in-house teams, and individuals worldwide for delivering exceptional results.

Today, I’m excited to announce all 18 winners of the 11th annual Search Engine Land Awards.

The 2025 Search Engine Land Awards winners

Best Use Of AI Technology In Search Marketing

  • 15x ROAS with AI: How CAMP Digital Redefined Paid Search for Home Services

Best Overall PPC Initiative – Small Business

  • Anchor Rides – Post-Hurricane PPC Comeback (AIMCLEAR)

Best Overall PPC Initiative – Enterprise

  • ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume

Best Commerce Search Marketing Initiative – PPC

  • Adwise & Azerty – 126% uplift in profit from paid advertising & 1 percent point net margin business uplift by advanced cross-channel bucketing

Best Local Search Marketing Initiative – PPC

  • How We Crushed Belron’s Lead Target by 238% With an AI-Powered Local Strategy (Adviso)

Best B2B Search Marketing Initiative – PPC

  • Blackbird PPC and Customer.io: Advanced Data Integration to Drive 239% Revenue Increase with 12% Greater Lead Efficiency, with MMM Future-Proofing 2025 Growth

Best Integration Of Search Into Omnichannel Marketing

  • How NBC used search to drive +2,573 accounts in a Full-Funnel Media Push (Adviso)

Best Overall SEO Initiative – Small Business

  • Digital Hitmen & Elite Tune: The Toyota Shift That Delivered 678% SEO ROI

Best Overall SEO Initiative – Enterprise

  • 825 Million Clicks, Zero Content Edits: How Amsive Engineered MSN’s Technical SEO Turnaround

Best Commerce Search Marketing Initiative – SEO

  • Scaling Non-Branded SEO for Assouline to Drive +26% Organic Revenue Uplift (Block & Tam)

Best Local Search Marketing Initiative – SEO

  • Building an Unbeatable Foundation for Success: Using Hyperlocal SEO to Build Exceptional ROI (Digital Hitmen)

Best B2B Search Marketing Initiative – SEO

  • Page One, Pipeline Won: The B2B SEO Playbook That Turned 320 Visitors into $10.75M in Pipeline (LeadCoverage)

Agency Of The Year – PPC

  • Driving Growth Where Search Happens: Stella Rising’s Paid Search Transformation

Agency Of The Year – SEO

  • How Amsive Rescued MSN’s Global Visibility Through Enterprise Technical SEO at Scale

In-House Team Of The Year – SEO

  • How the American Cancer Society’s Lean SEO Team Drove Enterprise-Wide Consolidation and AI Search Visibility Gains for Cancer.org

Search Marketer Of The Year

  • Mike King, founder and CEO of iPullRank

Small Agency Of The Year – PPC

  • ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume

Small Agency Of The Year – SEO

  • From Zero to Top of the Leaderboard: Bloom Digital Drives Big Growth With Small SEO Budgets

“I’m going to SMX Next!”

Select winners of the 2025 Search Engine Land Awards will be invited to speak live at SMX Next during our two ask-me-anything-style sessions. Bring your burning SEO and PPC questions to ask this award-winning panel of search marketers!

Register here for SMX Next (it’s free) if you haven’t yet.

Congrats again to all the winners. And huge thank yous to everyone who entered the 2025 Search Engine Land Awards, the finalists, and our fantastic panel of judges for this year’s awards.

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The agentic web is here: Why NLWeb makes schema your greatest SEO asset

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The agentic web is here: Why NLWeb makes schema your greatest SEO asset

The web’s purpose is shifting. Once a link graph – a network of pages for users and crawlers to navigate – it’s rapidly becoming a queryable knowledge graph. 

For technical SEOs, that means the goal has evolved from optimizing for clicks to optimizing for visibility and even direct machine interaction.

Enter NLWeb – Microsoft’s open-source bridge to the agentic web

At the forefront of this evolution is NLWeb (Natural Language Web), an open-source project developed by Microsoft. 

NLWeb simplifies the creation of natural language interfaces for any website, allowing publishers to transform existing sites into AI-powered applications where users and intelligent agents can query content conversationally – much like interacting with an AI assistant.

Developers suggest NLWeb could play a role similar to HTML in the emerging agentic web. 

Its open-source, standards-based design makes it technology-agnostic, ensuring compatibility across vendors and large language models (LLMs). 

This positions NLWeb as a foundational framework for long-term digital visibility.

Schema.org is your knowledge API: Why data quality is the NLWeb foundation

NLWeb proves that structured data isn’t just an SEO best practice for rich results – it’s the foundation of AI readiness. 

Its architecture is designed to convert a site’s existing structured data into a semantic, actionable interface for AI systems. 

In the age of NLWeb, a website is no longer just a destination. It’s a source of information that AI agents can query programmatically.

The NLWeb data pipeline

The technical requirements confirm that a high-quality schema.org implementation is the primary key to entry.

Data ingestion and format

The NLWeb toolkit begins by crawling the site and extracting the schema markup. 

The schema.org JSON-LD format is the preferred and most effective input for the system. 

This means the protocol consumes every detail, relationship, and property defined in your schema, from product types to organization entities. 

For any data not in JSON-LD, such as RSS feeds, NLWeb is engineered to convert it into schema.org types for effective use.

Semantic storage

Once collected, this structured data is stored in a vector database. This element is critical because it moves the interaction beyond traditional keyword matching. 

Vector databases represent text as mathematical vectors, allowing the AI to search based on semantic similarity and meaning. 

For example, the system can understand that a query using the term “structured data” is conceptually the same as content marked up with “schema markup.” 

This capacity for conceptual understanding is absolutely essential for enabling authentic conversational functionality.

Protocol connectivity

The final layer is the connectivity provided by the Model Context Protocol (MCP). 

Every NLWeb instance operates as an MCP server, an emerging standard for packaging and consistently exchanging data between various AI systems and agents. 

MCP is currently the most promising path forward for ensuring interoperability in the highly fragmented AI ecosystem.

The ultimate test of schema quality

Since NLWeb relies entirely on crawling and extracting schema markup, the precision, completeness, and interconnectedness of your site’s content knowledge graph determine success.

The key challenge for SEO teams is addressing technical debt. 

Custom, in-house solutions to manage AI ingestion are often high-cost, slow to adopt, and create systems that are difficult to scale or incompatible with future standards like MCP. 

NLWeb addresses the protocol’s complexity, but it cannot fix faulty data. 

If your structured data is poorly maintained, inaccurate, or missing critical entity relationships, the resulting vector database will store flawed semantic information. 

This leads inevitably to suboptimal outputs, potentially resulting in inaccurate conversational responses or “hallucinations” by the AI interface.

Robust, entity-first schema optimization is no longer just a way to win a rich result; it is the fundamental barrier to entry for the agentic web. 

By leveraging the structured data you already have, NLWeb allows you to unlock new value without starting from scratch, thereby future-proofing your digital strategy.

NLWeb vs. llms.txt: Protocol for action vs. static guidance

The need for AI crawlers to process web content efficiently has led to multiple proposed standards. 

A comparison between NLWeb and the proposed llms.txt file illustrates a clear divergence between dynamic interaction and passive guidance.

The llms.txt file is a proposed static standard designed to improve the efficiency of AI crawlers by:

  • Providing a curated, prioritized list of a website’s most important content – typically formatted in markdown.
  • Attempting to solve the legitimate technical problems of complex, JavaScript-loaded websites and the inherent limitations of an LLM’s context window.

In sharp contrast, NLWeb is a dynamic protocol that establishes a conversational API endpoint. 

Its purpose is not just to point to content, but to actively receive natural language queries, process the site’s knowledge graph, and return structured JSON responses using schema.org. 

NLWeb fundamentally changes the relationship from “AI reads the site” to “AI queries the site.”

Attribute NLWeb llms.txt
Primary goal Enables dynamic, conversational interaction and structured data output Improves crawler efficiency and guides static content ingestion
Operational model API/Protocol (active endpoint) Static Text File (passive guidance)
Data format used Schema.org JSON-LD Markdown
Adoption status Open project; connectors available for major LLMs, including Gemini, OpenAI, and Anthropic Proposed standard; not adopted by Google, OpenAI, or other major LLMs
Strategic advantage Unlocks existing schema investment for transactional AI uses, future-proofing content Reduces computational cost for LLM training/crawling

The market’s preference for dynamic utility is clear. Despite addressing a real technical challenge for crawlers, llms.txt has failed to gain traction so far. 

NLWeb’s functional superiority stems from its ability to enable richer, transactional AI interactions.

It allows AI agents to dynamically reason about and execute complex data queries using structured schema output.

The strategic imperative: Mandating a high-quality schema audit

While NLWeb is still an emerging open standard, its value is clear. 

It maximizes the utility and discoverability of specialized content that often sits deep in archives or databases. 

This value is realized through operational efficiency and stronger brand authority, rather than immediate traffic metrics.

Several organizations are already exploring how NLWeb could let users ask complex questions and receive intelligent answers that synthesize information from multiple resources – something traditional search struggles to deliver. 

The ROI comes from reducing user friction and reinforcing the brand as an authoritative, queryable knowledge source.

For website owners and digital marketing professionals, the path forward is undeniable: mandate an entity-first schema audit. 

Because NLWeb depends on schema markup, technical SEO teams must prioritize auditing existing JSON-LD for integrity, completeness, and interconnectedness. 

Minimalist schema is no longer enough – optimization must be entity-first.

Publishers should ensure their schema accurately reflects the relationships among all entities, products, services, locations, and personnel to provide the context necessary for precise semantic querying. 

The transition to the agentic web is already underway, and NLWeb offers the most viable open-source path to long-term visibility and utility. 

It’s a strategic necessity to ensure your organization can communicate effectively as AI agents and LLMs begin integrating conversational protocols for third-party content interaction.

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