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Structured data with schema for search and AI

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.

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

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.

Read more at Read More

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

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.

Read more at Read More

Google Search Console adds Query groups

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.

Read more at Read More

Web Design and Development San Diego

Introducing Query groups in Search Console Insights

We are excited to announce Query groups, a powerful Search Console Insights feature that groups
similar search queries. One of the challenges when analyzing search performance data is that there
are many different ways to write the same query: you might see a dozen different variations for a
single user question – including common misspellings, slightly different phrasing, and different languages.
Query groups solve this problem by grouping similar queries.

Read more at Read More

AI vs Content Marketers: The New Content Marketing Formula

It’s easy to fall into doom and gloom that AI is replacing content marketers. It’s really replacing outdated workflows, though.

Over 90 percent of large marketing teams now use AI to generate content. They’re moving faster, publishing more, and rethinking production from the ground up. But speed alone won’t make content perform.

Audiences tune out shallow, generic material. Human creativity still drives differentiation. Strategy, originality, and clear brand perspective separate useful content from noise.

The teams that win combine AI’s efficiency with human insight. That requires knowing where automation fits and where it doesn’t. If you haven’t defined how to use AI for content creation inside your workflow, now’s the time.

This piece explores what effective AI vs human content looks like today and how to build it without losing your edge.

Key Takeaways

  • Most companies have already integrated AI into their content workflows, but don’t fall in the trap of treating them as shortcuts rather than systems.
  • Content that earns visibility today is structured, specific, and backed by human perspective, not just keyword targeting.
  • Strategic AI use supports ideation, formatting, optimization, and repurposing, but quality control stays human.
  • Personalization, brand voice, and original data continue to drive trust and engagement.
  • Success comes from balancing scale with clarity. The best content performs because it’s relevant, not frequent.

Managing The AI Flood

AI-generated content has reshaped digital publishing. Brands produce more blog posts, email copy, and landing pages than ever. But volume brings saturation and diminishing returns.

Not all AI content is low quality, but much of it reads identically. Teams optimize for speed without strategy. The result? More output, less substance.

A graphic showing usage of AI-generated content by bloggers/

Content that still works doesn’t feel mass-produced. It stands out by doing one or more of these things:

  • Offers a clear point of view or original framework
  • Goes deeper than surface-level summaries
  • Reflects genuine understanding of the audience
  • Adds context, nuance, or experience AI can’t fake

Search engines adapt to this shift. Platforms like Google and Perplexity look at content with structure, specificity, and trust signals over keyword stuffing or volume. AI tools are more likely to cite content that demonstrates expertise and clarity.

The opportunity isn’t to publish more. Build better systems for quality and relevance at scale. Winning teams won’t lean on AI to fill gaps, but reinforce strengths.

Human guidance makes the difference. Without it, content becomes another drop in the flood.

Rebuilding The Content Workflow

AI accelerates content production. It also forces teams to rethink how work gets done.

Instead of replacing content professionals, AI shifts where their time and value go. Manual tasks like keyword clustering, formatting, or metadata writing now run through automation. What remains critical is work AI can’t do well: aligning content to business goals, telling compelling stories, and capturing audience nuance.

How does this work in practice? Writers, strategists, and editors move upstream. They spend more time setting direction, defining tone, and curating inputs. Downstream, AI helps turn those inputs into faster iterations, formatted assets, and scalable deliverables.

This shift creates a more responsive content engine. One that reaches insight faster. One that makes room for testing and repurposing without burning out your team.

The result? More consistent output, more flexibility, fewer bottlenecks.

To get there, rebuild the workflow around what your team does best, not just what AI does quickly.

The sections below break down how to apply this shift at each stage, from ideation to optimization, so you can create a system that scales without sacrificing value.

Ideation

Strong content starts with strong ideas. That’s still a human job.

AI makes the early stages faster. Instead of starting from scratch, marketers use AI to scan top-performing content, surface related questions, and generate keyword clusters in seconds. Tools like ChatGPT, Ubersuggest, and BuzzSumo help teams quickly identify gaps, trends, and angles worth exploring.

A graphic showing AI-assisted content ideation.

But ideation is only useful when it’s aligned with strategy. AI should support the process, not drive it. You need that human point of view as a starting point.

Real-Time Performance Feedback

AI doubles as a smart editor.

Tools like Clearscope, MarketMuse, and Surfer SEO give real-time scoring on keyword coverage, topic depth, readability, and search intent. You can spot weak sections, catch missing subtopics, and verify your draft aligns with how people actually search.

A graphic showing how real-time performance feedback works with AI.

Instead of waiting for performance to drop before making updates, fix issues before content even publishes. That means fewer rewrites and better outcomes from day one.

Brand Voice Support

One of the biggest risks with AI content? Sounding like everyone else. Brand voice systems help.

Feed AI tools with examples of your tone, preferred phrases, and messaging guardrails to guide outputs toward consistent brand reflection. Prompt libraries, templates, and style frameworks give AI clearer direction and reduce heavy editing later.

A graphic showing how to build brand voice systems for AI output.

But it’s not set-and-forget. Someone still needs to review and fine-tune. AI can help scale your voice, but it won’t define it for you.

Content Repurposing

Most content teams don’t need more ideas. They need more mileage from content they already have.

AI makes breaking down webinars, blog posts, or whitepapers into new formats easier. With the right content repurposing plan, turn a single piece into multiple social posts, email sequences, video scripts, or short-form summaries in minutes.

A graphic showing how content repurposing works at scale with AI support.

This approach saves time and extends the reach of your core ideas. The key is setting rules around tone and structure so AI keeps output aligned with your original intent.

Graphics

Visual content used to slow down many content workflows. Not anymore.

AI-powered design tools like Canva, Midjourney, and Runway help marketers produce branded graphics, thumbnails, and motion assets much faster. Instead of waiting days for design resources, teams create visuals in parallel with written content without sacrificing quality.

AI tools that can help with multimedia production.

This means faster turnarounds on social content, better visual support for blog posts, and more consistency across formats. As with writing, human review remains necessary, but AI handles much of the heavy lifting.

SEO Formatting

Formatting for SEO used to eat up hours, particularly at scale. AI tools now handle much of that backend work.

From writing meta descriptions and alt text to adding schema markup and internal links, automation streamlines the technical side of publishing. Tools like SEO.ai and Surfer can also suggest keyword tweaks and intent matches based on real-time SERP data.

A graphic showing how to automatically format SEO and metadata using AI.

This doesn’t replace SEO strategy, but it cuts down the grunt work. Teams can focus more on aligning content with search intent, not just checking boxes.

The New Age of AI-Optimized Content: What Does It Look Like?

The rise of AI hasn’t lowered the bar for content quality. It’s raised it.

With machine-generated content flooding every channel, visibility now depends on value, not volume. Search engines and users reward content that brings clarity, trust, and depth.

A graphic showing how to improve AI visibility for content.

Your content strategy needs to shift focus. Specificity, structure, and perspective matter more than keyword counts and content frequency.

AI-optimized content that performs well today typically checks a few key boxes:

  • Built around real expertise, often supported by proprietary data or firsthand experience
  • Clearly structured, using headings, bullets, and schema markup to improve readability and search parsing
  • Leads with utility, helping readers solve problems, take action, or understand something faster
  • Reflects your brand’s voice and positioning, not a generic blend of scraped internet copy
A graphic showing how to structure content for AI visibility.

Human content professionals have leverage here. AI can get a draft to 70 percent, but that last 30 percent (the part that connects, converts, or earns backlinks) still requires human input.

One of the most overlooked opportunities right now? Simply tightening your structure. Clear formatting helps search engines surface your content and makes it easier for generative tools like ChatGPT and Perplexity to cite and summarize it correctly.

AI can help get content out the door faster. But if you want that content to show up, earn trust, and drive results, human oversight isn’t optional. It’s the differentiator.

Multimedia Integration

A well-placed visual can do more than dress up a page. It boosts visibility, extends engagement, and increases the odds of being cited by generative search engines.

Search engines also reward content that blends formats. Multimedia helps break up long blocks of text, reinforces key takeaways, and signals structure that AI engines can easily parse.

A graphic showing how to properly integrate smart multimedia into AI-generated content.

To make it work, start planning visuals alongside your copy, not after the fact. That upfront alignment leads to stronger storytelling and assets that actually support performance, not just polish the page.

AI’s Impact on Content Distribution

Content doesn’t drive results if no one sees it. That’s always been true. What’s changed is how distribution works and who you’re optimizing for.

Today, your audience includes both people and machines. The rise of generative search and large language models (LLMs) means your content isn’t just being read by humans. It’s being crawled, summarized, and cited by AI systems that prioritize structure, metadata, and clarity.

A graphic explaining how to write to human and machine audiences.

To stay visible, your distribution strategy needs to reflect that.

Start with metadata. Schema markup, structured tags, and optimized alt text all help AI tools understand and surface your content across search, snippets, and summaries. This isn’t just a technical checkbox. It’s the infrastructure that supports discoverability.

Then think about format. Repurpose long-form assets into LinkedIn posts, email sequences, YouTube Shorts, or Reddit threads. Tailor messaging by platform. Adjust tone for different audiences. A one-size-fits-all approach wastes reach.

Finally, use automation to your advantage. Tools like Buffer, Zapier, and Hootsuite can help schedule, adapt, and push updates across multiple channels at once. That frees your team from repetitive tasks and ensures consistency wherever your audience finds you.

Distribution used to be about checking the promotion box. Now it’s a system with humans on one end and AI on the other.

Done well, distribution doesn’t just get more eyes on your content. It makes sure the right people and the right algorithms see it in the right place, at the right time.

Staying Ahead of the Content Curve

Predictability used to be a strength in content planning. But with AI constantly changing how content is created, distributed, and discovered, agility matters just as much.

Keeping your edge means paying attention to two things: where AI is going, and how your audience is reacting right now.

Start by tracking signals. Tools like Exploding Topics, Glimpse, and SparkToro help identify early trends and shifts in search behavior before they hit the mainstream. Combined with real-time performance data from platforms like GA4 or social analytics, you can spot what’s resonating and what’s falling flat while there’s still time to act.

An example of how to make real-time adjustments from engagement signals with AI content.

Adaptability is key. A/B testing thumbnails, headlines, or messaging lets you make micro-adjustments without overhauling your entire campaign. And monitoring where and how AI engines cite your content can highlight gaps worth closing or opportunities to double down on.

Future-proofing doesn’t mean locking in a rigid plan. It means building a system that can flex with your audience and the algorithms that serve them.

FAQs

Can AI-generated content rank in search engines?

Yes, but only if it’s high quality. Google doesn’t penalize AI content specifically. What matters is whether the content provides value, demonstrates expertise, and meets user intent. AI-assisted content that’s edited and enhanced by humans typically performs better than purely AI-generated material.

How do I balance AI vs human-generated content in my strategy?

Use AI for tasks like ideation, outlining, formatting, and repurposing. Keep humans involved in strategy, editing, brand voice, and final review. A good rule: AI can get you to 70 percent, but humans should handle the final 30 percent that makes content distinctive and valuable.

What are the risks of using too much AI in content creation?

Over-reliance on AI leads to generic, samey content that doesn’t stand out. Other risks include factual errors, lack of brand voice, and content that sounds robotic. Users and search engines increasingly favor content with clear human expertise and originality.

How is human vs AI content different in terms of engagement?

Human-created or human-edited content typically generates higher engagement because it includes personal experiences, emotional resonance, and authentic storytelling. AI content often lacks nuance and personality, which can reduce trust and engagement rates.

Conclusion

The shift to AI-assisted content isn’t slowing down. But speed and automation aren’t enough to drive results on their own. The real differentiator is how well your system blends efficiency with insight.

Human-led strategy still drives the most meaningful outcomes, whether that’s developing a content plan built around real audience data or shaping assets to align with how search and generative engines work today.

If you haven’t revisited your content approach recently, now’s the time. You can start by refining your SEO content strategy or building smarter processes around AI content optimization.

In a space full of content, only the most useful, intentional, and well-structured will rise to the top.

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A recap of the October 2025 SEO Update by Yoast

The message from this month’s SEO Update is clear: AI and data accuracy are reshaping how we plan, optimize, and measure SEO. This is not just a slate of updates, but a signal to rethink impressions, content creation, and tooling so you stay effective. Chris Scott, Yoast’s Senior Marketing Manager, hosted the session. Alex Moss and Carolyn Shelby shared deep dives on AI trends, Google updates, and Yoast product news.

Data and rankings in flux

A key shift centers on data. Google removed the num=100 parameter, which changed how much ranking data shows up per page in Google Search Console. The result isn’t a sudden performance drop; it’s a correction. Impressions can look lower because the data is being cleaned up, and that matters more than the raw numbers. Paid search data stays solid, since ads rely on precise counting for financial reasons.

AI content and media: use it, don’t rely on it

Sora 2 can generate short videos from text prompts, providing handy visuals to accompany blog posts. Use AI visuals to complement your core messaging, not to replace it. In e-commerce, Walmart, WooCommerce, and Shopify are testing AI-enabled shopping features. Don’t rush a full switch before major buying events.

Local SEO and engines beyond Google

Bing’s Business Manager now has a refreshed UI focused on local listings, signaling a push into local search. Diversifying beyond Google can reveal new AI-powered opportunities. It’s about testing where AI-driven search and shopping perform best, not moving budgets blindly.

AI mode and how people behave

Research into AI-dominant sessions shows a distinct pattern: users linger 50 to 80 seconds on AI-generated text, and clicks tend to be transactional. Intent patterns shift, too. Now, comparisons lead to review sites, decisive purchases land on product pages, and local tasks point to maps and assets.

Meta descriptions and AI generation

Google tested AI-generated descriptions for threads lacking meta content, but meta descriptions aren’t obsolete. Best practice is to lean on Yoast’s default meta templates (like %excerpt%) as a reliable fallback. Write with an inverted pyramid in mind, which puts key information first, so AI can extract it cleanly. Keep a fallback description in Yoast SEO so automation stays under your control.

AI in everyday workflows

ChatGPT updates push toward more human-to-human interactions, and tools like Slack can summarize threads and search discussions by meaning, not just keywords. Growth in AI usage feels steadier now; some younger users opt for other AI tools.

Insights from Microsoft and Google

The core rules haven’t changed: concise, unique, value-packed content wins. Shorter, focused writing works best for AI synthesis; trim fluff and sharpen clarity. The message is simple because clarity beats complexity, especially as AI becomes more central to how content is consumed.

Yoast product updates to watch

The Yoast SEO AI+ bundle adds AI Brand Insights to track mentions and citations in AI outputs, and pronoun support has been added to schema markup for inclusivity. If you’re tracking AI relevance beyond traditional signals, this bundle can be a smart addition.

Next actions and a quick invitation

For more news, you can join the next SEO Update by Yoast on November 24. The transcript, video, and news items are all available on the SEO Update by Yoast October Edition webinar page. For more information and options to watch future webinars, you can also visit the main Yoast webinars listings.

The post A recap of the October 2025 SEO Update by Yoast appeared first on Yoast.

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Top AI Visibility Tools in 2026

Noticed your traffic dropping even though your rankings look stable? You’re not alone.

AI tools like ChatGPT, Perplexity, and Google’s AI Overviews are now answering the same questions that used to send people to your site. 

If your brand isn’t showing up in those AI-generated responses, you may be losing visibility. And the tough part? You won’t be able to measure that lost visibility with traditional analytics tools.

That’s where AI visibility tools come in. They tell you when your brand shows up in AI answers, which platforms mention you, and how often your content gets cited. In short, they track your presence across large language models (LLMs) and AI search engines so you know if your LLM seeding efforts are paying off.

The good news is a handful of tools are already helping brands track their AI visibility. Some existing platforms have added AI tracking to their SEO suites. Others focus exclusively on LLM citations. 

Each gives a different way to see (and improve) your AI presence. Let’s look at some of the best AI visibility tools available right now.

Key Takeaways

  • AI visibility tools track brand mentions and content citations across LLM platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews.
  • Think of these tools as the AI-era version of SEO tools. They give you hard data on whether your optimization tactics are actually working.
  • Most platforms are still adapting alongside AI search behavior, so look for tools that update often.
  • The right tool for you depends on your budget, whether you want standalone tracking or built-in SEO features, and how technical your team is.
  • Combining AI visibility metrics with traditional analytics gives you the complete picture of content performance across all channels.

Why Are AI Visibility Tools Important?

The way people search is fundamentally changing. Gartner predicts traditional search engine volume will drop 25 percent by 2026 due to AI platform and virtual agent usage. 

This is the zero-click phenomenon at work. 

People are getting answers directly from AI platforms instead of clicking through to websites. That makes knowing where your brand appears in AI responses as vital as tracking your Google rankings.

AI visibility tools solve a measurement problem. They monitor which LLM platforms cite your content and your brand mentions in AI Overviews. They measure changes over time so you can evaluate whether your LLM SEO efforts are actually working.

Think of these tools as Google Analytics for AI search. Without this data, you’re guessing about what resonates with AI platforms. With it, you see exactly what content drives citations and what gets ignored. These tools reveal patterns in what content formats, topics, and structures earn the most AI citations.

Traditional SEO metrics like page views, rankings, and backlinks still matter. But they tell only part of the story. 

Don’t ignore the growing segment of your audience interacting with your content through AI platforms. They might not visit your site, but their interactions still influence visibility and authority. 

Combining standard analytics with AI visibility data shows the complete picture of your content’s reach and what’s actually driving results across channels.

Top 5 AI Visibility Tools on The Market

The LLM visibility tool market is growing fast. New platforms launch regularly with different features, tracking methods, and pricing structures. 

After comparing what’s out there, these five AI visibility tools stand out. They range from budget-friendly all-in-one platforms to enterprise-focused citation intelligence.

Ubersuggest

Ubersuggest's AI search visibility platform.

Ubersuggest has added new AI Visibility features to its SEO toolkit. The big win? You can now monitor AI citations and see how they connect to your traditional search performance, all from one dashboard.

Ubersuggest AI Visibility makes it easy to add AI visibility tracking into your marketing program. Key metrics the tool tracks include:

  • Brand Visibility: How often your brand gets mentioned across AI-generated answers in a given period. 
  • Industry Rank: Your average position compared to other brands in your space.
  • Top Prompts: The main questions people are asking in AI platforms relevant to your industry, and how your brand appears in those answers.
  • Competitor Visibility: How your brand’s presence in AI visibility trends compared to competitors over time.
The Ubersuggest AI visibilty dashboard.

Along with the easy-to-navigate interface, Ubersuggest’s pricing is a major advantage. Most enterprise tools charge per project or lock you into long-term contracts. Ubersuggest takes a different route with flat monthly pricing and unlimited project tracking. That means an agency managing 20 clients pays the same as someone tracking just two sites.

You also get full access to all the traditional SEO features Ubersuggest is known for, so you don’t have to pay for two separate platforms to see the full picture.

Because Ubersuggest is built on years of SEO infrastructure, its data is consistent and reliable. And some teams might not be comfortable with other new visibility tools, many of which launched in the past year and are still working out bugs in their tracking.

Profound

The Profound interface.

(Image Source)

Profound is a new platform specifically designed for enterprise brands that need detailed intelligence about how AI platforms discuss them. This goes beyond counting citations.

The system analyzes the context around every brand mention, including: 

  • Sentiment: Whether AI platforms position you positively or negatively.
  • Competitive mentions: Which competitors get mentioned alongside your brand. 
  • Authority: Topic clusters where you’re seen as an authority versus areas where others dominate. 

Profound is built for customization. Its team builds dashboards tailored to your industry, integrates with your existing systems, and creates reporting formats that match your organization’s workflow. 

Need specialized tracking for regulated industries? They configure it. Competitive intelligence can also be scaled across hundreds of queries, and alert systems can let you know if your brand suddenly drops from an AI response.

The tradeoff? Price. Annual contract costs typically start high and scale based on how many brands you track, query volume, and customization needs. This isn’t built for small businesses.

With that said, Profound’s depth and customization justify the cost for brands where AI visibility directly impacts market position and revenue.

Semrush

The SEMrush interface.

(Image Source)

Semrush added AI visibility tracking to its existing SEO suite. Already using the platform? The new features integrate smoothly into your workflow.

The tool monitors citations across major AI platforms and provides visibility scoring that works like domain authority, providing a single number showing how your AI presence compares to competitors over time.

The real benefit of Semrush’s functionality is that it connects AI visibility data with everything else it already tracks. You can see which pages earn both backlinks and AI citations. You can see whether content that ranks in traditional search also appears in AI responses. That integrated view helps you understand what’s working across all your marketing channels.

For teams trying to consolidate tools, this setup is efficient. You get traditional SEO and AI visibility data in one report, no platform-switching required.

The tradeoff is its agency pricing. Semrush limits how many projects you can track per account tier. Adding clients means upgrading plans or buying additional accounts. Managing 30-plus brands? Costs climb fast compared to platforms with unlimited project tracking.

Overall, this may be a smart add-on if you are already onboarded onto Semrush. But it might not be the most affordable option for smaller teams or tighter budgets.

Ahrefs

The Ahrefs interface.

(Image Source)

Ahrefs made its name with backlink analysis and competitive research before becoming one of the most popular SEO tools around. Its move into AI visibility adds another layer to an already powerful platform.

This new functionality tracks citations across AI platforms and lets you filter by specific engines, monitor changes over time, and compare your visibility to competitors. Standard stuff.

Ahrefs stands out by connecting link data with AI citations. Its backlink index is one of the largest available and updates frequently. The platform shows correlations between your link profile and AI visibility, revealing which linked pages get cited most often in AI responses.

That connection offers real insight. Content earning quality backlinks tends to appear more in AI citations. Understanding that relationship helps you identify what makes content citation-worthy and apply those patterns to other pieces. Combine that with Ahrefs’ broader SEO features, and you get a well-rounded picture of your brand visibility online.

The major caveat, though, is the pricing, which follows a similar structure as Semrush. Plans limit tracked projects, so costs increase as you scale. Five clients work fine. Fifty clients get expensive.

For teams that prioritize link building alongside AI visibility, Ahrefs handles both well. Just know you’ll pay premium prices.

ScrunchAI

The ScrunchAI interface.

(Image Source)

ScrunchAI is a newer offering that focuses exclusively on AI visibility. Already using other tools for standard optimization and just need LLM citation tracking? Scrunch’s specialized approach might fit.

The platform monitors brand appearances across ChatGPT, Claude, Perplexity, Google’s AI Overviews, Bing AI, and emerging AI search engines. Real-time tracking alerts you to citation frequency changes, new platforms surfacing your content, or sudden visibility drops. 

Where ScrunchAI stands out is that it tracks both citation quality and frequency. It can tell whether AI platforms position your brand as a primary resource, secondary resource, and if any misinformation shows up alongside your name.

ScrunchAI also provides recommendations based on your data. Certain content structures get cited more often? It suggests creating similar pieces. Missing from responses where competitors appear? It flags those gaps with specific topic ideas.

Another interesting feature is query simulation. You can run industry-specific prompts to see if your brand appears and compare results across different AI engines. That gives you a clear picture of where you’re strong and where to focus your next optimization push.

In terms of pricing, Scrunch lands in the middle of our list. Monthly plans scale based on query volume and update frequency rather than limiting projects. That makes costs predictable for agencies.

The tradeoff is betting on a newer company. Established platforms have proven track records. ScrunchAI is still building its reputation, though early users report solid performance and responsive support.

Choosing the Right AI Visibility Tool for You

Selecting an AI visibility tool requires matching capabilities with your specific constraints and goals. 

Start with three core questions: What’s your budget? How technical is your team? Do you need standalone AI tracking or an integrated SEO platform? Here are some key focus areas:

  • Budget determines realistic options. Tools like Ubersuggest provide AI visibility alongside comprehensive SEO features at accessible prices for small businesses and agencies. Enterprise platforms like Profound deliver granular intelligence but require substantial financial commitment that only makes sense at scale.
  • Technical capabilities matter. Some platforms assume comfort with data analysis and provide extensive export, API, and customization options. Others prioritize simplicity with clear dashboards and straightforward recommendations. Match the tool’s complexity to your team’s skills and bandwidth.
  • Consider your existing technology stack. Already investing in Ubersuggest, Semrush, or Ahrefs for SEO? Their AI visibility features extend current workflows. You avoid learning new interfaces and keep data centralized. If you’re starting from scratch or want laser focus on AI tracking, a specialized platform like ScrunchAI might be the better fit.
  • Consider your scaling needs. Requirements differ dramatically between tracking five websites versus managing 50 client accounts. Some tools charge per project or impose account limits, creating expensive scaling challenges. Others offer unlimited projects under single subscriptions, simplifying budgeting as you grow.
  • Data reliability should influence decisions. Newer tools might offer attractive features but lack infrastructure for consistent metrics. Established platforms benefit from years of data collection and algorithm refinement. Request demos, compare results across tools, and check user reviews before committing.

Finally, assess how tools adapt to AI search changes. AI search is changing at lightning speed, and the tools that don’t update will quickly fall behind. The best platforms have active roadmaps, regular feature updates, and expanding coverage across emerging AI engines.

FAQs

What is the best AI tool for increasing visibility?

The best AI visibility tool depends on your budget and needs. Ubersuggest offers strong value for small businesses and agencies, combining AI citation tracking with full SEO capabilities at accessible pricing. Enterprise brands might prefer Profound’s deeper analytics. Test several options to find which interface and features match your workflow best.

Are AI visibility tools better than traditional marketing methods?

AI visibility tools complement traditional marketing rather than replacing it. You still need solid content strategy, SEO fundamentals, and audience understanding. Think of them as an extension of your analytics, not a replacement. Use them alongside traditional metrics for a complete view of performance across all channels where audiences find information.

How do AI visibility tools integrate with existing SEO strategies?

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AI visibility tools track LLM citations the same way traditional tools monitor search rankings. Platforms like Ubersuggest, Semrush, and Ahrefs combine both metrics in unified dashboards. This lets you optimize content for standard search results and AI citations simultaneously, creating strategies that cover all the ways people discover information today.

Conclusion

AI search isn’t slowing down. Platforms that answer questions before users ever click a link are expanding fast. Tracking your presence in AI-generated responses is essential now.

The tools covered here provide visibility into how AI platforms cite your content and mention your brand. Some integrate AI tracking into broader SEO platforms. Others focus exclusively on LLM citations. Your choice depends on budget, needs, and existing systems.

Start measuring your AI visibility now. The brands paying attention today will outperform the ones waiting to catch up later. 

The tools exist. The data is available. Use it.

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How to use YouTube Ads to drive B2B conversions

How to use YouTube Ads to drive B2B conversions

When you think of video advertising on YouTube, you probably think of ecommerce:

  • Videos showing products.
  • Influencers doing an unboxing.
  • Other visuals of consumer products that lend themselves to the video format.

Even Google’s own case studies for video emphasize consumer-focused themes. Just look at the analysis of the top 2025 video ads.

See any B2B brands there? Me neither. 

It’s true that YouTube Ads perform very well for ecommerce advertising aimed at consumers. But YouTube can also help drive B2B leads. 

You might be scratching your head and saying, “But I’ve tried YouTube for B2B. It doesn’t convert.” And you would be right.

YouTube Ads for B2B rarely convert directly into leads. Complex products with long sales cycles are not going to sell themselves in one video.

But YouTube campaigns definitely have a positive influence on B2B lead generation – we’ve seen it across nearly all of our B2B clients.

Here are two case studies, featuring very different advertisers, that show how YouTube Ads can be used to increase B2B conversions.

Case study 1: Enterprise B2B SaaS advertiser

One of our enterprise B2B SaaS clients offers multiple business solutions.

Paid search is a strong lead source for most of them, but two struggled to convert – traffic was steady, yet the cost per lead was high.

When we dug in, we found that users weren’t aware of these solutions or how they addressed specific business needs. The landing page content wasn’t persuasive enough.

We tested YouTube video campaigns that clearly explained each solution’s value. The impact was undeniable.

Comparing search performance from the quarter before video to the quarter during, we saw key metrics – CTR, CPC, cost per lead, and conversion rate – all improve.

Enterprise B2B SaaS advertiser - Solution 1

Here, CTR improved significantly with the video live, which indicates that users had a better understanding of the solution after seeing the video.

This led to a lower CPC, which, combined with a slightly improved conversion rate, lowered cost per lead by 30%.

With the second solution, the results were even more dramatic.

For this solution, front-end metrics actually got worse: CTR declined, and CPC increased.

Search competition in this space was stiffer during the “after” period, which pushed CPCs up.

However, the campaigns still saw a 25% decrease in cost per lead, and conversion rates more than doubled.

In this instance, the video campaigns really helped explain how the solution can benefit users, which directly translated into better conversion rates from search.

Dig deeper: From Video Action to Demand Gen: What’s new in YouTube Ads and how to win

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Case study 2: Local B2B business

The second case involves a local B2B business.

For the first five months of 2025, this advertiser ran a small YouTube video campaign intended to drive consideration.

We had hoped the video would directly drive a few leads, and ran it on a Maximize Conversions bid strategy, but it never generated a single lead.

At the same time, CPLs across the entire account were rising, so in early June, we decided to pause YouTube and use the budget on campaigns that were directly driving leads.

That turned out to be a mistake.

CPLs on brand search campaigns rose by 47% when we stopped video. 

This is a business without much seasonality, and brand is usually less impacted by seasonality anyway, so at first, we were puzzled. Then we decided to relaunch video.

Voila! Brand search CPLs returned to their previous levels.

We suspected the video campaigns were contributing to the success of the brand campaigns, so we decided to try adding a Demand Gen campaign to the mix.

Brand CPLs decreased by 47%.

Not only were we able to return brand search CPLs to their original levels, but we were also able to cut them nearly in half when combined with YouTube and Demand Gen campaigns. 

During the whole nine-month period, YouTube and Demand Gen campaigns only generated two conversions directly. However, the positive impact on brand search performance was indisputable.

It’s important to stress here that we made other optimizations during the test periods for both clients, so the improvements in search are probably not 100% attributable to the addition of the video campaigns.

However, in the case of the enterprise client, the improvements for the solutions that ran video outpaced performance across the rest of the account.

And the fact that two very different advertisers saw correlated improvements in search performance lends further credence to the theory that video played an important role.

Dig deeper: How to measure YouTube ad success with KPIs for every marketing goal

Keys to impactful video campaigns

Even though these two cases involved very different clients, here are the key practices that made both video campaigns successful:

  • Use custom segments made up of high-performing search keywords. Don’t use broad targeting or in-market audiences unless you have a very large awareness budget.
  • If you have first-party audiences and want to run Demand Gen, use them for a lookalike audience. Otherwise, custom segments of strong search keywords work best.
  • Make your geo-targeting spot-on. Don’t waste spend on irrelevant regions. For the local B2B client, we carefully selected areas of the city that best met their needs. For the enterprise client, even though they wanted to reach a global audience, we took care with which countries we targeted.
  • Use short videos – no more than 15-30 seconds – and include your brand name and logo in the first few seconds.
  • Choose a Target CPV bid strategy. We were able to get CPV below $0.01, which got our message in front of as many users in the target audience as possible.
  • The more videos, the better. If you have 3, 4, 5, or more videos, use them. Even slight variations help minimize video fatigue and grab attention.

You don’t need huge budgets for this to work – in both cases, we spent less than 5% of the client’s total budget on video.

With the right targeting, you can keep costs very reasonable – and the campaigns pay for themselves in lower CPLs in search.

Dig deeper: 3 YouTube Ad formats you need to reach and engage viewers in 2025

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How to avoid marketing mix modeling mistakes that derail results

How to avoid marketing mix modeling mistakes that derail results

Marketing mix modeling (MMM) is having a moment in marketing measurement.

As privacy regulations limit user-level tracking, marketers are turning to it for reliable, cross-channel measurement. (We love it at my agency – MMM analyses often lead to smarter budget allocation with significant downstream impact.)

But as adoption grows, so do execution errors and misconceptions about what MMM can and can’t do. 

Despite its strategic potential, it’s often misused, misinterpreted, or oversold – leading to costly mistakes and credibility loss from unrealistic expectations.

MMM isn’t a black box. To produce meaningful insights, it demands context, strategy, iteration, and strong data. 

Context is especially critical. Without it, MMM becomes what I call a mathematical echo chamber – no external inputs and little connection to reality.

This article breaks down how to approach MMM correctly, avoid common pitfalls, and turn your analysis into real business value.

Execution errors

Too often, teams fixate on the modeling technique and overlook the broader system – data quality, assumptions, and stakeholder context. 

There are plenty of possible mistakes, but the ones I see most often are:

  • Using inconsistent, incomplete, or unvalidated spend and performance data.
  • Assuming immediate or linear responses to media spend, which oversimplifies reality.
  • Interpreting statistical relationships as proof of impact without experimentation.
  • Using MMM for daily campaign decisions despite its strategic design and lagging granularity.
  • Building models that are over-optimized in-sample but fail in the real world.

If you make any of these, your MMM efforts will be muddled and ineffective, and you will not get much buy-in for the initiative going forward.

Faulty expectations vs. reality

When run properly, MMM can offer highly valuable insights, but only within its appropriate use case. 

With good modeling and inputs, you can:

  • Reallocate budgets based on marginal ROI and saturation.
  • Forecast sales impact from various budget scenarios.
  • Set spending caps to avoid diminishing returns.
  • Show long-term contributions of brand versus performance channels.
  • Track media effectiveness over time and support cross-functional alignment.

What you cannot expect MMM to do:

  • Optimize daily media buying decisions.
  • Attribute at the user or creative level.
  • Replace lift tests or experimentation (which are a necessary complement to MMM).

In other words, treat MMM as a strategic GPS that needs other inputs to work well, not a tactical turn-by-turn navigation tool.

Misreadings of output

You can give three marketers the same MMM output, and they might have three very different interpretations of what it means and what to do next. 

We’ve got a handy chart of the ways people misread the data (and how to fix those mistakes):

Misreadings of output

The misinterpretation I’d like to spend a bit of time on here is the correlation/causation dynamic. 

Marketers need to understand that MMM is essentially a fancy correlation analysis that needs to be supplemented by incrementality testing, such as geo lift testing, to establish causation. 

Dig deeper: Why incrementality is the only metric that proves marketing’s real impact

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What you need for effective MMM analysis

MMM does involve coding, but it’s a lot more than that. 

It’s a cross-functional discipline involving data science, marketing, finance, and strategy. 

To get it right, you need:

1. Clean, longitudinal data

One note before I dive into the data elements you need to run MMM: data density is critical. 

For businesses without a huge pool of revenue-generating events (think of big SaaS platforms or car dealerships advertising online), use strategic proxy metrics that happen earlier in the purchase journey and provide strong predictors of revenue generation. 

With that in mind, here’s the data needed (or recommended) for your model:

  • Weekly data across 2–3 years.
  • Media spend by channel and campaign. (Region is recommended.)
  • Control variables (all recommended): Promos, pricing, and competitors.
    • Note: seasonality is baked into the model for Meta’s Robyn, one of my favorite MMM options.

2. Advanced modeling techniques

  • Adstock/lag functions to reflect delayed impact.
  • Saturation models (e.g., Hill curves) for diminishing returns.
  • Regularization or Bayesian priors to stabilize estimates.

3. Validation and iteration

Running an MMM analysis once and taking the results at face value is never going to get you the best possible insights. 

If you’re serious about adopting MMM, prepare to include the following in your process:

  • Cross-validation, holdout tests, geo-lift experiments.
  • Regular re-runs (quarterly or biannually) to stay aligned with the market.
  • Incorporation of other tools (e.g., MTA, A/B testing) for a full picture.

Dig deeper: MTA vs. MMM: Which marketing attribution model is right for you?

I highly recommend running analyses more than once and using different methods/platforms to identify commonalities and differences. 

In the visual comparing Robyn and Meridian’s output from a recent client analysis, both models attributed similar influence across most channels – a good sign that helps validate the model. 

But there’s a wrinkle: for channel 0, Meridian showed much higher organic influence and a slight bump in paid. 

That suggests we need additional testing before moving to action items.

Robyn vs Meridian

4. Stakeholder engagement

Even with top-tier MMM analyses, how you communicate the findings – and what they enable – is critical to getting buy-in from clients or management.

Before you start, align with stakeholders on KPIs, ROI definitions, and model assumptions to prevent surprises or misunderstandings later.

When you share results, include uncertainty ranges and clear action items that flow directly from your data. 

If you can’t answer the inevitable “So what?” question, you’re not ready to present your findings.

Better MMM becomes a competitive edge

Overall, the shift away from user-based tracking is healthy for the marketing industry. 

Initiatives like incrementality testing and MMM are finally getting their due as core parts of campaign analysis.

As major platforms level the optimization playing field with automation, running these analyses more effectively than your competitors is one way to drive differentiated growth.

Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future

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