Posts

Why does having insights across multiple LLMs matter for brand visibility?

/0 Comments/in , /by

Search today looks very different from what it did even a few years ago. Users are no longer browsing through SERPs to make up their own minds; instead, they are asking AI tools for conclusions, summaries, and recommendations. This shift changes how visibility is earned, how trust is formed, and how brands are evaluated during discovery. In AI-driven search, large language models interpret information, decide what matters, and present a narrative on behalf of the user.

Table of contents

Key takeaways

  • Search has evolved; users now rely on AI for conclusions instead of traditional SERPs
  • Conversational AI serves as a new discovery layer, users expect quick answers and insights
  • Brands must navigate varied interpretations of their presence across different LLMs
  • Yoast AI Brand Insights helps track brand mentions and identify gaps in AI visibility across models
  • Understanding LLM brand visibility is crucial for modern brand strategy and perception

The rise of conversational AI as a discovery layer

“Assistant engines and wider LLMs are the new gatekeepers between our content and the person discovering that content – our potential new audience.” — Alex Moss

Search is no longer confined to typing queries into a search engine and scanning a list of links. Today’s discovery journey frequently begins with a conversation, whether that’s a typed question in a chatbot, a voice prompt to an AI assistant, or an embedded AI feature inside a platform people use every day.

This shift has made conversational AI a new layer of discovery, where users expect direct answers, recommendations, and curated insights that help them make decisions and build brand perception more quickly and confidently.

Discovery is happening everywhere

Users are now encountering AI-powered discovery across a range of interfaces:

AI chat interfaces

Tools like ChatGPT allow users to ask open-ended questions and follow up in a conversational manner. These interfaces interpret intent and tailor responses in a way that feels natural, making them a go-to for exploratory search.

Also read: What is search intent and why is it important for SEO?

Answer engines

Platforms such as Perplexity synthesize information from multiple sources and often cite them. They act as research helpers, offering concise summaries or explanations to complex queries.

Embedded AI experiences

AI is increasingly built directly into search and discovery environments that people already use. Examples include AI-assisted summaries within search results, such as Google’s AI Overviews, as well as AI features embedded in browsers, operating systems, and apps. In these moments, users may not even think of themselves as “using AI,” yet AI is already influencing what information is surfaced first and how it is interpreted.

This broad distribution of AI discovery surfaces means users now expect accessibility of information regardless of where they are, whether in a chat, an app, or embedded in the places they work, shop, and explore online.

How people are using AI in their day-to-day discovery

Users interact with conversational AI for a wide range of purposes beyond traditional search. These models increasingly guide decisions, comparisons, and exploration, often earlier in the journey than classic search engines.

Here are some prominent ways people use LLMs today:

Product comparisons

ChatGPT gives a detailed brand comparison

Rather than visiting multiple sites and aggregating reviews, there are 54% users who ask AI to compare products or services directly, for example, “How does Brand A compare to Brand B?” and “What are the pros and cons of X vs Y?” AI synthesizes information into a concise summary that often feels more efficient than browsing search results.

“Best tools for…” queries

Result by ChatGPT for “best crm software for smbs.”

Did you know 47% of consumers have used AI to help make a purchase decision?

AI users frequently ask for ranked suggestions or curated lists such as “best SEO tools for small businesses” or “top content optimization software.” These queries serve as discovery moments, where brands can be suggested alongside context and reasoning.

Trust and validation checks

Many users prompt AI models to validate decisions or confirm perceptions, for example, “Is Brand X reputable?” or “What do people say about Service Y?” AI responses blend sentiment, context, and summarization into one narrative, affecting how trust is formed.

Also read: Why is summarizing essential for modern content?

Idea generation and research exploration

In a study by Yext, it was found that 42% users employ AI for early-stage exploration, such as brainstorming topics, gathering potential search intents, or understanding broad categories before narrowing down specifics. AI user archetypes range from creators who use AI for ideation to explorers seeking deeper discovery.

Local discovery and service search

local search results on chatgpt
ChatGPT recommendations for “best cheesecake places in Lucknow, India.”

AI is also used for local searches. For example, many users turn to AI tools to research local products or services, such as finding nearby businesses, comparing local options, or understanding community reputations. In a recent AI usage study by Yext, 68% of consumers reported using tools like ChatGPT to research local products or services, even as trust in AI for local information remains lower than traditional search.

In each of these moments, conversational AI doesn’t just surface brands; it frames them by summarizing strengths, weaknesses, use cases, and comparisons in a single response. These narratives become part of how users interpret relevance, trust, and fit far earlier in the decision-making process than in traditional search.

Not all LLMs interpret brands the same way

As conversational AI becomes a discovery layer, one assumption often sneaks in quietly: if your brand shows up well in one AI model, it must be showing up everywhere. In reality, that’s rarely the case. Large language models interpret, retrieve, and present brand information differently, which means relying on a single AI platform can give a very incomplete picture of your brand’s visibility.

To understand why, it helps to look at how some of the most widely used models approach answers and brand mentions.

How ChatGPT interprets brands

ChatGPT is often used as a general-purpose assistant. People turn to it for explanations, comparisons, brainstorming, and decision support. When it mentions brands, it tends to focus on contextual understanding rather than explicit sourcing. Brand mentions are frequently woven into explanations, recommendations, or summaries, sometimes without clear attribution.

From a visibility perspective, this means brands may appear:

  • As examples in broader explanations
  • As recommendations in “best tools” or comparison-style prompts
  • As part of a narrative rather than a cited source

The challenge is that brand mentions can feel correct and authoritative, while still being outdated, incomplete, or inconsistent, depending on how the prompt is phrased.

How Gemini interprets brands

Gemini is deeply connected to Google’s ecosystem, which influences how it understands and surfaces brand information. It leans more heavily on entities, structured data, and authoritative sources, and its outputs often reflect signals familiar to traditional SEO teams.

For brands, this means:

  • Visibility is closely tied to how well the brand is understood as an entity
  • Clear, consistent information across the web plays a bigger role
  • Mentions often align more closely with established sources

Gemini can feel more predictable in some cases, but that predictability depends on strong foundational signals and accurate brand representation across trusted platforms.

How Perplexity interprets brands

Perplexity positions itself as an answer engine rather than a general assistant. It emphasizes citations and source-backed responses, which makes it popular for research and comparison queries. When brands appear in Perplexity answers, they are often tied directly to cited articles, reviews, or documentation.

This creates a different visibility dynamic:

  • Brands may be surfaced only if they are referenced in cited sources
  • Freshness and topical relevance matter more
  • Competitors with stronger editorial or PR coverage may appear more often

Here, brand presence is tightly coupled with external content and how frequently that content is used as a reference.

How these models differ at a glance

AI Model How brands are surfaced What influences the visibility
ChatGPT Contextual mentions within explanations and recommendations Prompt phrasing, training data, general relevance
Gemini Entity-driven, aligned with authoritative sources Structured data, brand consistency, trusted signals
Perplexity Citation-based mentions tied to sources Content coverage, freshness, external references

Why brands need insights across multiple LLMs?

Once you see how differently large language models interpret brands, one thing becomes clear: looking at just one AI model gives you an incomplete picture. AI-driven discovery does not produce a single, consistent version of your brand. It produces multiple interpretations, shaped by the model, its data sources, and users’ interactions with it.

Must read: When AI gets your brand wrong: Real examples and how to fix it

Therefore, tracking across your brand across multiple LLM models is essential because:

Brand visibility is fragmented by default

Across different LLMs, the same brand can show up in very different ways:

  • Correctly represented in one model, where information is accurate and well-contextualized
  • Completely missing in another, even for relevant queries
  • Partially outdated or misrepresented in a third, depending on the sources being used

This fragmentation happens because each model processes and prioritizes information differently. Without visibility across models, it’s easy to assume your brand is ‘covered’ when, in reality, it may only be visible in one corner of the AI ecosystem.

Different audiences use different AI tools

AI usage is not concentrated in a single platform. People choose tools based on intent:

  • Some use conversational assistants for exploration and ideation
  • Others rely on citation-led answer engines for research
  • Many encounter AI passively through search or embedded experiences

If your brand appears in only one environment, you are effectively visible only to a subset of your audience. This mirrors challenges SEO teams already recognize from traditional search, where performance varies by device, location, and search feature. The difference is that with AI, these variations are less obvious and more challenging to track without dedicated insights.

Blind spots create real business risks

Limited visibility across LLMs doesn’t just affect awareness; it also impairs learning. Over time, it can lead to:

  • Inconsistent brand narratives, where AI tools describe your brand differently depending on where users ask
  • Missed demand, especially for comparison or “best tools for” queries
  • Competitors are being recommended instead, simply because they are more visible or better understood by a specific model

These outcomes are rarely intentional, but they can quietly influence brand perception and decision-making long before users reach your website.

So all these points point to one thing: a broader, multi-model view helps build a more complete understanding of brand visibility.

The challenge: LLM visibility is hard to measure

As brands start paying attention to how they appear in AI-generated content, a new problem becomes obvious: LLM visibility doesn’t behave like traditional search visibility. The signals are fragmented, opaque, and constantly changing, which makes tracking and understanding brand presence across AI models far more complex than tracking rankings or traffic.

Below are some key challenges brand marketers might face when trying to understand how their brand appears to large language models.

1. Lack of visibility across AI platforms

Different LLMs, such as ChatGPT, Gemini, and Perplexity, rely on various data sources, retrieval methods, and citation logic. As a result, the same brand may be mentioned prominently in one model, inconsistently in another, or not at all elsewhere.

Without a unified view, it’s difficult to answer basic questions like where your brand shows up, which AI tools mention it, and where the gaps are. This fragmentation makes it easy to overestimate visibility based on a single platform.

2. No clear insight into how AI describes your brand

AI models often mention brands as part of explanations, comparisons, or recommendations, but traditional analytics tools don’t capture how those brands are described. Teams lack visibility into tone, context, sentiment, or whether mentions are positive, neutral, or misleading.

This makes it hard to understand whether AI is reinforcing your intended brand positioning or subtly reshaping it in ways you can’t see.

3. No structured way to measure change over time

AI-generated answers are inherently dynamic. Small changes in prompts, updates to models, or shifts in underlying data can all influence how brands appear. Without consistent, longitudinal tracking, it’s nearly impossible to tell whether visibility is improving, declining, or simply fluctuating.

One-off checks may offer snapshots, but they don’t reveal trends or patterns that matter for long-term strategy.

4. Limited ability to benchmark against competitors

Seeing your brand mentioned in AI answers is a start, but it doesn’t tell you the whole story. The real question is what’s happening around it: which competitors appear more often, how they’re described, and who AI recommends when users are ready to decide.

Without comparative insights, teams struggle to understand whether AI visibility represents a competitive advantage or a missed opportunity.

5. Missing attribution and source clarity

Some AI models summarize or paraphrase information without clearly attributing sources. When brands are mentioned, it’s not always obvious which pages, articles, or properties influenced the response.

This lack of source visibility makes it difficult to connect AI mentions back to specific content efforts, PR coverage, or SEO work, leaving teams guessing what is actually driving brand representation.

6. Existing tools weren’t built for AI visibility

Traditional SEO and analytics platforms are designed around clicks, impressions, and rankings. They don’t capture AI-powered mentions, sentiment, or visibility trends because AI platforms don’t expose those signals in a structured way.

As a result, teams are left without reliable reporting for one of the fastest-growing discovery channels.

Together, these challenges point to a clear gap: brands need a new way to understand visibility that reflects how AI models surface and interpret information. This is where tools explicitly designed for AI-driven discovery, such as Yoast AI Brand Insights, come into play.

How does Yoast AI Brand Insights help?

It won’t be wrong to say that the AI-driven brand discovery can be fragmented and opaque; therefore, leading us to our next practical question: how do brand marketing teams actually make sense of it?

Traditional SEO tools weren’t built to answer that, which is where Yoast AI Brand Insights comes in. It’s designed to help users understand how brands appear in AI-generated answers and is available as part of Yoast SEO AI+.

Rather than focusing on rankings or clicks, Yoast AI Brand Insights focuses on visibility and interpretation across large language models.

Track brand mentions across multiple AI models

One of the biggest gaps in AI visibility is fragmentation. Brands may appear in one AI model but not in another, without any obvious signal to explain why. Yoast AI Brand Insights addresses this by tracking brand mentions across multiple AI platforms, including ChatGPT, Gemini, and Perplexity.

This gives teams a clearer view of where their brand appears, rather than relying on isolated checks or assumptions based on a single model.

Identify gaps, inconsistencies, and opportunities

AI-generated answers don’t just mention brands; they frame them. Yoast AI Brand Insights helps surface patterns in how a brand is described, making it easier to spot:

  • Where mentions are missing altogether
  • Where descriptions feel outdated or incomplete
  • Where competitors appear more frequently or more favorably

These insights turn AI visibility into something teams can actually act on, rather than a black box.

Shared insights for SEO, PR, and content teams

AI-driven discovery sits at the intersection of SEO, content, and brand communication. One of the strengths of Yoast AI Brand Insights is that it provides a shared view of AI visibility that multiple teams can use. SEO teams can connect AI mentions back to site signals, content teams can understand how messaging is interpreted, and PR or brand teams can see how external coverage influences AI narratives.

Instead of working in silos, teams get a common reference point for how the brand appears across AI-driven search experiences.

A natural extension of Yoast’s SEO philosophy

Yoast AI Brand Insights builds on principles Yoast has long emphasized: clarity, consistency, and understanding how search systems interpret content. As AI becomes part of how people discover brands, those same principles now apply beyond traditional search results and into AI-generated answers.

In that sense, Yoast AI Brand Insights isn’t about chasing AI trends. It’s about giving teams a more straightforward way to understand how their brand is represented, where discovery is increasingly happening.

From rankings to representation in AI-driven search

AI-driven discovery is no longer an edge case. It’s becoming a regular part of how people explore options, validate decisions, and form opinions about brands. As large language models continue to evolve, the question for brands is not whether they appear in AI-generated answers, but whether they understand how they appear, where they appear, and what story is being told on their behalf. Gaining visibility into that layer is quickly becoming a foundational part of modern brand and search strategy.

The post Why does having insights across multiple LLMs matter for brand visibility? appeared first on Yoast.

Read more at Read More

Bing Webmaster Tools testing new AI Performance report

/0 Comments/in , /by

Microsoft has been promising to give data on the performance of websites mentioned in AI results within Bing and Copilot since February 2023 and then again in April 2023. But then decided to let us down and only lump the data together with web queries, not giving us a clear view of how our sites perform within Bing’s AI experiences.

Now Bing is reportedly testing showing a new report within Bing Webmaster Tools named AI Performance report.

AI Performance report. This report is currently in a super limited beta – Microsoft has not announced anything about this publicly. But a source told us this report shows citation data from both Microsoft Copilot and partners. It shows the number of citations and the number of cited pages by day.

You can see how many times Copilot cited your website and across how many pages. It does not show you how many people clicked from those citations on Copilot to your site.

It does also let you see the data listed by “grounding queries” and “pages.” Grounding queries is likely not the full query entered into the search box on Copilot but how Bing interprets that query. Plus, it will show you the “intent” behind the query, whether it is a navigational, informational, or other form of query.

The report also shows you the specific pages cited by Copilot.

ETA. Again, Microsoft has not announced this report yet but some are seeing it go live within Bing Webmaster Tools under the Search Performance report named “AI Performance.” I do not know when you or I will gain access to the report.

Why we care. It is great to see more AI performance reporting coming from Bing Webmaster Tools, but I really do wish for click data. Every publisher, content creator, and site owner wants to know how the click-through rate from AI experiences compares to web search.

It just feels like all the search engines are deliberately hiding this data from us.

Read more at Read More

Google AI Overviews follow up questions jump you directly to AI Mode

/0 Comments/in , /by

Google will now jump you directly into AI Mode when you do a follow-up question from AI Overviews within Google Search. This makes the “transition to a conversation even more seamless,” Robby Stein, VP of Product, Google Search wrote.

Plus, Google AI Overviews are powered by Gemini 3 by default, globally.

AI Overviews jumping to AI Mode. We covered when Google was officially testing this back in December and also before Google confirmed the test in October 2025. The ask a follow-up question within the Google Search AI Overviews will jump you into a conversation directly in AI Mode.

Google said this is about “making the transition to a conversation even more seamless,” within Google Search.

Why is Google doing this? Google said that during its testing, it “found that people prefer an experience that flows naturally into a conversation – and that asking follow-up questions while keeping the context from AI Overviews makes Search more helpful.”

Here is how it works:

When you click on “Show more,” Google will overlay AI Mode directly over the search results. You can to click the X at the top right of the screen to go back to the search results. And all the sources are removed from this view, so much for sending more traffic to publishers and content creators…

Note, this is live on mobile only right now.

Gemini powering AI Overviews. Google also said that it is rolling out Gemini 3 as the default model for AI Overviews globally. Robby Stein said, “we’re making Gemini 3 the new default model for AI Overviews globally, so you get a best-in-class AI response right on the search results page, for questions where it’s helpful.”

This is different from his previous announcement about a week ago, where Gemini 3 Pro would power AI Overviews for complex queries for English globally for Google AI Pro & Ultra subs.

Now, Gemini 3 is the default model uses for AI Overviews globally.

Why we care. While Gemini 3 may provide better quality responses for AI Overviews, the bigger news is that Google officially rolling out that follow up questions go to AI Mode from Google Search’s AI Overviews.

This is a big deal, because this will likely result in even fewer clicks from Google Search to publishers and instead will drive more searchers into AI Mode.

AI Overviews show up at the top of the search results for many queries. It is hard enough to get clicks from those citation cards now, and it will be even harder as this new follow-up experience rolls out. Google is actively pushing those searchers from Search into AI Mode and not to your website.

Read more at Read More

Yahoo debuts Scout, an AI search and companion experience

/0 Comments/in , /by

Yahoo today launched the first version of its AI-powered answer engine, Yahoo Scout. Scout is available at scout.yahoo.com and is embedded across Yahoo’s network, including Yahoo News, Finance, Mail, and Search. Think of it as a Yahoo-branded AI companion designed to guide users directly within Yahoo’s properties.

What is Yahoo Scout. Yahoo Scout is Yahoo’s take on an AI search engine and companion, similar to Google’s AI Mode or OpenAI’s ChatGPT, but with a distinct Yahoo flair. The goal is to give Scout a real personality — fun, engaging, and easy for people of all ages to use and understand, Yahoo told me.

  • When you first visit Yahoo Scout, you’re greeted by a playful homepage with a search box, a catchy slogan, and an animated icon that makes the experience feel friendly and inviting.
  • Below the search box, Yahoo offers suggested searches, with filters for topics like news, finance, sports, shopping, and travel.
  • On the left, Scout shows your past queries, making it easy to jump back in where you left off.

Here’s a screenshot of the homepage. This one features a cowboy hat, but other versions include a crystal ball, a gold medal, a walking cartoon brain, and more.

Yahoo Scout’s advantage. The Yahoo Search team gave me early access to Yahoo Scout. While the interface feels familiar if you’ve used competing tools, the Yahoo-specific elements clearly set it apart.

Yahoo’s advantage over many AI search competitors is its massive, built-in audience across Mail, News, Finance, and Search. It has more than 500 million user profiles and deep data on queries, usage, intent, and behavior. Yahoo also maintains over one billion knowledge-graph entities and tracks 18 trillion consumer events and signals across its properties. Together, this gives Yahoo the ability to deliver more personal AI-driven search experiences and more accurately categorize queries.

Yahoo is the second largest email company and third largest search engine, the company told me.

Yahoo Scout can pull rich content from across Yahoo directly into its responses. This includes features like Yahoo Finance widgets, detailed financial data, tables and citations, weather, news, and more.

  • “Search is fundamentally changing, and our team has been inspired to use our decades of experience and extremely rare assets to create something uniquely useful for Yahoo’s hundreds of millions of monthly users. This beta launch is just the starting point. From search to our industry-leading verticals, Yahoo Scout will help our users accomplish their goals online faster and better than ever before,” said Jim Lanzone, CEO of Yahoo.

Sending traffic to you, the publisher. Scout is closely tied to Yahoo’s original mission: being a trusted guide to the internet, Lanzone said. From the ground up, Yahoo built Scout to honor the open web by driving traffic downstream to content creators.

Yahoo Scout responses use large, wide blue highlights across the text. When you hover over them, you can click through to the original source.

Each response also includes a “featured source” that’s easy to spot and select. Scout further emphasizes content with tables and imagery while surfacing relevant news articles and sources throughout its answers.

Early AI search engines did little to send traffic back to the sources behind their answers, Lanzone said. Yahoo wanted to set an example for how to do this the right way. There isn’t enough revenue for every publisher to rely on licensing deals with AI companies, and historically, the model that worked best was simple: send traffic back to the original sources.

Here’s an example of how Yahoo Scout links to its sources:

When you hover over the blue highlights, the source appears, and you can click through to visit it. The purple “Read more” featured-source section also aims to drive traffic downstream.

CTR expectations. I asked Yahoo about the expected click-through rate from Scout to publishers. They said they don’t know yet. Yahoo plans to learn from real-world usage once Scout goes public and iterate to improve downstream clicks. This is Scout’s first release, and real user data should be telling.

They expect queries in Yahoo Scout to be longer than in Yahoo Search, with lighter ad loads and a much higher click-through rate than the industry average.

Yahoo also told me it plans to give publishers access to impression and click data in the future, possibly through a Yahoo Webmaster Tools–style product. Crawling and indexing would remain separate, since that layer is still powered by Microsoft Bing.

Yahoo Scout in every Yahoo property. You’ll be able to access Yahoo Scout across all Yahoo properties.

  • Yahoo Mail will summarize emails with AI and extract actionable items, such as adding events to your calendar.
  • Yahoo Search will add AI summaries powered by Scout.
  • Yahoo News will surface key article highlights and include the daily digest audio summary.
  • Yahoo Finance will introduce a new Analyze button powered by Scout.

Examples of Yahoo Scout in action. Here are a few examples of Yahoo Scout. It’s not perfect, but for a six-month project, I’m impressed.

I asked Scout for help explaining how SEO works, and it delivered a solid response. SEO is complex, and not everyone will agree with every detail, but the answer was thoughtful and useful. There are citations throughout the summary:

I then asked it to share sources for finding content on the topic as a follow-up. There were clear missed opportunities to link out more, which I pointed out to Yahoo, and they agreed.

I asked Yahoo Scout how to navigate to the sources it mentioned, and at that point, it did provide links:

Here’s a screenshot of another citation that appears when you hover your mouse cursor over it.

Here are some other searches I tried:

  • Entertainment: Scout incorporates news articles, with larger graphics in clickable card formats.
  • Finance: Yahoo brings in Yahoo Finance. I was unable to generate stock charts, although in a demo I was given, I was shown that live. So maybe it was being worked on during my tests:
  • Weather: I was testing this Sunday morning, as the big snow storm was touching down in New York:

I was able to get a Yahoo Weather chart:

With tips on how to stay warm:

  • Sports: The Super Bowl is coming up, and I was hoping to get some predictions:

As a lifelong Jets fan, I asked whether the team has any chance of winning the Super Bowl in the next 10 years. The answer wasn’t encouraging, but I was happy to see a chart embedded directly in the response.

  • Shopping: And then Yahoo gave me some advice on how to dress during this weather:

Ads and commissions. Yahoo Scout will show ads at the bottom of some responses. It will also monetize commerce-related queries through affiliate commissions, a common web revenue model.

  • Yahoo told me the ads are still powered by Microsoft Advertising, but Yahoo controls how those ads appear within these interfaces.
  • These ads will be charged on a CPC basis, not an impression basis, as some other AI engines announced.

Here is a screenshot of a Progressive Insurance ad for questions about car insurance.

Here is a screenshot of product results that are labeled, “Yahoo may earn commission from these links.”

How Yahoo Scout came about. For about three years now, Yahoo has been hinting about making a return to the search game. In 2009, Yahoo made a deal with Microsoft to have Microsoft power Yahoo Search and that was the end of Yahoo building its own search technology. Literally, Yahoo has outsources Search since then and has not done its own search technology until now, with Yahoo Scout.

That is until now. About six month ago, Yahoo acquired Eric Feng’s company to lead up consumer search at Yahoo. Eric Feng is known for co-founding an online video platform startup called Mojiti, which was acquired by Hulu in 2007, in which Eric became the founding CTO and head of product at Hulu. But before that, he worked at Microsoft in the Research labs, working on solving problems with Search.

“Yahoo’s deep knowledge base, 30 years in the making, allows us to deliver guidance that our users can trust and easily understand, and will become even more personalized over the coming months,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group, the creators of Yahoo Scout. “Yahoo Scout now powers a new generation of intelligence experiences across Yahoo, seamlessly integrated into the products people use every day.”

Jim Lanzone, the CEO of Yahoo, who in his own right has a long history in search, as the CEO of Ask.com for many years, told me that Eric Feng has been instrumental in building out Yahoo Scout in the past 6 months. And there is so much more to come, this is just the first public release and you can expect many more interations and improvements to Yahoo Scout in the near future.

Anthropic. Yahoo Scout is not built on its own LLM, Yahoo partnered with Anthropic to use Claude as Yahoo Scout’s primary foundational AI model. Anthropic is one of the top artificial intelligence companies in the market. It has arguably the best AI for coders and coding frameworks named Claude. Anthropic was founded in 2021 by former members of OpenAI, including siblings Daniela Amodei and Dario Amodei, who serve as president and CEO, respectively. In September 2023, Amazon announced an investment of up to $4 billion. Google committed $2 billion the next month. As of November 2025, Anthropic has an estimated value of $350 billion.

While the foundational AI models use Anthropic, Yahoo has customized it and incorporates Yahoo’s proprietary data to make it unique and useful. Doing these searches on Anthropic will not give you anywhere close to the same experience as you would get on Yahoo Scout.

“When you’re serving hundreds of millions of users, you need AI that can do more than retrieve information – it has to reason, synthesize, and explain. Yahoo is building toward a more personalized, trustworthy kind of search, and Claude’s ability to deliver that quality of guidance at scale is at the heart of Yahoo Scout,” said Ami Vora, Head of Product at Anthropic.

Microsoft Bing. Plus, Microsoft Bing data is also incorporated into Yahoo Scout. The underlining search index is from Bing, but the responses, ranking, and experience is all Yahoo. “Yahoo Scout also builds on Yahoo’s long-standing relationship with Microsoft by leveraging Microsoft Bing’s grounding API. By combining this API with Yahoo’s trusted data and content ecosystem, Yahoo Scout ensures that answers are informed by authoritative sources from across the open web, Yahoo wrote.

Plus, Yahoo is also joining Microsoft’s Publisher Content Marketplace pilot. Microsoft’s Publisher Content Marketplace can help support revenue for publishers, the company said. Yahoo wrote this is, “reflecting a shared commitment to expanding publisher reach, connecting original work with new audiences, and supporting sustainable revenue opportunities for publishers.”

Hallucinations. I asked about hallucinations and Yahoo told me they put in a lot of guardrails to prevent hallucinations as much as possible. The Yahoo entity graph, the news content, and other Yahoo-specific data are used to ground the responses so that communications should be minimal and less than some other AI engines. In fact, they believe the hallucination rate would be “very low” compared to other AI engines.

Agents. Many AI engines are releasing agentic experiences, AI agents, to complete tasks for you. Google, OpenAI and Microsoft are investing big time into this.

Yahoo Scout has added some elements of this including inside of Yahoo Mail to add calendar events, smart compose features and more. Yahoo promises a lot more to come on this front.

Why we care. It’s an exciting time for search. For someone like me who has spent more than 20 years in search, it’s nice to see Yahoo step back into the space. Watching industry veterans like Jim Lanzone, Eric Feng, and Brian Provost take on search with AI is making it fun again, and I’m excited to see what Yahoo does next.

Availability. The Yahoo Scout answer engine is available today in beta for U.S. users at Scout.Yahoo.com and in the Yahoo Search app on iOS and Android. For more about Yahoo Scout, see this help document.

Read more at Read More

Is your account ready for Google AI Max? A pre-test checklist

/0 Comments/in , /by
Is your account ready for Google AI Max? A pre-test checklist

AI Max is Google’s latest foray into semi-keywordless targeting. 

While you need keywords for the system to have a starting place, Google uses signals beyond keywords in deciding how to show ads to searchers.

In accounts with a strong history of broad match success, AI Max can be highly effective at finding new conversions. 

If accounts are not well-optimized or have not been successful with broad match, AI Max can be a huge money pit.

To clear up a rumor before we get into the data: you do not have to use AI Max to have ads appear in AI Overviews. 

Broad match keywords can show ads in AI Overviews regardless of your AI Max usage. 

We’re looking at AI Max as a conversion expansion option, not just an option to show in AI Overviews.

This article examines the review steps you should take before you decide to test AI Max.

What to check before enabling AI Max

Accurate conversion tracking 

Your conversion tracking must be accurate, deduplicated, and focused on business outcomes. AI Max optimizes toward what you have defined as success. 

If you aren’t tracking all your conversions, or if your conversions are inflated, AI Max will be working from inaccurate data and making poor decisions.

Automated bidding with a conversion-focused strategy 

Broad match only works well when you have a bid strategy that is focused on conversions, such as:

Our experiments with AI Max have shown that it is much more predictable with one of the target options (Target CPA or Target ROAS) than with the max bid options (Maximize conversion value or Maximize conversions). 

Since the Max conversion options are meant to get you the most possible, regardless of the CPA or ROAS, they will often continue to spend your budget when the next set of conversions could have exceptionally high CPAs or very low ROAS.

If you use AI Max with one of the max bid options, pay close attention to your budget and the AI Max data.

Conversion volume

Technically, you can enable AI Max without any conversions for a campaign. 

However, with under 30 conversions per month, AI Max has been highly erratic. 

At over 100 conversions per month, it has done well more often than not, assuming you have had success with broad match in the past. 

In general, you will want to test AI Max in campaigns that have at least 30 conversions per month.

If you are going to test AI Max, starting with non-brand campaigns that have a high conversion volume will usually give you a better introduction to AI Max’s possibilities for your account.

No impression share lost due to budget

If you’re already losing impressions due to your budget, your handpicked keywords will receive even less budget if you enable AI Max. 

The goal is to spend as much as you can on your top keywords, and then have AI Max experiment with the budget we can’t spend. 

If you are already losing impressions due to your budget, then enabling AI Max usually results in poorer performance.

Have proven broad match success

AI Max will treat all of your keywords as broad match, and then expand even further than your broad match keywords. 

If you haven’t successfully used broad match, then enabling AI Max will be a waste of money.

You should first ensure that broad match can work for you, which might require reorganizing ad groups, testing new ads, and optimizing your landing pages. 

Only after you have consistently seen good results with broad match should you try AI Max.

Dig deeper: How to tell if Google’s AI Max for search is actually working

Should you use URL expansion? 

When you enable AI Max, you can expand URLs to other pages on your website. 

This means that Google can pick any page of your website to use as a landing page when AI Max triggers an ad.

Google allows you to exclude URLs. Most sites should exclude:

  • Help files and support pages.
  • Pages not built for conversions.
  • Pages that do not have conversion tracking enabled.
  • FAQs.
  • Blogs.
  • Old landing page tests that are still live.
  • Old website designs that are still live.
Google Ads - Add URL exclusions

A few people have found success with using AI Max with blogs and support pages. However, these seem to be exceptions more often than the standard result.

AI Max has struggled when there are many geographic landing pages. 

We’ve seen accounts that target different geographies by campaign, and each campaign has its own set of landing pages. 

AI Max has routinely mismatched the campaign’s geographic target with landing pages intended for other geographies. 

For example, your California campaigns are sending all of their traffic to landing pages dedicated to Texas traffic.

If you want to use AI Max URL expansion, and you have landing pages dedicated to various geographies, you will need to exclude all the landing pages that are irrelevant to the geography of your campaign.

For companies that create dedicated landing pages for each campaign or ad group, I have yet to see an example of AI Max finding better landing pages.

In every example, AI Max’s URL expansion has needed to be turned off. Eventually, this option might work for advertisers, but I have yet to see that happen.

You can review the URLs that Google is using and exclude them. If you turn on URL expansion, you will want to regularly review these URLs.

Dig deeper: AI Max in action: What early case studies and a new analysis script reveal

Get the newsletter search marketers rely on.

See terms.

Should you try automatically created assets? 

My great hope for AI Max is the automatically created assets. 

I wish I could enable this only for extensions. AI Max can help you scale messaging tremendously. 

It can go through all of your ad groups and automatically create sitelinks and callouts at the ad group level. 

This level of customization is one that many advertisers never have time to fully explore.

We had a client who enabled this feature, and suddenly, all their sitelinks linked to pages that were irrelevant to the keywords. 

We’ve seen other clients use this feature, and their callouts improved dramatically. 

Google still has a ways to go in how they auto-create assets, but this is a feature I have high hopes for.

Unfortunately, you can’t enable this feature for only ad assets (extensions). If you enable automatically created assets, Google will create additional RSA assets for you.

These assets can cause customer confusion by:

  • Making promises your brand doesn’t meet.
  • Using messaging that isn’t compliant with the law for regulated industries or doesn’t follow your brand guidelines.

You can write guidelines for how you want your ads to appear and rules on what shouldn’t be used. 

If you’re going to have Google automatically create assets, you’ll want to add guidance on how the ads should be created.

Note that term exclusions and text guidelines (Google’s official names for these features) don’t appear to be enabled in all accounts right now and may still be rolling out to advertisers.

Overall, Google’s auto-generated RSA assets have a poor track record, and if you enable them, you will want to regularly review what Google is creating on your behalf.

How to test AI Max

Since Google has a history of matching broad match keywords to other brands and generic keywords, AI Max has been very inconsistent with brand keywords.

I’d suggest starting with your top non-brand keywords to test AI Max. 

For most brands, there are more conversions to be had in non-brand expansion than in finding more people who are already searching for your brand.

AI Max can be enabled at the campaign or ad group level. 

One of the best ways to run a limited test with AI Max is to enable it only in a few ad groups that have a lot of conversion data and a successful history with broad match.

In the interface, enabling AI Max for only a few ad groups is painfully slow. 

You have to enable AI Max at the campaign level, then go into every ad group and turn it off where you don’t want it enabled.

The Google Ads Editor lets you turn AI Max on or off at the ad group level.

Google Ads - Settings for AI Max

If you want to test AI Max in only a few ad groups, then use the editor for your initial setup.

Dig deeper: When to trust Google Ads AI and when you shouldn’t

Is your account ready to test AI Max?

Google has long sought a keywordless targeting option for search. Its first step was Performance Max. 

AI Max is another foray to introduce advertisers to the idea that they don’t have to choose every keyword in their search campaigns.

Like all Google Ads products, they usually perform poorly when first launched. 

After a few years of data gathering, refinement, and additional advertiser controls, these products often prove successful.

AI Max is still a new product. Some accounts have had success with it. Others have only found failure. 

As with everything Google Ads-related, you should test it before widely adopting it.

The best way to test AI Max is to find non-brand ad groups with high conversion volume and a successful history with broad match keywords. 

Perform limited tests in these ad groups. If you find success, you can expand the ad groups that use AI Max.

During your tests, you must review your search terms, URLs, and auto-created assets. 

If you are not going to add these tasks to your workflow, then you are not ready to test every AI Max feature.

AI Max’s potential is enormous. However, it isn’t a good solution for everyone. Its ad writing can be quite poor. 

The new search terms can be hit-or-miss. You must babysit it. However, the time savings and potential are undeniable.

While I believe there is a bright future for AI Max, you must first ensure you complete all steps to verify that your account is ready to test it. 

If you follow the steps outlined in this article, you’ll know if you’re ready to test AI Max and see what these new campaign options can do for you.

Read more at Read More

4 Facebook ad templates that still work in 2026 (with real examples)

/0 Comments/in , /by
4 Facebook ad templates that still work in 2026 (with real examples)

Have you ever tried to find inspiration for ads by scrolling your own Facebook feed?

Then you know that most companies’ ads aren’t very compelling. Also, scrolling Facebook in this day and age is weirdly exhausting.

Here’s the truth: most high-performing ads in 2026 aren’t winning the day because they’re wildly original or uniquely “viral” (do we still call something that?). 

They’re winning because they follow the same repeatable templates that smart marketers have been using for decades. 

(Yes, even now. Even with AI. Even with “creative strategy” and words like “scrollable” being used non-ironically in business initiatives.)

This article goes back to basics, eschewing “inspiration” in favor of tried-and-true approaches.

Below are four Facebook ad templates you can use right now, regardless of what you’re selling, with real examples that show the strategy behind top brands’ creative.

1. Problem? Meet solution

Pain point → Relief → Simple next step

This is advertising 101. It worked in 1926, it works in 2026, and it’s still undefeated for a reason.

Despite what some business owners believe, customers don’t wake up thinking about your business. 

They wake up thinking about their life:

  • “I spent too much money.”
  • “I don’t have time.”
  • “I feel stuck.”
  • “I’m overwhelmed.”
  • “I can’t stay consistent.”

That means you’ve got to meet them where they are. 

If your customer doesn’t realize their situation is solvable, they won’t buy anything. 

That means, even if you’re the best solution in the world, until they recognize the problem, they won’t look for an answer.

Example: ClickUp

Facebook Ads - ClickUp

ClickUp takes a modern pain point that most tech workers struggle with on a daily basis, and reframes it into something that can actually be solved:

Overwhelmed by multiple tools and apps? Stop switching between them and use one platform that does it all.

This ad isn’t just selling “project management.” It’s selling:

  • Mental relief.
  • A single source of truth.
  • Less context switching, more productivity.
  • Team alignment.
  • The promise (though some might say illusion) of control.

Plug-and-play copy starter

Still dealing with [problem]?

You’re not alone – and you don’t have to stay stuck.

[Product/service] helps you [benefit] without [common objection].

Get started → [CTA]

Dig deeper: Meta Ads for lead gen: What you need to know

2. Can your competitors do this?

Unique selling point → Instant comparison → ‘Oh, hey’ moment

If you’re in a crowded industry fighting for market share (and in 2026, a lot of businesses are), the brands that stand out are the ones that make it easy for customers to answer one question:

  • Why should I choose you?

Let’s be clear: you don’t necessarily need a radical innovation or a show-stopping differentiator. 

Sometimes it’s how you do things, what you prioritize, or who you’re for. 

All that really matters is that you’re different in a way people can understand quickly and easily.

Example: The Woobles

Facebook Ads - The Woobles

Crocheting has been around forever. Beginner kits have existed for decades. Patterns have been sold in stores since before we were all born.

And yet, somehow, The Woobles managed to grab a huge chunk of market share in a craft that’s older than the automobile.

That’s impressive.

This ad shows exactly how they do it.

Instead of positioning crochet as “learn a new skill,” they highlight what makes them different, then continue stacking their differentiators in a way that makes the purchase feel almost inevitable:

  • Cute, modern projects people actually want to make.
  • Designed for true beginners.
  • Thicker yarn and a chunky hook.
  • Step-by-step video tutorials.

That’s really the point of a strong USP ad. It’s not just “we’re unique.” It’s “here’s why this is easier, better, and faster.”

Plug-and-play copy starter

Most [category] products do [expected thing].

Ours does [unexpected/uncommon benefit].

Here’s what makes it different:

  • [Differentiator 1]
  • [Differentiator 2]

Try it for yourself → [CTA]

Dig deeper: Rethinking Meta Ads AI: Best practices for better results

Get the newsletter search marketers rely on.

See terms.

3. Say more with less

Testimonial/UGC → Minimal brand talk → Trust does the selling

Not every ad needs to look and sound like an ad. In fact, some of the best-performing Facebook ads in 2026 are the ones that take you a second to realize they’re sponsored.

This is the “let the customer do the talking” template, and it’s everywhere on Instagram and TikTok because it works.

Think creator-style, user-generated content (UGC), testimonials, and review-driven ads that feel real, slightly imperfect, and way less polished than traditional brand messaging.

Oddly enough, the lack of polish is part of the appeal. It reads as “honest,” not “salesy.”

Example: Allbirds

Facebook Ads - Allbirds

Allbirds runs a simple, product-focused ad for the Tree Dasher 2, pairing a customer quote with a simple image of the shoe.

  • “Wore these @allbirds for 13 hours and could’ve gone another 13. I never want to take them off.”

That line pretty much does all the work for the ad.

It implies:

  • Comfort that lasts all day.
  • No break-in period.
  • Real-world wearability.

The creative itself is even straightforward: product image, a few lifestyle shots, and a clean layout. It’s not trying to be flashy, it’s trying to be believable. 

Plug-and-play copy starter

“I didn’t think anything would help, but this actually worked.”

[Show the proof]

If you’re dealing with [problem], try [product] → [CTA]

Dig deeper: How to test UGC and EGC ads in Meta campaigns

4. The ‘quick win’ checklist

3-5 bullets → Easy decision → Low-friction CTA

Sometimes people don’t want a story. They want clarity.

This template works especially well on Facebook because it’s built for how people actually scroll: fast, distracted, and looking for something that solves a problem right now.

Instead of writing paragraphs, you give them a handful of “yes, I want that” benefits they can absorb in two seconds. 

The “quick win” Checklist format:

  • Reduces decision fatigue.
  • Makes value instantly scannable.
  • Highlights benefits without over-explaining.
  • Works beautifully for cold audiences who don’t know your brand yet.

Example: Little Sleepies

Facebook Ads - Little Sleepies

Little Sleepies uses a simple visual and benefit callouts to answer the parent question underneath the question:

  • “Is this actually going to make my life easier?”

Instead of trying to be clever, the ad clearly lists the practical wins:

  • Double zippers for easier diaper changes.
  • Ultra-soft bamboo for comfort.
  • Fits longer (up to 3x) for better value.

This is a great reminder that in 2026, the ads that win aren’t always the funniest or most creative; they’re often the ones that make the buying decision feel effortless.

Plug-and-play copy starter

Everything you need to [achieve outcome]:

  • [Benefit 1]
  • [Benefit 2]
  • [Benefit 3]

Get it today → [CTA]

Dig deeper: How to get better results from Meta ads with vertical video formats

Templates beat inspiration every time

In 2026, the brands winning on Facebook aren’t the ones reinventing advertising every week or pouring money into slick branding campaigns.

They’re the ones who:

  • Choose a proven structure.
  • Write a clear hook.
  • Test variations quickly.
  • Let the results decide.

You don’t need inspiration every time you write a Facebook ad. You need structures you can trust.

Pick one template, write two variations, and test them against each other. Then repeat.

Read more at Read More

Why Search and Shopping ads stop scaling without demand

/0 Comments/in , /by
Why Search and Shopping ads stop scaling without demand

If you’ve spent any time in PPC communities, Reddit threads, Slack groups, or conference Q&As, you’ve probably noticed a recurring frustration: “Google Ads isn’t scaling. It’s not working, and we’re stuck.”

On the surface, everything looks fine. The campaigns are running, impression share is high, shopping feeds are clean, and budgets are flowing. But growth isn’t materializing.

This isn’t usually about “broken campaigns” – it’s about the limits of demand.

In niche markets or categories shaped by seasonality, growth is naturally capped.

Yes, running broad match or AI Max can expand your reach to adjacent queries, so impression share might not literally be 95%.

But these campaigns are still only capturing demand that already exists. Once you’ve covered the pool of relevant searches, you can’t spend your way into more.

That’s the uncomfortable truth: Google Ads doesn’t create demand. It captures it.

If fewer people are searching this month, or if your category naturally has a small audience, your results will reflect that.

You can dominate what’s there, but you can’t conjure demand out of thin air.

So when growth stalls, the real question isn’t “What’s wrong with Google Ads?” but “What are we doing to create demand that fuels future searches?”

Search and shopping = Demand capture, not creation

Let’s call Search and Shopping what they are: demand capture channels.

They’re excellent for getting in front of people when they’re ready to buy, or at least actively researching. But they are reactive by design.

Ads only appear once someone types a query. No query, no ad.

That’s why impression share (IS) can be deceptive.

A 90% IS looks like you’re winning (and you are). But if there are only 500 relevant searches in your market this month, you’ll never scale to 5,000 clicks just by raising bids.

Broad match and campaigns like AI Max can stretch coverage by surfacing adjacent queries.

But these still rely on intent. If nobody is searching for related terms, there’s nothing to match against.

Contrast this with platforms like Meta or TikTok, where more budget literally means more reach.

Search doesn’t work that way. It’s not a demand generator – it’s a closer.

Where demand really comes from

So if Search and Shopping can’t create demand, what does?

Marketers have long grouped channels into three buckets: owned, earned, and paid.

It’s old-school terminology, but it’s still the most practical way to break down where demand actually originates.

If Search and Shopping are just there to capture demand at the end, you need to understand which levers create it upstream.

Owned

These are the channels you control: your website, email, content, and CRM. They don’t usually create brand-new demand, but they’re critical for nurturing it.

Think of a D2C brand running a simple “VIP early access” sign-up before Black Friday. That list fuels branded searches once the sale goes live.

Or a SaaS company publishing an FAQ blog that shows up for early research queries, nudging prospects who later Google the brand directly.

Owned channels ensure that once curiosity is sparked, it’s effectively nurtured toward a search.

Earned

These are the channels you don’t directly pay for: PR mentions, SEO visibility, reviews, organic social, and word of mouth.

A product that lands in a holiday gift guide? Branded searches spike the next week.

A TikTok that goes viral organically? Google Trends charts it days later.

Positive Trustpilot reviews? They push people back to Google to check your site or compare pricing.

Earned channels matter because they carry credibility. They don’t just spark curiosity; they make people trust you enough to type your name into the search bar.

Paid

Paid media includes both demand-capture channels (Search and Shopping) and demand-creation channels.

Search and Shopping capture existing intent, but platforms like Meta, TikTok, YouTube, Pinterest, and Display create it.

These channels don’t wait for someone to type a query, they put your brand in front of people who weren’t already looking.

  • A TikTok showing your product in action.
  • A YouTube pre-roll highlighting your brand story.
  • A Pinterest ad that lands on someone’s gift board weeks before purchase.

These sparks generate curiosity, which later turn into branded searches.

While broad match and Performance Max might unearth “new” queries, they’re still intent-driven.

The real creation happens upstream, through paid channels designed to spark awareness.

Dig deeper: How paid, earned, shared, and owned media shape generative search visibility

The funnel without the fluff

You’ve likely heard this before, but it’s worth being specific about where Search and Shopping actually fit.

They’re strongest at conversion, but they also show up during the consideration phase of the buyer’s journey, when people are still comparing options.

Here’s how the funnel really works.

Awareness

This is the stage where people first notice you exist.

For example, a skincare brand could run TikTok ads showing its serum in action, or a B2B SaaS company might run YouTube pre-roll explaining a popular platform feature.

In retail, a promoted Pinterest pin could land on someone’s gift board long before purchase.

Tip: This is where Meta video campaigns, TikTok ads, YouTube pre-roll, Pinterest-promoted pins, PR placements, and influencer content live. These channels don’t wait for intent – they spark it.

Consideration

During this stage, people compare, research, and explore.

For example, that skincare shopper might read reviews, sign up for “early access,” and later search “best vitamin C serum.”

In B2B, a prospect could download a case study and then Google “top CRM tools for small businesses.”

Tip: This is where generic search campaigns (e.g., “best [product]” or “affordable [category]”), shopping ads with comparison queries, CRM nurture flows, SEO content, and retargeting via Meta/display/YouTube come in. This stage is about reassurance, education, and visibility while the prospect weighs their choices.

Conversion

The stage where people buy. For example, two weeks after first becoming aware of the brand, the skincare shopper searches “Brand X serum” and buys via Shopping.

After much comparison, the B2B prospect searches “[Vendor name] pricing” and completes a demo-request form.

Tip: This is where branded search, high-intent shopping queries, retargeting to cart abandoners, and PMax remarketing close the deal.

That’s why the funnel matters. If you only play at conversion, you miss those critical mid-funnel searches where people decide between you and your competitors.

Skip awareness and consideration, and your funnel isn’t a funnel at all – it’s a drinking straw. 

Get the newsletter search marketers rely on.

See terms.

What to do when search hits its ceiling

When growth stalls, the solution isn’t “spend more on search.” It’s fuelling demand earlier.

Here’s how to do exactly that, broken up by budget level.

If you’re working with smaller budgets, focus on high-leverage plays:

  • Grow your CRM list: Run simple lead-gen ads, like “sign up for early access” or “exclusive drops.” Even $300-$500 on Meta can build a list that costs nothing to email later.
  • Run warm-up campaigns: Low-cost video or carousel ads on Meta or TikTok build remarketing pools you can retarget with cheaper Google Display or YouTube Ads.
  • Optimize your site: Gift guides, FAQs, delivery cut-offs. A poor landing page wastes every click you’ve fought for.
  • Keep remarketing switched on: Display, YouTube, or PMax remarketing switched on is often cheaper than chasing new clicks in search.

If you’ve got bigger budgets, play full-funnel:

  • Run always-on awareness: Meta, YouTube, TikTok, Pinterest. Sequence your creative by teasing early, revealing mid-season, and then pushing offers when intent peaks.
  • Segment your CRM properly: VIPs deserve exclusives. Lapsed buyers need reactivation. Gift shoppers want bundles. Tailor the journeys.
  • Invest in influencers and PR: Gift guides, unboxings, trend-driven content. These placements fuel branded search demand faster than any keyword tweak.
  • Personalize your site: Recommendation engines and dynamic content keep people on the path to purchase.

Things everyone should check:

  • Check impression share: If you’re at 90%+, you’re near the ceiling. Broad match and AI Max might stretch coverage, but they won’t invent intent.
  • Track branded search: If branded queries aren’t rising, awareness is flat.
  • Keep remarketing on: It’s the lowest-hanging fruit.

Assets you need in place

Fix the basics before you pour money into awareness. Demand creation is wasted if your funnel leaks.

At a minimum, you need proper creative assets. Don’t just think about “a video” or “a few images.”

Different platforms require different formats and sizes, and if you don’t prepare variations, you’ll either be stuck with auto-cropping or miss placements altogether.

  • Meta: Vertical (Reels/Stories), square (Feed), and landscape (In-stream).
  • TikTok: Full-screen vertical, with captions/subtitles baked in as sound-off viewing is common.
  • YouTube: Horizontal 16:9 ratio for standard placements, but also vertical Shorts for mobile audiences.
  • Pinterest: Vertical lifestyle imagery tends to outperform product-only shots.
  • Display: Responsive formats mean you should plan both text + multiple image ratios so the algorithm has variety to test.

For small brands, this doesn’t mean expensive shoots. Scrappy user-generated content can be repurposed across platforms if you plan with aspect ratios in mind.

For bigger brands, building a creative matrix – every concept mapped across different formats and funnel stages – ensures consistency and saves on reshoots.

Landing pages

Don’t send awareness traffic to a generic homepage. Build pages that:

  • Answer FAQs
  • Highlight delivery cut-offs (critical in Q4)
  • Showcase bundles or gift guides for seasonal shoppers
  • For B2B: Tailor landing pages to industries or personas

CRM setup

Even a simple nurture flow is better than nothing. Capture the email at the awareness/consideration stage and follow up.

Larger brands should run segmentation and automated journeys:

  • VIPs: Exclusives.
  • Lapsed buyers: Reactivation flows.
  • Prospects: Educational sequences.

These assets make sure that when demand is created, it actually converts instead of leaking out of the funnel.

AI: Helpful, but not a shortcut

AI is everywhere right now. Tools like Performance Max, AI Max, and creative generators are powerful.

Used well, AI can save time and scale execution.

For example, generative AI can help brainstorm dozens of ad copy variations that you then refine for brand fit.

Or it can automate repetitive tasks, such as analyzing search term reports or adjusting bids, freeing you up to focus on strategy.

However, AI doesn’t change the rules of demand. It still relies on intent already being there. And if you let it run unchecked, you risk losing what makes your brand stand out.

Search Engine Land has repeatedly warned about this: over-reliance on AI can result in generic creative that lacks voice and originality, blending your ads into the crowd.

Think of AI as an accelerator: It can speed up execution, but it can’t define your brand, audience, or strategy. That still requires a human marketer.

Making it real for stakeholders, measuring demand creation

If you’re explaining this to a board or client, keep it simple:

  • Lead with this: Search responds to demand; it doesn’t generate it.
  • Show them impression share: If you’re already at 90%+, the problem isn’t coverage – it’s demand.
  • Point to branded search trends: Flat branded queries mean flat awareness.
  • Highlight competitor activity: Show where rivals are fuelling demand – Meta, TikTok, PR, or Pinterest. That’s why their branded search traffic is rising.

Don’t just show performance data. Show where the demand gap is.

Branded search is the clearest signal, but it isn’t the only one. Look at:

  • Direct traffic: More people typing your URL into their browser means brand awareness is working.
  • Organic search traffic (non-branded): If this grows, your content is pulling people in who may later convert via paid.
  • Social engagement and reach: Demand creation platforms build traction, even if the final conversion happens in Google.

Ultimately, owned, earned, and upper-funnel paid activity all create demand; Search and Shopping are there to capture it.

The ceiling isn’t Google Ads – it’s demand

The truth is, this is the direction PPC is heading.

Query growth is flattening, AI search is reshaping how results appear, and brand demand is becoming the real performance lever.

The next time someone says, “Google Ads isn’t driving traffic,” flip the question on them: Was there any demand to capture in the first place?

Because if you’re only running Search and Shopping, you can’t grow beyond the demand that already exists.

The brands that win aren’t the ones squeezing bids and obsessing over CPC swings.

They’re the ones consistently fuelling demand upstream: awareness, SEO, content, influencers, CRM, video, and social – all working together to prime the market.

So when growth stalls, the real question isn’t, “What’s wrong with Google Ads?” It’s “What are we doing to create demand that fuels future searches?”

Read more at Read More

EU puts Google’s AI and search data under DMA spotlight

/0 Comments/in , /by
Google vs. publishers: What the EU probe means for SEO, AI answers, and content rights

The European Commission has formally opened new proceedings to spell out how Google must share key Android features and Google Search data with rivals under the Digital Markets Act.

The Commission on Tuesday opened two formal “specification proceedings” to guide how Google must comply with key DMA obligations, effectively turning regulatory dialogue into a structured process with defined outcomes.

Why we care. The European Commission is escalating its oversight of Google under the Digital Markets Act, with moves that could reshape competition in mobile AI and search — and limit how much advantage Google can extract from its own platforms. If Google is required to share search data and Android AI capabilities more broadly, it could accelerate competition from alternative search engines and AI assistants, potentially fragmenting reach and measurement.

Over time, that may affect where advertisers spend, how much inventory is available, and how dependent campaigns are on Google-owned platforms.

First focus — Android and AI interoperability. Regulators are examining how Google must give third-party developers free and effective access to Android hardware and software features used by Google’s own AI services, including Gemini.

  • The goal is to ensure rival AI providers can integrate just as deeply into Android devices as Google’s first-party tools.

Second focus — search data sharing. The Commission is also moving to define how Google should share anonymised search ranking, query, click and view data with competing search engines on fair, reasonable and non-discriminatory terms.

  • That includes clarifying what data is shared, how it’s anonymised, who qualifies for access, and whether AI chatbot providers can tap into the dataset.

Between the lines. This isn’t just about compliance checklists. The Commission is signaling that AI services are now squarely in scope of DMA enforcement, especially where platform control over data and device features could tilt fast-growing markets before competitors have a chance to scale.

What’s next: Within three months, the Commission will send Google its preliminary findings and proposed measures. The full proceedings are set to conclude within six months, with non-confidential summaries published so third parties can weigh in.

The backdrop. Google has been required to comply with DMA obligations since March 2024, after being designated a gatekeeper across services including Search, Android, Chrome, YouTube, Maps, Shopping and online ads.

Bottom line. The EU is moving from theory to execution on the DMA — and Google’s handling of AI features and search data is becoming an early test of how aggressively regulators will shape competition in the next phase of the digital economy.

Read more at Read More

ChatGPT ads come with premium prices — and limited data

/0 Comments/in , /by
The agentic web is here: Why NLWeb makes schema your greatest SEO asset

OpenAI is pitching premium-priced ads in ChatGPT — with far less data than advertisers are used to getting.

What’s happening. According to a report, OpenAI is pricing ChatGPT ads at roughly $60 per 1,000 impressions — about three times higher than typical Meta ads. Despite the cost, advertisers will receive only high-level reporting, such as total impressions or clicks, with no insight into downstream actions like purchases.

Why we care. ChatGPT is emerging as a brand-new, high-attention ad environment — but one that comes with trade-offs. The high CPMs and limited reporting mean early tests will be more about brand exposure and learning than performance efficiency.

For marketers willing to experiment, this offers a first-mover chance to understand how ads perform inside AI conversations before the format scales or measurement improves.

The tradeoff. OpenAI has left the door open to expanding measurement in the future, but it has publicly committed to never selling user data to advertisers and keeping conversations private. That stance limits the kind of targeting and attribution advertisers expect from platforms like Google or Meta.

Who will see ads. The first ads will roll out in the coming weeks to users on ChatGPT’s free and lower-cost Go tiers, excluding users under 18 and conversations involving sensitive topics such as mental health or politics.

Between the lines. OpenAI is positioning ChatGPT ads as a premium, trust-first product — betting that context, attention, and brand safety can justify higher prices even without granular performance data.

Bottom line. ChatGPT ads may appeal to brands willing to pay more for visibility in a new AI-driven environment, but the lack of measurement will make performance-focused advertisers think twice.

Dig deeper. OpenAI Seeks Premium Prices in Early Ads Push (Subscription needed)

Read more at Read More

Google research points to a post-query future for search intent

/0 Comments/in , /by
Google intent extraction

Google is working toward a future where it understands what you want before you ever type a search.

Now Google is pushing that thinking onto the device itself, using small AI models that perform nearly as well as much larger ones.

What’s happening. In a research paper presented at EMNLP 2025, Google researchers show that a simple shift makes this possible: break “intent understanding” into smaller steps. When they do, small multimodal LLMs (MLLMs) become powerful enough to match systems like Gemini 1.5 Pro — while running faster, costing less, and keeping data on the device.

The future is intent extraction. Large AI models can already infer intent from user behavior, but they usually run in the cloud. That creates three problems. They’re slower. They’re more expensive. And they raise privacy concerns, because user actions can be sensitive.

Google’s solution is to split the task into two simple steps that small, on-device models can handle well.

  • Step one: Each screen interaction is summarized separately. The system records what was on the screen, what the user did, and a tentative guess about why they did it.
  • Step two: Another small model reviews only the factual parts of those summaries. It ignores the guesses and produces one short statement that explains the user’s overall goal for the session.
    • By keeping each step focused, the system avoids a common failure mode of small models: breaking down when asked to reason over long, messy histories all at once.

How the researchers measure success. Instead of asking whether an intent summary “looks similar” to the right answer, they use a method called Bi-Fact. Using its main quality metric, an F1 score, small models with the step-by-step approach consistently outperform other small-model methods:

  • Gemini 1.5 Flash, an 8B model, matches the performance of Gemini 1.5 Pro on mobile behavior data.
  • Hallucinations drop because speculative guesses are stripped out before the final intent is written.
  • Even with extra steps, the system runs faster and cheaper than cloud-based large models.

How it works. Intent is broken into small pieces of information, or facts. Then they measure which facts are missing and which ones were invented. This:

  • Shows how intent understanding fails, not just that it fails.
  • Reveals where systems tend to hallucinate meaning versus where they drop important details.

The paper also shows that messy training data hurts large, end-to-end models more than it hurts this step-by-step approach. When labels are noisy — which is common with real user behavior — the decomposed system holds up better.

Why we care. If Google wants agents that suggest actions or answers before people search, it needs to understand intent from user behavior (how people move through apps, browsers, and screens). This research moves this idea closer to reality. Keywords will still matter, but the query will be just one signal. In this future, you’ll have to optimize for clear, logical user journeys — not just the words typed at the end.

The Google Research blog post. Small models, big results: Achieving superior intent extraction through decomposition

Read more at Read More