Google Ads expands PMax Channel Reporting to account level

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Your guide to Google Ads Smart Bidding

Performance Max (PMax) advertisers just got a major visibility upgrade: Channel Reporting is now available at the account level, not just within individual campaigns.

How it works:

  • View and compare all PMax campaigns in a single reporting overview.
  • Segment by conversion metrics to understand what’s driving results.
  • Identify performance patterns across channels without jumping campaign to campaign.

Why we care. Until now, channel performance data was siloed within each PMax campaign. The new account-level reporting makes it easier to spot trends, compare results, and optimize across campaigns.

The big picture. Google notes that channel data is available for PMax campaigns “at this time” — a phrasing that suggests the feature could expand to other campaign types down the road.

Bottom line. More visibility, less friction. This change gives advertisers a faster, more complete view of PMax performance — and hints at broader reporting upgrades ahead.

First seen. This update was first picked up by Jun von Matt IMPACT’s Head of Google Ads, Thomas Eccel.

Read more at Read More

Why community is the antidote to AI overload in search marketing

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Why community is the antidote to AI overload in search marketing

In 2025, people aren’t just searching for answers anymore.

They’re looking for genuine responses from the people they trust most: 

  • Creators.
  • Communities.
  • Fellow brand supporters. 

In many ways, community has become an algorithm of its own.

AI-powered tools like Google Gemini, ChatGPT, and Perplexity have made knowledge more accessible than ever. 

But in doing so, they’ve also flattened it. 

Answers feel repetitive, citations pull from the same limited sources, and brand voices risk becoming interchangeable.

That’s where community comes in. 

While generative AI commoditizes information, community restores individuality. 

It offers what no model can compress into tokens: 

  • Authentic connection.
  • Lived experience.
  • Trust.

When democratized information becomes homogenized

I can still remember when Google – and later YouTube – made information feel democratized, putting knowledge at our fingertips like never before.

But with the rise of AI, that same accessibility now comes at a cost: everything starts to sound the same.

Every brand competing for similar keywords risks becoming interchangeable, sounding the same in AI-generated summaries that deliver information without distinction. 

Meanwhile, authority is concentrated into a small set of repeatedly cited sources, so users encounter little variation in what LLMs surface.

In some ways, this is similar to traditional SEO. 

But there’s an important difference: websites once gave us the chance to “get our brand over” and show what made our solution unique. 

That’s what feels lost in an AI-driven search experience.

Still, within this sameness lies opportunity. 

While brands fight for visibility inside an AI Overview, those with the strongest communities can not just stand apart – but truly stand alone.

Dig deeper: SEO for user activation, retention and community

Community as the differentiator

AI responses are built around compression – getting audiences to an answer as quickly and concisely as possible. 

Community, on the other hand, expands.

AI platforms tend to generalize first, then personalize only when prompted. 

Community works the opposite way: it personalizes from the start.

In my view, that’s the kind of user experience audiences will ultimately prefer – and it’s how brands will become the choice within their niche.

Think about:

  • A Reddit thread that discusses your product specifically. That’s not just another citation. It’s a living testimonial, open to being challenged or reinforced in real time.
  • A Discord server filled with engaged users doesn’t just provide customer support – it showcases the culture and identity your brand is building.
  • Social comment threads around a creator’s content show personality, emotion, and authenticity that no LLM can replicate.

Ultimately, your community gives your brand the one thing AI can’t compress or flatten into tokens: individuality. 

In a world of sameness, community is what gives your brand its voice back.

UGC? Hello, UGT! 

User-generated content (UGC) has long been viewed as central to search marketing – a key driver of discoverability.

That’s still true, but the conversation has matured. 

It’s no longer just about “content.” What truly matters now is user-generated trust (UGT).

This may sound like a subtle mindset shift, but it changes everything. 

Search marketing teams should focus on the real, ongoing conversations within communities that validate products and learn how to leverage those conversations wherever possible.

That’s where genuine user advocacy emerges. And it’s advocacy that increasingly shows up in SERPs and AI responses.

Whether it’s a YouTube video featured in search results, a Reddit thread highlighted in an AI answer, or a TikTok creator’s series, UGT creates organic momentum. 

It sends signals to both people and algorithms that your brand is credible, trustworthy, and the preferred choice.

Backlinks can be gamed, and citations scraped or manufactured.

UGC is about output. And UGT? It’s about advocacy and credibility – and that’s exactly what search marketing teams need to drive lasting results.

Dig deeper: Advanced tactics to maximize the SEO value of user-generated content

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How owned and earned communities build trust

When thinking about your brand community, there are two key considerations.

Owned communities

These are spaces where conversations and culture consistently reinforce or evolve your positioning and shape how you’re perceived.

They include:

  • Brand Discord channels. 
  • Slack groups.
  • Reddit forums.

Earned communities

These are the building blocks your brand participates in – where authenticity can either strengthen or undermine trust.

Think of:

  • Reddit threads not owned by your brand.
  • Facebook groups.
  • Quora discussions.
  • Comment threads.
  • Other spaces where people gather independently. 

Both owned and earned communities play a critical role in the smartest strategies. 

By seeding and nurturing conversations where your community and broader audience already gather.

By cultivating a “home” that is uniquely yours, you protect your brand against the homogenizing effect of AI-driven search.

Dig deeper: The rise of forums: Why Google prefers them and how to adapt

Why community is the secret sauce of search everywhere

Here’s a sobering thought: AI is only going to get better. 

They’ll become more skilled at surfacing consensus and amplifying shared rhetoric.

But consensus doesn’t drive differentiation. Community does.

A backlink can be replicated. A feature in a listicle can be matched. That’s just search marketing ping-pong.

A community, on the other hand, can’t be scraped, cloned, or copied.

Brands that invest in their communities today aren’t just building engagement.

They’re building something much more powerful: a moat of differentiation and individuality.

Community resists the sameness of AI-driven search. It’s what ensures your brand’s voice doesn’t just show up, but truly stands out.

LEGO Ideas: Community in action

One brand that proves the power of community as a competitive advantage – especially in an AI-driven world – is LEGO.

Through its LEGO Ideas platform, the company has turned its community into a creative engine for product ideation and a discovery layer that informs both content and product development.

Fans submit their ideas and vote on their favorites. The best and most popular are turned into real products. 

Everything from pop culture tie-ins (recently, a Wallace and Gromit set was greenlit) to architectural replicas has emerged through this process.

So why is this powerful from a search perspective?

There are two key reasons.

Authenticity at scale, organically

Every submission, vote, and comment is UGT in action. 

The community validates which ideas deserve attention, creating a visible signal of credibility long before a set hits the shelves.

Fan conversations fuel visibility

The conversations, forums, and social amplification around these fan-led projects fuel organic visibility. 

A single fan concept can spark thousands of blog posts, Reddit threads, YouTube videos, and TikToks – surfacing LEGO in contexts no AI citation list could ever replicate.

While competitors battle for presence in AI summaries or listicle roundups – vying to be labeled “Best Construction Toy” – LEGO has built differentiation through something much harder to copy: a living, breathing community that fuels product innovation, search visibility, and brand preference.

No amount of AI summarization can flatten a brand’s individuality when its users are constantly creating new stories about it.

Dig deeper: How to use social and forum data to inform next-level SEO strategies

From presence to preference: Why community wins

The future of search can’t be about simply being listed. 

Visibility alone is no longer enough.

Brands need to feel alive – human – and that happens through community.

AI can summarize anything, but it can’t replicate belonging. 

That’s why the brands investing in their communities today will be the ones that win tomorrow.

They won’t just be seen; they’ll be chosen. They’ll become the preference.

So, start by asking: where is my community already thriving? 

Listen, nurture, and amplify. 

That’s how you turn presence into preference in a world where every brand shows up.

Read more at Read More

The future of remarketing? Microsoft bets on impressions, not clicks

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The future of remarketing? Microsoft bets on impressions, not clicks

There’s a shift happening in digital advertising. 

For years, remarketing hinged on clicks: someone had to visit your site, trigger a pixel, and leave behind a trail you could follow with ads. 

But what if you could build your remarketing audience before they ever click?

That is the core promise of impression-based remarketing – a Microsoft Advertising-exclusive capability that lets advertisers build audiences (or exclusions) simply from users seeing their ads. 

No click. No form fill. Just an impression.

In a world of privacy shifts, AI-driven search, and fractured attention spans, this approach may not just be a nice-to-have – it could be the future.

(Disclosure: I work as Microsoft’s product liaison, and the perspectives shared here reflect my role inside Microsoft Advertising.)

What is impression-based remarketing? 

Impression-based remarketing is Microsoft Advertising’s super-powered audience targeting method. 

Instead of waiting for a user to take an action such as visiting your site, it lets you track and segment audiences based solely on ad visibility. 

Here is how it works in plain terms: 

If your ad is displayed on Bing search results, native placements, Copilot, or other Microsoft inventory, the person who saw it can be added to a remarketing list. 

That list can then be used for targeting, exclusions, or bid adjustments across eligible campaigns. 

Key operational details: 

  • You can define up to 20 sources (campaigns or ad groups that feed your remarketing lists). 
  • The audience membership window can be 1-30 days (seven days is often the sweet spot for balancing awareness and consumer sentiment). 
  • Any campaign type can be a source, but not all can be a target. For example, Premium streaming can feed lists but cannot be targeted directly. 
  • Emerging surfaces like Copilot impressions are eligible as both sources and targets, though granular reporting is not yet fully available. To clarify, only Showroom ads (currently in closed beta) can specifically target Copilot placements. 
  • If you use autobidding, Microsoft’s system will factor in your bid adjustments, meaning a +20% bid really will raise CPC or CPM. 

In short, it is the ability to remarket to people who have only seen your ad, which opens up a broader, top-of-funnel opportunity while respecting the growing limitations on tracking. 

Dig deeper: Microsoft Advertising expands remarketing list sources to 20 campaigns

How to use it – functionally and strategically 

Think of impression-based remarketing in two phases: 

  • Functional setup: The technical nuts and bolts. 
  • Strategic execution: Deciding which campaigns feed the lists, which campaigns target them, and what creative to use. 
Microsoft Ads impression-based remarketing - How to use it – functionally and strategically

Functional setup 

  • Build your audience lists
    • Identify the campaigns or ad groups that will act as sources. 
    • These are the ads whose impressions will populate your lists. 
  • Create associations
    • Associate your sources with the target campaigns where you will use the audiences for targeting, exclusions, or bid adjustments. 
    • At least one audience ad must be in your associations to make all campaign types eligible to target. 
  • Decide on membership duration
    • Seven days is often ideal to balance recency with volume, but your industry’s buying cycle may warrant shorter or longer windows. 
  • Layer on bid strategies
    • Keep in mind that bid adjustments impact CPC or CPM directly under auto-bidding. 
Microsoft Ads impression-based remarketing - Functional setup

Strategic execution 

This is where impression-based remarketing can go from “neat” to “needle-moving.” 

Empathize with the customer journey 

A first-time viewer is not ready for the same message as a warm lead. 

The most common mistake in Impression-based remarketing is running the same creative to people regardless of where they are in the funnel. 

For example: 

  • Cold audience (first exposure): Focus on brand awareness and curiosity hooks. 
  • Warm audience (saw an ad, maybe interacted with other brand assets): Lean into unique value propositions and proof points. 
  • Hot audience (familiar, showing intent signals): Shift toward urgency, offers, or clear conversion CTAs. 

Tailor messaging to decision-makers vs. influencers 

Not all buyers are the same. In B2B, especially, the person seeing your ad may not be the one signing the check. 

  • Decision-maker personas respond to concrete ROI, cost, terms, and support benefits. 
  • Influencer personas, those who need to convince the buyer, often respond better to emotional appeals, user stories, or tips on how to get leadership buy-in. 

Use micro-steps in the buyer’s journey 

Since the trigger is just an impression, do not assume you can skip stages. 

Instead of expecting someone to leap from “saw ad” to “buy,” map out micro-conversions: 

  • Move from awareness to engagement (click, video view). 
  • From engagement to consideration (content download, add to cart). 
  • From consideration to decision (purchase, sign-up). 

Sometimes this means setting ad groups, not just campaigns, as your sources and targets to allow for precise audience control.

Budget with conversion thresholds in mind 

If your targeting is too narrow, you might never gather enough impressions to reach performance significance.

Budgets should align with the audience sizes needed to meet your conversion goals.

Microsoft Ads impression-based remarketing - Strategic execution

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Why impressions are the future 

The shift to impression-based remarketing is not just about Microsoft offering a new targeting lever. 

It’s about survival in a rapidly changing ecosystem. 

1. Privacy is rewriting the rules 

With cookie deprecation, consent restrictions, and stricter data privacy laws, the reliable, click-based remarketing audiences of the past are disappearing. 

An impression, recorded server-side, does not rely on a user’s browser for tracking, making it a more resilient signal. 

2. AI-powered search changes user behavior 

As conversational AI like Microsoft Copilot, ChatGPT, and other assistants take center stage, the traditional search journey (“type query → click site → take action”) is being replaced. 

In many cases, users will get answers without ever clicking a link. 

This means advertisers must reach and influence people before they click, or even without them clicking at all. 

Dig deeper: How Microsoft Ads compares to Google Ads and when to use it

3. Sentiment and recall become the new metrics 

The old metrics, such as CTR, do not tell the whole story when much of the journey happens off-site. 

The future winners will be brands that: 

  • Create memorable touchpoints. 
  • Build positive sentiment before a user enters the buying stage. 
  • Stay top-of-mind when the moment of need arises. 

Impression-based remarketing allows you to intentionally re-engage based on visibility alone, which aligns perfectly with these goals. 

4. Redeeming undervalued placements 

Historically, advertisers have excluded certain placements, such as mobile games or sites with high ad density, because they seemed “low quality” in a click-through world. 

Those same environments can be very effective for brand imprinting. 

The user might not click in the moment, but repeated impressions in familiar contexts can drive recall later. 

Impression-based remarketing allows you to capitalize on these “slow burn” touches without overvaluing accidental clicks.  

Takeaways for advertisers 

If you are planning campaigns for the holiday season or for the AI-driven world we are already stepping into, here is the checklist to make impression-based remarketing work for you: 

  • Set it up now
    • Build your sources and associations. 
    • Keep the target list broad, but be selective with your sources. 
  • Map the journey
    • Identify what someone needs to see first, second, and third. 
    • Create dedicated creative for each stage. 
  • Respect personas
    • Decision-makers and influencers need different messaging. 
    • Avoid “one size fits all” creative blasts. 
  • Budget for volume and thresholds
    • Without enough impressions, your targeting power fades. 
    • Ensure campaigns have enough spend to feed the machine. 
  • Think beyond clicks
    • Use impression-based lists to drive brand familiarity, not just immediate conversions. 
    • Measure impact with recall and sentiment studies where possible. 

Impression-based remarketing: From feature to future

Impression-based remarketing is not just another targeting option. 

It is a structural shift in how advertisers can build relationships with their audiences. 

In a clickless, AI-mediated future, it lets you control the who and when of your targeting, even if the how of user interaction changes completely. 

Microsoft might have positioned it as a feature, but for savvy advertisers, it is a competitive moat. 

Dig deeper: How to maximize your Google Ads remarketing campaigns

Read more at Read More

Google traffic to news publishers is steady, but it isn’t traditional Search

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Google traffic news sites

Google has remained a stable source of traffic to news publishers over the past year. Although many websites have seen their traffic significantly impacted by Google’s AI Overviews, Chartbeat data shows that for 565 U.S. and UK news publishers:

  • Search referrals made up 19% of traffic in July, little changed since early 2019.
  • Google dominates search traffic: 96% of publisher referrals.
How publisher traffic referral types are stacking up.

Yes, but. “Search” here includes Google Discover, which is not traditional search. Discover is now the primary driver of Google referrals.

Why we care. Search traffic hasn’t collapsed. However, the stability is somewhat masked by a shift from traditional Google Search to Google Discover.

Dig deeper. Google says AI is boosting Search. Yes, but…

Direct traffic is shaky. Efforts to build a loyal, “type-in” audience have largely stalled, leaving publishers more dependent on Google and aggregators. Direct traffic to homepages and landing pages has fallen to 11.5% from a pandemic-era high of 16.3%.

Social keeps sinking. Social’s decline means fewer diversified referral sources:

  • Facebook referrals are down 50% since 2019, despite a recent bump.
  • X traffic is down 75% vs. 2019.
  • Only Reddit is surging – up 220% since 2019, boosted by Google visibility and an AI training deal (but it still sends less referrals than Facebook and X).

The report. Publisher traffic sources: Google steady but social and direct referrals are down, as reported by PressGazette

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Google finally gives visibility into Search Partner Network placements

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Why campaign-specific goals matter in Google Ads

Advertisers can now see exactly where their Search, Shopping, and App campaign ads are running across the Search Partner Network (SPN), with full site-level impression data.

How it works:

  • Reports list all SPN sites where your ads appeared.
  • Impression data is broken down at the site level.
  • Works like existing placement reports in Performance Max.

Why we care. Transparency has long been a sticking point with SPN. This update gives advertisers the visibility they’ve been asking for – and the ability to make smarter, brand-safe decisions.

The big picture. This change empowers advertisers to:

  • Audit brand suitability more effectively.
  • Optimize spend by analyzing which sites drive value.
  • Gain tighter control over campaign performance.

First seen. This update was first noted by Anthony Higman, founder and CEO of ADSQUIRE. He is still skeptical of Search Partner Networks despite it being an answer to a request advertisers have made for years:

  • “Still Most Likely Wont Be Participating In The Search Partner Network But This Is Unprecedented And What ALL Advertisers Have Been Requesting For Decades Now!!!”

Bottom line. Advertisers finally have the transparency and control needed to run on SPN confidently and optimize placements for better results.

Read more at Read More

Google replaces Content API for Shopping with new Merchant API

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Google Shopping Ads - Google Ads

Google announced it will shut down the Content API for Shopping on Aug. 18, 2026, officially making the Merchant API the new standard for managing Merchant Center accounts.

Why we care. For over a decade, advertisers and retailers have relied on the Content API to push product data into Google Shopping. The new Merchant API promises a simpler, more powerful way to control how products appear across both organic and ad surfaces – but it means developers and PPC teams need to start planning migrations now.

Details:

  • The Merchant API has been available in beta since May 2024, but is now generally available.
  • Google describes it as a “simplified interface” for scaling product feeds and gaining programmatic access to data, insights, and unique capabilities.
  • It will serve as the primary tool for product data management, spanning both paid and organic listings.

What’s next. The Content API remains available until August 2026, but Google urges advertisers to migrate sooner.

  • Help docs are live to guide developers through the transition.
  • Expect growing forum chatter as advertisers share migration challenges and best practices.

Bottom line. If your ecommerce business relies on the Content API, the clock is ticking. Moving to the Merchant API isn’t optional, and early adopters may gain a smoother path to scaling feeds and campaigns.

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Generative AI is changing search, but Google is still where people start: Study

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Generative AI Google search

Generative AI is reshaping how people find information — but it hasn’t replaced search engines like Google. That’s according to a new Nielsen Norman Group study:

  • While users increasingly experiment with ChatGPT, Gemini and AI Overviews, most still default to old habits: starting with Google.

Why we care. Google is a habit – and habits are hard to break. That gives Google a built-in edge: even as AI eats into clicks, Google remains the default starting point for users. That means organic visibility still matters for brands and businesses. AI is reshaping the journey, but it won’t erase search overnight.

The big picture. According to the study:

  • AI overviews = fewer clicks. People notice and often rely on Google’s AI summaries, reducing the need to visit websites. Not new, and still bad news for publishers.
  • AI chat boosts efficiency. Once users tried Gemini or ChatGPT for complex tasks, they found them faster and more useful than traditional search.
  • Search isn’t gone. Even heavy AI users still cross-check with Google or visit content pages. No participant relied solely on AI for all information needs.
  • Familiarity wins. Just as “Google” became a verb, some users now casually call ChatGPT “Chat.” Brand familiarity may be the biggest advantage in AI search.

Bottom line. Generative AI is changing how people research – but it’s an evolution, not a revolution. The biggest barrier to AI adoption isn’t accuracy or UX, it’s human habit.

About the data. Nielsen Norman Group conducted remote usability testing with nine participants in North America and UK, representing diverse demographics and levels of AI experience. Sessions explored how users approached real research tasks with search engines and AI tools.

The study. How AI Is Changing Search Behaviors

Dig deeper. Google’s AI Overviews are hurting clicks: Pew study

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Clicks rose, ROAS fell when Amazon left Google Shopping

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Amazon icon is seen on a mobile phone screen

After Amazon pulled its ads from Google Shopping on July 23, clicks became cheaper, and volumes rose, but the value of that traffic dropped. That’s according to a new study from Optmyzr, which analyzed 6,137 advertiser accounts.

By the numbers (all categories combined):

  • 📈 Clicks: +7.8%
  • 📉 CPC: -8.3%
  • 📉 Conversion Value: -5.5%
  • 📉 ROAS: -4.4%

Why we care. Less competition doesn’t automatically help advertisers, and more traffic doesn’t always mean better business. Amazon-trained shoppers still expected rock-bottom prices, fast shipping, and seamless buying. When competitors couldn’t deliver, conversion value fell.

Category winners and losers. Electronics was the clear winner. Retailers like Best Buy and Apple matched Amazon’s offer, driving +81% conversions and +7% ROAS. In other categories:

  • Home & Garden, Sporting Goods, Tools, Apparel: Fell into the volume trap – more clicks, but lower value and weaker ROAS.
  • Health & Beauty: Traffic converted, but at a lower per-sale value.
  • Apparel & Accessories: The largest category by volume, but saw a -9.5% drop in conversion value.

Between the lines. Amazon wasn’t just another bidder – it was shaping shopper expectations across categories. When Amazon left, those expectations didn’t reset, the study suggests.

What to watch. Optmyzr plans a follow-up analysis to see if delayed ecommerce conversions change the results.

Bottom line. For PPC advertisers, cheaper clicks aren’t a win if they don’t turn into profitable customers. Without Amazon-level pricing and convenience, many brands risk falling into the volume trap.

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5 B2B content types AI search engines love

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5 B2B content types AI search engines love

AI search is evolving fast, but early patterns are emerging. 

In our B2B client work, we’ve seen specific types of content consistently surface in LLM-driven results. 

These formats – when structured the right way – tend to get picked up, cited, and amplified by models like ChatGPT and Gemini.

This article breaks down five content types gaining notable AI search visibility, what makes them effective, and how to optimize them for LLM discovery:

  • Comparison pages.
  • Integration docs/open APIs.
  • Use case hubs.
  • Thought leadership on external platforms.
  • Product docs with schema.

1. Comparison pages

Our analysis shows that Gemini frequently surfaces “X vs. Y” content in AI Overviews and AI Mode – even when the query doesn’t ask explicitly for the comparison.

does carbon steel rust - AI Overview

What to include

  • Publish /vs/ pages with pros, cons, pricing, use case match, and schema. 
  • Do this for any competitors that bring in a decent volume of comparison queries, along with any comparisons that are easily related to your product or service.

2. Integration docs/open APIs

Our analysis has provided numerous instances of GPTs and Copilot citing SaaS APIs and dev docs in answers.

Example

  • A ChatGPT prompt for “setting up span metrics for backend services” cited a docs page from performance monitoring company Sentry in a list of best practices.
SaaS APIs and dev docs in AI answers

What to include

  • Maintain clear documentation + changelogs with versioning and schema.

Dig deeper: The future of B2B authority building in the AI search era

3. Use case hubs

We’ve seen clear indicators that AI Search prefers content that ties features to real business problems.

Example 

  • Vanta’s SOC 2 compliance resource appears prominently in a ChatGPT answer to “SOC 2 compliance automation for startups.”
SOC 2 compliance automation for startups - ChatGPT

What to include

  • Build intent-driven use case pages with testimonials and product mapping.

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4. Thought leadership on external platforms

LLMs pick up posts from company experts, including founders, SMEs, and established thought leaders, on outlets like Medium and Dev.to for strategy-based questions.

Example

How to find the best OpenSearch provider for cerntalized logging?

What to include 

  • Syndicate posts from a company founder, SME, or brand ambassador with a unique POV, then include a canonical link back to the business website.

5. Product docs with schema

Gemini AI Mode lifts from product docs if they’re structured with FAQs, How-to sections, and/or breadcrumb structured data.

Example

Metal 3d printer - AI Mode
recommended cash flow analysis tools for doc processing - AI Mode

What to include 

  • Add FAQPage, HowTo, breadcrumb structured data, and SoftwareApplication schema types to product docs.

3 overarching recommendations

You should never veer from the E-E-A-T principles that have long underpinned traditional SEO. Those same tenets will serve you well for LLM discovery, too. 

Beyond them, however, there are a few LLM-specific steps to consider if your goal is to increase AI search visibility.

I’ll break down three key recommendations.

Optimize for multi-modal support

AI search systems are increasingly retrieving and synthesizing multimodal content (think: images, charts, tables, videos) to better answer user queries. 

Flex your content across multiple media types to provide more useful, scannable, and engaging answers for users. 

Specific recommendations:

  • Ensure images and videos remain crawlable for search and AI bots. 
  • Serve images via clean HTML and avoid lazy-loading with JavaScript-only rendering, since LLM-based scrapers may not render JavaScript-heavy elements. 
  • Images should use descriptive alt text that includes topic context. 
  • Add captions to images and videos with an explanation right below or beside the visual. 
  • Use <figure>, <table>, etc., with contextually correct markup to help parse tables, figures, and lists.
  • Avoid images of tables. Use HTML tables instead for a machine-readable format supporting tokenization and summarization.

Optimize for chunk-level retrieval

AI search engines don’t index or retrieve whole pages.

They break content into passages or “chunks” and retrieve the most relevant segments for synthesis. 

Optimize each section like a standalone snippet. 

Specific recommendations:

  • Don’t rely on needing the whole page for context. Each chunk should be independently understandable. 
  • Keep passages semantically tight and self-contained. 
  • Focus on one idea per section: keep each passage tightly focused on a single concept. 
  • Use structured, accessible, and well-formatted HTML with clear subheadings (H2/H3) for every subtopic.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Optimize for answer synthesis

AI search engines synthesize multiple chunks from different sources into a coherent response. 

Aim to make your content easy to extract and logically structured to fit into a multi-source answer.

Specific recommendations:

  • Summarize complex ideas clearly, then expand (A clearly structured “Summary” or “Key takeaways”).
  • Start answers with a direct, concise sentence.
  • Favor a factual, non-promotional tone. 
  • Use structured data to help AI models better classify and extract structured answers.
  • Use natural language Q&A format.

Create B2B content that wins in AI search

An added benefit of these five content types is that they span multiple intent stages – helping you attract prospects and guide them through the funnel. 

Just as important: make sure your AI search measurement systems are in place (we use Profound, GA, and qualitative research) so you can track impact over time. 

And stay tuned to reports and industry updates to keep pace with new developments. 

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A technical SEO blueprint for GEO: Optimize for AI-powered search

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Technical SEO GEO

When it comes to AI-powered search, visibility isn’t just about ranking – it’s about being included in the answer itself.

That’s why generative engine optimization (GEO) matters. The same technical SEO practices that help search engines crawl, index, evaluate, and rank your content also improve your chances of being pulled into AI-generated responses.

The good news? If your technical SEO is already strong, you’re halfway there. The rest comes down to knowing which optimizations do double duty: improving your rankings while boosting your visibility in generative results.

This article breaks down four technical pillars with the biggest impact on GEO success:

  • Schema markup.
  • Site speed and performance.
  • Content structure.
  • Technical infrastructure.

1. Schema markup: Speaking AI’s language

Schema has long been essential for SEO because it removes ambiguity. Search engines use it to understand content type, identify entities, and trigger rich results.

For GEO, schema clarity is even more important. LLMs favor structured data because it reduces ambiguity and speeds extraction. If your content is marked up clearly, it’s more likely to be selected and cited.

Priority schema types for GEO

Focus on evergreen types that improve visibility:

  • FAQPage: Clearly labeled Q&A helps LLMs match user queries and surface your answers.
  • HowTo: Structured step-by-step processes are easy for AI to extract.
  • Product / Service: Defines pricing, availability, and specifications for accurate inclusion.
  • Article / NewsArticle with Author: Authorship adds a trust signal to your content.
  • Organization / LocalBusiness: Reinforces your identity, entity clarity, and local authority.
  • Review / AggregateRating: Provides social proof that AI engines use as quality signals.
  • VideoObject / ImageObject: Makes your multimedia easier for AI to find and feature.
  • BreadcrumbList: Improves context and page hierarchy mapping.

Implementation best practices

  • Use JSON-LD format (Google’s recommended approach).
  • Test rigorously with Google’s Rich Results Test and Schema Markup Validator.
  • Keep markup synced with your visible content – outdated schema erodes trust.
  • Don’t overdo it: mark up only what helps explain the content.

Bottom line: Schema improves your chances of being cited in AI answers, keeping competitors out of the box.

2. Site speed and performance: A (dis)qualifying factor

In SEO, speed has been a Google ranking factor since 2010. In GEO, speed is often a qualifier.

Generative engines pull from billions of pages. If yours is slow or unstable, they can skip it in favor of faster, more reliable sources.

Quick performance wins

  • Compress images; use WebP or AVIF; enable lazy loading.
  • Eliminate render-blocking CSS and JavaScript.
  • Target a server response time (TTFB) under 200ms.
  • Use a CDN to reduce latency.

Bottom line: Speed could be a tiebreaker between equally relevant sources. Faster pages have higher odds of inclusion in AI-generated answers – and they convert better once users click through.

3. Content structure: Making information machine-readable

LLMs rely on clarity. The easier it is for machines to parse and organize your content, the more likely it is to appear in AI-generated results.

Structural essentials

  • Logical URLs: Short, descriptive paths (e.g., /services/website-design/) clarify hierarchy.
  • Internal linking: Use bidirectional linking – pillar pages to subpages and vice versa – to reinforce topical authority.
  • Header tags (H1–H6): Follow a logical hierarchy and avoid skipping levels.
  • Structured elements: Tables, lists, and ordered steps are easier for LLMs to extract than long paragraphs.

Bottom line: Well-structured content signals topical authority, giving your site a better chance of being included in comprehensive AI answers.

4. Technical infrastructure: Ensuring discovery and trust

Even the best schema, structure, and speed won’t matter if LLMs can’t access or trust your content. Your technical infrastructure underpins GEO.

Key considerations

  • Crawlability: Ensure your important pages are accessible to Googlebot and Bingbot, since many LLMs rely on those indexes.
  • Freshness signals: Use accurate publication/modification dates, XML sitemaps with <lastmod>, and visible update notes.
  • Security: HTTPS, valid SSL, and security headers (CSP, X-Content-Type-Options, X-Frame-Options) establish trust.
  • JavaScript rendering: Don’t hide core content behind heavy client-side rendering. Use server-side rendering for anything essential.

Bottom line: If search or generative engines can’t crawl, verify freshness, or trust your site, your content won’t be considered – no matter how authoritative it is.

Building for search and AI success

The technical elements that drive GEO success aren’t new. They build on SEO fundamentals you already know:

  • Schema.
  • Performance.
  • Structure.
  • Infrastructure.

But in the AI era, these aren’t just best practices – they’re the deciding factors between being featured and being forgotten.

Getting this right will preserve your search visibility and put your content at the center of AI-driven answers.

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