Cohorts, Clusters, And The Coming AI Ad System via @sejournal, @DuaneForrester

The funnel didn’t disappear. It went invisible.

Marketers spent decades perfecting the funnel: awareness, consideration, conversion. We built personas. We mapped content to stages. We watched users click, scroll, bounce, convert. Everything was visible.

But GenAI doesn’t show its hand.

The funnel still exists, it’s just hidden inside the model. Every time someone prompts ChatGPT or Perplexity, they reveal their place in a decision journey.

Not by filling out a form or triggering a pixel, but through the prompt fingerprint embedded in their question.

That’s the new funnel. You’re still being evaluated. Still being chosen. But the targeting is now invisible, inferred, and dynamic.

And most marketers have no idea it’s happening. In fairness, I think only the cohort portion of this is actively happening today.

The ad system I explore here is purely theoretical (though Google appears to be working in a similar direction currently, and its rollout could be realistic, soon – links below).

TL;DR: This article doesn’t just explain how I think GenAI is reshaping audience targeting; it introduces three new concepts I think you’ll need to understand the next evolution of paid media: Prompt Fingerprints, Embedding Fingerprints, and Intent Vector Bidding. 

The funnel isn’t gone. It’s embedded. And it’s about to start building and placing ads on its own.

About the terminology: 

Prompt Fingerprint and Intent Vector Bidding, I believe, are net-new terms for our industry, coined here to describe how future LLM-based systems could group users and auction ad space.

Conceptually, Intent Vector Bidding aligns with work already being done behind the scenes at Google (and I’m sure elsewhere), though I don’t believe they use this phrase. 

Embedding Fingerprint draws from AI research but is reframed here as a brand-side construct to power targeting and retrieval inside GenAI systems.

This article was written over the last three weeks of July, and I was happy to find an article on August 4 talking about the concepts I’m exploring for a future paid ads bidding system.

Coincidental, but validating. The link to that article is below.

What Cohort Targeting Used To Be

In the pre-AI era, cohort targeting was built around observable behaviors.

• Retargeting audiences built from cookies and pixels.
• Segments shaped by demographics, location, and device.
• Lookalikes trained on customer traits and CRM lists.

We mapped campaigns to persona types and funnel stages. A 42-year-old dad in Ohio was mid-funnel if he clicked a product video. An 18-year-old in Mumbai was top-funnel if he downloaded an ebook.

These were guesses, good ones, often, but still blunt instruments. And they were built on identifiers that don’t necessarily survive the GenAI shift.

Prompts Are The New Personas

Large language models don’t need to know who you are. They don’t really need to track you. They don’t care where you came from. They only care what you ask, and how you ask it.

Every prompt is vectorized. That means it’s turned into a mathematical representation of meaning, called an embedding. These vectors capture everything the model can glean from your input:

• Topical domain.
• Familiarity and depth.
• Sentiment and urgency.
• Stage of intent.

LLMs use this signal to group prompts with similar meaning, even if they come from completely different types of people.

And that’s how new cohorts can form. Not from identity. From intent.

Right now, most marketers are still optimizing for keywords, and missing the bigger picture. Keywords describe what someone is searching for. Prompt fingerprints describe why and how.

Someone asking “quietest portable generator for camping” isn’t just looking for a product, they’re signaling lifestyle priorities (minimal noise, portability, outdoor use) and stage (comparison shopping).

That single prompt tells the model far more than any demographic profile ever could.

And crucially, that person is joining a cohort of other prompters asking similar questions in similar ways. If your content isn’t semantically aligned with that group, it’s not just less visible. It’s excluded.

New Concept: Prompt Fingerprint

A unique embedding signature derived from a user’s language, structure, and inferred intent within a prompt. This fingerprint is your new persona.

It’s what the model actually sees and what it uses to determine which answers (and potentially which ads) you receive. (More on those ads later!)

When Context Creates The Cohort

Let’s say the Toronto Maple Leafs just won the Stanley Cup (hey, a guy can dream, right?!). Across the city, thousands of people start prompting:

• “Where to celebrate in Toronto tonight?”
• “Best bars near Scotiabank Arena open late?”
• “Leaf’s victory parade time and location?”

None of these users knows each other. Some are teenagers, others are retirees. Some are local, others are visiting. Some are hardcore fans, some just like to party. But to the model, they’re now a momentary cohort; a group connected by real-time context, not long-term traits.

This is a fundamental break from everything digital marketers are used to. We’ve always grouped people by identity: age, interests, behavior, psychographics. But LLMs group people by situational similarity.

That creates new marketing opportunities and new blind spots.

Imagine you sell travel gear. A major snowstorm is forecast to slam into the Northeast U.S.

Within hours, prompts spike around early departures, snowproof duffel bags, and waterproof boots. A travel-stress cohort forms: people trying to escape before the storm hits. They’re not a segment you planned for. They’re a moment the system saw before you did.

If your content or product is aligned with that moment, you need a system that detects, matches, and delivers immediately. That’s what makes system-embedded ad tech essential.

You’re not buying audiences anymore. You’re buying alignment with the now, with a moment in time.

And this part is real today.

While the inner workings of commercial GenAI systems remain opaque, cluster-like behavior is often visible within a single platform session.

When you ask a string of similar questions in one ChatGPT or Gemini session, you may encounter repeated phrasing, brand mentions, or answer structure. That consistency suggests the model is grouping prompts by embedded meaning, not demographics or declared traits.

I cannot find studies or examples of this behavior being recorded, so please drop a comment if you have a source for such data. I keep hearing about it, but cannot find dedicated data.

Looking Forward

Entire classes of micro-cohorts may form and disappear within hours. To reach them, you’ll need AI-powered, system-embedded ad systems that can:

• Detect the cohort’s emergence through real-time prompt patterns.
• Generate ads aligned with the cohort’s immediate need.
• Place and optimize those ads before the window closes.

Humans can’t move at that speed. AI can. And it has to because the opportunity vanishes with the context.

Sidebar: What I Think Is Real Vs. What I Think Is Coming

• Prompt Fingerprints – Live Today: Every GenAI system turns your prompt into a vector embedding. It’s already the foundation of how models interpret meaning.
• Cohort Clustering by Prompt Similarity – Active Now: You can observe this in tools like ChatGPT and Gemini. Similar prompts return similar answers, meaning the system is clustering users based on shared intent.
• Embedding Fingerprints – Possible Today: If brands structure their content for vectorization, they can create an embedding signature that aligns with relevant prompts. Most don’t yet.
• Intent Vector Bidding – Emerging Theory: Almost in the market today. Given current ad platform trends, this kind of bidding system is likely being explored widely across platforms.

Why Old-School Personas Will Work Less Effectively

Age. Income. ZIP code. None of that maps cleanly in vector space.

In the GenAI era, two people with radically different demographics might prompt in nearly identical ways and be served the same answers as a result.

It’s not about who you are. It’s about how your question fits into the model’s understanding of the world.

The classic marketing persona is much less reliable as a targeting unit. I’m suggesting the new unit is the Prompt Fingerprint, and marketers who ignore that shift may find themselves omitted from the conversation entirely.

The Funnel Is Still There — You Just Can’t See It

Here’s the thing: LLMs do understand funnel stages.

They just don’t label them the way marketers do. They infer them from phrasing, specificity, and structure.

• TOFU: “Best folding kayaks for beginners”
• MOFU: “Oru Inlet vs. Tucktec comparison”
• BOFU: “Oru kayak discount codes July 2025”

These are prompt-level indicators of funnel stage. And if your content doesn’t align with how those prompts are formed, it likely won’t get retrieved.

Want to stay visible? Start mapping your content to the language patterns of funnel-stage prompts, not just to topics or keywords.

Embedding Fingerprints: The New Targeting Payload

It’s not just prompts that get vectorized. Your content does, too.

Every product page, blog post, or ad you write forms its own Embedding Fingerprint, a vector signature that reflects what your message actually means in the model’s understanding.

Repurposed Concept: Embedding Fingerprint

Originally used in machine learning to describe the vector signature of a piece of data, this concept is reframed here for content strategy.

An embedding fingerprint becomes the reusable vector signature tied to a brand, product, or message – a semantic identity that determines cohort alignment in GenAI systems.

If your content’s fingerprint aligns closely with a user’s prompt fingerprint, it’s more likely to be retrieved. If not, it’s effectively invisible, no matter how “optimized” it may be in traditional terms.

Intent Vector Bidding: A Possible New Advertising Paradigm

So, what happens when GenAI systems all start monetizing this behavior?

You could get a new kind of auction. One where the bid isn’t for a keyword or a user profile, per se, but for alignment.

New Concept: Intent Vector Bidding

A real-time ad bidding mechanism where placement is determined by alignment between a user’s prompt intent vector and an advertiser’s content vector.

To be clear: this is not live today in any public, commercial ad platform that I am aware of. But I think it’s well within reach. Models already understand alignment. Prompt clustering is already happening.

What’s missing is the infrastructure to let advertisers fully plug in. And you can bet the major players (OpenAI, Google, Meta, Microsoft, Amazon, etc.) are already thinking this way. Google is already looking at this openly.

We’ve Been Heading Here All Along

The shift toward LLM-native ad platforms might sound radical, but in reality, we’ve been headed this way for over a decade.

Step by step, platform by platform, advertisers have been ceding control to automation, often without realizing they were walking toward full autonomy.

Before we trace the path, please keep in mind that while I do have some background in the paid ad world, it’s much less than many of you.

I’m attempting to keep my date ranges and tech evolutions accurate, and I believe they are, but others may have a different view.

My point here isn’t historical accuracy, it’s to demonstrate a continual, directional progression, not nail down on which day of which year did Google do X.

And, I’ll add, maybe I’m entirely off base with my thinking here, but it’s still been interesting to map all this out, especially since Google has already been digging in on a similar concept.

1. From Manual Control To Rule-Based Efficiency

• Early 2000s – 2015

In the early days of search and display, marketers controlled everything: keyword targeting, match types, ad copy, placements, and bidding.

Power users lived inside tools like AdWords Editor, manually optimizing bids by time of day, device type, and conversion rate.

Automation started small, with rule-based scripts for bid adjustments, budget caps, and geo-targeting refinements. You were still the pilot, just with some helpful instruments.

2. From Rule-Based Logic To AI-Guided Bidding

• 2015 – 2018

Then came Smart Bidding.

Google introduced Target CPA, Target ROAS, and Enhanced CPC: bid strategies powered by machine learning models that ingested real-time auction data (device, time, location, conversion likelihood) and made granular decisions on your behalf.

Marketers set the goal, but the system chose the path. Control shifted from how to what result you want. This was a foundational step toward AI-defined outcomes.

3. From AI-Guided Bidding To Creative Automation

• 2018 – 2023

Next came the automation of the message itself.

Responsive Search Ads let advertisers upload multiple headlines and descriptions and Google handled the permutations and combinations.

Meta and TikTok adopted similar dynamic creative formats.

Then Google launched Performance Max (2021), a turning point that eliminated keywords entirely.

• You provide assets and conversion goals.
• The system decides where and when to show your ads, whether across Search, YouTube, Display, Gmail, Maps, and more.
• Targeting becomes opaque. Placement is more invisible. Strategy becomes trust.

You’re no longer steering the vehicle. You’re defining the destination and expecting the algorithm gets you there efficiently.

4. From Creative Automation To Generative Execution

• 2023–2025
  

The model doesn’t just optimize messages anymore; it writes them.

• Meta’s AI Sandbox generates headlines and CTAs from a prompt.
• TikTok’s Creative Assistant produces hook-driven video scripts on demand.
• Third-party tools and GPT-based agents build full ad campaigns, including copy and targeting.
• Google’s Veo 3 and Veo 3 Fast now live on Vertex AI, generate polished ads and social clips from text or image-to-video inputs, optimized for rapid iteration and programmatic use.

This isn’t sci-fi. It’s what’s coming to market today.

5. What Comes Next – And Why It’s Inevitable

The final leap is where you don’t submit an ad, you instead submit your business.

A fully LLM-native ad platform would:

• Accept your brand’s value propositions, certifications, product specs, creative assets, brand guidelines, company vision statements, and guardrails.
• Monitor emergent cohorts in real time based on prompt clusters and conversation spikes.
• Inject your brand into those moments if, and only if, your business’s vector aligns with the cohort’s intent.
• Charge you automatically for participation in that alignment.

You wouldn’t target. You wouldn’t build campaigns. You’d just feed the system and monitor how well it performs as a semantic extension of your business.

The ad platform becomes a meaning-based proxy for your company, an intent-aware agent acting on your behalf.

That’s not speculative science fiction. It’s a natural endpoint of the road we’re already on, I believe. Performance Max removed the steering wheel. Generative AI threw out the copywriter. Prompt-aligned retrieval will take care of the rest.

Building The LLM-Native Ad Platform

This is a theoretical suggestion of what could be our future for paid ads within AI-generated answer systems.

To make Intent Vector Bidding real at scale, the underlying ad platform will have to evolve dramatically. I don’t see this as a plug-in bolted onto legacy PPC infrastructure.

It will be a fully native layer inside LLM-based systems, one that replaces both creative generation and ad placement management.

Here’s how it could work:

1. Advertiser Input Shifts From Campaigns To Data Feeds

Instead of building ads manually, businesses upload:

• Targeted keywords, concepts, and product entities.
• Multimedia assets: images, videos, audio clips.
• Credentials: certifications, affiliations, licenses.
• Brand guidelines: tone, voice, claims to avoid.
• Business limitations: geography, availability, compliance.
• Structured value props and pricing tiers.

2. The System Becomes The Creative + Placement Engine

The LLM:

• Detects emerging prompt cohorts.
• Matches intent vectors to advertiser fingerprints.
• Constructs and injects ads on the fly, using aligned assets and messaging.
• Adjusts tone and detail based on prompt stage (TOFU vs BOFU).

3. Billing Becomes Automated And Embedded

• Accounts are pre-funded or credit-card linked.
• Ad spend is triggered by real-time participation in retrieval or output injection.
• No ad reps. No auctions you manage. Just vector-aligned outcomes billed per engagement, view, or inclusion.
• Ad creation and placement become a single-price-point item as the system manages all, in real time.

If you want some more thoughts on this concept, or one that’s closely related, Cindy Krum was recently on Shelley Walsh’s IMHO show, where she talked about whether she thinks Google will put ads inside Gemini’s answers, and it was an interesting discussion.

You should give it a listen. And this report on Google suggests this is not only here now, but expanding.

The Human Role Doesn’t Disappear – It Evolves

Marketers and ad teams won’t be eliminated. Instead, they’ll become the data stewards and strategic interpreters of the system.

• Expectation setting: Clients will need help understanding why their content shows up (or doesn’t) in GenAI outputs.
• Data maintenance: The system is only as good as the assets you feed it, and relevance and freshness matter.
• Governance and constraints: Humans will define ethical limits, messaging boundaries, and exclusions.
• Training and iteration: AI ad visibility will rely on live outputs and observed responses, not static dashboards. You’ll tune prompts, inputs, and outputs based on what the system retrieves and how often it surfaces your content.

In this model, the ad strategist becomes part translator, part data curator, part retrieval mechanic.

And the ad platform? It becomes autonomous, context-driven, and functionally invisible, until you realize your product’s already been included in the buyer’s decision … and you’ve been billed accordingly.

A Closer Look: Intent Vector Bidding In Action

Imagine you’re an outdoor gear brand and there’s a sudden heatwave hitting the Pacific Northwest. Across Oregon and Washington, people begin prompting:

• “Best ultralight tents for summer hiking”
• “Camping gear for extreme heat”
• “Stay cool while backpacking in July”

The model recognizes a spike in semantically similar prompts and data from news sources, etc. A heatwave cohort forms.

At the same time, your brand has a product page and ad copy about breathable mesh tents and high-vent airflow systems.

If your content has been vectorized (or if your system embeds an ad payload with a strong Embedding Fingerprint), it’s eligible to enter the auction.

But this isn’t a bid based on demographic data or historical retargeting. It’s based on how closely your product vector aligns with the live cohort’s prompt vectors.

The LLM chooses the most semantically aligned match. The better your alignment, the more likely your product is included in the AI’s answer, or inserted into the contextual ad slot within the response.

No campaign setup. No segmented audience targeting. Just semantic match at machine speed. This is where creative, product, and performance converge, and that convergence rewrites what it means to “win” in modern advertising.

What Marketers Can Do Right Now

There’s no dashboard that will tell you which Prompt Fingerprints you’re aligned with. That’s the hard part.

But you can start by thinking like a model until tools start to develop features that allow you to model your Prompt Fingerprint.

Start with:

• Simulated prompt testing: Use GPT-4 (or Gemini or any other) to generate sample queries by funnel stage and see what brands get retrieved.
• Create content for multi-cohort resonance: for example, a camping blog that aligns with both eco-conscious minimalists and adventure-seeking parents.
• Build your own prompt libraries: Classify by intent stage, specificity, and phrasing. Use these to guide creative briefs, content chunking, and SEO.
• Track AI summaries: In platforms like Perplexity, Gemini, and ChatGPT, your brand might influence answers even when you’re not explicitly mentioned. Your goal is to become the attributed source, not just a silent contributor.

In this new, genAI version of search, you’re no longer optimizing for page views. You’re optimizing for retrievability by semantic proximity.

The Rise Of The Prompt-Native Brand

Some brands will begin designing entire messaging strategies around prompt behavior. These prompt-native brands won’t wait for traffic to arrive. They’ll engineer their content to surf the wave of prompt clusters as they form.

• Product copy structured to match MOFU queries.
• Comparison pages written in prompt-first language.
• AI ad copy tuned by cohort spike detection.

And eventually, new brands will emerge that never even needed a traditional website. Their entire presence will exist in AI conversations.

Built, tuned, and served directly into LLMs via vector-aligned content and Intent Vector Bids.

Wrapping Up

This is the next funnel, and it’s not a page. It’s a probability field. The funnel didn’t disappear. It just went invisible.

In traditional marketing, we mapped clear stages (awareness, interest, decision) and built content to match. That funnel still exists. But now it lives inside the model. It’s inferred, not declared. It’s shaped by prompts, not click paths.

And if your content doesn’t align with what the model sees in that moment, you’re missing in the retrieval.

More Resources:

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This post was originally published on Duane Forrester Decodes.

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Featured Image: NicoElNino/Shutterstock

AI Crawler Optimization Tips

Generative AI platforms such as ChatGPT, Perplexity, and Claude now execute live web searches with all prompts. Ensuring a site is crawlable by AI bots is therefore essential for mentions and citations on those platforms.

Here’s how to optimize a website for AI crawlers.

Disable JavaScript

Make sure your pages are readable with JavaScript disabled.

Unlike Google’s crawler, AI bots are immature. Many tests from industry practitioners confirm AI crawlers cannot always render JavaScript.

Most publishers and businesses no longer worry about JavaScript crawlability since Google has rendered those pages for years. Hence there’s a huge number of JavaScript-heavy sites.

The Chrome browser can render a site without JavaScript. To activate:

• Go to your site using Chrome.

• Open Web Developer tools at View > Developer > Developer Tools.

• Click Settings (behind the gear icon) on the right side of the panel.

• Scroll down and check the option “Disable JavaScript” under “Debugger.”

Now browse your site, making sure:

• All essential content is visible, especially behind tabs and drop-down menus.

• The navigation menu and other links are clickable.

• For video embeds, there’s an option to click to the original video, access a transcript, or both.

You can use Aiso, an AI optimization platform, to ensure AI bots can access and crawl your site. With a free trial, the platform allows a few free checks. Go to the “Website crawlability” section and enter your URL.

The tool will conduct a thorough review with suggestions on improving access for AI crawlers and even show the appearance of pages with JavaScript disabled.

Ensure AI Access

Make sure your site allows access for AI bots. Some content management platforms and plugins disallow AI access by default — site owners are often unaware.

To check, review your robots.txt file at [yoursite.com]/robots.txt.

The AI platforms themselves can interpret the file to ensure it allows access. Paste your robots.text URL into a ChatGPT prompt, for example, and request an analysis.

Structured Data

Structured data markup, such as from Schema.org, can also help ensure visibility.

Schema markup makes it easier for AI bots to extract essential information from a page (or bypass a block) without crawling it in full.

For example, many website FAQ sections have collapsible elements that prevent access to AI bots. Schema’s FAQPage Type replicates all questions and answers, enabling bot visibility.

Similarly, Schema’s Article Type can communicate context and authorship of content.

Google Ecommerce SERP Features 2025 Vs. 2024 via @sejournal, @Kevin_Indig

In 2024, Google turned the SERP into a storefront.

In 2025, it turned it into a marketplace with an AI-based mind of its own.

Over the past 12 months, Google has layered AI into nearly every inch of the shopping search experience by merging organic results with product listings, rolling out AI Overviews that replace traditional product grids, and introducing a full-screen “AI Mode.”

Meanwhile, ChatGPT is inching closer to becoming a personalized shopping assistant, but for now, the most dramatic shifts for SEOs are still happening inside Google.

To understand the impact, I revisited a set of 35,000+ U.S. shopping queries I first analyzed in July 2024.

In today’s Memo, I’m breaking down the state of Google Shopping SERPs in 2025. A year later, the landscape looks … different:

• AI Overviews have started to displace classic ecommerce SERP features.
• Image packs dominate the page.
• Discussion forums are on the decline.

Plus, an exclusive comparison of 2024 vs. 2025 ecommerce SERP features and a full, detailed checklist of optimizations for the SERP features that matter most today (available for premium subscribers. I show you exactly how I do this).

This memo breaks down exactly what’s changed in Google’s shopping SERPs over the past year. Let’s goooooo.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

In the last 12 months, Google hasn’t just transformed itself into a publisher that serves up content to answer queries right in the SERP (via AI Overviews and AI Mode). It’s also built out an extensive marketplace for shopping queries.

However, Google now provides a whole slew of SERP features and AI features for ecommerce queries that are at least as impactful as AIOs and AI Mode.

Meanwhile, ChatGPT & Co. are starting to include product recommendations with links, reviews, buy buttons, and recommendations directly in the chat. (But this analysis focuses on Google results only.)

To better understand the key trends for Google shopping queries, in July 2024, I analyzed 35,305 keywords across product categories like fashion, beds, plants, and automotive in the U.S. over the last five months using seoClarity.

We’re revisiting that data today, examining those same keywords and categories for July 2025.

The results:

1. AI Overviews have started to replace product grids.
2. Ecommerce SERPs are increasingly visual.
3. There are more question-related SERP features (like People Also Ask), less UGC.
4. Fewer videos are appearing across the SERPs for product-related searches.

About the data:

• This data specifically covers Google search results and features. It doesn’t include ChatGPT, Perplexity, etc. However, we’ll touch on this briefly below.
• Over 35,000 search queries were analyzed, and the same group was examined in both July 2024 and July 2025.
• The search queries analyzed include product-related queries across a broad spectrum, from brand terms (like Walmart) to individual products (iPads) and categories (e-bikes).
• If you’re curious about the exact list of Google shopping SERP features included in this analysis, they’re included at the bottom of this memo.

Before we dig into the findings…

In Google’s shift from search engine to ecommerce marketplace (and from search engine to publisher), Google has merged as much as possible into the SERP page.

Web results and the shopping tab for shopping searches were combined as a response to Amazon’s long-standing dominance.

The shopping tab still exists, sure.

But for product-related searches, the main search page and the Google shopping experience look incredibly similar, with the Shopping tab streamlined to a product-grid experience only.

In June 2024, I reported in Critical SERP Features of Google’s shopping marketplace:

• Google has fully transitioned into a shopping marketplace by adding product filters to search result pages and implementing a direct checkout option.
• These new features create an ecommerce search experience within Google Search and may significantly impact the organic traffic merchants and retailers rely on.
• Google has quietly introduced a direct checkout feature that allows merchants to link free listings directly to their checkout pages.
• Google’s move to a shopping marketplace was likely driven by the need to compete with Amazon’s successful advertising business.
• Google faces the challenge of balancing its role as a search engine with the need to generate revenue through its shopping marketplace, especially considering its dependence on partners for logistics.

And now?

Google’s layered AI and personalized SERP features into the shopping experience as well.

Below are the Google SERP features I’ll be examining in this year-over-year (YoY) analysis, specifically, with a quick synopsis if you’re not familiar.

• Images: A horizontal carousel of image results related to the query pulled from product pages or image-rich content; usually appear at the top or mid-page and link to Google Images or directly to source pages.
• Products: Displays a visual grid or carousel of products with titles, images, prices, reviews, and merchants. This includes free product listings (organic) and Product Listing Ads (PLAs) (paid).
• People Also Ask (PAA): Related questions users frequently ask. Clicking a question reveals a source link. (These often inform Google’s understanding of search intent and user curiosity.)
• Things To Know: An AI-driven feature that breaks a topic into subtopics and frequently misunderstood concepts. Found mostly on broad, educational, or commercial-intent queries, this is Google’s way of guiding users deeper into a topic and understanding deeper search intent.
• Discussion and Forums: Highlights relevant threads from platforms like Reddit, Quora, and niche forums. Answers are often community-generated and authentic. Replaced some traditional “People Also Ask” real estate for shopping or reviews queries.
• Knowledge Graph: Displays structured facts about a person, brand, product, or topic-sourced from trusted databases. Appears in a right-hand sidebar or embedded box.
• Buying Guide: A feature that explains what to consider when shopping for a product, e.g., “What to look for in a DSLR camera.” Usually placed mid-page for commerce-intent queries. It mimics a human assistant or product expert’s advice. Contains snippets and links to sources.
• Local Listing: Shows local business listings with map, ratings, hours, and quick call/location links. Prominent in searches with local intent like “shoe store near me” or “coffee shops in Detroit.”
• AI Overview: Generative AI summary at the top of the SERP that answers the query using information synthesized from multiple sources. For shopping queries, it often includes product summaries.
• Video: A carousel or block of video content, mostly from YouTube, but also from other video-hosting platforms. May include timestamps, captions, or “key moments” for long videos.
• Answer Box (a.k.a. Featured Snippet): A direct answer to a query extracted from a single web page, shown at the top of the SERP in a stylized box. Often used for factual or how-to queries. Includes the source link.
• Free Product Listings: Organic product results submitted via Google Merchant Center feeds. These listings show in the Shopping tab and occasionally in the main SERP product grid (distinct from paid Shopping ads).
• From sources across the web: A content block showing opinions or quotes on a product or topic from a variety of sites. Often used in AI Overviews or product reviews to surface aggregated user sentiment or editorial input.
• FAQ: An expandable schema-driven block showing common questions and answers sourced from a specific page. Typically appears under a site’s organic result when FAQ schema is properly implemented.
• PPC: Sponsored links shown at the top or bottom of the SERP, marked “Sponsored” or “Ad.” These can show up as text, product images/grids, etc.

In addition to the standard SERP features tracked in this analysis via the above list, here’s a look at the current Google shopping marketplace SERP features and/or elements (like toggle filters) that we’re dealing with at the halfway point of 2025.

• AI Mode (Full-Screen): Interactive, immersive full-page AI shopping experience with filters and buy links.
• Shopping filters inline: Dynamic filters (brand, color, price) within AI Mode and Shopping grids.
• Virtual try-on: This feature was recently released. It’s a generative AI module showing clothes on diverse body types (expanding by category).
• Price tracking/alerts: Users can track price drops and get alerts via Gmail or Chrome. Honestly, a pretty great tool.
• Popular stores/top stores: Scrollable carousel of prominent retailers for the product category.
• Product sites (EU market): Organic feature that shows prominent ecommerce domains (due to regulatory changes in the EU).
• Trending products/popular products: Highlights products rising in popularity based on recent search activity.
• Merchant star ratings: Display review scores and counts in summaries or tiles.
• Free shipping/returns labels: Highlighted callouts in product tiles.
• “Verified by Google” merchant badges: Google-trusted seller icon in some listings.
• Quick comparison panels: Side-by-side spec or feature comparisons (this is an early-stage rollout, similar to Amazon’s product comparison panel or module).

To illustrate with an example, let’s say you are looking for kayaks (summertime!).

On desktop (logged-in), Google will now show you product filters on the left sidebar and “Popular products” carousels in the middle on top of classic organic results, but under ads, of course.

Directly under the shopping product grids, you have traditional organic results along with an on-SERP Buying Guide, similar to People Also Ask questions (which is also included further down the page).

Both the Buying Guide and People Also Ask features deliver answers with links to original content.

On mobile, you get product filters at the top, ads above organic results, and product carousels in the form of Popular products or “Products for you.”

This experience doesn’t look very different from Amazon … which is the whole point.

Google’s shopping experience lets users explore products on a variety of marketplaces, like Amazon, Walmart, eBay, Etsy, & Co.

From an SEO perspective, the prominent position of product grid (listings) and filters likely significantly impacts CTR, organic visibility, and ultimately, revenue.

But let’s take a look at the same search via AI Mode.

Below is the desktop experience via Chrome.

I’ve zoomed out here so you get the whole view, but it takes the user two to three scrolls to get to the product grid when in a standard view.

Here on mobile, getting to product recommendations takes several scrolls. In one instance, I received a result that included a list of places near me in my city where I could get a kayak.

Keeping the current Google shopping SERP experience in mind, here’s what the data shows.

This is the most noteworthy shift found in the data, as you can probably guess.

Since March 2025, when Google began rolling out AI Overviews more aggressively, they’ve also started replacing (organic) product grids.

The graph above might look like it represents minimal changes when you examine it in a timeline view, but you can see the trend even better when moving AIOs to a second y-axis (below).

I expect AI Overviews to still show the product grids searchers have become accustomed to, although they might take a different form.

When searching for [which camera tripod should I buy?], for example, we find an AI Overview at the top with specific product recommendations.

Of course, AI Mode takes that a step further with richer product recommendations and buying guides.

(Shoutout to The New York Times and the other five sources for this AI Mode answer … which now don’t see an ad impression or affiliate click.)

As a result of this shift, which I predict will only increase over time, tracking your brand mentions and product links in AI Overviews becomes critical. Skip this at your own risk.

Here, you’ll see the increase in image packs over time, with a big shift in March 2025.

Image packs for ecommerce-related queries grew from ~60% in 2024 to a new baseline of over 90% of keywords in 2025.

Also, notice how Google systematically tests SERP layouts between core updates (e.g., the dip in the graph above happens between the March and June 2025 Core Updates).

Having strong product images, which are properly optimized, continues to be crucial for ecommerce search.

Since January 2025, Google has shown more People Also Asked (PAA) features at the cost of Discussions & Forums.

Even though Reddit is the second most visible site on the web, I’m surprised to see more PAA – two years after Google removed FAQ rich snippets from the SERPs.

This is something you want to consider tracking for queries that are directly related to your products, if you’re not doing so already. (You can do this in classic SEO tools like Semrush or Ahrefs, for example.)

Since August 2024, Google has systematically reduced the number of videos in the ecommerce search results.

It seems that images have taken a lot of the real estate videos that used to own.

As a result, videos are less important in ecommerce search, while images are increasingly more important.

If you’ve been creating and optimizing videos and haven’t seen the SEO results you wanted for your products/site, this could be your signal to invest in other types of content.

While this analysis covers Google SERP data specifically, it’d be a miss to not discuss the new shopping features in ChatGPT.

However, we don’t yet have months and months of data on LLM-based conversational product recommendations to give us good, clear information, so I anticipate there will be more analysis ahead once more time passes.

ChatGPT’s shopping experience is starting to look a lot like Google’s  – but with a twist: Instead of viewing lists of blue links or multiple product grids, it curates a conversational shortlist with minimal product listings included.

No affiliate links and no paid ads (yet).

OpenAI integrates real-time product data from tools like Klarna and Shopify, allowing ChatGPT to surface up-to-date prices, availability, reviews, and product details in a shoppable card-style format.

ChatGPT also offers a “Why you might like this” and “What people are saying” generative summary when a specific product is clicked.

OpenAI offers the following guidance about how these products are selected [source]:

A product appears in the visual carousel when ChatGPT perceives it’s relevant to the user’s intent. ChatGPT assesses intent based on the user’s query and other available context, such as memories or custom instructions….

When determining which products to surface, ChatGPT considers:

• Structured metadata from third-party providers (e.g., price, product description) and other third-party content (e.g., reviews).

• Model responses generated by ChatGPT before it considers any new search results. Learn more.

• OpenAI safety standards.

Depending on the user’s needs, some of these factors will be more relevant than others. For example, if the user specifies a budget of $30, ChatGPT will focus more on price, whereas if price isn’t important, it may focus on other aspects instead.

OpenAI also explains how merchants are selected for products [source]:

When a user clicks on a product, we may show a list of merchants offering it. This list is generated based on merchant and product metadata we receive from third-party providers. Currently, the order in which we display merchants is predominantly determined by these providers….

To that end, we’re exploring ways for merchants to provide us their product feeds directly, which will help ensure more accurate and current listings. If you’re interested in participating, complete the interest form here, and we’ll notify you once submissions open.

That being said, it takes some trial and error to trigger product recommendations directly in the chat.

For instance, the prompt [can you help me find the best kayaks for beginners] results in an output that includes product recommendations, while the query [what are the best kayaks for beginners] results in a list without shopping results, features, or links.

Prompts with action-oriented language like “can you help me” and “will you find” may have a higher likelihood of offering shopping results directly in the chat, while queries like “what is the best” and “what are the best” and “compare the features of” may result in a variety of recommendations.

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Featured Image: Paulo Bobita/Search Engine Journal