AI has infinitely sped up the hype cycle in marketing.
So when the term “vibe marketing” came onto the scene, you may have rolled your eyes for a moment before you said, “I have to try this.”
In basic terms, vibe marketing means using AI to run entire marketing workflows. Usually, this involves a combination of:
Vibe coding: No-code AI tools where you type what you want (e.g., “Build me a landing page”), and the tool spins it up
AI agents: Always-on assistants that handle background tasks, like checking your inbox for leads or updating your CRM
And whether or not they consider themselves “vibe marketers,” many teams are already doing this.
In a survey of marketing teams doing $100m+ in revenue, GrowthLoop found that more than a third of those teams use AI to optimize campaigns or predict customer behavior.
And those embedding AI into their processes report more effective strategies.
So, is vibe marketing the next wave of marketing methodology? Or just more AI hype?
In this guide, we’re diving into real-world case studies that show how marketers are using AI in their daily workflows.
Plus, we’ll test the hype against reality based on my own experiments and the perspective of industry experts.
Vibe Marketing vs. Traditional Marketing
With vibe marketing, things like campaigns, segmentation, and competitor analysis can happen in the background. So you can focus more on creative work and strategy.
Here’s how it stacks up against traditional marketing:
Task
Traditional Marketing
Vibe Marketing
Campaign creation
Weeks of strategy, briefs, handoffs, and approvals
Concepts, landing pages, and emails drafted in hours
Audience segmentation
Manual data exports and persona-building
AI builds real-time dynamic segments
Competitive analysis
Manual research on competitor websites, social feeds, reports
Automated data scraping and AI summaries
Performance reporting
Hours compiling data into slides
Real-time dashboards + plain-English insights
This all sounds incredible, and it’s all technically possible for marketing teams today.
But here’s the catch: AI workflows are still clunky and experimental.
Hootsuite reports that while 83% of marketers say their AI budgets have increased, 4 in 10 companies waste at least
10% of their AI budget on tools that didn’t deliver.
Bottom line: Don’t expect AI workflows to run your marketing overnight. Sometimes building them takes longer than doing the task manually (I learned that firsthand — more on that later).
So, what does vibe marketing look like when it does work?
6 Examples of Vibe Marketing in the Wild
Vibe marketing can seem like a vague concept.
But when we talk about using AI to automate social listening workflows, follow up with inbound leads, or run competitive analysis, all of a sudden this ambiguous concept takes on real-world meaning.
We’ll see six examples of brands using vibe marketing in their daily workflows.
Plus, how you can copy these ideas into your own strategy.
1. Build Enterprise-Level Campaigns Without Reliance on Technical Teams
The biggest slowdown in most campaigns isn’t the marketing work itself. It’s the wait for other teams to deliver what you need.
At the job site, Indeed, those delays stretched to an average of 3.5 months per campaign.
Even simple requests — like defining an audience segment — meant analysts had to pull data from their warehouse. Then, engineers had to reformat it before marketing could use it.
With vibe marketing, the team broke that bottleneck.
They used the AI platform GrowthLoop to turn raw customer data into ready-to-use segments.
Now, their team can type a plain-English prompt (e.g. “nurses in the U.S. who searched jobs in the last 30 days but haven’t applied”) and instantly generate that segment.
Instead of waiting a whole quarter to get in front of job seekers, the team can now react to hiring needs in almost real time.
Try It Yourself:
If you’re on an enterprise team already using a data warehouse tool, GrowthLoop’s makes it easy to type a goal, generate audiences, and send them directly into campaigns.
On the other hand, let’s say you keep customer data in a CRM or spreadsheet — names, emails, recent purchases.
With a tool like Clay, you can import those leads and use the built-in AI to enrich them with more data.
Then, you can create campaigns that automatically go out based on that enrichment.
For example, when a company has received funding in the last three months, they can be automatically added to a campaign.
In seconds, you’ve got a list ready to target.
What makes this powerful isn’t just faster data access.
It’s the AI layer that turns raw information into something marketing can actually act on, without waiting on anyone else.
2. Automate Social Listening Workflows
Getting a lot of mentions on social media is great — until it isn’t. Some social media managers can spend hours every day sifting through comments and posts that tag the brand.
More than just being a tedious task, this is completely unsustainable.
Which is exactly what Webflow’s two-person social team realized.
Between Reddit, X, YouTube, and forums, they faced 500+ daily mentions. But only a handful actually needed a human reply.
Finding those few was like looking for needles in a haystack.
So, they built an AI workflow to do the sorting for them.
The system scans every mention, tags it by sentiment and urgency, and pushes the important ones straight into Slack.
Out of 500+ daily posts, the team now sees just 10–15 that matter most — and responds within the hour.
Pick one high-volume channel — maybe Reddit, X, or even a busy community forum.
Use a tool like Gumloop or Apify to pull in mentions of your brand. Then, run them through an AI categorizer to flag sentiment and urgency.
Start small, check the tags for accuracy, and only then scale to other platforms.
Note: To take this workflow a step further, add a tool like ManyChat or Yuma.ai to generate automated responses to posts and DMs. Entrepreneur Candace Junée did this and saw a 118% increase in leads while saving 15 hours per month answering Instagram DMs.
3. Create On-Brand Content Assets
Ever tried to turn a 40-page technical document into a blog post or campaign copy?
The content is there, but shaping it into something clear — and in your brand’s voice and style — takes time.
At Pilot Company, with multiple sub-brands and channels to manage, that challenge multiplied.
Writers spent hours summarizing technical docs into usable briefs. Designers waited for copy that matched the right tone before prototypes could move forward.
And inconsistencies crept in across brands.
So, the team used Jasper to help build consistency in style and tone.
They used the tool’s summarizer to condense long technical documents into actionable outlines, and the brand-voice model to keep messaging aligned across sub-brands.
Designers could even pull realistic placeholder text without waiting on writers.
The result: Each team member saved 3–5 hours a week, freeing them up for strategy and storytelling instead of slogging through documents.
Try It Yourself:
With a tool like Jasper, you can add specific instructions about your brand voice, audience, and even include source material to show what great content looks like for your brand.
Then, you can use it to create copy and content for entire campaigns.
You can also use tools like Notion AI, Claude, or ChatGPT to turn long documentation into campaign content.
Start by inputting your brand voice, style, target audience, and any other details that might be useful. Then, upload documentation and ask the AI to turn it into specific pieces of content.
Test the tools to find your favorite. Make sure to give specific instructions on what kind of output you’re looking for.
Use AI to generate briefs, draft first passes, or speed up design prototypes — and reserve human time for the creative polish.
On paper, 500+ inbound marketing leads a day looks like a dream for a small agency.
But for Tiddle, a six-person influencer agency, it was a nightmare.
They were buried in the flood of messages, with only a few that were worth pursuing. Sorting through the noise ate up 6–8 hours a day — time that should’ve gone into client campaigns and outreach.
Instead of hiring more staff, they brought in AI.
Using Lindy, every inbound email was screened automatically.
Low-quality offers were politely declined, while promising ones were flagged and routed to the right person.
If terms weren’t a fit, the AI could even suggest counteroffers.
The team went from slogging through hundreds of emails to focusing only on the 10–15 real opportunities that mattered.
As Tiddle’s CEO, Mike Hahn, says, “Every deal we’ve closed in the last few months came from Lindy surfacing the right conversations.”
Try It Yourself:
Pick one channel where inbound volume is overwhelming (email, DMs, LinkedIn).
Define the “must-haves” for a qualified lead (budget, offer type, brand fit), then use a tool like Lindy or Clay to screen and tag incoming requests.
You can even set up conditional logic so the tool can change how it responds based on specific conditions.
Note: Small companies aren’t the only ones making use of AI for inbound leads. Ariel Kelmen, president and CMO of Salesforce, recently said that they use AI agents to handle interactive follow-ups with leads. And those agents manage the first 80% of the conversation.
5. Build Hyper-Personalization for Your Ideal Customer Profiles
“Hi [first name]…” personalization doesn’t cut it anymore. But manually tailoring every message to your ideal customer profiles (ICPs) is impossible to scale.
Oren Greenberg, a solo marketing consultant, faced this problem.
And since there was no system that fit his ideals of hyperpersonalization, Oren built his own.
He coded a workflow in Replit that filtered a 50,000-company dataset, excluded existing contacts, and generated outreach tailored to each company’s stage and challenges.
The result: outreach so specific it only makes sense for the intended recipient.
Pro tip: Hyper-personalization works only if you deeply understand your ICP — AI can’t do that thinking for you. But once you know who you’re selling to, it can scale bespoke messaging in ways you couldn’t manually.
Try It Yourself
If you’re a highly technical person with the skills and know-how to recreate something like this in a vibe-coding tool, then by all means have at it.
For the rest of us, using a tool like Clay is a fast path to get 80% of the way there.
Start by defining your ICP.
Then use Clay to pull in business data, filter it against your ICP criteria, and enrich it with extra context.
With that data in place, you can add an AI-powered column that drafts personalized outreach for each prospect.
Run a pilot batch of 50–100 and iterate until the system feels like true one-to-one messaging.
6. Run Competitive Analysis
New marketing roles often start with 30-60 days of slow discovery.
Who are the real competitors? What do customers actually care about? What language do they use?
Semrush’s former VP of Brand Marketing Olga Andrienko found a way to shortcut that process.
Before Day 1 at a new job, she suggests running an AI-powered competitive analysis.
Pull your site and the top competitors’ pages, transcribe the most-viewed YouTube reviews, and mine Reddit and forums for repeated complaints.
Then, feed that into an AI summarizer to surface frequent feature praise or criticism and real customer phrasing. Tools like Google Opal or Gemini help cross-link those insights into a positioning map.
Whether you’re stepping into a new role, launching a campaign, or scoping out a new market, the same workflow applies.
First, pick your brand and three competitors. With a scraper tool like Apify, get your website copy and grab a handful of top YouTube reviews and forum threads.
Then, feed those into a tool like Claude, Gemini, or ChatGPT to summarize and analyze the data.
Extract the top five pains and language customers use, and sketch a one-page positioning map you can bring to meetings.
That way, you start your campaign with clarity — not uncertainty.
My Disastrous Vibe Marketing Experiment (What I Learned the Hard Way So You Don’t Have To)
Giving you examples is great, but I wanted to put all this to the test and see if I could build a usable AI workflow for myself. (Spoiler: It did not go well.)
Goal: Save time replying to LinkedIn comments without losing my voice.
Constraints: Something I could test immediately, for free, and that would actually be useful.
Method: Build a workflow that scrapes comments, learns my style, and drafts replies I could approve before posting.
Time spent: 4+ hours
1st Attempt
First, I created an account in PhantomBuster, a tool that automates actions on social platforms like LinkedIn.
Then, I connected my LinkedIn account and set up the “LinkedIn Post Commenter and Liker Scraper” tool.
I asked it to retrieve only comments from my LinkedIn posts from recent days, which it did successfully.
Next, I created a new “Scenario” in Make, a no-code automation and AI agent tool, and added PhantomBuster as the start of that workflow.
Then, I built a Make AI Agent that would draw from my previous posts to learn my voice..
I added that Make AI Agent into the workflow, giving it instructions to analyze the comments scraped by PhantomBuster and produce a reply.
And finally, I added Google Docs as the final output. The idea was to create a document where I could see both the original comment and the AI-generated reply.
The whole workflow ran successfully, which I took as a win and closed up shop for the night.
But when I opened my laptop the next day to check all the wonderful replies my new AI buddy had written for me, all I found was this lovely Google Doc:
Still undeterred, I decided to try something different.
2nd Attempt
Along the same lines, I wanted to build an automated AI workflow that would scrape content from LinkedIn that I’m interested in. Then, write comments in my voice and style using my existing content as a foundation.
I used a similar workflow: PhantomBuster to scrape the content, Make AI Agents to analyze and write comments, and getting the final output in a Google Sheet.
Unfortunately, that gave me the exact same result (only this time in spreadsheet format, woohoo!):
What especially irked me was that the automations themselves were running successfully. But I still had no output.
So after more than four hours of work (and a lot of back-and-forth with ChatGPT), I finally gave up.
Could I have figured out this AI workflow eventually? Yes, I have no doubt.
But at that point, how much time would I be saving?
Does a little time saved on writing comments justify spending hours building an AI workflow (and what should’ve been a relatively simple one, at that)?
Here’s what I learned from this experiment:
If you’ve been secretly feeling a little skeptical about vibe marketing, you were right
The folks building vibe-coded apps and AI workflows in five minutes have years of practice. The rest of us can’t expect the same speed.
The tools that are currently available for vibe coding and AI automations aren’t ready yet for the average user to just jump in and build
If someone with a background in tech (me) struggled so much with a simple workflow, imagine the challenge of something more complex
And while it’s true that others are seeing success with vibe marketing (like the examples that we saw above), there are also clear downsides.
It’s Not All a Bed of Roses: The Caveats of Vibe Marketing
Vibe marketing is like any new marketing buzzword: We all love to join in the hype, even if we don’t quite get it.
The problem is, the hype can obscure reality.
After running my own experiments, I also talked with other experts in the field. What emerged was a clear pattern — vibe marketing is powerful, but the gaps between promise and practice are real.
It’s Harder Than It Looks
The idea that you can tinker around with AI for five minutes and produce a usable workflow just isn’t feasible for the majority of us.
And yet, that’s the promise we’re seeing over and over again:
This all sounds great, but we’re marketers: We know better.
Simple automations? Sure.
But robust, real-world systems usually need engineering support or serious AI chops.
Without that, you risk fragile prototypes that break the first time they’re stress-tested.
Oren Greenberg, the AI marketing consultant we talked about earlier, told me:
“The level of hype is out of this world. Vibe coding is cool, and there are a few people who’ve built a nice small business out of it. But it’s mostly the vendors who are minting cash.”
Here’s the point: Don’t get swept up in the hype. Check the source.
The Infrastructure Is Messy
AI workflows look slick in a demo. But in practice, you have to plug into your marketing stack.
And that’s where things get complicated.
For example, you might build the perfect AI agent to score inbound leads, only to realize that your CRM can’t accept the data the way you need.
As Austin Hay, Co-Founder of Clarity and MarTech teacher at Reforge, noted in a recent interview:
“Everyone’s excited about unstructured data, but unstructured data is useless when it needs to play nice with structured systems.”
For traditional marketing teams, this means your AI workflows may not play well with your company’s established martech systems.
And if your tech’s API documentation is outdated (or worse, nonexistent), it will be nearly impossible to vibe code your way to integrations between existing tools.
AI Can’t Invent Outside its Datasets
Another misconception around vibe marketing is that you can throw any messy, undefined problem at an AI agent and it will figure it out.
The reality is less glamorous.
AI thrives on patterns it’s seen before. Point it at a well-scoped, repeatable task, and it shines.
But ask it to invent outside of its training data — or solve a fuzzy, novel problem — and you’ll end up with loops, errors, and wasted hours.
Speed Only Works When You Know Where You’re Going
AI can help you move fast. But if you don’t know what metrics matter and where you want your workflows to lead, faster will just mean getting lost sooner.
Marketers who succeed with vibe coding are the ones who define the finish line first. AI then becomes a vehicle to reach those goals faster, not a substitute for setting them.
Kevin White, Head of Marketing at Scrunch AI, put it this way in a recent interview:
“AI multiplies the abilities of people who already know their craft. Treat it as a force multiplier for your expertise rather than a substitute for it.”
Vibe Marketing Tools Free Up Time…But for What?
As more marketers build AI workflows and vibe code their way to productivity, a philosophical question arises: why?
AI workflows and automations free up time (when they work). But, what are we freeing up time for?
By eliminating the busywork, we’ve saved only the most demanding tasks for ourselves. And while creating and strategizing may be what we enjoy most, it’s impossible for most people to do that kind of mentally-taxing work for eight hours straight.
“In conversations with CMOs, it’s clear that GenAI has become a core part of how modern marketing teams operate. What separates the winners is a commitment not just to scaling the technology, but to empowering the people who use it. Those CMOs investing in tools and talent are the ones rewriting the playbook.”
Ready to Try Your Own Vibe Marketing Experiment?
Vibe marketing isn’t snake oil. But it’s not a silver bullet, either.
The hype can make it feel like anyone can vibe code and automate their way to a marketing edge. But the reality is far more nuanced.
The marketers getting real value from vibe marketing are the ones with strong fundamentals, clear goals, and often a layer of engineering support behind them.
For the rest of us, the takeaway is simple:
Vibe marketing is worth experimenting with, but it won’t replace strategy, judgment, or hard-won expertise.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-03 14:08:552025-10-03 14:08:55Vibe Marketing: Hype, Reality, and Real Case Studies
ChatGPT recommends products — complete with photos, pricing, and purchase links — to its 700 million weekly users.
And now customers can complete purchases without leaving the chat.
BIG deal.
But will ChatGPT recommend your products?
That’s not automatic. And you can’t pay for placement.
What you can do is optimize your site so ChatGPT understands what you sell, trusts your brand, and surfaces your products when buyers search. This guide shows you how.
You’ll learn the eight-step framework for getting featured in ChatGPT Shopping.
I also spoke with Leigh McKenzie, Backlinko’s Head of Growth and founder of the ecommerce brand UnderFit, to get his insights on what’s actually working.
First, let’s look at how ChatGPT decides which products make the cut.
How ChatGPT Shopping Works
ChatGPT Shopping kicks in automatically for some shopping intent prompts.
While it doesn’t fire every time, I found it appears more often than not after testing 100+ prompts.
The key? Typing a prompt with clear buying intent.
Like “e-bikes that can handle potholes.”
Instead of just explaining things or offering advice, ChatGPT Shopping recommends specific products.
This includes product images, pricing, and links to online stores and websites where users can make a purchase.
Side note: The ChatGPT Shopping experience isn’t consistent. Even with the same prompt, the carousel may (or may not) show. It can also appear at the top, middle, or bottom of the chat. This variability suggests the feature is still evolving.
If your store gets recommended, countless high-intent shoppers will see your products.
For example, when I tested the e-bike query, ChatGPT gave me a brief explanation of what features to prioritize.
But it also provided a visual product carousel with eight products, each in its own card with key details.
(It looks similar to Google Shopping ads, except you don’t have to pay for them.)
Clicking on any card opens a side panel with:
Additional product photos
A list of stores, prices, and direct links
A short “why you might like this” summary
Sentiment pulled from reviews and forums
From there, users simply click “Visit” to reach the merchant’s product page.
But this experience is changing.
As of September 2025, OpenAI is rolling out Instant Checkout — a feature that lets shoppers buy directly inside ChatGPT.
This is a huge shift.
ChatGPT is no longer just a product discovery tool. It’s a full shopping destination.
Right now, Instant Checkout is only available to Etsy sellers in the United States.
But OpenAI plans to expand this feature to Shopify merchants and other countries soon.
Not on either platform?
They’re also accepting applications for merchants to build their own integrations. (More on this in Step #7.)
How ChatGPT Selects Products to Recommend
A shopper describes what they’re looking for (“running shoes with arch support under $150”), and ChatGPT’s AI goes to work.
It scans the web for the most relevant products based on that request.
And weighs details like product names, descriptions, features, reviews, brand authority, and other signals to find the best matches.
If your product checks the right boxes — and the information on your site is clear and crawlable — it has a chance to be recommended.
ChatGPT may also consider the user’s location and preferences when making recommendations.
Ultimately, all product recommendations must also pass through OpenAI’s safety systems.
This filters out low-quality, misleading, or unsafe products.
So, what does all of this mean for you?
ChatGPT Shopping is evolving fast — and the brands that keep up will win the most visibility.
Here’s how to ensure ChatGPT can understand, trust, and recommend your products.
1. Add Structured Schema Markup to Your Site
ChatGPT needs structured data to understand what you sell.
Schema markup is code that labels key details on your product pages (and website as a whole): name, price, description, availability, reviews, and more.
It turns raw HTML into data AI tools can parse instantly.
Without it, ChatGPT (and other AI systems) have to guess what’s on your page.
With it, they see clean, structured information they can confidently include in product recommendations.
At a minimum, your product schema should include:
Product: Name, description, brand, image, and identifiers (GTIN, SKU, MPN)
Offer: Price, currency, availability, and URL
Review: Individual reviews with reviewer names and ratings
It may look intimidating, but many content management systems — like WordPress, Shopify, and Wix — offer plugins or built-in tools that generate the markup for you automatically.
Once your markup is in place, test that it’s working correctly using Google’s Rich Results Test or Schema.org’s validator.
These tools make it easy to check that your structured data is valid, visible, and error-free.
Pro tip: Go beyond the schema basics. Add AggregateRating for average review scores or FAQPage markup to answer common buyer questions. The more context you provide, the easier it is for AI to surface your product in response to specific prompts.
2. Create and Maintain a High-Quality Product Feed
A product feed is a structured file that packages up your product details and sends them to platforms like Google Merchant Center, Shopify, and Etsy.
It includes details like titles, prices, availability, images, links, and more.
ChatGPT may use data from major platforms like Google to decide which products to recommend.
Pro tip: Want to add your product feed directly to ChatGPT? OpenAI will notify interested merchants when this feature is available. Fill out the Merchant Application form for consideration.
For example, if your Google Shopping feed is outdated, incomplete, or inaccurate, ChatGPT may return bad information about your products.
Or skip recommending them entirely.
That’s why a high-quality, up-to-date product feed is critical.
Side note: If you’re on an ecommerce platform like WooCommerce or Shopify, feeds are usually created automatically.
But keeping feeds accurate is easier said than done.
There are a lot of moving parts, like site updates, refresh schedules, and third-party tools.
And it only takes one slip for mismatches to creep in.
Here are a few common product feed issues — and how to fix them:
Product Feed Problem
Why It Happens
Fix
Price Mismatch
Feed not refreshed, sync delay
Enable daily/real-time feed updates. Use one consistent pricing source.
Inaccurate availability
Inventory updates on site, but feed refresh lags
Sync stock levels in real-time whenever possible. Double-check before campaigns.
Wrong or Truncated Title
Feed title auto-truncated or different from H1/meta
Align feed titles with on-page H1/meta. Keep product names consistent.
Incorrect image
Feed defaults to first gallery image
Set hero/product image as primary in CMS and feed
Missing reviews
Reviews hidden in JS or not in schema
Add Review and AggregateRating schema in HTML
Conflicting schema
Multiple apps/plugins overwrite each other
Use one schema source. Validate with Schema.org or Google’s Rich Results test
Automation keeps most updates in sync. And manual checks before major launches or sales help catch anything that may slip through.
Here’s how Leigh maintains a balance of the two for his ecommerce store:
“I keep all my product data in a spreadsheet. Whenever I change a product detail, I update it there first. WooCommerce uses that data to update my site’s pages and schema automatically. Then, Channable takes the same spreadsheet and syncs those updates into my product feeds. That way, my site and my feeds are always pulling from the same source, so everything stays consistent.”
3. Make Sure AI Bots Can Read Your Site
If ChatGPT can’t read your site, it can’t recommend your products.
Two simple technical issues block many ecommerce sites from showing up: hidden content and restricted crawlers.
Check for JavaScript
Many AI bots — including ChatGPT — still struggle with content that only loads via JavaScript.
If key details aren’t in the page’s raw HTML, the bot might never see them.
This includes your product descriptions, prices, and images.
Eek.
Here’s how to check if that’s happening:
Pull up a product page on your website or online store
In Google Chrome, go to “Settings” > “Privacy and security” > Site Settings
Under “Content,” click “JavaScript” and toggle “Don’t allow sites to use JavaScript”
Reload the product page you’re testing
If your product details disappear, it means they’re only loading through JavaScript.
To fix this, work with a developer to ensure all essential information is in your site’s raw HTML.
They’re onboarding merchants on a rolling basis and will reach out when you’re accepted.
Once you’re in the pipeline, you’ll need to:
Provide a structured product feed that meets OpenAI’s product feed specs. Leigh recommends starting with your existing Google feed and updating it as needed to meet OpenAI’s requirements.
Enable ACP checkout. ACP lets ChatGPT place and complete orders in your system. If you’re on Stripe, setup can be as simple as one line of code. If not, you can still integrate using Stripe’s Shared Payment Token API or the Delegated Payments Spec — no provider switch required.
Connect your payment provider. You’ll still process transactions and remain the merchant of record.
Pass certification requirements. OpenAI requires sandbox testing and end-to-end checks before you go live.
Pro tip: Even if ChatGPT Instant Checkout isn’t available for your store yet, preparing your product data, feeds, and backend now will help you move faster when it is. This should give you a head start as this feature gains popularity.
8. Track Your ChatGPT Visibility
It’s not enough to show up in ChatGPT Shopping.
You also need to measure how well you’re performing.
Start with tracking traffic.
The easiest way is through OpenAI’s built-in UTM tag.
utm_source=chatgpt.com
This is code that OpenAI automatically adds to all outbound links. And looks like this:
Set up a custom segment in Google Analytics to track and analyze ChatGPT traffic to your site.
Once that’s done, look for patterns:
Is ChatGPT traffic increasing month over month — or slowing down?
How does the conversion rate compare to other channels?
Do visitors stick around or bounce right away?
Side note: Not every ChatGPT mention will be traceable. Some users see your product in a chat and search your brand directly on Google instead of clicking. Look for spikes in branded search traffic or direct visits to gauge the broader impact of LLMs.
But traffic only tells you what happens after people click.
You also need to measure what happens before — specifically, which prompts surface your products.
To do this, it helps to understand the kinds of prompts shoppers type.
Most fall into four buckets.
Price-based: “Best dog food bowl under $20,” “luxury ceramic dog bowl”
Use-case: “Dog bowl for messy eaters,” “raised bowls for large breeds”
Feature-based: “Non-slip stainless steel dog bowl,” “slow feeder BPA-free”
Problem-solution: “Dog bowl that keeps ants out,” “dog bowl that doesn’t slide on tile”
Think of these buckets as templates.
Test prompts in each category and ask yourself:
Does your product show up? If so, are the details accurate?
If not, who does — and why? (Are their reviews fresher, their authority stronger, or their copy closer to buyer language?)
Repeatedly run these checks to gather more data.
You’ll learn which prompts lead to product mentions, how your LLM visibility changes, and how buyers talk about your brand.
Rather automate this process?
Tools like Semrush’s AI SEO Toolkit let you:
Track which prompts surface your products
Monitor brand sentiment
Compare visibility in different platforms
Beyond ChatGPT Shopping: Your AI Visibility Playbook
There isn’t a magic formula for getting ChatGPT to recommend your products.
But the brands that consistently get recommended all have three things in common:
A rock-solid technical foundation
Clear, buyer-focused product copy
Strong trust signals across the web
Get these right, and you’re not just optimizing for ChatGPT Shopping.
You’re setting yourself up to be discovered across EVERY AI platform out there.
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If I had a dollar for every time someone said “know your audience,” I could retire from marketing altogether.
And yet, most teams are completely winging it.
Too often, marketers equate audience research with half-baked customer relationship management (CRM) data, some social media metrics, and a few buyer interviews.
But that’s just organizing information you already have.
Real audience research means discovering what you don’t know yet.
It’s the exact words people use when they’re frustrated. The solutions they’ve already tried and dismissed. The moment they decide to trust one source over another.
When you get this right, you move from guessing what might work to creating content from what your audience is already telling you.
In this guide, I’ll show you how to find those insights across five key channels, with practical tactics you can use right away.
Download our free Audience Research Tracker
As you go through these methods, I’ll show you how to capture insights in our Audience Research Tracker and turn them into actionable content ideas.
Why You Can’t Skip Audience Research
If you’ve ever lost hours scrolling TikTok or binge-watched “just one more episode” on Netflix until midnight, you’ve experienced the power of audience research.
Platforms like Netflix, YouTube, and TikTok own our attention because they know us better than we know ourselves.
They’ve built this advantage by making audience research a core function.
Netflix, for example, treats “Consumer Insights” as one of its nine core research areas, which shows just how pivotal understanding users is to their success.
For these winning brands, audience research isn’t an afterthought.
It shapes everything: what gets built, how products are positioned, and which messages resonate
And the payoff is massive — delivering experiences tailored to your customers that keep them coming back for more.
In stark contrast, many marketing teams run on fragments.
SEOs chase keywords, social focuses on engagement, and product marketing fine-tunes messaging. Everyone has a piece of the puzzle, but no one can put it together.
As a result, campaigns are designed for specific channels instead of real people.
Done well, audience research can close this gap to:
Sharpen your messaging that customers find relatable
Prevent wasted spend by showing you where people actually are
Speed up creative cycles by giving teams validated insights to work with
In short: This research legwork aligns marketing with real customer needs, winning customer trust in the process.
And the good news is you don’t need Big Tech’s expensive resources to pull this off.
I’ll show you how to conduct audience research and out-empathize the competition with your existing team and budget.
5 Channels to Conduct Audience Research for Content Marketing
Your buyers are already telling you what they want. You just need to listen carefully.
Let’s learn how.
Make sure to download our tracker and jot down all the information from your audience research techniques.
Tap Into Intel Within Your Company
Some of the most valuable audience insights are already within your reach, sitting with your sales and customer success (CS) teams.
These groups are on the front lines.
They regularly interact with prospects and customers about their frustrations, aspirations, objections, and goals.
For marketers figuring out how to conduct audience research, collecting these insights is a great starting point.
Here’s how:
Source of Insight
How It Works
What You’ll Learn
Listen to conversations
Sit in on sales demos, onboarding calls, or quarterly check-ins
Use a simple template to document key takeaways
How buyers describe challenges
Words and phrases they repeat
Factors they prioritize
Sync with frontline teams
Run regular sessions with sales, CS, product, and marketing to share notes
Common challenges
Objections that block deals
Features customers love or struggle with
Interview & survey customers
Conduct 1:1 interviews with prospects and customers
Use surveys to validate patterns
Why buyers looked for a solution
Their decision-making process
Alternatives considered
Listen and Capture First-Hand Conversations
The fastest way to understand your audience is to literally listen.
Sit in on a sales demo, a customer onboarding call, or a quarterly check-in meeting.
This will bring you raw insights you can’t get from surveys, like:
The way buyers frame their challenges
The decision factors they prioritize
The words they repeat
But listening alone isn’t enough.
You need a simple system to document the key takeaways from every conversation and share them across teams.
Here’s an example of what that might look like for a fictional coffee brand:
Our Audience Research Tracker will help you distill these conversations into meaningful content opportunities.
You can jot down recurring problem statements in your buyers’ language and identify their biggest pain points.
Then, prioritize ideas based on our four key parameters like urgency, business value, and more.
Sync with Frontline Teams
Another way to capture these insights is by regularly connecting with your customer-facing teams.
When teams work in silos, each one only sees a part of the puzzle.
This creates a disconnect in your customer experience because no one has the whole picture of what buyers want.
That’s why it’s worth setting up regular cross-team sessions for marketing, sales, customer success, and product teams to compare notes.
These sessions can surface insights that no single team could uncover on its own.
Interview and Survey Customers
Besides internal data, hearing directly from buyers can give you a deeper, more reliable understanding of what drives their decisions.
Customer interviews provide essential context about the why behind their behavior.
You can find out:
How they first discovered your product or category
What pain points pushed them to look for a solution
The decision-making process they followed
What alternatives did they consider
With surveys, you can validate these insights and see which ones apply broadly versus one-off anecdotes.
The bottom line: Before spending anything on new research, look inward to collect and process information you already have.
Use Reddit for Unfiltered Conversations
Unlike other social media platforms, Reddit gives you access to candid and often brutally honest conversations.
Take this post on frustrating skincare routines.
It voices raw and real emotions that people face when dealing with skincare challenges.
And in the comments, there are even more stories and nuanced perspectives.
They offer crucial insights about the audience, like “skincare feels like a tough road of trial and error” and the “emotional toll of poor skin health.”
So, how do you use Reddit to know your buyers better?
Start with the Right Filters
Reddit’s filters make it easy to sift through posts and find what matters most.
You can sort results by:
Relevance: Best for finding posts that match your keyword directly
Top: Surfaces the most upvoted posts over a time period
Hot: Shows recently trending posts with the most upvotes
Comment count: Sorts posts with the most comments
New: Shows you the freshest discussions
Plus, you can filter results by timeframe to see what’s trending now versus what’s been a consistent pain point over time.
In my search for “moisturizer for oily skin,” filtering by “Relevance” shows the closest matches, while “Hot” surfaces the most recently upvoted posts.
Pro tip: Use Google with the search operator site:reddit.com “keyword.” This often works better than Reddit’s native search, especially if you’re looking for niche phrases.
Find the Right Subreddits
While it’s easy to find bigger and popular subreddits, it’s equally important to look for smaller, niche spaces where your audience might hang out.
Remember, the same buyers may express themselves differently depending on the space they’re in.
For instance, a skincare brand could find valuable insights across:
r/SkincareAddiction: Broad, general skincare conversations
r/AsianBeauty: Discussions centered on Asian markets
r/30PlusSkincare: Catering to an older demographic
Each subreddit reflects a different slice of the audience.
Read Posts and Comments Like a Researcher
A good Reddit post will give you context into people’s problems, goals, and lived experiences.
But the comments add more nuance to the original post. This is where people expand on the issue, discuss solutions, and share personal stories.
Here’s a post where the original poster (OP) shares their concerns about using Retinol, an ingredient known for its anti-aging properties.
Other Redditors share their take and advice on this issue, highlighting some alternatives to consider.
For a skincare brand, this post is helpful to understand:
Buyers’ concerns regarding Retinol
Commonly used and recommended solutions
Based on these insights, the brand can create content focusing on the best practices for Retinol use. Another great idea is to make a beginners’ guide for using Retinol and taking care of your skin.
Besides, Reddit also offers something other platforms can’t: clear signals of what not to do.
Upvotes highlight ideas and opinions people love. Downvotes show the perspectives or advice they reject.
Find AMAs (Ask Me Anything)
Ask Me Anything (AMAs) can be a gateway to your audience’s biggest questions or issues they’re curious about.
Any industry expert or influencer with trusted credentials can host an AMA.
Here’s an example from a certified dermatologist.
Questions asked in this thread reveal issues where people need an expert’s guidance.
For example, one Redditor asked for basic skincare regimens while another shared a question about stretch marks.
Pay attention to questions with high upvotes. Those are the ones that most people want advice on.
Check Out YouTube Comments and Videos
YouTube is the second-largest search engine where people go to solve problems, compare options, and learn new skills.
Naturally, it can reveal a lot about your buyers.
An audience intelligence tool like Sparktoro is a good starting point for YouTube research.
When you enter any keyword, it lists the most relevant YouTube channels for this audience.
Visit these channels and extract rich insights based on the steps I explain below.
For example, this video comparing stainless steel pans with cast iron skillets tells you the creator’s subjective take on the topic.
But when you scroll through the comments, you’ll find which option people prefer — and why.
Here’s a quick and easy process to document insights from as many YouTube videos as you want.
Copy comments from every video in one go. Then, paste them into ChatGPT or any LLM tool of your choice.
Share this prompt to extract common pain points and themes:
I have added a collection of YouTube comments below. Please analyze them as if you’re conducting target audience research.
Identify:
→ The most common themes and topics people talk about
→ Motivations, desires, or positive outcomes they want
→ Patterns in language (words/phrases that repeat often)
Present your findings in a structured summary. Create a table highlighting frequent pain points, frustrations, or complaints, and add users’ quotes for each pain point.
Here are the comments:
[Paste comments]
This way, you can turn hundreds of scattered thoughts into a structured list of what your audience actually struggles with in their exact words.
I tried this myself and here’s how it went:
I found a clear breakdown of my audience’s pain points spelled out in their exact words.
Add these to our research tracker — and just like that, I have topics for my next few Instagram reels, like “Health concerns around non-stick pans” and “Why stainless steel pans are better than non-stick.”
Learn From User-Generated Content
Beyond comments, user-generated content (UGC) can also offer a direct line into what your buyers care about.
Think product reviews, unboxing videos, comparisons, or even vlogs where people share how they use a product.
Notice the kind of pros and cons that people highlight in these videos.
For example, this YouTube creator made a video about his decision to stop using Hexclad pans.
He explains:
Why he bought these pans
What went wrong with these products
What alternatives he considered and switched to
Use these insights to understand key buying factors and some pain points worth exploring.
Explore Social Media Platforms Your Buyers Use
Social media works best as an audience research method when you know where your buyers actually spend their time.
Tools like Similarweb make this easier by showing you which channels your audience prefers.
Add your website and a few key competitors to get started, like this example with TechCrunch, Wired, and other competitors.
Here’s how the tool breaks down each brand’s audience share on different social media platforms:
The takeaway: Identify the platforms that matter most to your buyers and dig deep into those spaces.
LinkedIn
On LinkedIn, start by identifying people who fit your Ideal Customer Profile (ICP).
Pay close attention to the posts they share — their wins, failures, roadblocks, and processes.
These real-world updates reveal where your product or service can make a meaningful impact.
For example, if your ICP includes customer success teams, this LinkedIn post shows how leaders are experimenting with AI tools.
It highlights both opportunities and gaps you could address — like growing interest in a trend (opportunity) or frequent complaints (gap).
To scale your research, use LinkedIn Sales Navigator to apply filters and zero in on the right people within your ICP.
For example, you can filter results by industry, keywords, location, seniority, language, and more of these filters.
Instagram
Instagram hashtags are a great way to discover audience interests.
Start with broad themes like #mealprepideas to see what’s trending.
Each hashtag (like a keyword) surfaces a collection of posts tagged with this term.
Look for posts with high engagement because they signal what truly resonates.
For instance, this post earned over 393k likes because it offered clear, visual recipe ideas that people found useful.
Like LinkedIn, you can also follow influencers or niche creators in your space to get closer to your audience.
Their posts (and especially the comments) often pinpoint the questions, frustrations, and goals your buyers are struggling with.
TikTok
To use TikTok as an audience research method, create a fresh account dedicated to your niche.
Interact only with videos specific to your space, and TikTok’s algorithm will start curating a feed of trending content.
Once you see relevant videos, dive into the comments to spot recurring themes and pain points.
For example, the comments on this meal prep video include many questions about the containers and the recipe.
You can also search for your keywords and toggle between “Top,” “Users,” “Videos,” and “LIVE” content to explore different kinds of content on the app.
X
X has powerful tools for audience research if you know where to look.
Use the advanced search function to filter posts by keyword, engagement, account, or time frame.
Another underrated feature for target audience research: “Lists.”
It lets you build a curated feed of accounts you want to hear more from, like potential customers, influencers, or industry voices.
You can either follow existing lists or create a new one.
For instance, searching for “vibe coding” lists shows ready-made feeds you can tap into for insights.
Compare Platforms with Semrush Social Tracker
Semrush’s Social Tracker helps you zoom out and learn more about your audience from multiple channels at once.
It pulls data from Instagram, Facebook, LinkedIn, X, YouTube, Pinterest, and TikTok, so you can see how your audience interacts with different channels.
With this report, you can identify which platforms generate the strongest engagement from your target audience.
And it’s easier to spot popular post formats (Reels, carousels, videos, etc.) and hashtags that drive interaction.
To get started, connect your social accounts and add competitor profiles in Social Tracker.
Use the “Overview” tab to compare follower growth, posting activity, and engagement side by side.
Then, jump to platform-specific tabs to get in-depth reports for each platform.
Mine Customer Reviews
A single customer review may just be one person’s opinion.
But when you analyze these reviews at scale, clear patterns start to emerge.
For starters, look for factors that led people to buy a product. Or, notice the cons people mention in low-rated reviews.
Both indicate pain points you can target.
For example, this customer calls out weak product durability and a disappointing warranty process.
You also want to find what success looks like for your potential customers. Is it saving time, cutting costs, improving quality, or something else?
Document the features they call out as good or bad.
This insight can shape your messaging and even suggest improvements for your offering.
See if reviews pinpoint any competitors that people considered. Note why they chose or didn’t choose a specific brand over others.
Similar to the exercise I suggested for YouTube, collect product reviews from different platforms.
Ask any LLM tool to analyze these reviews and prepare a list of pain points.
Here are the review platforms you should check out based on your business type:
The more you know about your buyers, the stronger results your marketing efforts can produce.
What’s even better, your audience is already leaving signals about what they want.
When you listen closely and capture these insights, you can create content and launch campaigns that hit closer to home.
Download our Audience Research Tracker to easily document this data and turn these insights into content opportunities.
Next up: Wondering how to tie all your audience-centric content ideas together? Check out our guide on building a customer-focused content strategy to put this research to work.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-02 13:35:112025-10-02 13:35:11Audience Research: Stop Guessing What Your Buyers Care About
We’re excited to announce the beta release of Yoast AI Brand Insights, available as part of the Yoast SEO AI+ package. This new tool helps you understand how your brand appears in AI-powered answers, and where you can improve your visibility. Ideal for bloggers, marketers, and brand managers, Yoast AI Brand Insights gives you an overview of your brand presence across tools like ChatGPT, Perplexity, and Gemini.
For years, Yoast has helped you get found in search engines. Recently though, search is changing. People aren’t just using Google anymore, they’re turning to AI tools like ChatGPT for answers. Those answers often mention brand names as recommendations. So here’s the big question: when AI tools answer questions in your niche, does your brand show up? Our new tool, Yoast AI Brand Insights (beta), helps you find out.
Yoast AI Brand Insights lets you see when and how your brand appears in AI-generated answers and helps you understand where you need to focus your effort to improve your visibility.
Why Yoast AI Brand Insights matters, now
AI-powered answers are shaping customer decisions faster than ever. Visitors from AI search are often more likely to convert than those from regular search. It’s no surprise, because asking an AI-powered chatbot can feel like getting a personal recommendation. Afterall, word of mouth remains one of the most powerful ways to build trust and spark interest.
Most analytics tools can’t tell you how your brand appears in AI answers, or if it’s mentioned at all. With more people turning to tools like ChatGPT, Perplexity, and Gemini for advice, that’s a big blind spot if you are trying to get your name out there.
Yoast AI Brand Insights aims to close that gap. You’ll see when and how your brand appears, what’s being said, and where the information comes from, so you can take action to ensure your brand is part of the conversation.
See how you stack up against other brands mentioned in your prompts
With just a few clicks, you can:
Check if your brand is mentioned in AI-generated answers for relevant search queries
Benchmark against competitors: see how often your brand comes up
Understand the sentiment connected to your brand: positive, neutral, or negative
Find the sources AI tools use when they mention you
Track your progress over time so you can respond to changes quickly
Pricing & getting started
Yoast SEO AI+ is priced at $29.90/month, billed annually ($358.80 plus VAT). The plan includes one automated brand analysis per week per brand, so you can track and compare how your brand is showing up in AI-powered search over time. With each purchase of Yoast SEO AI+ you recieve one extra brand.
With this package you also get the full value of Yoast WooCommerce SEO, which includes everything from Yoast SEO Premium, News SEO, Local SEO, and Video SEO, in addition to one free seat of the Yoast SEO Google Docs add-on.
For marketers, this means you no longer need to patch together separate solutions for on-page SEO, ecommerce optimization, content creation, or LLM visibility. Everything you need to analyze, optimize, and grow your brand presence is included in one complete package.
How to get started
Login with MyYoast: secure, single sign-on for all your Yoast tools and products.
Open Yoast AI Brand Insights: You can find it near the Yoast SEO Academy
Set up your brand: add your brand’s name and a short introduction to your business
Run your scan: we’ll find relevant AI search queries for you, you can use them or tweak them to your liking.
Review your results: see relevant mentions and their sources, your brand sentiment, and the AI Visibility Index in an easy-to-read dashboard
Yoast AI Brand Insights is now available in beta as part of Yoast SEO AI+. This is your chance to be among the first to explore how your brand shows up in AI-powered search. We’d love your thoughts as we refine the tool, your thoughts here.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-01 10:07:322025-10-01 10:07:32Introducing a new AI-powered package: Track your brand in AI search
Content summarization isn’t a new idea. It goes back to the 1950s when Hans Peter Luhn at IBM introduced one of the first algorithms to summarize text. Back then, the goal was straightforward: identify the most important words in a piece of writing and create a shorter version. What began as a technical experiment has now evolved into a fundamental aspect of how we read, learn, and share information. Summarization allows us to cut through overwhelming amounts of text and focus on what really matters, shaping everything from research and education to marketing and SEO.
In this article, we’ll explore why summarizing is essential for modern content and how both humans and AI-driven tools are making information more accessible, trustworthy, and impactful.
What is content summarization?
Content summarization is the process of condensing a large piece of high-quality content into a shorter version while keeping the essential points intact. The aim is straightforward: to produce a clear and concise summary that accurately represents the meaning of the original text without overwhelming the reader.
Summarization makes information easier to process. Imagine reading a lengthy report or book but only needing the key takeaways for a meeting. It also helps individuals and businesses grasp the core message quickly, saving time and effort.
There are two main approaches to summarize moder content:
Manual or human-driven content summarization
Think back to the last time you turned a long article into a short brief for a colleague; that’s a perfect example and explanation of manual content summarization. In this approach, a human reads, weighs what matters, and rewrites the core points for easy digestion of information.
Manual content summarization requires critical thinking to spot what matters and language skills to explain important information clearly and concisely.
Clear advantages of human-driven content summarization are:
The ability to notice nuance and implied meaning
Flexibility to shape tone and level of detail for a specific audience
The creativity to link ideas or highlight unexpected relevance
Judgment to keep or discard details based on purpose
This human-led method complements content summarization AI, giving summaries a thoughtful, audience-aware edge.
AI-driven content summarization
The other approach is powered by technology. AI-driven content summarization utilizes natural language processing and machine learning to rapidly scan through text and generate summaries in seconds. It typically works in two ways:
Extractive summarization, where the AI selects the most important sentences directly from the content
Abstractive summarization, where the AI generates new sentences that capture the main ideas in a more natural way
The benefits are clear: speed, consistency, and scalability. AI can summarize website content, reports, or articles far faster than a human team. However, it has limits. Context can be missed, and nuances like sarcasm or cultural references may be overlooked. The quality also depends on the AI model and the original text.
Both manual and AI-driven summarization play a crucial role today. Humans bring nuance and creativity, while AI delivers efficiency and scale. Together, they make summarization an essential tool for modern communication.
What are some of the core benefits of content summarization?
Turning lengthy information into clear takeaways is more than convenient. It makes content meaningful, easier to use, and far more effective in learning and communication. Whether done manually or supported by AI tools, summarization offers key benefits:
Enhances learning and study preparation
Summarizing strengthens comprehension and critical thinking by distilling main ideas and separating them from supporting details. Students and professionals can also rely on concise notes that save time when revising or preparing presentations.
Improves focus and communication
Condensing text sharpens concentration on what matters most. It also trains you to express ideas in a precise and structured way, which enhances both writing and verbal skills.
Saves time and scales with AI tools
Summaries allow readers to absorb essential points without having to read hours of content. With AI tools, this process scales further, reducing large volumes of text into clear insights within minutes.
Boosts accessibility and approachability
Summarization makes complex or lengthy content approachable and accessible for diverse audiences. Multilingual AI tools extend this further, breaking down language barriers and ensuring knowledge reaches a global audience.
Why summarization matters in the modern content landscape?
We live in an age of too much information and too little time. Every day, there is more content than anyone can read, which means people make split-second choices about what to open, skim, or ignore. This makes it more important that your content presents clear takeaways upfront before readers move on. Content summarization is how you win that first, critical moment of attention.
Information overload
Digital work and life produce an enormous flood of text, messages, reports, and notifications. This makes it challenging for readers to find the right signal in the noise. Therefore, text summaries act as a filter, surfacing the most relevant facts so readers and teams can act faster and with less cognitive friction.
People scan and skim, so clarity wins
Web reading behavior has been stable for years: most users scan pages rather than read every word. Good summaries present the core idea in a scannable form, increasing the chance your content is understood and used. That scannability also improves the odds of search engines and AI LLM comprehension surfacing your content as a quick response to user queries.
Trust and clarity for readers and systems
A clear and crisp text summary signals that the author understands their topic and values the reader’s time. That builds trust. On the search side, concise and well-structured summaries are what engines and AI systems prefer when generating featured snippets or AI overviews. Being chosen for a snippet or overview can boost visibility and credibility in search results.
Faster decision-making
When stakeholders, readers, or customers need to act quickly, summaries provide the necessary context to make informed decisions. Whether it is an executive skimming a report or a user checking if an article answers their question, summaries reduce the time to relevance and accelerate outcomes. This is also why structured summaries can increase the chance of being surfaced by search features that prioritize immediate answers.
Prominent use cases of content summarization
Content summarization is not a nice-to-have. It is one of the main reasons modern content continues to work for busy humans and businesses. Below are the most practical and high-impact ways in which the summarization of modern content is currently being used.
Business reports
Executives and teams rely on concise summaries to make informed decisions quickly and effectively. Executive summaries and one-page briefs transform dense reports into actionable insights, enabling stakeholders to determine what requires attention and what can be deferred. Effective summaries reduce meeting time, expedite approvals, and enhance alignment across teams.
Educational content
Students and educators use summaries to focus on core concepts and to prepare study notes. AI-driven summarization tools can generate revision guides, extract exam-relevant points, and turn long lectures or papers into study-friendly formats. These tools can support personalized learning and speed up content creation for instructors.
Marketing strategies and reporting
Marketers rely on summaries to present campaign performance, highlight key KPIs, and share learnings without overwhelming stakeholders. Condensed campaign briefs and executive summaries enable teams to iterate faster, align on priorities, and uncover insights for strategic changes. Summaries also make it easier to compare campaigns and track trends over time.
Readers and listeners increasingly prefer bite-sized overviews. Newsrooms use short summaries and AI-powered digests to connect busy audiences with high-quality reporting. Podcasts and newsletters pair episode or article summaries with timestamps and highlights to improve discoverability and retention. Summaries help users decide what to read, listen to, or save for later.
Content Summarization & SEO: Does it Benefit in Boosting Organic Visibility?
Did you know that content summarization can help your SEO strategy? Search engines prioritize clarity, relevance, and user engagement, and concise summaries play a role in meeting those criteria. They not only shape a smoother user experience but also help search engines quickly grasp the core themes of your content.
Boosting click-through rates
Summaries also support higher CTRs in search results. A clear and compelling meta description written as a summary can serve as a strong preview of the page. For example, a blog on “10 Healthy Recipes” with a summary that highlights “quick breakfasts, vegetarian lunches, and easy weeknight dinners” is more likely to attract clicks than a generic description.
Improving indexing and relevance
From a technical standpoint, summarization helps search engines with indexing and relevance. Algorithms rely on context and keywords, and well-written summaries bring focus to the essence of your content. This is especially important for long-form blogs, case studies, or reports where the main ideas may otherwise get buried.
Winning featured snippets
Another growing benefit is visibility in featured snippets and People Also Ask sections. Summaries that clearly answer a query or highlight structured takeaways increase the chances of being pulled into these high-visibility SERP features, directly boosting organic reach.
Extending multi-channel visibility
Content summarization also creates multi-channel opportunities. The same summaries can be repurposed as social media captions, newsletter highlights, or even adapted for voice search, where users want concise and direct answers.
Supporting AI and LLMs
Lastly, in the age of AI, summaries provide context for LLMs (large language models). Clean, structured summaries make it easier for AI to process and reference your content, which extends your reach beyond search engines into how content is surfaced across AI-powered tools.
How to write SEO-friendly content summaries with Yoast?
The basics of an effective summary are simple: keep it clear, concise, and focused on the main points while signalling relevance to both readers and search engines.
This is exactly where Yoast can make your life easier. With AI Summarize, you can generate instant, editable bullet-point takeaways that boost scannability for readers and improve how search engines interpret your content.
Want to take it further? Yoast SEO Premium unlocks extended AI features, smarter keyword optimization, and advanced SEO tools that save you time while improving your visibility in search.
A smarter analysis in Yoast SEO Premium
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
AI text summarization uses artificial intelligence to condense text, audio, or video content into shorter, more digestible content. Rather than just cutting words, it preserves key ideas and context, making information easier to absorb.
Today, summarization relies on large language models (LLMs), which not only extract sentences but also interpret nuance and generate concise, natural-sounding summaries.
How does AI text summarization work?
AI text summarization relies on a combination of sophisticated systems that help a large-language model deeply understand the content, decipher patterns, and generate content summaries without losing any important facts.
Here’s a brief overview of the process of AI-powered content summarization:
Understanding context: AI models analyze entire documents, identifying relationships, sentiment, and flow rather than just looking at keywords, allowing the AI models to understand at a deeper level
Generating abstractive summaries: Unlike extractive methods, which simply copy existing sentences, abstractive summarization paraphrases or rephrases content to convey the essence in fresh, coherent language
Fine-tuning for accuracy: LLMs can be trained on specific domains such as news, legal, or scientific content, so the summaries reflect the right tone, terminology, and level of detail
Benefits of AI text summarization
The true power of AI summarization lies in the value it creates. By blending scale with accuracy, it turns information overload into actionable knowledge.
Scales content summarization: Handles hundreds of pages or documents in minutes, which would otherwise require hours of manual effort
Ensures consistency: Produces summaries in a uniform style and structure, making information easier to compare and use
Saves time and costs: Frees up teams, researchers, and analysts to focus on insights instead of spending time reading
Improves accessibility: Makes complex content digestible for wider audiences, including those unfamiliar with technical details
Supports accuracy with human oversight: Editors can refine summaries quickly while still benefiting from automation
Practical use cases of AI summarization
AI summarization is not just theoretical. It has already become part of how businesses, teams, and individuals manage daily information flow. Here are some of the common applications of AI summarization which have become a part of our live:
Meetings: Automatically captures key points, decisions, and action items in real time
Onboarding: Condenses company or project documentation so new team members can understand essentials quickly
Daily recaps: Summarizes Slack, Teams, or email threads into clear, concise updates
Surfacing information: Extracts relevant context from long reports, technical documents, or customer feedback, ensuring that critical insights are never overlooked
In fact, AI agents are already being used in professional settings to summarize key provisions in documents, with 38% of professionals relying on these tools to expedite the review process. This demonstrates that AI summarization is not just a future possibility, but an integral part of how modern teams manage complex information.
In summary, don’t skip the summary!
Summarization is no longer a sidekick in your content strategy; it is the main character. It fuels faster human learning, strengthens SEO by making your pages clearer to search engines, and ensures AI systems don’t misrepresent your brand. When your content is easy to scan, you reduce bounce rates, improve trust, and increase visibility across platforms where attention spans are short.
This is exactly where a tool like Yoast SEO Premium becomes invaluable. With features like AI Summarize, you can instantly generate key takeaways that work for readers, search engines, and AI overviews alike. Instead of manually condensing every piece of content, you achieve clarity at scale while maintaining editorial control. Summarization is not just about making content shorter; it is about making it smarter, and Yoast helps you do it with ease.
So, to summarize the summary: invest in doing this right, because the future of content depends on it.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-01 08:55:342025-10-01 08:55:34Why is summarizing essential for modern content?
Google is piloting a Labs section inside the Google Ads platform, giving advertisers a centralized place to access early-stage experiments and tools.
Why we care. Instead of quietly testing features across scattered accounts, Google appears to be consolidating experiments into a single hub, making it easier for advertisers to discover and try new features before full rollout.
Details.
The Labs section shows up at the bottom of the left-hand menu in select accounts.
Early tests include features like “missed growth opportunities,” which highlight potential campaign improvements through bid or budget adjustments.
Not all advertisers will have access to it.
First seen. The update was first spotted by Vojtěch Audy, PPC specialist at Na volné noze.
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Google is enhancing Meridian, its open-source Marketing Mix Model (MMM), to help marketers make smarter, more precise budget decisions.
Why we care. Understanding ROI across channels is increasingly critical. These new Meridian updates allow for a more precise understanding of what drives sales, factoring in both media spend and non-media variables like pricing and promotions.
The big picture. Here’s what’s new:
Non-media variables: Marketers can now include pricing, promotions, and other business levers to measure their impact on sales more accurately.
Channel-level contribution priors: New features let you guide the MMM with your own business knowledge, improving the relevance of insights.
Longer-term effects: Enhanced binomial adstock decay functions track how upper-funnel media influences purchases weeks later.
Optimized spending: Marginal ROI (mROI) priors help pinpoint where the next dollar delivers the highest return, using past campaign performance.
Support and community. Meridian now has 30 certified global partners and an active Discord community, offering guidance to deploy the tool effectively and translate insights into business growth.
The bottom line: These updates aim to transform Meridian from a measurement tool to a strategic partner for budget allocation. This means Meridian can help marketers optimize spend across immediate conversions and longer-term brand impact.
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The history of search offers clues about where we’re headed in the AI era – but there’s more to learn and more to do to move forward with practical steps.
AI changes the way people search. Instead of short queries that once required digging through blended results, users can now ask complex questions and get direct answers.
Much of the work to optimize for AI, though, overlaps with what SEO professionals have been doing for years. Our community is already adapting and well-positioned to take on this shift.
This article outlines practical steps to navigate the evolving landscape.
SEO, AEO, GEO: Defining the new terms
Before diving in, it’s worth addressing the terminology. It’s still new, and no single label has fully crystallized for this AI layer on top of SEO.
Two terms have gained traction:
AEO (answer engine optimization), which focuses on optimizing content so it’s chosen as the answer in AI-driven results like Google’s AI Overviews.
GEO (generative engine optimization), which describes a broader approach across generative AI platforms.
Neither feels perfect. AEO is a bit clunky, and GEO risks confusion with geography and local search.
Still, these are the terms currently in use. For simplicity, I’ll use GEO in this article.
While tools like ChatGPT are undoubtedly cool – and early adopters, myself included, are spending a lot of time tinkering – the broader story is different.
For most people, AI will matter less as a standalone tool and more through its integration into phones, web browsers, and search engines.
GEO is a new layer that sits above SEO.
Often, tools like ChatGPT will search and compile information.
These tools provide a layer of abstraction and do much of the grunt work.
They still scan the digital world and then collate that information to simplify things for the user, while search engines like Google continue to provide the best overall digital map of the world.
If traditional SEO was about matching keywords, AEO is about being the best answer – and the easiest to integrate into an AI response.
GEO: Strategic foundations
There’s no firm consensus on GEO tactics, but most of what’s recommended is simply good SEO.
That said, tactics that lost ground in the zero-click landscape may regain utility in AI Mode, where the AI does the deep dive and collates information for users.
Here are some basic strategic foundations to put in place to set yourself up for visibility in AI tools.
1. Focus on your customers
I’ve long championed bringing traditional marketing thinking into SEO, and GEO is the natural evolution of that approach.
Know your audience. Create personas, gather feedback, and define their goals, pain points, and the jobs they rely on your product or service to support.
Customer insight is key to building a customer-first strategy that helps you stand out in the age of AI.
2. Real expertise wins
The web is full of derivative content that does little to stand out. This creates a problem for AI.
Model collapse happens when AI keeps training on AI-generated content without new signals, leading to increasingly stale and inaccurate results.
The solution is what humans are still best at – fresh insights from:
Interviews.
Original research.
Proprietary data
These provide AI with something new – and worth citing.
That’s an opportunity. Have a voice, and bring something original to the table.
AI is helping people research and make purchase decisions. Your role is to be part of the discussion.
Modern SEOs are in good shape – much of what AI requires builds on the strategic SEO work we already do.
Add in some PR, social media, and content creation (which often sit under the SEO umbrella), and you’re well on your way to a functional GEO strategy.
Getting started is crucial. To stay ahead:
Create content worth quoting: Write the piece an AI (or a human journalist) would want to reference. That means clear answers, evidence, original insights, and a point of view – not filler.
Anticipate the full conversation: Don’t just answer the first question – answer the follow-ups, too. If someone asks, “How does AI change SEO?” they’ll also want to know, “What should I do about it?” Build that into your content.
Structure for machines and humans: Use headings, lists, FAQs, and concise summaries to help AI parse your work. But don’t forget the narrative depth that keeps people reading.
Diversify your discovery footprint: Don’t rely on Google alone. Research your audience, understand their hangouts, and publish in the formats and places where they ask questions today: LinkedIn, YouTube, podcasts,and industry forums. AI tools crawl all of it.
Focus on authority signals: Show the human behind the content. Add author bios, cite sources, and link to your work elsewhere. AI engines, like search engines before them, lean on trust and authority.
Experiment, measure, refine: Try different formats, test and measure how your content shows up in AI summaries, track brand mentions, and adapt. SEO has always been iterative – this new era is no different.
The opportunity in the chaos
As SEO evolves into GEO – or whatever it may end up being called – this really is the best approach.
There’s no doubt a lot of change is happening.
But much of it is part of the same gradual evolution we’ve seen before, where clicks declined and Google started answering questions directly.
AI now makes it even easier for customers to find the information they’re looking for.
They may not read it on your site – at least not initially – but the AI will, and that’s the point.
Another strength we have as SEOs is that change is constant.
If you’ve been in SEO for any length of time, you’ve lived through Panda, Penguin, Mobilegeddon, BERT, helpful content updates – the list is long (and may cause PTSD for many of us).
The key is to treat this as the next evolution. AI is being integrated into search, and it will likely become the way the masses adopt the technology.
Don’t see this as the death of SEO.
Instead, view SEO and AI (or GEO/AEO, etc.) as close, contributing partners, and evolve your plan to match the changing landscape.
Your job as a marketer is to feed these tools the information they need to point customers to you and your clients.
This shift will likely mean fewer short-term manipulations and tactical opportunities – but better results for businesses that do the basics well.
At its core, good SEO/GEO is just good marketing: understanding your customers, meeting their needs, and communicating clearly.
Amid the chaos lies opportunity. For those willing to embrace the challenge, experiment with new tools, and keep going.
That’s what we’ve always done as SEOs, which is why we’re best positioned to embrace this new world.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/09/Thriving-in-AI-search-starts-with-SEO-fundamentals-4nKDbJ.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-09-30 15:00:002025-09-30 15:00:00Thriving in AI search starts with SEO fundamentals
Before this, figuring out what AI Max was doing felt like looking into a black box.
Now, we can finally see the data and make smarter decisions.
Evaluating AI Max: Metrics that matter
When you’re looking at AI Max performance, start with the basics.
Open your search term tab and look for the Search terms and landing pages from AI Max option.
This lets you see AI Max results separately from your exact match, phrase match, and broad match keywords.
Compare conversion share and budget share
The first thing to check is how many conversions come from AI Max versus your regular keywords.
If AI Max is bringing in 30% of your conversions but eating up 60% of your budget, you know something needs attention.
Look at the cost per conversion, too.
AI Max might cost more at first, but that’s normal while Google learns what works for your business.
Look beyond cost – focus on conversion rates
Don’t just focus on the cost. Pay attention to conversion rates.
AI Max often finds people who are ready to buy but use different words than you expected. These new search terms can be gold mines if you spot the patterns.
One of AI Max’s biggest benefits is finding search terms you never thought to target.
Look at your search terms report and filter for AI Max queries. You’ll probably see some surprises.
Let’s say you sell running shoes and target “best running shoes.” AI Max might show your ads for “comfortable jogging sneakers” or “shoes for morning runs.”
These are people who want the same thing but use different words. The smart move is to add these high-performing terms to your regular keyword lists.
Identify irrelevant traffic
If you’re getting clicks from people searching for “cheap shoes” when you sell premium products, add “cheap” as a negative keyword.
AI Max respects negative keywords, so use them to guide the system toward higher-quality queries.
Every AI system needs time to learn, and AI Max is no different.
Plan for a learning phase
The first few weeks often look expensive because Google is still figuring out what works. Don’t panic if your costs jump initially.
Track your performance daily during the first month
As AI Max learns your patterns, you should see costs stabilize and conversion rates improve. If things keep getting worse after three weeks, then it’s time to make changes.
Keep an eye on the types of search terms AI Max finds over time
Early on, you might see lots of random queries. As the system learns, the terms should become more relevant to your business goals.
The biggest mistake people make is changing too much, too fast.
AI Max needs data to work properly, and constantly adjusting things prevents the system from learning.
That said, some problems need quick fixes.
If AI Max is spending money on completely irrelevant searches, add negative keywords immediately.
If the AI creates ads that violate your brand guidelines, remove those assets right away.
Watch your overall account performance, not just AI Max numbers.
Sometimes, AI Max might look expensive on its own, but it actually helps your other campaigns perform better by capturing different types of traffic.
Planning for the future
AI Max is still evolving, and Google keeps adding new features.
To adapt:
Build reporting systems that can grow with these changes.
Set up automated reports for the metrics that matter most to your business.
Don’t try to control everything.
The businesses seeing the best results from AI Max are the ones that:
Set clear goals.
Provide good data.
Let the system do its job.
Your role shifts from managing keywords to managing strategy.
Start testing AI Max on a small scale if you’re nervous about it. For example:
Create one campaign with AI Max enabled and compare it to your existing campaigns.
Run an AI Max for Search campaign experiment and let Google evaluate if the experiment is statistically valid.
Once you see how it works for your specific business, you can decide whether to expand.
Case studies: AI Max in action
These are early results from a limited data set and shouldn’t be viewed as statistically significant.
I’m sharing them to illustrate what I’ve seen so far – but the sample size is small and the timeframe short. Take these numbers with caution.
Case 1: Tourism and travel
This advertiser already had a solid search setup and was seeing good results.
Growth, however, was difficult because of heavy competition and the fact that strong keywords were already in play within a modern search structure.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.11
1.47%
Broad match
€0.09
3.79%
Exact match
€0.53
9.00%
Exact match (close variant)
€0.22
7.11%
Phrase match
€0.16
6.25%
Phrase match (close variant)
€0.11
3.27%
AI Max generated additional conversions, but relative to the existing setup the impact was limited.
The conversion rate was much lower than other match types.
Because the average CPC was low, there was no cost spike, but performance still lagged.
Broad match – also known for surfacing broader, newer queries – had an even lower CPC (€0.09) and a conversion rate more than twice that of AI Max.
In this account, AI Max’s contribution was minor.
Search term overlap analysis showed AI Max had a 22.5% overlap rate, meaning 77.5% of queries were new to the campaign.
That’s a fairly good sign in terms of query discovery.
Case 2: Fashion ecommerce
This account focused on women’s clothing and already had a well-optimized campaign.
The goal was to expand reach during the competitive holiday season, when exact match keywords became increasingly expensive.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.08
2.15%
Broad match
€0.12
2.89%
Exact match
€0.67
8.45%
Exact match (close variant)
€0.28
6.78%
Phrase match
€0.19
5.92%
Phrase match (close variant)
€0.14
4.11%
AI Max performed well here, delivering the lowest CPC at €0.08 and a respectable 2.15% conversion rate.
Although conversion rates were lower than exact and phrase match, the cheaper clicks kept cost per acquisition competitive.
AI Max also captured fashion-related long-tail searches and seasonal queries that the existing keyword set had missed.
Notably, AI Max outperformed broad match with both lower costs and better conversion rates.
This suggests its ability to better understand product context and user intent – especially important in fashion, where search terminology is diverse.
Search term overlap analysis showed only an 18.7% overlap rate, meaning 81.3% of queries were completely new.
That level of query discovery was valuable for extending reach in a highly competitive market.
Case 3: B2B SaaS
This account promoted project management software and had a mature strategy focused on high-intent keywords.
Conversion tracking was strong, measuring both MQLs and SQLs.
The client wanted to test AI Max for additional lead generation opportunities.
Match type
Avg. CPC (€)
CVR (%)
AI Max
€0.89
0.76%
Broad match
€0.72
1.23%
Exact match
€1.84
4.67%
Exact match (close variant)
€1.22
3.91%
Phrase match
€1.05
3.44%
Phrase match (close variant)
€0.94
2.88%
In this case, AI Max struggled. Despite a reasonable CPC of €0.89, the conversion rate was just 0.76%.
That pushed CPA well above the client’s target, making AI Max the worst-performing match type in the account.
It tended to capture too many informational searches from users not yet ready to convert.
Even broad match, typically associated with lower-intent traffic, outperformed AI Max with a 1.23% conversion rate at a lower CPC.
The complexity of the B2B buying cycle favored exact and phrase match keywords over AI Max’s broader interpretation.
Search term overlap analysis showed a 31.4% overlap rate, leaving 68.6% of queries as new.
However, these were mostly low-intent informational searches that didn’t align with SQL goals – underscoring the importance of high-quality conversion tracking when evaluating AI Max.
Wider industry sentiment
Advertiser feedback so far mirrors these mixed results.
In a recent poll by Adriaan Dekker, more than 50% of respondents reported neutral outcomes from AI Max, while 16% saw good results and 28% reported poor performance.
Tips to analyze AI Max search terms
You can analyze AI Max queries in Google Sheets using a few simple formulas. If your search term report has the term in column A and match type in column B:
To check whether a search term appears in both AI Max and another match type:
To count how many match types trigger a given term:
=COUNTIFS($A:$A;A2) can be used to count how many match types trigger on that search term.
To measure query length with an n-gram analysis:
=(LEN(A1)-LEN(SUBSTITUTE(A1," ","")))+1
These checks show whether AI Max is surfacing unique queries, overlapping with existing match types, or favoring short-tail vs. long-tail terms.
Because AI Max is still in early stages, it’s hard to draw firm conclusions.
Performance may improve as the system learns from more data, or remain flat if your setup already covers most transactional queries.
That’s the question advertisers will answer in the coming months as more tests and learnings emerge.
So far, results can be positive, neutral, or negative.
In my experience, neutral to negative outcomes are more common – especially in accounts with strong existing setups, where AI Max has fewer opportunities to add value.
A Google Ads script to uncover AI Max insights
To make analyzing AI Max performance easier, I created a Google Ads script that automatically pulls data into Google Sheets for deeper analysis.
It saves hours of manual work and includes the exact formulas mentioned earlier in this article, so you can immediately spot overlap rates and query patterns without manual setup.
The script creates two tabs in your Sheet:
AI Max: Performance Max search term data with headlines, landing pages, and performance metrics.
Search term analysis: A full comparison of all match types, including AI Max, with automated formulas.
The analysis covers:
Overlap detection between AI Max and other match types.
Query length analysis (short-tail vs. long-tail).
Match type frequency counts to identify competitive terms.
Automatic cost conversion from Google’s micro format into readable currency.
How to use it:
Create a new Google Sheet and copy the URL.
In Google Ads, go to Tools > Scripts.
Paste the script code and update the SHEET_URL variable.
Run the script to automatically populate your analysis.
With this setup, you can quickly calculate the same metrics I used in the case studies – like the 22.5% overlap rate in the tourism account or the 81.3% new query discovery in fashion.
The automated workflow makes it easier to see whether AI Max is surfacing genuine new opportunities or simply redistributing existing traffic.
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Google AI Mode is getting more visual by providing a more graphical response to some of your queries, including your shopping search queries. Google can do this by using its new visual version of the query fan-out technique it has used with AI Overviews and AI Mode.
Google AI Mode, for certain queries, particularly those related to shopping, will respond with images and graphics. These are aimed at sparking inspiration, Robby Stein, VP of Product Management at Google Search, told Search Engine Land.
AI Mode will be able to not just understand your query in a text-based manner, but also understand your query visually and respond with both textual and visual responses. It is a new and updated fluid, ongoing conversation in AI Mode that “sparks inspiration,” Google said.
Visual search fan-out technique. Google’s fan out technique is able breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf. Now, Google can also do this visually by looking at the image and text query input for query analysis and various image region analysis, including meta data and context around the image. Then Google AI Mode can render a visual grid of responses to your query.
“This means AI Mode can perform a more comprehensive analysis of an image, recognizing subtle details and secondary objects in addition to the primary subjects – and then runs multiple queries in the background. This helps it understand the full visual context and the nuance of your natural language question to deliver highly relevant visual results,” Google wrote.
AI Mode is more visual. Now when you search for some queries in AI Mode, the responses will be much more graphical and visual, right up front. Yes, AI Mode may have responded with images before, but now the images are higher and more prominent in some of the responses.
Plus, you can conduct follow up questions on the visual responses.
“You’ll see rich visuals that match the vibe you’re looking for, and can follow up in whatever way is most natural for you, like asking for more options with dark tones and bold prints. Each image has a link, so you can click out and learn more when something catches your eye. And because the experience is multimodal, you can also start your search by uploading an image or snapping a photo,” Google added.
Shopping in AI Mode. Lilian Rincon, VP Product Management Google Shopping, told us one of the best places for a visual experience is with shopping in Google Search. A more visual AI Mode helps you shop conversationally with fresh shopping data, can lead to a better shopping experience.
With the addition of Google’s Shopping Graph of 50 billion product listings, where 2 billion products are refreshed daily every hour, the responses are not just inspirational but detailed and helpful.
AI Mode for Shopping can not just give you ideas on what to put in your living room but also help you find the perfect article of clothing, in your color, style and fit.
Here is a video of this in action:
More details. This is launching today in Google AI Mode in the US in English. These are free listings, not shopping ads, and currently have no paid model, including no affiliate model. While Google has ads in AI Overviews, Google is only experimenting with ads in AI Mode, and there are no more details on ads right now for this experience.
Agentic experiences, like helping you buy and find what your looking for, is here for some areas now in Search Labs. But Google said they want the final purchase to happen directly on the retailer’s site.
Why we care. A new, more visual and graphical experience in AI Mode, may be a better search experience for some searchers and for some queries. Google is experimenting with a lot of changes to Google Search and is rapidly trying new interfaces and technologies.
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