Google is quietly testing a new way to make Shopping ads feel more local. Select ads using local inventory feeds now display the merchant’s city or town directly above the product title — think “London” or “Tonbridge” — giving shoppers a clearer sense of where the store is based.
Why we care. The new location labels make Shopping ads feel more local and trustworthy, helping nearby retailers stand out in crowded results. Clear city or town indicators can increase click-through rates and drive more in-store visits from shoppers who prefer buying close to home.
It also gives merchants using local inventory feeds a competitive edge by highlighting proximity without needing new ad formats or extra setup.
How it works. The label appears within Shopping ads that already use local inventory data. It joins existing formats like:
In-store
Pickup later
Curbside pickup
But unlike those, this label focuses purely on the store’s location, not fulfillment options.
The catch. Google hasn’t officially announced the feature. Details on rollout, eligibility, and technical requirements remain unknown.
Between the lines. Merchants using local inventory feeds may get a visibility boost if they operate in recognisable or high-trust locations. For users, it’s another nudge to choose nearby retailers over marketplace or long-distance sellers.
First seen. This update was spotted by PPC News Feed founder Hana Kobzová.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/location-shopping-ads-EZRhH0.jpg?fit=1280%2C720&ssl=17201280http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-08 18:01:312025-12-08 18:01:31Google Shopping Ads now show merchant location labels
You can now purchase products directly within ChatGPT.
That’s right, OpenAI recently announced a new feature that turns ChatGPT into a personal shopping assistant. You ask for something, and it doesn’t just recommend it. It finds it, prices it, and even helps you check out all in one chat.
They’re calling it Instant Checkout, and it’s already rolling out with help from e-commerce giants like Stripe and Walmart. The feature enables OpenAI to pull in real-time product listings and personalized suggestions.
It’s still early days, but this is a big deal for e-commerce brands. It opens up an entirely new kind of shopping experience; one where everything from product discovery and research to checkout all happens in a single interface. And with new ChatGPT ads already hitting the ecosystem, it’s clear this is a major market shift.
Key Takeaways
ChatGPT now supports in-chat shopping with real-time product listings and checkout through partners like Walmart.
Users interact with the feature using natural language prompts, making product discovery more conversational than keyword-based.
Product visibility depends on clean data: use schema markup, clear product names, and natural descriptions.
E-commerce brands must adapt fast. AI-driven recommendations are transforming the way customers browse and make purchases.
Optimizing for ChatGPT shopping requires mobile speed, fresh reviews, and structured product content.
What Do We Know About ChatGPT Shopping and How It Works?
Here’s what we know so far: ChatGPT can now help users discover and buy products directly in the chat interface.
The feature is called Instant Checkout, and it’s powered by OpenAI’s integration with tools like Stripe and Shopify, with Walmart also recently partnering for early rollout. The service is available to all U.S. users of ChatGPT, regardless of their tier.
What It Looks Like in Action
Let’s say you ask ChatGPT for “espresso machines under $200.” ChatGPT doesn’t just return a list of brands; it provides:
Curated product suggestions from across major retailers
All of this happens through integrations with online retailers and APIs that deliver live product data behind the scenes. The interesting thing is that brands don’t pay for this visibility in ChatGPT’s shopping function.
Where Google Shopping results are based on brands’ paid ad campaigns or Google’s search algorithm, ChatGPT shopping is more conversational and organic. It focuses on the people (what people are saying bout this product online, what the reviews are, etc.).
Built on Conversational Search
What makes this different is the user experience (UX). You’re not clicking through filters and category pages; you’re chatting. You refine your request like a conversation, asking questions like, “What about ones with arch support?” or “Can you find those in women’s sizes?” That’s a huge shift in how product discovery happens.
So, how does it choose what to show you? The platform analyzes structured metadata and previous model responses. It will look back on how it handled similar queries before it ever touches new search results.
The personalization potential is what makes this even more powerful. ChatGPT will be able to tailor your shopping experience by elevating or demoting various factors of your results based on your needs. For example, if you have a shopping budget of $50, ChatGPT can elevate price as a “signal” and only show you appropriate results. OpenAI is doubling down on the modern customer’s need for personalization.
Is ChatGPT Just Another Shopping Assistant?
Not exactly. Yes, it gives you product recommendations like other AI shopping assistants.
However, ChatGPT takes it a step further by allowing you to shop in a way that feels like texting with a smart, well-informed friend.
Here’s what sets it apart:
Conversational search: You don’t have to use exact filters or keywords. You can talk to it naturally and refine your search.
Live product data: ChatGPT pulls real-time pricing and availability from partner retailers.
Built-in checkout: With select partners, you can complete a purchase directly in the chat.
This changes the experience from “browse and compare” to “ask and buy.”
That kind of frictionless experience makes it especially appealing for time-strapped users, mobile shoppers, and anyone who already uses ChatGPT regularly. It takes online shopping from endless options to making an informed and personalized decision quickly.
How ChatGPT Shopping Will Impact E-Commerce
ChatGPT isn’t just adding shopping features. This will rewrite how people discover and buy products.
Instead of browsing categories or scrolling search results, users now get personalized recommendations just by asking a question. That creates a new funnel, one that starts with natural language. This could be new territory for many e-commerce brands.
Discovery Is Getting More Personal
In traditional search, people type product-focused keywords. With ChatGPT, they might say:
“I need a thoughtful gift under $50 for a coworker.” Or “What are some comfy sneakers for walking in Europe this winter?”
These are context-rich prompts that AI can interpret and respond to with curated product suggestions. Brands with clear, structured product data and natural-language copy will excel in this type of environment.
Product Pages Matter More Than Ever
AI pulls data from your listings, descriptions, and reviews. If your content is outdated or poorly structured, you might not even show up to ChatGPT shoppers.
And with impulse buys likely to spike in this kind of frictionless experience, your clarity and trust signals can make or break a sale.
This is the next frontier of AI in e-commerce. The game is constantly evolving, and now it’s about showing up where customers are asking questions and ensuring your brand is one of the first answers shown.
How To Optimize Your E-Commerce Product Pages for ChatGPT Shopping
If you want your products to show up in ChatGPT’s recommendations, your product pages need more than nice images and a sale price. You need structure, clarity, and language that AI understands.
Here’s how to get there:
1. Use Product Schema Markup
Structured data helps AI understand what’s on your page. Add product schema so ChatGPT (and other tools) can pull in your:
Price
Availability
Reviews
Product name and image
This is the foundation. Without it, you’re invisible to most recommendation engines.
2. Write Natural, Benefit-Focused Descriptions
ChatGPT’s main focus here is pulling product info and providing an output that sounds conversational. Rewrite your descriptions to sound like how people talk:
Don’t: “Ergonomic, breathable mesh back with tilt-lock feature”
Do: “Keeps you cool and comfortable during long workdays”
3. Keep Product Names Clear
Avoid overly clever names. “The Cloudstep LX” might sound cool, but no one’s searching for that. Try: “Men’s Waterproof Running Shoes – Cloudstep LX”.
4. Feature Fresh Reviews and Ratings
Recent social proof helps both users and AI understand what’s worth recommending. Keep reviews visible and up-to-date.
5. Speed Up Your Mobile Site
A slow page kills conversions, especially if someone’s trying to buy right in the moment. Optimize images, reduce scripts, and test your load time on mobile to ensure the best user experience.
FAQs
How do you use ChatGPT for shopping?
To use ChatGPT for shopping, start a conversation with a shopping-related prompt like “Find me wireless earbuds under $100.” If you’re using ChatGPT Plus, you’ll get product recommendations that also include links. Some users may also have access to built-in checkout through select partners.
Conclusion
ChatGPT shopping is a new channel, not just a new feature. One where conversation replaces search bars and product discovery happens through real-time, AI-driven recommendations.
If you’re in e-commerce, now’s the time to adapt. That means optimizing your product pages with proper schema markup and making sure your content speaks the way real people do.
Your potential customers are already chatting. The question is: is your brand ready to be part of that conversation?
If your marketing still treats everyone the same, you’re falling behind.
Audience segmentation is what turns generic campaigns into personalized, high-performing ones. Segmented email campaigns can generate a 760 percent increase in revenue compared to non-segmented ones.
That same principle applies across paid ads, social content, product messaging, and just about any other marketing channel you can think of.
Without segmentation, you’re guessing what your audience wants. That leads to wasted ad spend, and low engagement.
Segmentation gives you an edge. It helps you deliver the right message, to the right people, at the right time.
In this guide, you’ll learn what audience segmentation is, how the different types work, and how to apply them to drive better results across your funnel.
Key Takeaways
Audience segmentation is the process of dividing your broader audience into smaller, more specific groups.
Segmentation helps improve engagement, click-through rates, and conversions across every channel.
There are five core types: demographic, geographic, psychographic, behavioral, and firmographic (which is specifically for B2B).
Good segmentation starts with real data, not assumptions, and improves over time.
The most effective marketing strategies use segmentation to deliver more personalized and relevant messaging.
What Is Audience Segmentation?
Audience segmentation is the process of dividing your broader audience into smaller, more specific groups based on shared characteristics. These characteristics can be demographic, geographic, behavioral, or even psychographic.
The goal is simple: understand your audience better so you can speak to them more effectively.
Think of it like this: you wouldn’t send the same message to a first-time visitor and a loyal customer. And you wouldn’t talk to a 23-year-old in the same way you’d market to a 65-year-old. Segmentation helps you avoid that one-size-fits-none approach.
This isn’t just a tactic for email marketers, either. It’s a core part of building relevant campaigns across paid ads, landing pages, SMS, product marketing, and more.
Here’s what segmentation unlocks:
More personalized content and offers
Smarter ad targeting
Higher engagement rates
Better alignment across your marketing funnel
Audience segmentation often gets confused with defining your target audience. But while defining a target audience helps you understand who you’re going after at a high level, segmentation helps you break that audience down into actionable groups for more precise messaging.
Most marketers aren’t struggling with a lack of data. The challenge is turning that data into action.
That’s where customer and audience segmentation creates real value. When you group your audience based on shared traits or behaviors, you can tailor your messaging, timing, and channels to what actually resonates.
Brands that use segmentation typically see:
Higher open and click-through rates
Increased customer lifetime value
Lower cost per acquisition (CPA)
More efficient use of ad budgets
65 percent of consumers expect personalization in their customer experience. And it’s not limited to email. Whether you’re running Google Ads, building a product launch campaign, or personalizing a homepage—segmentation improves performance across the board.
It also allows you to meet customers where they are in their journey. Someone new to your brand might need education. A returning customer may be ready for an upsell. With segmentation, you can deliver the right message at the right moment.
Types of Audience Segmentation
There are several ways to segment your audience. Each type gives you a different lens into what drives your customers’ behavior. The best strategies use a mix of these, depending on your goals, product, and data.
Here are the five most common types of audience segmentation:
Demographic Segmentation
This is the most straightforward method. You segment based on traits like:
Age
Gender
Income level
Education
Marital status
Example: A clothing brand might promote its premium line to high-income professionals while marketing basics to students or entry-level workers.
Geographic Segmentation
Here, you group users by physical location:
Country or region
Climate
City size
Urban vs. rural
Example: A food delivery app might market lunch deals to users in busy cities while promoting family meals in suburban areas.
Psychographic Segmentation
This method looks at the “why” behind your customer’s actions:
Personality traits
Interests and hobbies
Lifestyle choices
Core values
Example: A fitness brand might market high-performance gear to athletes and eco-friendly materials to sustainability-minded shoppers.
Behavioral Segmentation
Segment based on how people interact with your brand:
Purchase history
Engagement level
Brand loyalty
Product usage
Example: A SaaS company might send upgrade offers to heavy users and reactivation emails to inactive accounts.
Firmographic Segmentation (B2B Only)
This is the B2B version of demographic segmentation:
Company size
Industry
Revenue
Location
Decision-maker role
Example: A software vendor might offer enterprise features to large corporations and budget-friendly plans to startups.
Real-World Segmentation Examples Across Channels
Segmentation works across every channel you’re using. The tactics change, but the principle stays the same: send the right message to the right person.
Email Marketing: New subscribers get your welcome series. Inactive customers (90+ days) get a win-back offer with a discount. Same list, different messages based on engagement level.
Paid Advertising:Cart abandoners see retargeting ads featuring the exact product they left behind. Cold audiences see brand awareness content and educational posts. Match the ad creative to where they are in the funnel.
Content Personalization: SaaS visitors see automation guides and workflow content. E-commerce brands see conversion optimization and retention posts. Your CMS can handle this with simple behavioral tags based on past visits.
Product Rollouts: Power users get early beta access to new features. Light users get the stable release later with more documentation. This reduces your support burden and makes heavy users feel valued.
SMS Marketing: Previous buyers in specific zip codes get flash sale alerts for local stores. First-time visitors get a welcome discount. High intent plus geographic relevance equals higher conversion rates.
The channel doesn’t matter. What matters is matching the message to the person and where they are in their journey.
How To Segment Your Audience, Step-By-Step
Getting started with segmentation doesn’t have to be complex. Here’s a simple process you can use to organize your audience into actionable groups.
1. Start With Data You Already Have
Look at what’s in your CRM, email platform, or analytics tool. Useful data often includes location, purchase history, on-site behavior, and sign-up source.
2. Define Your Most Important Attributes
Based on your goals, decide which traits matter most. For an e-commerce brand, it could be past purchase behavior. For a SaaS company, it might be usage level or company size.
3. Build Initial Segments
Group your audience using filters like:
“Has purchased in last 30 days”
“Visited pricing page but didn’t convert”
“Signed up from Facebook campaign”
Start simple. You can get more granular later.
4. Map Each Segment to the Customer Journey
Think about where each group is in their decision-making process. Someone early in the funnel needs education. A returning visitor might need an incentive.
If you haven’t done this yet, use customer journey mapping to connect segments to meaningful actions.
5. Test, Learn, and Refine
Segmentation isn’t one-and-done. Use A/B testing to refine your messaging, offers, and timing by segment. Drop what doesn’t work. Scale what does.
Best Practices for Audience Segmentation (That Actually Work)
Anyone can slice up an email list but effective segmentation goes beyond basic filters. Here are a few proven tips to get better results without overcomplicating your strategy.
Use Real Data, Not Assumptions
Avoid guessing what people care about. Use actual behavior, survey responses, or analytics to guide how you group your audience.
Keep Segments Useful, Not Just Accurate
A perfect audience profile is useless if it’s too small to act on. Prioritize segments that tie directly to your business goals—like conversions, upsells, or retention.
Don’t Over-Personalize
Over-segmentation can create unnecessary complexity. You don’t need 30 different versions of the same email. Focus on meaningful variations that actually move metrics.
Update Your Segments Regularly
Customer behavior changes. Segments should too. Review and refresh your data often to avoid targeting stale or irrelevant groups.
Align Segments With Personas
Your audience groups should reflect the same needs and motivations as your core buyer personas. If you don’t have a clear set, start with this guide to building an accurate customer persona.
I see the same mistakes over and over. Avoid these pitfalls to get better results from your segmentation strategy.
Segmenting too early. You need data before you can segment effectively. If you’re working with a brand-new list or product, focus on collecting behavioral data first. Premature segmentation based on assumptions will waste time and money.
Creating too many micro-segments. A segment with 47 people isn’t actionable. Keep your segments large enough to matter. If a group is too small to justify custom creative or messaging, fold it into a larger segment.
Using outdated data. Someone who bought six months ago isn’t in the same segment as someone who bought yesterday. Refresh your segments quarterly at minimum. Monthly is better for fast-moving businesses.
Segmenting but not personalizing. Building segments means nothing if you send the same message to everyone. Each segment should get tailored copy, offers, or creative. Otherwise, you’re just organizing your list for no reason.
Ignoring overlap between segments. People can belong to multiple groups. A high-value customer might also be geographically close to your store. Think about how segments intersect and prioritize which message matters most.
Not testing segment performance. Track metrics by segment. If one group consistently underperforms, either refine the segment definition or adjust your messaging. Segmentation without measurement is guesswork.
FAQs
What is audience segmentation?
Audience segmentation is the process of dividing your broader audience into smaller groups based on traits like behavior, interests, demographics, or location. It helps you deliver more targeted and relevant marketing.
What are the types of audience segmentation?
The most common types include demographic, geographic, psychographic, behavioral, and firmographic segmentation. Each one gives you a different way to understand and connect with your audience.
How do you segment your audience effectively?
Start with data you already have—like purchase history or engagement. Then group users based on shared traits, align segments to the customer journey, and continuously refine based on performance.
Conclusion
Audience segmentation isn’t a tactic you add later. It’s where effective marketing starts.
By breaking your audience into meaningful groups, you gain the ability to tailor messages, prioritize the right channels, and improve your results across the board. Whether you’re building email campaigns, running paid ads, or planning content, segmentation keeps your strategy focused and relevant.
Start with the data you already have. Pick one or two segments that align with your goals. Then test, learn, and scale.
The more precise your segmentation, the more personal your marketing will feel and the better it will perform.
Need help building a segmentation strategy that actually drives results? Check out my consulting services for hands-on support.
AI chat is the number one source B2B buyers use to shortlist software.
Not review sites. Not vendor websites. Not salespeople. AI chat.
G2’s 2025 survey of 1,000+ decision makers found that 87% say AI tools like ChatGPT, Perplexity, and Gemini are changing how they research software.
Half of SaaS buyers now start in AI chat instead of Google Search.
They’re “one-shotting” their research with prompts like “Give me CRM solutions for a large gym that work on iPads.”
What used to take hours of “Google —> right-click —> open new tab” is condensed to minutes.
If your product doesn’t show up when buyers ask AI to recommend solutions in your category, you’re losing deals before they begin.
This guide shows you exactly how to change that.
I’ll walk you through:
How AI visibility works for SaaS
Why some brands dominate AI answers
What you can do to make sure AI recommends you
Side note: The data in this article comes from Semrush’s AI Visibility Index (August 2025), focusing on the Digital Tech and Software category.
The 3 Types of AI Visibility for SaaS Brands
There are three ways your brand can show up in AI search:
Brand mentions
Citations
Recommendations
Type 1: Brand Mentions
Brand mentions mean your brand appears in the AI’s answer.
It’s not always an endorsement. It’s simply the AI recognizing your brand as relevant to the topic.
For example, I asked ChatGPT:
“How can remote teams stay aligned on projects?”
ChatGPT outlined a few tactics and listed several tools, naming specific brands as examples with no extra context about any of them.
At this level, how AI talks about your brand is super important. AKA: brand sentiment.
A positive tone builds early trust while a negative one sets bad expectations.
Let me show you what I mean.
I asked ChatGPT:
“What do marketers on Reddit say about top reporting dashboards.”
ChatGPT summarized Reddit’s discussions, listed a few tools, and included criticisms about some products.
If I were evaluating dashboards, the negative details about AgencyAnalytics and Looker Studio would create a subtle bias that would follow me as I continued my research.
That’s no bueno.
So make sure sentiment around your mentions leans positive.
Just go to “AI Visibility” > “Perception” and you’ll see key sentiment drivers surrounding your brand. The tool will show you Brand Strength Factors (positive influence on sentiment) and Areas for Improvement (negative sentiment factors).
Type 2: Citations
Citations are instances of AI using your content as a source.
It’s a strong signal that the AI trusts your brand and is using your content to build its answer.
In Google AI Mode, citations show up as clickable links on the right-hand side of the response.
In ChatGPT, they appear as footnotes or small inline links.
Citations come with two complications.
First, they’re not as visible as brand mentions.
The footnote-style links are easy to miss, which means you probably won’t get meaningful traffic from these citations.
The AI can use your content, but not mention your brand.
Semrush’s AI Visibility Index report calls this the “Zapier Paradox.”
In the Google AI Mode dataset, Zapier was the most-cited domain in the entire software category. It appeared in around 21% of the analyzed prompts.
Yet it ranked only #44 for brand mentions.
That means the AI trusts Zapier’s content enough to use it constantly.
But that trust hasn’t translated into more visibility for the brand itself.
That doesn’t mean citations are useless. Far from it, since they’re still the only method of sending users directly from AI search to your website.
But if you’re cited for an answer that recommends other brands (like Zapier often is), the citation isn’t super useful for your brand.
That’s why you want the third type of AI visibility.
Type 3: Product Recommendations
Product recommendations are where the AI moves from “here are some options” to “here’s what you should choose.”
To get recommended, your brand typically needs two things working in your favor:
Positive sentiment
Enough verified facts for the AI to feel confident putting your name forward
For example, when I asked:
“Which CRM is best for small businesses?”
ChatGPT recommended six CRM platforms.
Each one came with a breakdown of strengths.
That’s the AI directly influencing my consideration set.
And when the AI wraps up the answer with the top three CRMs, these three brands stay top of mind.
As the reader, I’m thinking:
“Alrighty. These are the tools I should probably compare.”
That’s the power of SaaS product recommendations in AI search.
The AI isn’t just helping me explore the category. It’s shaping the shortlist I walk away with.
How AI Models Choose Which SaaS Brands to Surface
When AI answers a query, it cross-checks sources.
It compares what you say about your product with its training data. Along with what the rest of the internet says about you.
If the details line up, you’ve got consensus and consistency: two forces that drive visibility in AI search.
Consensus
Consensus happens when many credible places describe your product in the same way.
In practice, the AI is looking for alignment across sources like:
Review sites (G2, Capterra, TrustRadius)
Industry blogs and SaaS publishers
Expert posts on LinkedIn or in public newsletters
User communities like Reddit and Quora
Your own website and documentation
Basically: anywhere your product is being talked about in a credible context.
Take Asana, for example.
It routinely appears in AI answers about project management tools.
And you can see why when you look at its footprint online.
Across multiple places, you’ll find the same core description repeated from their website to Capterra to Reddit.
All of these brand mentions alone help boost Asana’s potential visibility.
But when they also all point to the same story, that’s consensus. This helps AI feel confident surfacing the brand repeatedly.
Consistency
Consistency means the details match everywhere they appear.
When AI scans the web, it’s looking for verifiable facts. If those specifics line up, it trusts them.
But, if those signals don’t match, the model becomes unsure.
(Just like you would if five people gave you five different versions of the same “fact.”)
For example, let’s say:
Your pricing page says your Standard plan includes unlimited reports
Your help center says Standard users get 50 reports a month
Recent reviews say they hit limits after a week
Now you’ve got three conflicting stories.
When the AI sees that, it can’t tell which one is true. It might use the right one, or it might use the wrong one. Or it might not use any of them.
That’s why data hygiene matters in AI search.
The key facts about your brand should be consistent everywhere your brand is described.
The Content That Dominates SaaS AI Search
Not all content carries the same weight in SaaS AI search.
Some formats show up repeatedly because they help models verify what’s true about a product.
Review Platforms
Review platforms are some of the most heavily cited sources in SaaS AI search.
These sites, including G2, Capterra, and TrustRadius, give AI unfiltered, third-party proof about your product.
The platforms help the model validate:
Who you are
What your product actually does
How reliable it is
How users feel about it
In other words, this is where AI goes to separate your claims from real user experience.
And the data shows how influential they are.
According to Semrush’s AI Visibility Index, G2 is the 4th most-cited source for ChatGPT and 6th for Google AI Mode across the entire tech and SaaS category.
That tells us that:
Review platforms aren’t just buyer research hubs
They’re part of an AI’s verification layer
What people say about you in these places becomes part of the material the AI uses when describing your brand.
Best-of listicles and tool roundups give LLMs structured, pre-sorted information that they can easily digest.
These articles hand the AI a ready-made map of a category, including:
Who the key players are
How the tools differ
Which products consistently show up together
The AI regularly pulls from a mix of major publishers, niche SaaS blogs, and established industry media.
For example, when I asked for the top AI SEO tools, Google AI Mode’s citations included a bunch of best lists.
Every roundup, comparison post, or “best tools for X” mention becomes one more anchor AI tools can grab when they’re trying to answer a question about your category.
Pro tip: Don’t ignore your own media. AI models also use company-owned content as reference material. So create your own well-structured roundups and comparison pages in the niches where your product plays.
For example, when I asked ChatGPT whether Omnisend or Mailchimp is better for ecommerce, one of the citations was Omnisend’s own blog post comparing the two tools.
In other words: their own content helped shape the AI’s narrative.
Documentation & Product Knowledge Bases
AI also uses your product documentation to understand how your product works: what it does, who it’s for, and what its technical capabilities are.
For example, when I asked Google AI Mode, “Is Semrush good for enterprise?” the model pulled from several Semrush-owned pages:
The Enterprise landing page
A press release on the enterprise platform
A blog on “What Is Enterprise SEO”
An enterprise client case study
Together, those pages gave the model context to understand Semrush’s enterprise offering.
One more thing:
Make sure your content is well-structured, clear, and complete.
If it’s vague or lacks key details, the AI might look elsewhere to fill the gaps.
The Semrush study shows this clearly with pricing.
When SaaS brands don’t publish transparent pricing, AI models fill the blanks using community speculation. This speculation is often tied to negative sentiment.
So, how do you structure your content for better AI visibility?
Use:
Clear, explicit content using conversational language
Clean formatting that makes details easy to extract
Tables, charts, and Q&A blocks that package information neatly
Headings that signal hierarchy
Want the full breakdown? Our article on how to rank in AI search walks you through the full process.
Video Content
Text may fuel most AI answers, but video (especially YouTube) has become a meaningful signal, too.
In fact, YouTube is the 10th most-cited source in Google AI Mode for SaaS-related prompts.
This means AI isn’t just reading the web. It’s also learning from what people show and say on camera.
For SaaS brands, that’s a real visibility lever.
If your product appears in YouTube reviews, tutorials, comparisons, or walkthroughs, the AI can pull those details straight into its explanations.
For example, when I asked Google AI Mode whether the paid version of HubSpot is worth it, one of the citations was a YouTube review.
If you don’t have a YouTube presence yet, it’s worth planning for.
Start by getting your product included in other creators’ reviews and tutorials.
Then build out your own YouTube channel to control the narrative long-term.
What This Shift Means for Your SaaS Brand
If you’ve already put in the work on your SaaS SEO basics, you’re already in a good position.
But AI search adds a new layer, and it requires a few more steps to stay visible.
Make AI Visibility a Company-Wide Effort
AI search visibility isn’t something marketing can brute-force on its own since consensus and consistency play such a major part.
Multiple teams should keep their corners of the internet aligned in your brand story.
This means:
Marketing keeps claims factual and up to date
Product Marketing ensures documentation, changelogs, and feature pages match what’s actually live
Customer Success helps maintain accurate review-site profiles
PR/Comms monitors media mentions so nothing drifts off-message
To make that doable, create a simple internal “source of truth” every team can follow.
This doesn’t need to be a 100-page brand bible.
Start with:
Exact product names, tier names, and feature labels
The approved value props and phrasing you want repeated everywhere
Performance claims or metrics that should stay consistent across your site, docs, and press
Integration names and technical terms written the same way across all surfaces
Example of a Brand That’s Winning in AI Search (Slack)
Slack ranks ninth overall in the Digital Technology/Software category for AI visibility.
That visibility isn’t tied to one use case or category, as Slack shows up everywhere for various queries.
From prompts about remote work to team communication and the best tools for small businesses.
Here’s what they’re doing that you can steal:
Slack Owns Their Category (Not Just Brand-Specific Prompts)
Slack doesn’t only show up when someone searches for “Slack.”
They show up for everything inside their category, in prompts about:
Use cases: “team chat for remote work”
Features: “tools with shared channels”
Problems: “how to align remote teams”
Price: “team communication tools”
Showing up in these various category prompts builds early recognition.
This then affects what happens next as the user goes deeper into their buying journey.
For example, a user might start an AI conversation with:
“Which is better, Slack or Teams?”
Slack shows up in the citations because they’ve published content that answers that question.
Now, let’s say the user sees a drawback in the AI’s answer.
The user might follow up with:
“What are Slack’s security concerns?”
And Slack again shows up in the citations, this time through their own blog content.
Slack is actively shaping the conversation.
As the user moves from comparison to evaluation to decision, Slack’s content keeps appearing in the AI’s reasoning.
In short: Slack gets to influence the story at every step of the buyer journey.
Slack’s Messaging Is Clear
One thing Slack absolutely nails is message consistency.
Everywhere you look — their website, their docs, their review profiles, their blog — you get the same story about what Slack does and who it’s for.
Go to their site and you’ll see pages laying out features, use cases, and integrations. All in plain, straightforward language.
Even their blog posts break down new features in that same accessible tone.
That clarity matters because it makes it incredibly easy for AI to learn what’s what.
When your content follows a simple structure of “Here’s the feature, here’s what it does, here’s how it works,” the model can easily classify information.
But Slack doesn’t just do this on their site.
Jump over to their review profiles and you’ll find the exact same messaging — the same features, same categories, same positioning.
That consistency is a big plus.
When your messaging stays the same across every channel, you give the AI reliable information to work with.
Slack Is Present Everywhere LLMs Go for Answers
Slack has a footprint across every layer that large language models pull from.
The community layer: Reddit threads, Quora discussions, and YouTube reviews:
The expert layer: SaaS tutorials, niche SaaS blogs, and trusted industry publishers:
The verification layer: G2, Capterra, and TrustRadius:
This breadth matters because it helps LLMs find patterns.
When Slack’s value prop, features, and positioning appear the same way across all three layers, the AI treats that agreement as “high-confidence” information.
This gives the AI zero doubts about what Slack does and what it offers — and therefore what kinds of queries the AI should recommend Slack for.
Help AI Find and Feature Your SaaS Brand
For SaaS AI search, the game is simple:
Show up everywhere the AI looks.
For software companies, that means being intentional about what you publish, how you structure it, and where you show up across the web.
You don’t just need to “write more content.”
You need to create the right content, in the right places, in the right formats that AI models rely on.
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The holiday season is one of the most lucrative and competitive times of the year for app marketers. With users in the mood to browse, buy, travel, and celebrate, it’s a golden window to capture attention, drive installs, and boost engagement.
As shoppers embrace gifting, experiences, self-improvement, and more, the period presents the perfect opportunity to connect your app with seasonal behaviors – but success depends on how effectively you plan and execute.
By developing tailored mobile user acquisition strategies and creative campaigns that resonate with the festive mindset, you can strengthen visibility, fuel app installs, and turn short-term peaks into long-term growth.
In this blog, we’ll explore how to craft high-performing seasonal campaigns that resonate with the festive mindset and keep your app top of mind during the busiest shopping season of the year.
Key Takeaways
Adapt creatives and messaging to align with seasonal moods and trends.
Use limited-time offers to drive urgency and engagement.
Upweight marketing budgets to capitalize on peak seasonal activity.
Leverage user-generated content (UGC) to boost authenticity and reach.
Optimize Apple Search Ads and Custom Product Pages to maximize visibility.
Upgrade your Creatives to Match the Season
To stay competitive and maximize results, your creative approach must reflect the holiday spirit. Users are actively searching for seasonal inspiration, so aligning your visuals, copy, and value proposition with this period can dramatically increase engagement.
1. Seasonal Visuals
Incorporating festive design elements such as colors, typography, and imagery helps your app feel relevant and timely. Use holiday cues that create an emotional connection, ensuring to stay on brand and balanced.
Pair this with seasonal messaging that captures attention, whether that’s highlighting limited-time features, discounts, or ways your app enhances the holidays. Done well, these creatives signal that your app is current, relatable, and part of the seasonal excitement.
2. Themed Messaging
Adapt your tone and messaging to reflect the joy and energy of the season. Phrases like “Get in the Holiday spirit” or “Make gifting easier this year” can help your campaign feel conversational and relevant. If you’ve added new features or updated your app for the holidays, make sure they are clearly showcased in your ad copy and store listing. This is a great way to let users (new and returning) know that you have fresh and relevant content, products, and deals for the season.
3. Create Value for Users
Ask yourself how your app adds value during the holidays. Whether it helps users manage gift lists, discover deals, or stay organized, communicate that benefit clearly. The goal is to position your app as useful, not just festive.
4. Limited-Time Offers
Exclusive promotions and time-sensitive deals are powerful conversion drivers. Use clear CTAs like “Limited-time offer” or “Ends soon” to build urgency. In your visuals, spotlight these offers alongside seasonal products or app features.
For instance, Mixbook – an online photo book and personalized gift creation platform – ran a paid acquisition campaign offering 50% off during the holiday season. The combination of festive imagery and a compelling offer helped the brand capture high-intent users when purchase intent was at its peak.
Source: Mixbook Facebook Ads
Upweight Your Budgets for Seasonal Campaigns
The holidays aren’t the time for evenly distributed spend. Competition is higher, but so is opportunity, meaning strategic budget allocation is key.
Focus your spend where you can achieve the greatest impact and concentrate on high-performing channels and audiences rather than spreading budgets thinly. A good approach can be to prioritize one or two paid acquisition channels that align closely with your highest-performing segments, to ensure you’re investing where impact will be the greatest.
For example:
Travel apps often see surges in December and again in January, when users plan trips for the new year. Increasing budgets during these moments ensures you capture high-intent users when they’re most likely to convert.
Shopping apps should front-load investment in November and early December to align with Black Friday, Cyber Monday, and Christmas activity. Visibility during these periods delivers stronger ROI than a steady year-round spend.
By investing more heavily during high-intent windows, you’re positioning your app to be seen when users are most motivated to act.
Source: Jet2holidays Christmas Screenshots
Leverage User-Generated Content (UGC) to Drive Engagement
Seasonal campaigns don’t have to rely solely on paid creatives. User-generated content adds authenticity, builds trust, and stretches your budget further.
UGC allows users to share real experiences, and during the holidays, these organic stories resonate more than any brand-produced ad.
Some ways you can harness user-generated content effectively:
Showcase genuine testimonials: Feature authentic reviews in your app store listings and ads. For example, a productivity app could highlight how users managed their holiday planning with ease.
Run holiday-themed contests: Encourage users to share festive photos or stories connected to your app, such as “Best Holiday Recipe” or “Gift Guide Challenge.”.
Create a holiday hashtag campaign: Build a seasonal hashtag to increase visibility and encourage sharing.
Feature user success stories: Share real examples of how users benefited from your app during past holiday seasons to demonstrate real-world value.
Incorporate UGC in Ads: Ads featuring real users often outperform studio-produced creative in engagement and CTR.
Benefits of UGC:
Wider reach: User posts expose your app to their personal networks.
Increased trust: Audiences are more likely to believe peer recommendations over branded messages.
Cost-effectiveness: Repurposing authentic content reduces production costs.
Higher engagement: UGC blends naturally into social feeds and typically generates higher engagement on social media.
Use Apple Search Ads to Accelerate Your Seasonal Growth
Apple Search Ads (ASA) are one of the most effective ways to reach high-intent users, people already searching for apps like yours. During the holidays, when search behavior shifts and competition increases, optimising your ASA strategy is essential.
Seasonal keyword research: Identify seasonal search terms and trends using ASO tools. Keywords like “holiday planner,” “gift ideas,” or “Christmas shopping” can unlock new audiences during this period.
Seasonal Custom Product Pages (CPP): Custom Product Pages allow you to tailor visuals and messaging for specific keywords or campaigns. Update your CPPs with festive creatives, special offers, or limited-time product features to deliver a more relevant user experience.
Plan for Higher Competition: Expect CPCs to rise during peak seasons, so factor that into your forecasts. To maintain ROI, prioritize creative testing – visuals, messaging, and offers that can help you convert at a higher rate when competition is stronger.
Maximizing Lifetime Value of Seasonal Installers
Seasonal campaigns can generate huge bursts of installs, but the real value lies in retention. Many users acquired during holiday periods are motivated by discounts or limited-time offers – meaning they risk churning once promotions end.
To counter this, segment seasonal installers early and design retention campaigns around their behavior:
Offer exclusive post-season promotions or loyalty rewards.
Send early-access invitations for future sales or events.
Reinforce values through personalized push notifications or in-app messages that highlight ongoing benefits.
By nurturing these users beyond the holiday period, you can turn one-off installs into long-term, high-value customers.
Seasonal Growth Beyond Retail
While shopping and eCommerce apps experience some of the most visible holiday peaks, seasonal user-acquisition opportunities span almost every vertical. The key is to identify the moments that matter most for your audience and align your campaign strategy around them.
Travel & Experiences: December and January are peak planning months. Apps can use “escape the cold” or “plan your next adventure” narratives to capture high-intent travelers and early-year bookings.
Fitness & Wellness: The new year is synonymous with fresh starts. Fitness, nutrition, and mindfulness apps can capitalize on this momentum with “reset” or “new routine” messaging.
Finance & Money Management: After the holiday spending rush, users often turn to budgeting and saving. Finance apps can position themselves as the go-to solution for taking control in January.
Entertainment & Streaming: With people spending more time at home, apps in entertainment, gaming, and streaming can highlight shared experiences, relaxation, or discovery.
Food & Delivery: From festive feasts to New Year get-togethers, delivery and recipe apps can tap into convenience, celebration, and seasonal indulgence.
Productivity & Learning: As goals and resolutions take shape in early Q1, these apps can drive engagement by helping users stay organized, productive, and inspired.
Conclusion
The holiday season presents a unique opportunity for app marketers to connect with users at scale, but seizing that opportunity takes strategy, timing, and creativity.
From festive creatives and limited-time offers to smart budget allocation, user-generated content, and Apple Search Ads, every element of your user acquisition strategy should work together to maximize performance.
And remember, seasonality isn’t just about the holidays, it’s about harnessing moments. By aligning your app marketing with user behaviors and mindsets throughout the year, you can build campaigns that not only drive downloads but sustain growth long after the festive season ends.
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Google is expanding its customer lifecycle capabilities in Google Analytics, launching new audience templates and dynamic remarketing features designed to make high-value targeting and re-engagement easier for advertisers.
Driving the news. Google has introduced two new suggested audience templates in GA to help advertisers instantly build lifecycle segments:
High-Value Purchasers — powered by purchase count or lifetime value, with Google adding a new LTV percentile field so marketers can isolate their top-tier customers.
Disengaged Purchasers — defined by days since last purchase, giving Google a built-in way to help brands re-engage lapsed buyers.
Google designed these templates to sync directly with Google Ads customer lifecycle goals, including high-value new customer acquisition and re-engagement modes.
Google’s next move: dynamic remarketing inside GA. Google is also bringing display dynamic remarketing directly into Analytics, letting brands show personalized product-based ads to past site visitors without needing to build remarketing setups externally.
Once advertisers implement Google’s recommended eCommerce event collection, Analytics will automatically share dynamic remarketing data with linked Google Ads accounts — as long as personalized advertising is enabled.
Why we care. Google is making it much easier to target the customers who matter — high-value buyers and lapsed purchasers — without building complex audiences from scratch. These new templates and dynamic remarketing tools create faster, smarter ways to drive acquisition, retention, and repeat purchases directly from Google Analytics.
Google is giving you more precise lifecycle targeting with less manual work, and that can translate directly into better performance and more profitable campaigns.
The big picture. Google is tightening its ecosystem, giving advertisers more automated ways to identify, activate, and re-engage customers — all fueled by audience intelligence built inside Google Analytics.
The bottom line. Google is doubling down on lifecycle marketing by turning Google Analytics into an even stronger audience engine for Google Ads.
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Google rolled out a long-awaited Performance Max (PMax) reporting upgrade, giving advertisers their first clear look at how Search Partners affect campaign results.
Driving the news. The update is now live in Google Ads and adds Search Partners to the PMax channel performance tables. Advertisers can now see:
How Search Partners contribute to PMax results.
Whether they add incremental value.
How their performance compares with other PMax channels.
Total spend going to Search Partners.
What’s changing. The added transparency shows how PMax spreads budget across channels – especially in search – and helps confirm whether Search Partners traffic is profitable or pulling down efficiency.
Why we care. Search Partners activity has long been hidden inside PMax, making it hard for advertisers to see where spend was going or gauge its impact, but the new reporting line finally brings visibility to this opaque slice of search inventory. With that clarity, teams can assess incremental value, compare performance against other PMax channels, and make smarter optimization and budgeting decisions. In short, you can now measure spend that was previously invisible, and that insight can directly influence performance and profitability.
The big picture. The update may look small, but it’s a meaningful step toward unpacking how PMax works. For accounts running PMax at scale or analyzing profitability by channel, isolating Search Partners data can shape optimization, budgeting, and broader strategy.
First seen. Google Ads specialist Aleksejus Podpruginas first spotted the update and shared it on LinkedIn.
Bottom line. PMax is finally revealing a missing piece of the puzzle, giving advertisers a clearer view of how Google’s automation spends their money.
OpenAI CEO Sam Altman issued an all-hands “code red” to improve ChatGPT – a move that could delay the company’s advertising plans – according to an internal memo obtained by The Wall Street Journal.
Driving the news. Altman told employees the company must urgently improve ChatGPT’s personalization, speed, reliability, and ability to handle a wider range of questions.
Daily calls, temporary team reassignments, and a companywide push now center on one priority: make ChatGPT better, fast.
Nick Turley, who leads ChatGPT, said the team is focused on growing the assistant and making it feel “more intuitive and personal.”
Why now? Competition is catching up. The memo signals rising pressure on several fronts:
Google: Its upgraded Gemini model topped OpenAI on key benchmarks last month.
User growth: Gemini’s ecosystem jumped from 450 million monthly active users in July to 650 million in October, helped by new tools like the Nano Banana image generator.
Anthropic: Gaining ground with enterprise customers as the “safer, more predictable” LLM provider.
OpenAI is also facing heavy financial strain, with planned data center investments in the hundreds of billions, while the company remains unprofitable and reliant on constant fundraising. Internal forecasts suggest that OpenAI must get roughly $200 billion in revenue by 2030 to become profitable.
What’s getting delayed. To refocus on ChatGPT quality, Altman said OpenAI is pushing back work on:
Advertising initiatives.
AI agents for health and shopping.
A personal assistant called Pulse.
What’s next. Altman told staff a new reasoning model arriving next week is already outperforming Google’s latest Gemini release.
OpenAI previously declared a “code orange” over ChatGPT quality – part of an internal urgency scale (yellow → orange → red). GPT-5’s August launch drew criticism for feeling colder, being less helpful on simple tasks, and acting too cautiously. A November update made the model feel warmer and better at following instructions.
Why we care. It appears that OpenAI is pausing its rollout of ChatGPT ads to focus on product quality. That means advertisers hoping to use ChatGPT as an ad channel will have to wait longer.
Flashback. This isn’t the first code-red moment in the AI arms race. Google once issued its own “code red” because of OpenAI. In December 2022, after ChatGPT went viral, Google CEO Sundar Pichai declared a companywide code red, calling the chatbot an existential threat to Google Search. What followed:
Founders returned: Larry Page and Sergey Brin rejoined product meetings after years away.
Search overhauled: Google accelerated plans to add conversational features to Search.
Product surge: A leaked slide deck outlined 20+ new AI products and a demo of a chatbot-powered version of Search.
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Google will shut down three long-running Google Groups forums for advertising developers early next year as it moves all technical support into official channels.
Driving the news. Google will stop responding to new posts on Jan. 28. The forums will stay online as read-only archives until later in 2026, when Google plans to disable posting entirely.
After Jan. 28:
Support agents will no longer reply in Google Groups.
Replies to existing threads will create a new email ticket with Google support.
Existing content will remain available for reference, including past discussions and fixes.
The shift. Google said it’s consolidating support to “streamline technical support channels” and move developers toward official tools with better tracking and response workflows.
Where developers should go now. Google’s updated documentation now points to these official channels:
Why we care. These forums have long served as open Q&A hubs for developers, helping teams troubleshoot issues across the Google Ads API, Ads Scripts, and the Campaign Manager 360 API. With the forums going away, all troubleshooting will shift to official support, forcing developers to adjust workflows, share more detailed logs, and rely less on community-driven fixes. The way advertisers solve problems is changing, and preparation will help prevent downtime and lost performance.
What Google wants from developers. To speed up resolutions, Google urges developers to include complete diagnostic details when filing tickets, such as:
Google Ads API: request ID, full request + response logs
Bottom line. Google is shuttering its public troubleshooting forums in favor of standardized, direct support – a move that may streamline issue handling but could shrink the pool of community-shared knowledge over time.
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AI search is reshaping how ecommerce brands get discovered.
One week, your products show up in ChatGPT. The next week, they’re replaced by competitors.
For many brands, this uncertainty can feel overwhelming.
Organic visibility now depends less on rankings and keywords, and more on how LLMs gather information, which platforms they rely on, and what signals help them highlight your brand.
In this guide, I’ll explain this crucial shift in detail.
I’ll unpack:
What actually shapes visibility inside AI answers
The business impact of compressed buyer journeys and broken attribution
How you can build lasting relevance in this new search ecosystem
The 3 Types of AI Visibility for Ecommerce Brands
If you’re familiar with SEO, getting AI visibility is similar. It starts with how search systems decide what to display.
But for years, ecommerce SEO was a linear equation: rank = visibility = traffic (and then conversions).
AI search is changing that.
LLMs summarize, compare, and recommend products, all in one place.
In short: Shoppers can discover your products, check alternatives, and make buying decisions within AI chats.
In this new setup, brands compete across three different discovery models.
Type 1: Brand Mentions
Mentions drive product discovery and build top-of-funnel LLM visibility for your brand.
This is where your brand gets featured in AI-generated answers, often without a link to your site.
Mentions often come from reputation signals like:
Reddit posts
Media coverage
User reviews
Social discussions
Put simply, you become part of the conversation.
For new or emerging brands, this is often the first touchpoint to reach shoppers through AI.
Type 2: Citations
Citations are linked references within AI-generated results, like a footnote in an essay.
With citations, LLMs attribute specific information, claims, or data points to your pages.
Your brand becomes a source of truth in AI responses and gains credibility.
How?
When an AI tool cites your brand, it signals to shoppers that you’re an authoritative voice.
Plus, citations can support your positioning. The AI tools can pull your framing and product narrative into their response. Not someone else’s.
Type 3: Product Recommendations
AI platforms actively recommend products for a shopper’s specific needs and concerns.
This is the most impactful layer for ecommerce brands.
Your products can show up with pricing, ratings, and other details.
This type of visibility effectively merges discovery and purchase in one place.
This happens when the LLM reviews the query, compares options, and picks your product as the best fit.
Showing up in the list of recommended products makes your brand a part of the decision interface.
Shoppers can compare specs, prices, and reviews — or even purchase — right in the AI chatbot or search tool itself.
How AI Models Choose Which Ecommerce Brands to Surface
AI visibility as a discipline is still evolving rapidly. But there are clear patterns to which ecommerce brands get seen and which get sidelined.
Two driving forces at play are: consensus and consistency.
Consensus
With traditional search, ecommerce brands could build domain authority through activities like link building and digital PR. Strong pages from an authority perspective tended to perform well in search results.
In AI search, LLMs don’t evaluate your website and product pages in isolation. Authority is built from a consensus across sources.
LLMs ask: “What do credible sources agree on about this product?”
To decide which brands and products deserve visibility, LLMs cross-reference multiple sources, like:
Reddit threads
YouTube videos
Industry reports
Customer reviews
Trusted publishers
Community discussions
So, a glowing review on your PDP might mean little if customers on Amazon consistently leave 1-star ratings.
And a publisher’s feature loses impact if Reddit users repeatedly recommend your competitors instead.
In other words: No single source determines your likelihood of being mentioned or cited. It’s the pattern of consensus across multiple platforms that does this.
For example:
Keychron frequently shows up when you use AI search tools to find mechanical keyboards.
This happens because the brand has earned trust through various sources:
Review sites like PCMag and Tom’s Guide rank Keychron in their top recommendations
Keychron’s Amazon pages are detailed with positive reviews and an average rating of 4.4 stars
Multiple Reddit threads in subreddits like r/MechanicalKeyboards and r/macbook recommend the brand
Several YouTube videos feature Keychron in their roundup of mechanical keyboards
Each trust signal on its own is valuable.
But when taken together, LLMs see a pattern of independent sources validating the same brand/product for a specific use case.
Consistency
LLMs don’t crawl and rank pages the way traditional search engines do.
Instead, when answering a product-related query, an AI model might pull:
Your product name from your Shopify store
Pricing from Google Merchant Center
Key specs from Amazon
Opinions from users on Reddit
If your product title is “stainless steel” on Amazon but “brushed metal” on Walmart, the LLM can’t decide which is correct. This inconsistency could make the AI tool less likely to include any information about your product. Or it could include the wrong information.
This is why data hygiene is crucial for building AI visibility.
You need to maintain a clean, synchronized identity for every product across every channel.
Your product attributes should follow the same pattern across your site, marketplaces, and feeds:
Model numbers
Dimensions
Materials
Weights
Prices
LLMs use these data points to match your products to queries and validate claims across sources.
Your Amazon listing, your Shopify store, your Google Merchant feed — all sources need to tell the same story with the same data.
So, the same SKU name, image, and product description should appear everywhere your product appears.
Finally, outdated data signals decay, and models may deprioritize products with outdated info.
When you change a price or update a key spec, that change should be visible everywhere. Stock availability, pricing, and features should always be up to date.
Types of Content That Dominate Ecommerce AI Search
We’re seeing clear patterns in what gets cited, mentioned, or ignored in AI search for ecommerce.
Understanding these patterns can be the difference between hoping you show up and knowing how to position your brand so that you do show up.
Here’s what’s currently doing well in AI search for ecommerce:
Top Cited Sources
I wanted to see which brands are cited most frequently in LLM responses for ecommerce queries — so I tested it.
I picked nine popular ecommerce niches and searched category-specific queries across ChatGPT, Claude, Perplexity, and AI Mode.
Based on the responses, I made a list of five popular brands showing up frequently for each vertical.
Then, I jumped to the “Competitor Research” tab in Semrush’s AI Visibility Toolkit to run a gap analysis for these five brands in each category.
The “Sources” tab showed which domains LLMs cite most frequently, like this for the “outdoor travel & gear” niche:
This data reveals where LLMs pull product information, and which platforms matter most in your vertical.
Here’s what this data tells you:
Reddit: Reddit is a top-cited source for nearly every industry. If people aren’t discussing your brand in relevant subreddits, invest in Reddit marketing.
YouTube: It’s another universal citation source. Video content from creators and users feeds into AI answers. That means having a YouTube presence can be a huge visibility lever for most ecommerce verticals.
Category-specific platforms: Generic sources like Amazon appear everywhere. But niche platforms (like Petco, Barbend, Sephora) carry weight in their verticals.
Wikipedia: It’s a top source for categories like outdoor gear, healthy drinks, and gadgets. This is where product context and category education matter a lot alongside the likes of specs and pricing.
Going beyond these top-cited platforms, here are the kinds of content LLMs link to most frequently for ecommerce queries:
Publisher Listicles
These are product roundups, buying guides, and comparison posts from established media outlets.
For example, I asked ChatGPT for the best Bluetooth speaker recommendations.
It cites publishers like TechRadar, Rtings.com, and Stereo Guide for this response.
Getting featured in these listicles means you’re part of the source material LLMs use to compile information.
AI models use publisher listicles as sources because they:
Compare multiple products in one place
Refresh their recommendations periodically, providing recency signals
Include specific, comparable details like price ranges, key specs, and pros/cons lists
Fulfill high editorial standards and so may appear more trustworthy than user-generated content
Retailer Product Pages
Retailers like Amazon, Walmart, and Target are among the most frequently cited sources for product queries.
When I asked Perplexity about the NutriBullet Turbo, it cited the product pages from the likes of Walmart and Macy’s.
These PDPs provide structured data points like ratings, pricing, and key specs.
AI models often rely on these product pages because they:
Include structured, machine-readable product data like specs, dimensions, materials, and pricing
Aggregate hundreds or thousands of customer reviews as social proof
Show real-time availability and pricing
Lab Tests and Expert Reviews
In-depth product testing content from experts is another important source for citations.
These websites test products systematically and publish detailed findings.
LLMs can then use this empirical data as the basis for their responses.
For example, I asked Claude to find the best mattress for side sleepers.
The tool references sites like NapLab, Consumer Reports, and Sleep Foundation for data-backed recommendations.
AI models consider lab test or expert review content for citations because they:
Compare products against consistent criteria and benchmarks
Show credibility with independent, systematic evaluation processes
Include measurable data to explain their top-ranked recommendations
Periodically update their recommendations to offer fresh, authoritative data
Reddit Threads and Community Discussions
Conversations on Reddit, Facebook groups, and YouTube comments frequently appear in AI responses.
This is especially true for subjective queries like “Is X worth it?” or “What do people actually think about Y?”
I tested this myself by asking Perplexity whether the Instant Pot Duo is worth buying.
It pulled insights from multiple Reddit threads, a Facebook group, and a YouTube video to respond based on real user input.
Brands that get mentioned positively across multiple Reddit threads build “cultural proof.”
And those organic discussions about your brand feed directly into AI training data and real-time search results.
AI models pull from these communities because they:
Present an aggregated sentiment from community discussions
Contain contrasting opinions and insights to objectively review products
Show different use cases and pain points that a product can tackle
Highlight a product’s pros and cons based on firsthand experience
Comparison Posts
Content that compares two or more products can also help LLMs find the right brands to mention in their response.
When I ask AI Mode for alternatives to the supplement brand Athletic Greens, it mentions five options.
The sources include several comparison articles (alongside some roundups).
Being included in this type of content (even if you’re not the winner) can help build your visibility.
This could be Brand A vs. Brand B blog posts, YouTube videos, review sites, and social media discussions.
AI models refer to these resources because they:
Answer buyers’ questions by comparing two or more products
Focus on decision-making criteria and help people make informed decisions
Let’s now consider the business impact of this AI search setup for your ecommerce brand.
The Compressed Buyer Journey
The traditional ecommerce funnel was built on multiple touchpoints.
A shopper might:
Google a product category
Read reviews on multiple different sites
Check Reddit and YouTube
Visit brand websites to compare prices
Return days later to buy
Each step was an opportunity for your brand to show up, make an impression, and win their trust.
For a lot of purchase decisions, AI search collapses this entire journey into a single interaction.
The same shoppers can now go to AI tools and ask, “What’s the best air fryer for a small kitchen?”
They get a single response with buying criteria, product recommendations, pricing, ratings, and more.
Now, clearly this isn’t going to happen for every purchase decision. These tools are still new for one thing, and it takes a lot to majorly shift buyer behavior. (And of course, SEO is not dead.)
But discovery, evaluation, and consideration CAN all happen in one response now. The AI agent performs the research labor.
That means you have fewer chances to influence buyers.
In the past, if a shopper didn’t discover you in organic search, they might find you through a review site, a Reddit thread, or a retargeting ad.
In other words: You could lose the first touchpoint and still win the sale three touchpoints later.
With AI search, you might only get one shot: the initial response.
For many ecommerce queries, AI tools give you a curated list of options. If you’re not in that initial answer, you don’t exist in the decision process.
Take action: Build an AI search strategy using our Seen & Trusted Brand Framework to increase the probability of your brand getting featured in AI responses.
The Visibility Paradox
Your brand might frequently show up in AI search. But your analytics show flat traffic and zero conversions traced back to AI tools.
Here’s why:
Not all AI visibility is created equal.
Your brand can appear in 10 different AI responses and drive 10 completely different business outcomes.
It all depends on how you’re presented.
Here’s what the visibility spectrum actually looks like for ecommerce brands:
Visibility Type
Example
Business Outcome
Mentioned without context
“Popular air fryer brands include Ninja, Cosori, Instant Pot, and Philips.”
Value: Brand awareness Purchase Likelihood: Low
Mentioned with attributes
“Cosori is known for its large capacity and intuitive controls.”
“The Cosori 5.8-quart model includes 11 presets, uses 85% less oil than deep frying, fits a 3-pound chicken, and costs around $120.”
Value: Active consideration and purchase Purchase Likelihood: High
That means getting mentioned is table stakes, not the end goal.
Building brand awareness without differentiation just makes you a part of the crowd.
To drive real sales, you need to earn citations and product recommendations.
The brands winning in AI search are:
Cited as trustworthy sources
Recommended for specific use cases
Attribution Gets Murky
When shoppers find products through AI but buy elsewhere, analytics tools can’t track the whole journey.
This creates two problems:
You can’t prove the ROI of AI search: Even if AI mentions are driving consideration, you’ll get zero or limited data on that. You won’t see the prompt the user asked or the response from the tool.
You can’t optimize what you can’t measure: When you don’t know how people are discovering you in AI answers, you can’t A/B test your way to better visibility. The feedback loop is broken.
Tools like Semrush’s AI SEO Toolkit are closing this gap by showing how your brand and competitors appear in AI search.
I used the tool to check the AI visibility and search performance for Vuori, an athleisure brand.
The brand has a score of 76 against the industry average of 82, and is frequently mentioned AND cited in AI responses.
The toolkit also identifies specific prompts where your brand is mentioned or missing.
This makes it easy to spot exactly which type of queries are driving visibility and which represent missed opportunities.
For example, here’s a list of prompts where LLMs don’t feature Vuori, but do mention its competitors.
Go to the “Cited Sources” tab to find out the websites that LLMs most commonly refer to for your industry-related queries.
For Vuori, it’s sites like Reddit, Men’s Health, Forbes, and more.
The “Source Opportunities” tab will give you a list of key sites that mention your competitors, but not you. These are sites you should aim to get your brand included on.
Besides tracking your own AI visibility, the AI SEO Toolkit also lets you monitor your competitors’ performance on AI platforms.
The “Competitor Research” report compares you to your biggest competitors in terms of overall AI visibility.
It also highlights topics and prompts where other brands are featured, but you aren’t.
Example of a Brand That’s Winning in AI Search: Caraway
If you want to see what winning in AI search actually looks like, look at the cookware brand, Caraway.
When you ask AI about the “best bakeware set” or the “best ceramic pans,” Caraway almost always makes the shortlist.
Data from Semrush’s AI SEO Toolkit shows that Caraway also outweighs its biggest competitors in AI visibility.
Let’s break down how Caraway built this advantage.
Showing Up Where LLMs Look
Caraway is frequently featured on publishers like Taste of Home, Good Housekeeping, and Food and Wine.
These are the actual sources LLMs cite when constructing answers about cookware-related queries.
For example, here’s a paragraph from the Food and Wine article ChatGPT cited as a source, which mentions the attributes ChatGPT used in its recommendation:
Caraway also earns mentions through organic discussions on Reddit, Quora, and kitchen forums.
Retailer Evidence That AI Can Cite
Caraway’s clean Amazon Brand Store and on-site product pages also make it easily citable.
These product listings and pages give LLMs concrete signals like:
Multiple in-stock SKUs with visible sales velocity (“500+ bought in the past month”)
Product rating and volume
Rich media files
These retailer PDPs become credible sources for verifying pricing, availability, or product specs.
Strong Affiliate Presence
Caraway also runs an affiliate program, and the brand makes it frictionless for publishers to feature its products through:
Affiliate networks: Links are available through major networks like Skimlinks and Sovrn/Commerce
Amazon compatibility: Editors can also use Amazon Associates links for Caraway’s stocked SKUs
Reviewer support: The brand provides an affiliate kit, including link types, banner ads, text links, and email copy
This all makes it easy for Caraway to work with influencers and other publishers to promote its products. And these publishers can then appear as citations when AI tools make their recommendations.
For example, all the highlighted sources in the ChatGPT conversation below contain Caraway affiliate links:
Part of the Category Narrative
Many style media and mainstream outlets reference Caraway in their content.
Here’s a recent example from an Architectural Digest interview featuring the cookware set as an essential kitchen item.
This creates more authority for the brand in the cookware and kitchen category.
Make AI Work for Your Ecommerce Brand
You now know how the game works and who’s winning. It’s your turn to play it.
But there’s a lot to do.
Making your site readable by LLMs, opmtimizing your structured data, and setting up automated product feeds are just stratching the surface.
Our comprehensive Ecommerce AIO Guide gives you alll of the actionable tactics to consistently show up in AI results.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-28 15:00:212025-11-28 15:00:21How Ecommerce Brands Actually Get Discovered In AI Search
