Happy Friday!

Here's what you need to know this week across organic and paid that matters for your eCom store:

01 · llms.txt is a dead end for AI visibility: 97% of the files get zero traffic, and Google says they can't help anyway

What it means for you: the file is self-reported (untrustworthy by design, like the old meta-keywords tag), the bots that actually generate citations barely touch it, and Google’s own Mueller says it can’t help AI pick your site over a competitor’s. If you’re on Shopify, it’s auto-generated for you, so there’s nothing to do, but if you’re not, it’s still not where AI visibility is won, as that’s crawlable pages, strong PDPs, internal links and earned mentions across the sources the models trust. However for future-proofing it’s worth implementing on your non-Shopify store as it won’t hurt. Furthermore, I want to be clear here that Mueller said it’s more to help an agent that’s already on your site or check out, i.e. instructions for a visitor who’s walked in, not a way to get discovered in the first place.

02 · ChatGPT Ads adds AI creative generation as it's quietly becoming a fully paid channel

What it means for you: The story isn't that AI can generate ads (Meta, Google, TikTok and Amazon already do), it's that ChatGPT is maturing into a complete paid channel DTC brands will soon buy performance against, and your edge there won't be "we can make creative", but the product data, imagery, and offer you feed it. Potentially trial a small test budget early and treat your feed and brand assets as the input that decides whether it works, which is the same lesson as AI Max in row 03.

03 · AI Max is coming to Shopping: your feed and PDPs, not your bids, will decide paid performance

What it means for you: once Google interprets your feed semantically and picks its own landing pages, weak PDPs and thin feeds won't just hurt SEO, they’ll cap your paid ROAS, so the brands that win flip AI Max on after a feed–PDP alignment audit, not before. This is the whole discovery loop in one feature: organic, GEO and product data are now paid inputs, and the team that owns them as a single brief pulls ahead.

To read more:

1. 97% of llms.txt files got no requests, Ahrefs data shows — Ahrefs · Google's Mueller says llms.txt can't help LLMs differentiate sites — Search Engine Journal

2. OpenAI moves to automate ad creative — Digiday

3. AI Max for Shopping – NOVOS

From NOVOS

Three stories, and the one line that connects them is the unglamorous foundations (your content, feed, PDPs and earned mentions), that now silently decide your paid ROAS and your AI visibility. That's what we help with :)

Want to chat about this?

Happy Friday!

Here's what you need to know this week across organic and paid that matters for your eCom store:

01 · Google Search Console is rolling out native Generative AI performance reports for your AI Overviews & AI Mode visibility, finally straight from Google

What it means for you: long overdue and genuinely useful, AI visibility is reported natively in GSC instead of being stitched together from third-party trackers, so you can finally see which pages are appearing in AI Mode. We’ve reviewed it over the last few days across multiple clients, though, and the initial enthusiasm has tapered off because it's pages-only (no query data yet), still in limited testing, and, honestly, underwhelming in practice so far. Worth baselining the moment you get access, but the real value lands when query-level data follows and eventually clicks measurement.

02 · Sephora becomes the first prestige beauty retailer to let shoppers discover and buy inside Google's AI with no site visit needed

What it means for you: the storefront is quietly relocating into the AI layer, and the brands that win are the ones that the assistant trusts enough to recommend before a product page ever loads. If your PDPs/PLPs, reviews and overall brand signals aren't readable to these systems, you're not in the consideration set & will be the option the agent never surfaces.

03 · Google's putting Gemini-written ads inside AI Mode answers & the AI writes your product's pitch for you

What it means for you: ads are moving into the AI answer itself (Conversational Discovery, Highlighted Answers, AI-written Shopping explainers), and Gemini generates the "why this product" copy from your product data and site content, so your feed and PDP quality now silently decide what your paid ads even say. The brands winning here won't be the ones bidding higher; they'll be the ones whose structured data gives Gemini a better story to tell.

To read more:

1. Google launches Search Generative AI performance reports in Search Console — Google Search Central

2. Sephora is bringing prestige beauty shopping into Google’s AI ecosystem — Glossy

3. A new generation of ads for the AI era of Search — Google

Since it’s our first issue of the weekly news, here are two more from us :)

⚖️ UK-first: Google must now let publishers opt out of AI Overviews without losing their ranking: Built for news publishers bleeding referral traffic, but for most DTC brands, opting out of the AI answer means opting out of the buying decision. The useful parts are buried in the ruling: Google now has to share clearer AI-engagement metrics, so better visibility data is coming (hence why point 1 above rolled out so quickly 👀). → UK media groups win power to opt out of Google AI search summaries — The Guardian

📱 Your product feed is now the most underrated lever in eCom: Feeds stopped being a Google Shopping input and became the structured-data layer that decides whether Google, Meta, TikTok, marketplaces and LLMs can surface you at all, and most are riddled with weak titles and missing attributes quietly capping visibility before bidding or creative even gets a say. Fix the source-of-truth data once, and every channel lifts together; it’s a rare lever sitting across SEO, paid, eCom and AI discovery at the same time. → The most expensive blind spot in eCommerce: your product feed — NOVOS

Google’s AI Max Is Coming to Shopping

Success won’t just depend on your campaigns. It will increasingly be driven by the quality of your PDPs and product feed.

AI Max for Shopping is coming. After a year on Search campaigns, Shopping is the next surface Google has announced for AI Max. It’s not live yet, but Google’s trajectory is increasingly clear.

This isn’t simply another automated feature. It’s a shift in how performance gets generated, and the gap will widen for brands still treating feeds and PDPs as passive.

In reality, campaign structure and management still matter, but the main driver behind AI Max performance will increasingly be the strength of your PDPs and product feed.

A useful matrix I use to audit Google Ads accounts illustrates this clearly: if the landing pages are weak, the account won’t scale efficiently, and performance will leak. That becomes even more relevant with AI Max.

What AI Max Actually Does

For years, Shopping worked like this: Title = your keyword strategy. You didn’t bid on keywords, but your titles were your targeting. The title has been the single biggest lever in the feed.

That changes with AI Max. Three core features:

Text Customisation generates ad copy on the fly, pulling from your Merchant Center feed, your existing ads, and your landing pages. You’re providing the raw material. Google writes the ad.

Final URL Expansion chooses the best landing page for a query, even if that’s not the URL you set. If your training guide is a better fit than the generic product page, Google routes traffic there.

Optimal Format Selection decides whether the query is best answered with a text ad, a Shopping listing, or both, and picks dynamically.

Translation: Google will no longer just read your feed. It will interpret it. Matching becomes semantic, not term-based. Every attribute, in the feed and on your site, becomes training data.

Why This Is an SEO, GEO and Product Feed Challenge

AI Max has two primary input sources: your Merchant Center feed and your site content. Each AI Max feature draws from one or both.

Final URL Expansion needs proper site architecture. It can only route between pages that it can crawl, understand, and rank. If your site has thin PDPs, weak category pages, or content trapped behind JavaScript, the “best landing page” Google picks will be the least bad one.

Text Customisation needs intent-rich content in both feed and site. Google generates ad copy by reading your Merchant Center attributes (title, description, product highlights, vertical-specific fields) and your landing pages. If your feed is sparse and your PDP is a pile of feature bullets and generic marketing prose, the AI has nothing useful to extract. It produces generic ads.

Optimal Format Selection needs alignment. When feed, page, and supporting content tell different stories, Google sees a fragmented product. Consistency is now an ad performance signal.

A note on GEO. It’s not a separate discipline competing with SEO. It’s an adaptation of SEO for how AI systems retrieve and rerank content. If you don’t have good SEO, you won’t have good GEO, because AI engines need a source to retrieve from. Many AI crawlers still can’t read JavaScript, so technical SEO is non-negotiable.

The same principles apply to AI Max. It’s a paid surface that consumes the outputs of your SEO, GEO and product feed work. Brands that have invested in those foundations will see AI Max amplify their work. Brands that haven’t will see it expose every weakness at the same time.

Where to Focus in the Feed

1. Title (still the strongest signal). Context-rich, not keyword-stuffed. Structure: Brand + Product Type + Key Attribute + Use Case + Variant.

2. Description (massively underused). Front-load use cases, problems solved, and differentiators in the first 160 characters. Cut the “premium quality” fluff.

3. product_type and google_product_category. Use the full taxonomy path, not just the top level.

4. Product highlights. 4 to 6 distinct benefit bullets.

5. Additional images. Lifestyle and detail shots, not just pack shots.

6. Vertical-specific attributes. Apparel: material, pattern, fit. Beauty: ingredients, skin type. Home: dimensions, room, style. These unlock long-tail queries you’d never bid on manually.

7. PDP alignment. Final URL Expansion only works if your pages answer: Who is this for? When should I use it? Why over alternatives? Add use cases, comparisons, and FAQs.

The Convergence Point Is the Page and the Feed

The feed and the landing page are no longer just paid concerns. They’re the convergence point of Paid, SEO, GEO, and CRO.

A modern PDP, and a modern feed entry, has to do several jobs at once: answer a specific intent, structure content for extraction, carry rich schema and attribute signals, align with each other, and demonstrate expertise. None of these are new individually. What’s new is that they’re all in the same brief.

The Org Problem

Most teams are structured around channels because that’s how budgets are allocated. There’s no single owner of product data and landing pages as a strategic asset. Yet feed quality, PDP content, structured data, and on-page relevance all feed the same system. Brands that get this working as one will pull ahead.

The Shift

Before: you optimised campaigns. With AI Max, you optimise the inputs into Google’s AI.

Most advertisers will switch this on, expecting performance gains. Reality: AI is only as good as the data you feed it.

AI Max success will not be decided by how well the Paid team optimises bids. It will mainly be influenced by how well the SEO, GEO and product feed foundations were built. The Paid team turning AI Max on is just the moment when that prior work becomes visible.

According to Google: “Advertisers that activate AI Max in Search campaigns will typically see 14% more conversions or conversion value at a similar CPA/ROAS.”

That 14% should be seen as an average baseline rather than a ceiling. Some brands have seen significantly stronger results with AI Max:

• L’Oréal: +67% CTR, -31% CPA, and 2x CVR

• Royal Canin: +263% conversions and -73% CPA

These uplifts are often driven by richer context, stronger product data, and better PDPs, all signals that were previously underutilised before AI Max.

What to Retain

Invest in your product data. Feed and PDPs are no longer hygiene tasks; they’re the foundation of future Shopping performance. Brands that treat product data as a strategic asset will get the maximum benefit from AI Max. Those who don’t will quietly fall behind.

Sources

• https://blog.google/products/ads-commerce/ai-max-for-shopping/

• https://support.google.com/google-ads/answer/15910366?hl=en-GB

• https://business.google.com/uk/think/ai-excellence/loreal-ai-max-search-marketing-strategy/

• https://business.google.com/uk/think/ai-excellence/how-brands-use-ai-max-search-campaigns/

Executive Summary

Agentic commerce represents a shift in how digital transactions happen. Instead of simply helping people find products, AI agents are increasingly performing tasks on their behalf. Users can delegate actions such as searching, comparing options, ordering, and paying, stepping in only when needed. Early experiments in China show how this model works at scale. Platforms such as Meituan, Alibaba, Ant Group, and ByteDance are testing systems that interpret user intent and execute transactions across tightly integrated digital ecosystems. China has become a test environment because several enabling conditions coexist (more below). Maybe you won’t be acting on all of this today, but if your board asks what you need to do to stay ahead, you’ll have a good answer.

We’re all reading about how AI agents will be the ones searching and shopping in the future.

You can get your head around it in theory.

But how does it actually look in practice, day to day?

I came across this HBR research piece (link at the end) and thought it was essential reading for anyone in eCommerce right now. It’s one of the clearest pictures I’ve seen of where the industry is heading, and China is already living it.

The short version: AI agents aren’t just helping people shop. They’re already doing the shopping for them.

Meituan’s Xiaomei agent is the standout example. A user says, “Order my usual lunch, but deliver it 20 minutes later,” and the agent handles everything. Interprets intent, applies preferences, and completes the transaction. No browsing, no clicking, no funnel.

This is what we’re all calling “agentic commerce”, and it’s shifting from novelty to structural norm in China’s biggest platforms.

Agentic commerce represents a shift in how digital transactions happen. Instead of simply helping people find products, AI agents are increasingly performing tasks on their behalf.

Early experiments in China show how this model works at scale.

First of All - Why China?

It comes down to infrastructure. Five conditions have aligned there that don’t exist together anywhere else yet:

• Embedded digital payments (Alipay, WeChat Pay)

• Dense logistics networks

• Super-app ecosystems spanning multiple services

• A consumer base that adopts new behaviour fast

• And a regulatory environment that lets things run before the rules are written

Worth noting: 83% of consumers in China see AI products as more beneficial than not. In the US, that figure is 39%. The UK isn’t there yet, but we typically mirror similar behaviour to the US market, albeit a bit slower.

What This Actually Means For Your Brand

Maybe you won’t be acting on all of this today, but if your board asks what you need to do to stay ahead, you’ll have a good answer.

The traditional marketing funnel assumes a human is doing the browsing, clicking, and deciding.

Agentic commerce removes the human from the middle. This is already happening with certain stages of the Search Funnel, i.e., comparisons and ‘best under X’ searches.

An AI agent filters the options before your customer ever sees them.

That creates a new dual requirement: be persuasive to people, but eligible to machines.

What we’ve noticed with clients is that they over-focus on the last click and a user handing over full control to buy a product, but agentic is more than that.

Agents embedded within Search

As of May 20th, Google also announced how agents will now be playing a much bigger part within Search itself > https://blog.google/products-and-platforms/products/search/search-io-2026/

Competing For The “Agent Answer,” Not The Human Funnel

Two big shifts follow from this.

Brand marketing stays important, but for a different reason. Humans still input preferences into an agent. “Order from Nike,” not “find me any trainer.” Brand meaning and preference still matter upstream.

Performance marketing gets disrupted the most. Click-through rates, conversion optimisation, traffic acquisition... all of that logic assumes human attention is the scarce resource. When agents do the filtering, you’re no longer competing for clicks. You’re competing to be included in the agent’s answer.

The researchers call this competing for the “agent shelf.” What gets you on it has nothing to do with creative or media spend. It’s service reliability, fulfilment rates, clear return policies, structured product data, and clean operational signals. Machine-readable trust.

The Two Things Worth Acting On Now

1. Treat agent eligibility signals as a marketing priority, not just an ops metric

We know brand sentiment plays a big part in AI search - but what can you do about it?

Right now, a weak returns policy or a slow dispute resolution time costs you a few customer service tickets.

In an agentic world, it costs you placement on the agent’s answer entirely. The penalty for operational issues moves from “annoying” to “invisible.”

If you want to see what this looks like in action, join our webinar early June when our team, along with SEO lead Abishek from YuMove, will be running through how they’ve mined customer service data to support SEO+GEO: https://luma.com/488dv35h

2. Map which parts of your customer journey could be delegated to an agent in the future

The researchers frame this as an “agent delegation map.” The core question is which decisions in your customer’s journey can move to autopilot, which must stay human, and where are the non-negotiable checkpoints?

The key focus is on the bracket on the left.

Research, shortlist creation, and final selection are the three steps where brands either make it onto an agent’s shortlist or don’t, and that happens before the customer is ever involved.

For every step in your journey, ask two questions:

1. Does this require a genuinely new human decision, or is it just friction?

2. If an agent is handling it, what signals is it using to decide whether to include your brand at all?

Are we just writing for robots again?

No. It's not about being "robot-friendly" - it's about being agent-eligible.

With SEO, we optimised for bot-readable HTML because search engines decided what to show humans.

With agentic commerce, the structure is the same, but the stakes are different. Users still set intent, but the agent filters what they see. So yes, you need machine-readable trust signals, but you’re not writing for robots. You’re writing for humans who’ve delegated their filtering to one.

What changes is that you can’t hide operational weaknesses anymore. A clunky returns process that annoyed a few customers before? Now it’s a data point the agent weighs before your brand is even shown. Sloppy product data that users could work around? The agent can’t.

You’re still building for your customer. You’re just making sure the agent can understand what you’re offering well enough to recommend you.

The Bottom Line

This isn’t a 2030 story; in the US, Walmart and Google already announced a partnership in early 2026 to integrate Walmart’s inventory directly into Gemini for agent-led shopping.

Consumers are already delegating search to agents.

It will expand further down the funnel.

The question isn’t whether your customers will delegate their shopping to AI, it’s whether your brand is designed to be chosen by the agent acting on their behalf, not just the person browsing.

This post has been a summary and adaptation for you, but it’s worth a read in full (if you have a subscription).

Source: Research: What China’s AI Agents Reveal About the Future of Commerce, Harvard Business Review, April 2026 https://hbr.org/2026/04/research-what-chinas-ai-agents-reveal-about-the-future-of-commerce*

Creative now drives up to 80% of performance on Meta. Which means one thing: if you can’t produce enough of it, you won’t scale.

You Can Probably Feel It Already

CPMs are creeping up. Frequency is rising. Your best creatives don’t last as long as they used to. And when the pressure hits to produce more, nothing is ready.

Here’s a pattern that shows up almost everywhere. Promo periods get fresh creative. BAU periods run on whatever is left. It works, until it doesn’t. Frequency creeps up, performance dips, and the instinct is to wait for the next promo to fix it. The real issue is that BAU creative never receives the same level of investment.

Why Creative Volume Matters More Than Ever

Meta no longer relies on audiences the way it used to. It relies on signals. And today, your creatives are your targeting.

Every new creative gives the algorithm a new angle, a new hook, a new entry point into the auction. Without that constant feed of fresh signals, learning slows, frequency rises, CPMs increase, and growth stalls.

The Question Most Teams Never Answer

Most teams know they need more creatives. But they don’t know how many.

Great creative is table stakes. But, great creative at low volume still starves the algorithm. The question most brands never ask is not just “is this good?” but “do we have enough of it?” And almost no one knows the answer.

Step 1: Find Your Number

Before you fix your output, you need to know what you’re aiming at. That’s where the calculator comes in.

👉 https://thisisnovos.com/paid-media-creative-calculator/

Takes less than a minute. Input your spend, your CPA, and your testing allocation. It gives you a target: how many concepts you need per month and what that looks like week by week.

What That Number Usually Looks Like

Say you’re spending £200K/month, with an £80 CPA, and allocating 10% to testing.

That’s roughly 63 new concepts per month. With 2 formats per concept, that becomes ~126 deliverables per month, or 30+ assets per week.

Most brands at this level are producing 10 to 20 creatives a month. That’s not wrong. It’s just not enough. The algorithm is underfed, and performance reflects it.

One important note: this doesn’t mean 63 new concepts. Most of these should be variations of what already works: new hooks, new edits, new formats, each speaking to a specific persona.

Knowing The Number Is The Easy Part

Most teams get stuck the moment they see it. Producing at that level, every week, is a different challenge entirely. Too slow, too expensive, and most agencies aren’t built for this kind of output.

That’s the real problem to solve.

What We Built at NOVOS

Studio by NOVOS is a creative engine built for exactly this. Powered by the latest AI models, it produces still and video creatives at scale, iterating on what works and adapting assets across personas, hooks, and formats. These aren’t generic AI outputs. The models are guided by our team to ensure every asset is grounded in your brand, your offer, and what the data shows is working.

New assets every week, without the cost structure of a traditional agency.

The brands that win are the ones that learn the fastest. To do that, you need a constant pipeline.

Ready To Start?

Get your number with the calculator first.

Then, if you want to build the system to actually hit it, book a free demo. We’ll show you how Studio by NOVOS works end-to-end.

Personas are having a revival.

Up until recently, personas have often been owned and utilised most by brand teams. However, historically, I’ve seen again and again personas being created, filed away, and never actively used. As discussed in our recent post, the way we are searching with AI is leading us to longer and more personalised search behaviour. Personas are the ideal solution to help capture these searches by understanding and segmenting your audience.

With the increasing importance of product pages due to the rise of AI and LLMs (which can supplant your research to consideration stages and get straight to product recommendations), personas are getting a fresh purpose and can help you build rich, relevant and genuinely useful product-based content on PDPs. Read on for our practical guide to leveraging personas to level up your PDPs.

What Makes A Helpful Persona?

Making sure your personas have enough helpful detail in them is vital. A common pitfall with personas is that they simply aren’t detailed enough to be useful on a meaningful level. Take the following example of a top-level persona, the likes of which I have seen many times.

Example of a sub-par persona:

Yes, there’s the idea of a persona here, but there’s serious, useful detail lacking in ‘about’ and ‘needs’, and overly broad or optimistic products and brands.

When developing your personas, these are things that actually make them useful:

• Specificity: aim for concrete details on demographics, motivators, needs and behaviours. This makes it easier to create products, content, messaging and experiences for a real type of person.

• Be concise: whilst specifics are important, strike the right balance between rich vs overwhelming detail. Whilst vague personas are common, we also see some that try to describe everything. Stick to the most important aspects to ensure they’re helpful and not hard to use.

• Realism: make sure you represent how real customers behave, not how you wish they’d behave. Whilst it can be tempting to push personas to have more product affinities with your offering, or align their motivations with your brand, being unrealistic will ultimately harm the success of your strategy.

Tips on How to Build Out Personas

1. Delve into your audience research & analysis

Most businesses will have carried out some sort of research or analysis on their customer base, and this is a useful starting point. Other sources of audience information can be: customer support feedback, feedback from sales teams, analytics data (e.g. from your CMS or GA4), Reddit analysis, third-party tools like SparkToro, and more. Pool what you can and start to get a shape of your key personas. How many to aim for depends on the breadth of your offering, but 3-6 is a manageable starting point.

2. Flesh out persona outlines with AI assistance

Once you’ve got an outline of your key personas from your audience, using LLMs can be a great way to flesh these out into useful profiles. Try to give as much useful information as you can in your prompting to get the best outcome. Feed in your relevant data, profile outlines and a good overview of your brand. From there you can prompt your chosen AI assistant to use all this information to outline a complete persona.

Pain points, motivations (buying and emotional) and demographics are absolute must-haves to make your personas useful. Other facets you can consider include:

• Interests

• Traits (lifestyle and personal)

• Product affinities

• Job/industry (and seniority where relevant)

• Influences

• Environment

• Decision barriers

Review, iterate on, and edit what LLMs give you until you’re happy, remembering to aim for specific, concise and realistic profiles.

3. Perfect is the enemy of progress - you can iterate over time

It’s also worth keeping in mind that, as with many things, perfection can be the enemy of progress. I’ve worked with brands that start to get bogged down in the detail and data when building out personas - are the ages quite right according to analytics? Should we map all the pain points customer support encounters to a persona? Should we wait and create a new persona for a new product range we’re planning to launch in 2 months? Ultimately, getting out an agreed-upon MVP that gives you direction and an audience-focused mindset for improving your content and templates will be much more beneficial than delaying for perfection. It’s also positive to see personas as something you return to periodically and evolve with your brand over time.

So, How Do My Shiny New Personas Help My Product Pages?

By having a clear understanding of your personas, you can better tailor your product content and PDP templates so that they’re more helpful, user-centric and persuasive. Take ‘Simon the skincare starter’ again - we’ve iterated on the basic persona, and now we’ve mapped:

• Pain point: he doesn’t understand and is overwhelmed by skincare jargon and ingredients

• Pain point: long and complex skincare routines don’t fit into his daily routine

• Pain point: he doesn’t know which brands to choose or trust

• Motivation: wants to look polished, healthy, and like looks after himself well both in professional and social/dating settings

• Motivation: prefers brands that don’t feel overly cosmetic or beauty-led

• Motivation: social proof and guidance (straightforward advice and endorsements)

All this information can then be used to tailor product pages (and PLPs or content!) to provide a better experience for your persona. In practice, think about how you can take your basic product specs and make these more useful and relevant to your persona - for example:

• ‘SPF 30’ becomes ‘SPF 30 to protect against sun damage and premature ageing’

• ‘Contains hyaluronic acid’ becomes ‘Deeply hydrates and locks in moisture for smooth, fresh and brighter skin’

• ‘Gel formula’ becomes ‘Lightweight, fast-absorbing gel formula’

These are all designed to use clear, benefit-led and jargon-free language to appeal to Simon, aligning with some of his pain points and motivations.

Taking It Further With Product Attributes

We also recommend signposting helpful attributes in your product page (even better if you back them up with structured data). By going beyond basic attributes like product type, gender, colour etc, you can help your visitors understand if your product is well-suited to them. As well as offering clear benefits/functions like above, consider adding who or what use-cases your products are best for, and attributes specific to your niche. Following our example, in skincare, that might be things like skin type, product format, active ingredients, UV protection level, etc.

This detailed and user-centric approach has many benefits. Not only are you providing a richer and more helpful experience for your visitors, but this same information is also helpful for being cited by LLMs. With this rich, clearly defined information, you can help show your product is a great fit for the super long-tail “exactly what I want” type searches users opt for in LLMs that we’ve talked about previously. Furthermore, this exact same information can have a positive impact on AI Max and PMax paid campaigns, with additional details allowing smarter search term matching and discovery of new opportunities and emerging queries.

Scaling Changes With Page Template Features

One more helpful way to utilise your personas is to consider how their motivations and pain points can be aligned/alleviated within your page templates. For example, our Simon persona is motivated by social proof - seeing people like him have success with products and endorse them - as well as having a pain point around not knowing which brands are trustworthy. In this instance, a UGC/social or review module including real-life user imagery or videos on PDPs could satisfy both of his needs and be very helpful, encouraging conversion.

Chat to Us About Your Personas & Product Pages

Inspired to give your personas a new lease of life and see how they can be better utilised to enrich your PDPs? Get in touch to see how we can shape a persona-centric strategy for your brand.

The buzz around GEO has quietened in 2026 as the major AI platforms have shifted priorities. But the data tells a much more interesting story than the hype cycle suggests, and it matters if you’re an eCom leader.

We dug into both Semrush’s clickstream data and a citation study from SEJ from the last 18 months to understand what’s actually happening with ChatGPT traffic, and the picture is counterintuitive.

The Traffic Paradox

ChatGPT’s total user base peaked around October 2025 at roughly 650 million, then declined by around 10% through to February 2026. Gemini 3 was launched in November, which matches this decline.

But referral traffic - the visits ChatGPT sends out to the rest of the internet has done the opposite. It climbed from around 50 million monthly visits in late 2024 to nearly 290 million by early 2026. And it’s still accelerating.

Fewer users. More outbound traffic. That’s the paradox.

This suggests the product is shifting from a destination to a more pass-through/research tool for its engaged base.

Fewer Searches, More Clicks

It gets stranger. The share of ChatGPT prompts that trigger a web search has fallen from a peak of around 54% in September 2025 to roughly 28% by February 2026.

There are two ways to read this:

1. Either each search-enabled session is converting harder ie users are clicking through more when links appear or

2. ChatGPT is increasingly driving brand + web discovery without searching at all, recommending brands + sites from its own knowledge and shaping what users go to next.

Either way, the implication is the same: visibility inside AI responses matters whether or not a web search was triggered to generate that response.

Our read: ChatGPT is shedding its casual users. The ones who treated it as a novelty or a quick-answer tool. The users who remain are research-heavy, intent-rich, and more likely to follow a citation out to a website. The product is shifting from a destination to a pass-through discovery layer.

The Citation Surface Is Shrinking

In addition to all the data above, the number of cited domains per response has dropped by roughly 20% over the past few months.

That means fewer websites are competing for visibility inside each ChatGPT answer.

An SE Ranking analysis of 129,000 domains found that referring domains were the strongest predictor of ChatGPT citation likelihood, with a threshold effect kicking in at 32,000 referring domains (backlinks or digital pr links).

What This Means For Retailers

Retail remains one of the lowest industries for ChatGPT referral traffic. But the trajectory matters more than the current volume.

Conclusion: Despite the User Hit, ChatGPT Continues to Become a Brand Discovery and Trust Layer

Two things are happening in that journey: discovery and trust-building.

If your brand isn’t surfacing in AI responses, you’re missing the top of a new funnel.

If it is, users are likely heading to Google next to confirm, which means your organic presence needs to back up whatever ChatGPT is saying about you.

A reference from GPT can completely bypass the remaining funnel, taking users from discovery to purchase. An experience called anchoring bias.

What To Do Now

Treat GEO as a brand discovery channel. Every investment you make in SEO across authority building, content quality, and structured data is also what drives visibility in AI answers across ChatGPT, Google’s AI Overviews, and Gemini. This isn’t an either/or.

Get your product data ready for the agentic web. The next phase is AI agents that shop, compare, and transact on behalf of users. If your product feeds, attributes, and structured data aren’t clean and complete, you won’t exist in this upcoming (more commercial) AI world.

Don’t wait for the volume to arrive before you start optimising. The citation surface is shrinking now. The brands that build authority and visibility in AI responses today will be the ones capturing referral traffic at scale when it hits your category.

Sources:

• Full SEMrush data + source for charts: https://www.semrush.com/blog/chatgpt-search-insights/?ck_subscriber_id=2055546593& Top SEO News Of the Week [SEOFOMO - April 12, 2026] - 21362492

• SE article + data: https://www.searchenginejournal.com/chatgpt-search-is-citing-fewer-sites-data-shows/571219/?ck_subscriber_id=2055546593&utm_source=convertkit&utm_medium=email&utm_campaign=📊 Search Updates & Top SEO News Of the Week [SEOFOMO - April 12, 2026] - 21362492

Most eCommerce teams don’t have a traffic problem.

They have a visibility problem.

And in most cases, that problem starts with something they barely manage:

their product feed.

What’s Changing (and Why This Matters Now)

There’s a lot of noise around ChatGPT, Gemini, and AI-driven search.

The assumption is that these systems will simply crawl your website and figure everything out.

That’s not how it works.

AI-driven discovery is increasingly powered by structured product data.

That’s why OpenAI is already encouraging merchants to submit feeds directly:
https://chatgpt.com/merchants/

At the same time, Google’s AI ecosystem is heavily built on Shopping data and Merchant Center feeds.

The direction is clear: The cleaner and richer your product data is, the more likely you are to be surfaced.

Not just in ads. Everywhere.

Your Feed Is No Longer Just a Paid Media Input

For a long time, feeds were treated as a Google Shopping dependency.

That is outdated.

Your product feed now powers visibility across:

• Google Shopping and AI search

• Meta and TikTok catalogues

• Marketplaces

• AI assistants like ChatGPT

And with platforms like Shopify increasingly integrated into discovery systems, your catalogue and feed are becoming direct inputs into how products are surfaced.

Your feed is not distributed anymore.

It is eligible for discovery.

Where Most Brands Get It Wrong

Most feeds are simple exports.

They reflect how products are named internally, not how people search.

For example:

“Gold diamond ring”

That is accurate.

But it is not how demand looks.

People search with context:

“I’m looking for an engagement ring under £2k, something that still looks nice and not cheap, any ideas?”

If your feed does not reflect that, platforms struggle to match your product to those queries.

And when matching fails, visibility drops before anything else even matters.

What Your Feed Is Actually Doing

At a system level, your feed is not just a list of products.

It is the dataset platforms use to:

• Understand your catalogue

• Classify your products

• Match them to queries

• Rank them against competitors

That relies on structured signals like:

• Titles

• Categories

• Attributes

• Price

• Availability

• Identifiers

• Images

The more complete and aligned these are with real demand, the more visibility you unlock.

Why This Is Bigger Than Paid Media

One of the biggest misconceptions is that feed optimisation sits within paid media.

It doesn’t.

Your feed sits at the intersection of:

• SEO

• paid media

• ecommerce

• AI discovery

It is one of the few layers that impacts all of them at once.

Which makes it one of the highest leverage areas in your stack.

The Role of Your Website (and Why It Still Matters)

Your feed does not operate in isolation.

Your product pages and structured data reinforce what your feed is saying.

They provide:

• context

• consistency

• trust signals

If your feed says one thing and your PDP says another, platforms lose confidence.

But this is the key point:

Your website helps validate your product.
Your feed determines whether it gets surfaced at scale.

One supports. The other distributes.

You Don’t Need More Feeds. You Need Better Data

A common reaction is to create multiple feeds for different channels.

That is rarely the solution.

What actually matters is:

• having a clean, consistent source of truth

• structuring your data properly

• being able to adapt it depending on the platform

If your core data is strong, you can scale it.

If it is weak, multiplying feeds just multiplies inefficiency.

Where things usually break

Before any optimisation, most feeds have foundational issues:

• Missing attributes

• Weak or generic titles

• Poor categorisation

• Inconsistent pricing

• Gaps between feed and site

These are not minor issues.

They directly impact whether your products are:

• Eligible

• Matchable

• Competitive

What This Really Comes Down To

We are moving from a world where:

Your website drives discovery

to one where:

Your data drives discovery.

Your website still converts.

But your feed determines whether you get the chance.

Final Thought

If your product feed is weak:

• You show less

• You match less

• You compete less

Across Google, Meta, TikTok, marketplaces, and now AI systems like ChatGPT.

And no amount of bidding, creative, or CRO will fully compensate for that.

Because before you optimise performance, you need to earn visibility.

And today, that starts with your feed.

LLMs have fundamentally changed search. Customers are now searching in hyper long tail, referred to as ‘this is exactly what I want’ search. LLM technology means this search intent can now be satisfied.

Search is now personalised. Customers are demanding personalised product information. If your site and product information aren’t personalised to signpost how your customers are searching, you are less likely to reach them now, and increasingly so in the future.

Search has changed. Have you?

How Search Behaviour Has Changed

The ultra longtail “this is exactly what I want” situational search

People are no longer searching with keywords; they are searching with personal “this is exactly what I want” situations. It is the rise of situation search.

Source: https://ecommerce.thisisnovos.com/p/ai-search-future-proofing-your-content

Search has changed; the customer hasn’t

The customer is the same

The customer has not changed. Our customers still have the same situations; the same pain points to solve, or goals to achieve, for which our products may be the answer.

Search has changed.

How the customer searches for this information, and how this information is retrieved and presented, is now fundamentally different.

AI and large language models are now advanced enough to understand complex queries and reliably match the intent behind these queries to relevant sources with accuracy.

Previously, even if we had detailed customer persona information, because the vast majority of customers ended up searching for a product in a generally similar way, the actions we could take onsite were often limited. PLPs and categories ruled the roost. So this is where SEO actions were concentrated.

The depth of a search considered long tail may have also been 4-5 words long. This was still broadly predictable with only 1-2 key attributes. Ultimately, they lacked depth, so they could often be answered by PLPs or relatively generic content on PDPs. Now, customers are searching for ultra-long-tail terms, up to and surpassing 20+ words. There’s far too much context to answer without personalisation onsite. Situational search needs to be optimised for.

• Longtail before - “Black rugby boots for backs”

• Longtail now - “I am a 31-year-old man looking for rugby boots that I can wear on a muddy pitch. I play outside centre and want them to protect my feet. I want them to be flashy and last at least a couple of seasons.”

Search behaviour has changed

However, the ability for AI Search platforms to understand the context of a search and return exact information and pages for situational searches has changed this. PDPs, which contain more specific and tailored information, are on the rise. And since an eCom website typically contains many more PDPs than PLPs, the depth and specificity of information we can provide has expanded.

We can match personas’ situation searches with on-site PDP content.

Search is Now Personalised; Are You?

We can build out various customer journeys based on the specific situations that those customers would likely be searching for.

Situational searches are rarely random. They are usually expressions of the same motivations that sit behind your customer personas.

Just like in CRM, the dream is 1:1 email for every customer - i.e., personalised retention. However, the reality is this isn’t possible, CRM teams segment their database to personalise messaging - we need to take the same approach for Search. Search is now so vast with a hyper-long tail; if you talk to everyone, you talk to no one. You need to segment search using personas (i.e., personalisation).

This means persona research and mapping, often used in brand or CRM strategy, can now directly inform SEO content strategy. Different customer personas will require and relate to different types of content. For example, a client we work with has 3 distinct customer personas, shown below:

These content requirements may sometimes overlap; for example, both the timeless stylista and thoughtful gift giver would like to see customer user-generated content as social proof before purchasing.

However, content recommendations may also sometimes contradict one another. The Engaged Trendsetter does not want to see user-generated content, as they want to be first-movers within the trend.

How Can You Predict What Your Customer Will Search?

The other side of the story is - what information is AI looking for when customers search? What information is relevant to this page/topic? And this is where a query fan out can help us.

AI search engines don’t interpret queries as single keywords. Instead, they break questions into clusters of related sub-questions, often called query fan-outs. A query fan out takes a topic and breaks it down into related sub-topics or queries.

Using query fan-out mapping to key page types, we can then get an understanding of the type of follow-up questions AI may be looking to answer based on a given topic.

By understanding what AI is looking for, this can then be cross-referenced with the information you already have on a given page to see where your page performs well and where it lacks information that AI may be looking for.

Cross-Referencing Personas Situational Search with Query Fan Out Mapping

By then cross-referencing the type of information your customers want, with the information your pages are missing, you can find both relevant and useful content to add to a page, which benefits your customers, traditional search, and AI search.

Signposting Attributes

A common question we get asked is, since all situational searches are so unique, how can we target this across our site?

We’ll never be able to target every aspect of every situational search. However, using our key customer personas, we can signpost the key attributes of searches onsite. For example:

“I am a 31 year old man looking for rugby boots that I can wear on a muddy pitch. I play outside centre and want them to protect my feet. I want them to be flashy and last at least a couple of seasons.”

In this sense, a single situational search can be broken into its constituent parts, which can then be used as guidance for the types of info/attributes to be used across a variety of product pages, even if they sell very different products.

The “best for” product attribute

Previously, almost all products across an eCommerce site were presented as the best option for any customer. By defining who a product was best suited for, the implication was that it also must not be good for another customer, so this was avoided entirely.

With this new wave of situational search, with customers actively and specifically describing what they want, and don’t want, it helps to signpost when to use (or even not use) a particular product.

This is best suited when you have enough products to match the different needs of many customers. Counter-intuitively, by defining who a product is NOT for, you also may have higher customer satisfaction since they can compare and contrast what is best for their own needs.

The Challenges

Scalability

The biggest challenge with optimising for situational search is the scalability of changes. The content needs to be far more in-depth and across far more pages (1000s of PDPs vs 10s of categories). This represents a big challenge for many eCom sites.

Some AI tools, such as emfas or Ocula, can help to scale this across PDPs, whilst creating content that is in line with your brand and original - however, this should be approached with due caution. We’ve had success leveraging AI to create content of various forms, but it’s essential to avoid being generic.

Search Has Changed — Your Strategy Needs to Catch Up

The shift from keyword to situational search isn’t coming; it’s already here. The good news is that the foundation you need probably already exists in your business: your customer personas. By mapping who your customers are, what situations drive them to search, and what attributes matter most to them, you can build content that AI search engines want to surface and customers actually want to find.

The brands that win won’t be the ones who optimise for every possible query. They’ll be the ones who know their customer well enough to meet them in the exact moment they’re ready to buy.

Want to see how your PDPs perform against situational search? We offer a free AI visibility audit for eCommerce brands. Email dan@thisisnovos.com to claim yours or get in touch via our website.

AI search is often talked about as if it’s one single entity, one shift towards a new future for search and one single channel, but the reality is very different.

The same brand, asked about in two different LLMs, can be described in two completely different ways. Different sources and different signals present two different truths.

If you’re working in PR and you’re thinking more about how you can influence visibility in AI search, fundamentally understanding that different LLMs can present different results should be at the core of your thinking when developing your PR strategy for AI.

AI Doesn’t Agree On Who You Are

One of the biggest misconceptions that brands make about AI search is assuming that AI has a single understanding of who that brand is - the truth of the matter is, it doesn’t.

LLMs, such as ChatGPT, Perplexity and Google Gemini, don’t have one unified view of the information across the web. Each model is developed differently, has its own way of being trained and has different views of what a ‘good answer’ looks like.

Simply put, this means that your brand can appear authoritative in one platform, like ChatGPT, but completely invisible and irrelevant in another, say Perplexity. For eCommerce brands, too, there can be a critical difference in how a brand is viewed. ChatGPT may view you as commercial, whereas Perplexity may see you as purely informational - a critical distinction when you’re trying to sell products.

From a PR perspective, this is happening now, and it ultimately is adding a layer of complexity to PR strategies because PRs are no longer just trying to influence how journalists talk about a brand; we’re also trying to influence how machines understand the brand, how they categorise the brand and how they present all of that to users.

The Citation Split - Why Different LLMs Trust Different Sources

If you ask the same question to two different LLMs, you’ll typically see a clear difference in the sources that are cited.

An example below for a brand that sells products around gut health, the commonly cited domains for each LLM are strikingly different, and this is top-level before even taking into account the different personas of people searching:

ChatGPT frequently cites:

Perplexity frequently cites:

Anecdotally, the big difference in this example is that Perplexity is clearly more information-driven for this topic; they’re using sources that deliver clear evidence-backed information around health. Whereas ChatGPT, by prioritising sites such as The Independent and the Evening Standard are showing it is delivering direct product information, particularly for The Independent, who are well known for its IndyBest category that is focused on recommending products directly to consumers.

It’s also important to understand that both of these approaches are valid and need to be factored into any holistic PR strategy.

In my previous post on Substack, I shared another real-life example for one of our clients where Perplexity cited BBC Good Food for a health-related query, whilst ChatGPT relied on the NHS website instead.

This isn’t random for the two models; they interpret things differently, and they trust different sources altogether. Essentially, each model makes judgment calls about credibility, risk, and the quality of information provided based on how the model has been designed and trained.

The intricacies of the different models can be difficult to understand, but the implication is simple for any brand trying to build visibility on AI platforms:

Two LLMs can tell different stories about the same brand, but both believe they are totally correct.

What This Means for PR

Traditionally, PR as a channel has been measured by moments:

• A huge campaign pulling in tons of coverage

• A spike in new links built to the site

• A standout piece of coverage on a dream publication

But AI looks at these things differently and doesn’t necessarily reward these moments in time. AI rewards consistency, and it rewards patterns.

Now we know and understand that different LLMs trust different sources, we know that a single type of coverage sporadically will never be enough for consistent success across AI search and across different models. Any strategy that targets just one publication type and just one content format risks shaping a brand for a single model and encouraging invisibility across others.

This is why brands that can be considered to have great PR strategies are still missing from some AI-generated answers across some platforms; they simply aren’t understanding the need for variety.

Strong coverage will not instantly grant you visibility within AI search, but consistency matched with relevancy and a variety of different sources does deliver the visibility that brands are looking for.

PR is in the middle of a shift from just purely trying to gain attention to building consensus across all of the different AI models - this is where a brand can achieve consistent success across the board.

The Importance of Personas

There’s an extra layer to this of equal importance, and that is personas.

LLMs aren’t just answering questions; they’re providing a search experience that is unique to the individual. Think of this from your own point of view as a user, the search experience is fundamentally different, how you search, and the response you’re receiving is different to searching on Google. You give more detail, more insight, and LLMs essentially want to reward that information you’re giving by personalising your search experience and tailoring answers to exactly who you are and citing sources that you trust.

This creates an additional level of complexity and presents a challenge for any PR strategy:

1. Platform differences - ChatGPT v Perplexity v Google Gemini

2. Persona differences - Expert Led v Value Led v Research Focused

A brand may be well represented with a value-driven persona, but it may lack the depth of understanding to surface for the more research-focused persona - this is where a standard PR strategy could fall down.

A media list built for one persona type simply doesn’t have the breadth and complexity to shape how all AI models and personas within them understand your brand. PR strategies now need to influence on a more granular level, and they need to understand who is asking the questions, the publications they consume and the type of information they’re looking for.

How The SEED Framework Can Help Shape Your New PR Strategies

If we now understand that LLMs build a narrative around your brand based on the signals they trust, then your PR strategy can’t be viewed through a single lens and it can’t have single channel thinking - as an example, for an eCommerce brand, this means your PR strategy can’t just focus on product led coverage if you want to achieve success across all aspects of AI search.

The challenge isn’t about choosing which AI model to target; it’s about building a strategy that reinforces the same understanding of the brand across all of them. This is why we’ve structured our approach around our SEED framework.

S-ituational Relevance

Ensuring your brand appears consistently in the correct context, not just in high authority publications.

E-xpert Led Content

As has been the case with the traditional search model, expert-led content builds trust and credibility for your brand. LLMs are heavily influenced by expert comms, and a solid PR strategy should always be looking to bring expertise into play. Consistency with expert-led content allows for consensus to build across all AI models and will help the LLMs distinguish your brand from competitors.

E-nriched Product Data

Securing coverage that effectively communicates what you’re selling, your price point and why it’s different from competitors helps LLMs connect your products to the intent behind people searching, and it also allows them to marry your products up with the correct personas.

D-istinct Brand Message

Brand messaging, as has always been the case when marketing a brand, ties everything together. Consistent messaging across earned, owned and social channels allows AI to form a clear understanding of your brand, what you should be known for and why you have authority within your given niche.

Our SEED framework isn’t about building a strategy that works for one AI model; it’s about building consensus across multiple models and driving consistency over time to ensure that AI visibility is strong over a sustained period.

Multi-Model Visibility Is What We’re Looking For In 2026

Visibility in AI search is going to continue to shoot up the list of priorities for brands in 2026, but as the year progresses, generically appearing in “AI search” won’t give the success we’re all looking for.

The real consideration when understanding how successful a brand is within AI search comes when you look to analyse if you’re visible across multiple LLMs, if you’re presenting your expertise for the right topics and if you deliver consistency.

When understanding if a PR strategy has had the desired impact with AI search, consider multi-model visibility - or put simply, how consistently is your brand surfaced across ChatGPT, Perplexity, Google Gemini and any other AI model that may emerge throughout the year.

If AI search is going to continue to fragment across multiple different LLMs, then PR can be the channel that connects them.

Now that the initial hype around Google and Shopify’s Universal Commerce Protocol (UCP) announcement has settled down, it’s a good time to take a closer look at what it all means.

The Universal Commerce Protocol (UCP) is a newly emerging open standard protocol co-developed by Google and Shopify to provide a common language for businesses, AI, and payment providers to allow complete and secure agentic shopping to take place without the need for bespoke requirements across AI platforms, payment providers, and online businesses.

Although it is an open protocol, it was co-developed by Google and Shopify and, in collaboration with 20 industry leaders, including Etsy and Walmart, has been endorsed by global partners across the ecosystem, including Stripe, American Express, and Mastercard.

As an open protocol, any AI platform and CMS can leverage it; however, since it was co-developed by Google and Shopify, these platforms have the jump. Adopting UCP will allow agentic actions via Google AI Mode and Gemini, including, most notably (for now), integrated checkout.

Enabling UCP on Google AI Mode and Gemini

Preparing Merchant Centre for agentic search

A full breakdown of how to do this can be found directly from the source here; however, to summarise, you will need:

1. Return policy and Customer support info configured in your Merchant Centre settings

2. An updated product feed with three new attributes (recommended to be provided as a supplementary feed)

   1. native_commerce attribute, which opts in a product for integrated checkout

   2. consumer_notice attribute, which has sub-attributes consumer_notice_type and consumer_notice_message, which are used to display mandatory legal warnings for products

   3. merchant_item_id - only required if your ID attribute does not match the product ID expected by your Checkout API.

As things stand, it doesn’t appear that any new or specifically more detailed product attributes are required via the product feed. This may change; however, we can get some insight into the information that may help to become more discoverable through Shopify.

Business Agents

Although less talked about, alongside the launch of UCP, Google have also announced “Business Agents”; a branded AI agent in which shoppers can converse directly with a brand via Gemini and AI Mode for eligible stores (therefore extremely limited at this stage). It will be interesting to see if this gets expanded across other brands and if brands and customers engage with this.

UCP on Shopify

In Shopify’s documentation on how to build agentic platforms with their agentic catalogue API (which allows an agent to access all Shopify store products) it mentions that one of the retrieval parameters is categories, which uses their Shopify Standard Product Taxonomy. This is the same taxonomy used in product information as category metafields.

Although this is about building an agentic platform, rather than product discovery, this does suggest that this category information will be vital for the discovery and retrieval of your products following a search via AI. This means ensuring all your products have their categories and subcategory metafields accurately mapped is key!

Indeed, looking at Shopify’s own diagram of how agentic shopping would work, this does seem to marry up:

The fact that Shopify co-created UCP with Google suggests that they know the format and type of information that’s required to expand product reach, and presumably gives Shopify stores the early-mover advantage until other platforms catch up to provide product information in the same format.

How To Assign Categories & Category Metafields

Product categories and category metafields can be viewed and assigned via the individual backend product pages.

Example 1 - Product with no category assigned.

A product with no category assigned may have a suggested parent category given:

Once a category has been assigned, further subcategories can be added as category metafields. As you include more information here, Shopify may give recommendations or suggestions on which metafields and information to include.

Example 2 - Product with existing category and category metafield information

For products that already have a category and some category metafields assigned, Shopify will often give suggestions, as well as additional metafields to add.

Our recommendation is to provide as much information here as possible to give Shopify and any platform that will be using UCP as much context as possible in order to discover and retrieve your product in response to a search.

Previewing Your Shopify Product Cards

Shopify is also allowing US websites to preview what their product summary cards will look like in AI, as well as selecting the source information for various fields to ensure that the information is as complete and correct as possible.

Settings > Sales Channels

This then allows you to see a preview of how your products will appear in AI shopping, as well as confirm which product details align with various elements. Crucially, the categorisation and further categorisation are key features here. This screen also allows you to define additional product grouping AND product variants so AI can fully understand your products and their relationships.

Shopify Knowledge Base App

Shopify have also created a Knowledge Base app in which you can provide information about your store, including FAQs about your store. This, for now, won’t improve the reach or discoverability in AI, but simply the accuracy of AI answers; however, given its timing of release alongside UCP, it definitely seems to be a related push from Shopify.

ChatGPT Feed vs UCP?

I personally was fully expecting enhanced feeds to be a requirement for each of the AI platforms, similar to that which OpenAI & ChatGPT have launched, to become the norm for appearing in AI search.

As things stand from this initial introduction to UCP from Google, other than a few new attributes to become eligible for agentic search, this isn’t the case. It will be interesting to see if the possible attributes expand, or if this information is gathered from elsewhere in the ecosystem (for example, the categorisation via the CMS which Shopify have).

For now, I would still prepare for expanded shopping feed attributes for ChatGPT, until they announce otherwise.

Agentic Commerce for non-Shopify eCommerce Websites

The introduction of Universal Commerce Protocol means that different platforms within the ecosystem can still communicate with one another. Stripe, in December 2025, announced their Agentic Commerce Suite; their own standard of AI discoverability; uploading product catalogues to allow stores to sell through AI agents managed via Stripe. They have already announced that they will support UCP standards.

In a recent webinar, STRIPE announced:

• STRIPE has the agentic commerce protocol (ACP), which is their version of Google’s universal commerce protocol (UCP).

• STRIPE has an agentic commerce suite which allows retailers to upload their product catalogues and be eligible for agentic commerce in agents (i.e., the different AI platforms) of their choice. This supports both ACP and UCP.

  • Currently, this suite is only available to selected retailers in the US, but they are looking to roll out further

• Product catalogues can be integrated either by API or by CSV

  • CSV is essentially a product feed - remains to be seen what attributes would be needed/good, although STRIPE did say a complete product feed would be a priority

  • Also remains to be seen what info the API takes from the CMS

• ACP supposedly supports subscription models (unclear if UCP does at this stage), but products that require personalisation may need a more unique solution

All in all, the announcement of UCP should bring a sigh of relief for marketers as it simplifies how to become discovered across AI platforms.

As more information comes out about exactly how to optimise for USP, it will be interesting to see more detail on:

• How exactly can brands “optimise” for UCP?

• How does on-site content, feeds, and backend categorisation/product information combine within UCP

• Where is the best place/places to provide this information? Is it in feeds directly to AI agents, or in the backend CMS?

As eCommerce specialists, we often encounter resistance to some of the tried-and-tested SEO techniques we recommend when working with luxury and premium brands. While these strategies are widely considered best practice across all types of websites, they can sometimes clash with brand aesthetics or internal marketing priorities, particularly when there’s a strong emphasis on visual design or curated user journeys (e.g. product page breadcrumbs). In short, brand always trumps SEO best practices at these businesses.

That said, the landscape is shifting. More and more premium brands are beginning to focus on GEO/AEO and all things AI Search, particularly having a strong awareness that these LLM platforms could be the future of brand discovery and brand perception. Over 9 months ago, we wrote about how SEO is shifting to become more of a brand channel thanks to AI search.

However, the challenge luxury brands will face is a general neglect of SEO best practices. Bad SEO means bad GEO/AEO. And in our GEO guide for CMOs, you’ll find this table, which outlines stage one of any GEO/AEO is discovery, which is brand discovery.

The key point to emphasise here is that these premium brands will naturally rank very well for branded searches, but for brand discovery across LLMs, your brand needs to be visible for non-brand terms or at least attempt to own the narrative around your brand.

In this post, we break down a number of core SEO techniques we often recommend, explain why they matter for both users and search engines, and showcase how some of the biggest premium brands in the world have successfully implemented them, each in a way that still respects their unique tone and visual identity.

Whether you’re part of a luxury fashion house, a premium beauty brand, or a high-end homeware label, you’ll find actionable insights and adaptable approaches to elevate your organic performance while avoiding clashes with your brand team.

The key message here is that before you think about AI search, you need to sort the fundamentals of SEO for discoverability.

Please refer to these jump links to different sections of the article if you’re looking for specific SEO/GEO examples from premium brands.

Internal linking techniques:

• Optimised mega menu

• Subcategory linking on category pages (with images)

• Static category page links on the homepage

• HTML sitemaps

• Long breadcrumb links on product pages (above the fold)

• Long breadcrumb links on product pages (below the fold)

AI / LLM-friendly content

• Strathberry

On-page SEO techniques

• Search engine-friendly product naming conventions

• E-E-A-T content - traceability information

• E-E-A-T content - product care information

• Brands with optimised title tags on their homepage

Optimised Mega Menu - Anya Hindmarch & Burberry

In SEO, it’s crucial to link to your most important pages directly from the main site menu.

Placing these links at the top signals to Google that they hold high value. Because the menu appears on every page of the website, it significantly boosts the internal link equity of those key pages.

Some clients hesitate to include too many items in their menu, concerned it might appear cluttered or overwhelming. This is a valid concern, as an overloaded menu can lead to decision fatigue or “analysis paralysis.” That said, with thoughtful design and a mixture of internal linking strategies, it’s possible to create a menu that is both visually clean and highly effective.

Below, you’ll find a proposed structure that strikes a balance between SEO priorities and brand design aesthetics from Anya Hindmarch and a larger, more in-depth menu from Burberry.

Anya Hindmarch

Burberry

HTML Sitemap - Prada

HTML sitemaps are one of the most effective internal linking strategies we use (outside of the menu). They help Google and other search engines crawl your website by clearly directing it to all of your category and subcategory pages on site.

If your website has a large number of collections, we recommend organising the sitemap using headings, like in the Prada and Gucci example below, to make it easier for both users and crawlers to navigate.

When creating your HTML sitemap, be sure to exclude any pages that:

• Return a 404 error

• Redirect to another page

• Are product pages (as these change frequently)

• Are blog posts or low-priority pages that don’t need to be indexed

Below, you’ll find an example from Prada, which uses sectioned headings for clarity, and Gucci, which takes a more minimalist approach while still maintaining effective structure.

Prada

Gucci

Static Category Linking on The Homepage - Chloé

Just like the site menu, having static homepage links helps signal to Google which pages are most important on your website by reinforcing their visibility and relevance.

While many brands are cautious about adding static links to the homepage, since it’s prime real estate and is often shared by multiple teams, there are smart, subtle ways to do it.

For instance, these links can be placed further down the page or “below the fold,” allowing room for high-impact content, such as campaign visuals, promotional graphics, or featured products.

Below, you’ll see how Chloé and Gucci implement this technique elegantly, ensuring strong SEO support while maintaining a clean and refined design.

Chloé

Gucci

Long Breadcrumbs on Category Pages - Kate Spade & Louis Vuitton

Long breadcrumbs are one of the first SEO recommendations we make to new clients, and for good reason. They enhance user navigation, provide Google with a clear and efficient crawl path, and communicate the structure of your site from the homepage down to individual product pages.

There are several ways to implement long breadcrumbs across your site. The most common placement is in the top corner of product detail pages (PDPs) and collection pages (see Kate Spade example).

However, some brands choose to position them below the fold based on design preferences rather than functionality (see Louis Vuitton example for this).

From an SEO standpoint, placing breadcrumbs at the top of the page is ideal. This not only improves visibility for users, especially on mobile, but also signals to Google that breadcrumbs are an important part of the page structure. While placing them below the fold can still be effective, it’s generally less efficient and more likely to be overlooked by mobile users.

Breadcrumb links above the fold, Kate Spade

Breadcrumb links below the fold

Subcategory Links On a Collection Page - Louis Vuitton

Subcategory links are typically placed above the fold on collection pages and offer users a way to explore different variations within a broader product category. These links are highly valuable from an SEO and user experience perspective, as they provide deeper access to pages that may not be easily found through the main menu or homepage.

Different brands take different approaches to how these links are displayed. For example, Balenciaga uses imagery to visually highlight available subcategories, creating a more immersive browsing experience. In contrast, Mulberry opts for a more minimal approach, featuring simple hyperlinked text at the top of the product listing page to guide users subtly without distracting from the product grid.

Subcategory Links On a Collection Page with Images - Balenciaga

Subcategory links on a collection page with images - Hermes

Subcategory links on a collection page (discrete design) - Mulberry

Adding “Luxury” or “Designer” in The Home Page Title Tag

Including keywords like “luxury,” “designer”, or “premium” in your homepage title tag is a smart way to signal the value and positioning of your products to search engines. This helps ensure you’re targeting the right audience; those actively looking for high-end products, rather than competing for broad, generic terms like “coat” or “dress,” where you’ll appear alongside lower-priced competitors and marketplaces.

Optimising in this way not only improves your visibility to the right customers but also helps manage user expectations. For example, if someone clicks on your “coats” page expecting high-street pricing and sees premium prices instead, they’re likely to bounce. Clear positioning upfront can reduce this mismatch and attract shoppers who are genuinely looking for high-quality, designer-level products.

Below are examples of several premium brands that have successfully adopted this strategy.

Search Engine-Friendly Product Naming Conventions

It’s great to see brands like Prada and Burberry adopting optimised product naming and descriptive anchor text techniques on their PLPs.

Too often, we see brands use creative or overly abstract product names that don’t clearly communicate what the item is, its colour, or material. For example, products might be listed under a unique product name [see examples below] or described using unconventional colour names like “Mocha Mist” instead of “brown,” or “Pistachio” instead of “green.” While it’s completely fine, and often on-brand, to use these unique descriptors, it’s important to also include clear, searchable context.

Adding terms like “green” after “pistachio” or “wool coat,” after a unique product name ensures both Google and users can better understand, search for, and find the products they’re looking for. It’s all about combining creativity with clarity to support both brand voice and discoverability.

Optimised Collection Page H1

As with the homepage H1, and all H1s, it’s important to clearly set context for the collection page. For example, Burberry includes “cashmere” in the H1 for their scarves collections.

This signals to both users and search engines that the products sit at a higher price point than a generic “women’s scarves” or “men’s scarves” H1 would.

Using the material within the H1 is a smart and effective way to manage user expectations and position the collection appropriately, particularly if you want to avoid explicitly using terms like “luxury” or “premium.”

LLM & AI-Friendly Content

Repair information with an optimised & branded H1

Whilst it might seem obvious that it is the Louis Vuitton repairs page if it is on the Louis Vuitton website and says ‘repairs’ it’s much more search engine friendly and avoids stockists of your brand outranking you for that term.

Strathberry ‘what fits’ content

Not only does this approach improve user experience, but it also increases the likelihood of your products appearing in LLM-generated answers for more descriptive, intent-led queries such as “best designer handbag for commuting that can fit a 15-inch MacBook Pro.” By clearly surfacing attributes like material, use case, and size within key on-page elements, you give LLMs the context they need to confidently reference your products in relevant recommendations.

They even take it a step further by listing the make and model of electrical items that don’t fit.

Anya Hindmarch Specifications & Materials

Clear, easy-to-scan subheadings such as “Inside Features” and “Outside Features,” combined with transparent sustainability information from Anya Hindmarch, create an experience that works seamlessly for users, search engines, and LLMs alike, making it easy to fully understand the product and feel informed before purchase.

Care Instructions & Traceability From Chloé

These traceability and care information sections strongly reinforce the authority and expertise elements of Google’s E-E-A-T framework. They demonstrate the thought, craftsmanship, and responsibility that go into producing each item, while highlighting the brand’s commitment to transparency. This allows users to assess the product’s global footprint with confidence, and positions the brand to rank and appear in LLM responses for broader fan-out queries such as “designer bag made in Europe.”

Conclusion

Why it’s important to adapt your designer marketing strategy and what to avoid

This is a clear example of how a designer brand’s perceived authority can be diluted or misinterpreted by Google. From a simple search for “Louis Vuitton bridal shoes” (which has an average monthly search volume of around 70), the top result returned is Christian Louboutin France, a completely different brand altogether.

This highlights that we can’t assume that search engines will automatically understand brand intent, hierarchy, or relevance, even when it feels obvious to a human user. Without clear, structured signals around product categories, use cases, and brand associations, search engines (and increasingly LLMs) may fill in the gaps incorrectly.

It also shows an SEO page opportunity that Louis Vuitton is missing out on.

Actively guiding how your brand and products are interpreted is now essential to maintaining visibility, authority, and accuracy in both traditional search results and AI-generated answers.

At the start of 2025 on LinkedIn (and in subsequent discussions with clients and partners), the central question was: Has AI finally killed SEO?

If you’ve been reading this Substack throughout 2025, you’d know by now that GEO/AEO is very much an SEO responsibility and area of ownership. You may think, “It was just LinkedIn chatter,” but you’d be surprised how many highly respectable people still use the platform for news and education.

Towards the end of 2024, an interim CMO we’ve worked with for nearly five years told me: “I’m telling my clients not to invest in SEO anymore because Google won’t be here in three years.”

Two years later, here we are. It doesn’t look like Google is going bust in the next 12 months; in fact, it is arguably stronger than ever, which is the first prediction we’re kicking off with for 2026.

Google’s Dominance Shows

2024 was the year of Bard and the “Google is dead” narrative. Critics claimed they couldn’t innovate. However, in 2025, Gemini continued to grow from strength to strength, resulting in a major new model release at the end of the year.

The impact:

• Increased Gemini app downloads to take the number 1 download spot.

• A shift in GPT’s priorities, the “code red” by Altman to improve GPT quality.

• Potentially… a change in brand perception?

It further illustrated the juggernaut GPT is up against.

While GPT has often held the upper hand in model quality, we predict that in 2026, Google’s ecosystem will play a much larger role.

Google owns:

1. The Search Engine (Google)

2. The AI (Gemini)

3. The Browser (Chrome)

4. The Operating System (Android)

Why does this matter? Over 50% of searches happen on mobile. If you have an Android device, Gemini is embedded into the operating system. It isn’t a designated app you have to navigate to; it is an “always-on” assistant.

This is interesting because it can:

• Read your screen.

• Take direct actions.

Why should eCom brands care? Imagine saying, “Look at my basket. What am I missing, and are there other brands offering similar products for less?” Gemini reads the screen, provides four options, and outlines each brand’s USP. You then say, “Open brands 1, 3, and 4 in Chrome,” and Gemini executes, opening each homepage in your Chrome app. (p.s. Good luck tracking this user journey!)

This integration of AI Search, Browser, and OS is a game-changer. The above is a real example I did over the Christmas holidays, which reduced my basket cost by over 60%.

This is a basic example; imagine what will be possible in the next 12–24 months.

The industry's longer-term prediction is that it will shift toward “agents buying on your behalf,” referred to as ‘Agentic Commerce, ’ though we don’t expect it to take off in 2026 (see more below).

So two predictions here:

• Attribution of these models remains impossible; referral traffic is only a fraction of the benefit

• 2026 is the year for Google AI - AI mode will become more prominent in the UK, reinforced by Shopify's recent announcement of a direct integration with AI Mode.

Blue-Sky Thinking: Will GPT Survive?

A few of our clients are now asking: Will GPT survive? It has allowed Claude to move in on “productivity” tasks, and it seems to have lost the first-mover advantage to Google in terms of the actual model. Furthermore, it still isn’t fully monetised.

Meanwhile, Apple is lagging in AI. The FT predicts Tim Cook could leave Apple in 2026, why wouldn’t Apple buy OpenAI? They would instantly gain the ecosystem needed to compete: AI Search + Browser + Operating System. While this isn’t going to happen, it makes for a compelling “what if.”

Agentic Commerce: The Rise of Feeds

This was one of our 2025 predictions. While the requirements for GPT shopping feeds have been announced, the ability to submit a feed is not yet available. We expect them to land in 2026. These feeds will be more dynamic, allowing daily optimisations to influence results.

Throughout 2025, our focus was on the “discovery” aspect of AI Search, optimising PDPs and detailed, persona- and need-based content.

In 2026, GPT will finally introduce more commercial capabilities, enabling trackable sessions and revenue in tools like GA4 and Shopify.

As of January 2026, Shopify and Google have already announced their connection through UCP - “the common language between platforms, businesses and agents”, more here: https://ucp.dev/

You can see how this will eventually lead to the Agentic commerce point referenced previously.

There are two parts here:

• The ability to submit feeds and get the commercial shopping tiles in GPT, which will have a strong impact

• The ability of technology to integrate will take off in 2026; however, we think the actual buyer behaviour of searchers to actually allow an agent to buy on their behalf is going to take a while to catch on

Stripe is expected to announce its own version of agentic checkouts in early 2026, which is good news for non-Shopify brands.

GPT Ads: A No-Show?

This could go two ways. Gemini took GPT by surprise with its latest model, forcing a shift in priorities or a “code red”, as Altman called it. Consequently, OpenAI must choose:

1. Monetise immediately to generate income and compete.

2. Delay ads to focus on the user experience before “damaging” results with sponsored content.

All talk and headlines point to option 2, but the astronomical cash burn makes you think: Can they really go another 12 months without better monetisation?

More Ads at the Top of the Funnel

Google ‘beta-tested’ AI mode towards the end of 2025 - our clients saw mixed results.

Despite the results, it’s a clear indication of where Google is going with ad targeting. I always struggled to see how they’d change targeting to be able to monetise the hyper long tail searches of AI - the answer is your website content.

We discussed the topic of the influence GEO/AEO content could have on your ad targeting in this post, here.

In December, we also collaborated on a webinar on the same topic with our friends at Vervaunt, Pion333rs and Glara. We’ll share this with you next week.

Monetising the top of the funnel

If this approach works, it could be massive for Google. Historically, 80/90% of search clicks went to SEO.

Google has always struggled to monetise this part of search - if they can crack it, that’s 8/9x more opportunity for monetisation. Granted, CPCs will be lower due to fewer commercial and competitive queries, but volume will be astronomical.

Zero-Click Search Continues

We predicted this last year, and anyone in SEO knows CTR has been declining for years. However, recent data from SparkToro shows that CTR actually increased in the last two quarters of 2025. Google has already stated it expects this trend to continue.

By September 2025, roughly half of the lost clicks came back. However, if you’ve been in SEO as long as I have, you know this is a decade-long strategy. In 2015, Google released Featured Snippets, pulling comparison results directly into the SERP (and tanking Expedia’s share price in the process). Google’s goal for over 10 years has been to move from a referral engine to an experience engine.

Even if the numbers changed unexpectedly toward the end of 2025, there’s a broader trend at play, and we expect CTR to continue to decline.

The Data Drought

We do not expect more AI-specific performance data from Google or GPT this year. We want AI results segmented in GSC, and we want a “GSC equivalent” for GPT - but for now, we can only dream. As a result, the GEO ranking tools like Profound and Glara will be an essential tool in an SEO’s arsenal.

The previously referenced agentic commerce + feeds will support the commercial aspect of AI search. Using the same data reference previously, if 80/90% of Google's traditional search is discovery, overly focusing on the commercial last click means you are missing out on the discovery aspect of AI search.

Why “GEO/AEO” Terms Are Here to Stay

At the end of 2024, we predicted these terms would disappear. We were wrong. While people now know it falls under the SEO umbrella, we still need a term to differentiate “Traditional SEO” from “AI Search” activity.

The SEO industry actively rebels against the term “GEO,” but broader marketers and mainstream publications continue to use it. My advice? Embrace the term. This is the most attention SEO has received in years. It would be classic SEOs to miss this opportunity by being pedantic about terminology.

2026 wish: We all just settle on one term and stick with that.

The Strain on Publishers

The publisher model has been hit hard by “zero-click” search. Referral traffic from Google has been their lifeblood for years - but fewer page visits mean fewer opportunities for monetisation, creating fresh challenges for media outlets hoping to sustain growth.

This matters for SEO and eCom brands because we rely on these publications for the links and coverage that drive authority.

Current defence strategies include:

• Selling content: Direct annual agreements with AI models.

• Owned Audiences: Shifting toward app-based and video content.

• Gated Content: Using apps to collect first-party data (emails), which is more valuable than traditional display ads.

Video content remains a stronghold because it is too complex for AI models to fully “own” yet. Models might cite a clip, but the user still needs to visit the site for the full context.

Why you should care? Over 90% of AI search results are influenced by Earned Media, not your owned website.

The Great PDP Overhaul of 2026

In 2025, e-commerce brands were busy educating themselves and freeing up resources about AI Search. Now that the planning is done, we expect a wave of implementation. 2026 will be the year of the AI-optimised Product Detail Page (PDP).

If you want inspiration, read our previous post by Laura: https://ecommerce.thisisnovos.com/p/real-life-examples-of-ai-optimised.

Conclusion: Stop Waiting for the Dust to Settle

As we move into 2026, our message to brands and marketers is simple: stop waiting for the dust to settle.

Whether you call it SEO, GEO, or AEO, the objective is the same: ensuring your brand is the most authoritative, visible, and shoppable answer across an increasingly complex search ecosystem.

We often hear, “It’s only a fraction of our traffic,” or “You can’t measure the ROI yet.” That framing misses the point. AI Search is not just a traffic channel. It is rapidly becoming a brand-discovery channel, and the brands that win there will compound their advantage well before reporting feels comfortable.

We’re also asked about the split between SEO and AEO. In reality, there is no split. Everything we do for AI Search strengthens traditional SEO foundations.

The only time SEO becomes the sole focus is when a brand’s technical baseline is so weak that it prevents discovery.

The “Great PDP Overhaul” is already underway, and the gap between brands that act and those that wait will only widen. You’re essentially choosing between two camps:

Camp 1

• Waiting for platforms to explain the rules.

• Prioritising dashboards over strategic direction.

• Delaying investment until measurement feels familiar.

• Avoiding difficult conversations about declining search visibility.

Camp 2

• Accepting imperfect data as the new normal.

• Investing in fundamentals: authority, relevance, and high-quality content for real brand personas.

• Prioritising model visibility, even when it can’t be fully measured.

• Educating stakeholders early and leading through uncomfortable change.

The brands that win won’t be the ones with perfect data. They’ll be the ones who moved first.

You already know which camp wins. Have a great 2026.

LLM content is quickly becoming the new “SEO-optimised content” in terms of reputation, quality expectations, and its ability to drive performance. And I completely understand the hesitation. Is this just another trend everyone’s expected to jump on? Do we really need to overhaul every PDP on our sites for an LLM that’s still evolving, and experts (including SEO’s) don’t fully understand yet?

But what if the goal wasn’t to optimise for AI, search engines, or customers… but for all three at once? That’s a direction worth taking.

As subscribers, you’re likely already familiar with our S.E.E.D. framework, which we recently launched here at NOVOS. Below are examples of content designed to serve customers, search engines, and LLMs, while staying firmly grounded in the E’s of our S.E.E.D framework - expert-led content and enriched product data.

Expert-Led Content

Digital and Brand PR are more important than ever in meeting the fundamentals of AI-driven search. The old saying “there’s no such thing as bad press” no longer holds true. Unlike traditional Google search, which didn’t evaluate sentiment or the nuances of linked coverage, AI systems can interpret, understand, and summarise it.

Ask ChatGPT “tell me about X brand,” and you’ll often see a list of pros and cons, summaries of Trustpilot or Google reviews, and even sentiment drawn from user forums like Reddit or Mumsnet - though this behaviour is predicted to (hopefully) move on.

To lay the groundwork for your AI content strategy, you need to know what LLMs already know about your brand and products.

Research Your Own Brand

• “Is X brand good quality?”

• “Tell me about X brand”

• “Is X or X brand better for [enter buying persona characteristics]”

• “Who are the best brands for [x product]”

By doing so, you will gain a better understanding of what your baseline brand perception is and the areas that need addressing.

Enriched Product Data

It’s no secret that LLMs are now favouring product pages (PDPs) over the category and homepage. Google is acting more like a PLP experience now. The Google search results are now the category page, and all the results are the best-suited product pages from brands.

This means that we are skipping a whole step of the historical user journey of homepage > category page > product page, and instead, we go straight to PDPs. So, PDPs have to do a lot more heavy lifting than they did previously.

Now, PDPs are responsible for getting your brands’ USPs, product information and brand values across to the customer. We can’t rely on the traditional user journey of the homepage > category page > product page to educate our customers anymore.

When writing your product page copy, consider the following data points:

What does your product ‘pair well with’

• This is mainly applicable for food/drink and clothing products, but it’s a great way of subtly cross-selling whilst also showing your expertise.

• E.g. does this ready-to-drink cocktail go well with a charcuterie board or a steak dinner?

• Do these brown suede boots pair well with a midi dress or a leather skirt?

Below are some of my favourite examples from Lick and Hoka.

The Ordinary: They’ve gone above and beyond by adding a “do not use with” section on their PDPs, flipping the “best paired with” recommendations on their head.

What is your product designer or best for?

• If this is an item of footwear, is it a comfortable shoe for both commuting and everyday, or is it better suited to going out?

• Is it a dark colour that would work better in a bright room?

• Is it a bigger model that would require a bigger room?

• Is it suited to a high-traffic area?

• Can it be put in the washing machine?

Once again, Lick and Hoka perform strongly in these content areas.

Finsiterre have taken this one step further and done intra-product comparisons to educate users, Google and LLMs on which product is best for what.

What are the material/texture options?

• Are each of these fabrics better suited to different models?

• Do each of these fabrics have qualities that are preferable to different buyer personas, e.g. a smart velvet sofa is better for consumers with pets, whereas the X model has a firmer back to it so is better for people with back issues, etc.

Lick, again, has nailed this on their PDPs.

How can you educate them on quality check guidelines?

• What reassurances and quality check services do you consider in the process?

• I like the example below from ASICS, which summarises their customer reviews so that you can get an overall perception of the product quality and comfort.

• Clearly displaying warranties is another easy way to do so.

Provide information on durability

• What is the likely lifetime value of this product

• Can it withstand high traffic areas?

Product turnaround time

• This is particularly important around gifting seasons like Christmas and Mother’s/Father’s Day, as you want to manage expectations for last-minute gift buyers.

• Below you can find a really detailed example from Nations Photo Lab.

Size comparisons and education

• Mention that it is inches, not centimetres (for LLMs) and provide a conversion.

• Mention if this is considered a standard /small/medium/large size (if/where appropriate).

• If it is an object, like a poster or item of furniture, a graphic showing the product next to an everyday item can be really helpful for users.

Below is an example from Etsy, guiding users on intra-product size comparisons:

• E.g. a photo print next to an iPhone, or a rug beneath a standard two-seater sofa.

Below is an example from Finisterre on how you can explain the model sizes.

Transparent size comparisons from Hoka: these are great assets for customers as well as for providing information to LLMs to answer queries like “Do Hoka running half zips come up small? I’m a size X, and X feet tall and don’t know which size to buy.”

To Summarise

Being cited in articles, listicles and trade features is more important than ever. These mentions significantly boost your visibility in LLMs and have become a crucial brand channel.

Your product detail pages (PDPs), meanwhile, are what actually teach LLMs about your products. Queries in LLMs tend to be 1.5–2× longer than Google searches, and you can’t rely on a single marketing persona anymore. To reach a wider audience, you need to clearly communicate your products’ key features, from ideal pairings and sizing to standout benefits, so both Google and LLMs can accurately understand and surface your offering.

Yes, this can feel like a lot to fit onto one product page, but there are smart, non-cluttered ways to do it.

And remember: human attention spans are shrinking, and the average online reading age in the UK is just 9–11 years old. Keep your content short, snappy and visual, and above all, make it educational.

Finally, if you missed our previous post, everything we’ve mentioned in this post could actually be helping your Google ad targeting in 2026.

Since work has really ramped up on optimising for AI Search, one of the most frequent questions we get asked is how we’re optimising for Reddit, since it’s often one of the highest cited sources.

The Data Reddit as Citations for LLMs

This is a very fair question, since Reddit almost ubiquitously tops the charts as the most cited domain in studies on the topic. Even with a notable drop in citations around September 2025, Reddit remains at the top of the pile.

Source - https://www.semrush.com/blog/most-cited-domains-ai/

Profound Client Reports

We see a similar trend for citations for clients we’re tracking, with Reddit often near the business end of the top cited domains. This does change per topic, however, so it’s definitely worth ensuring we’re looking at at least that level of granularity when it comes to making decisions and actions off the back of it.

Looking at a topic level, it can be easier to scale actual actions off the back of this since the top 5 cited pages account for over 20% of citations in this specific topic.

Why is Reddit So Highly Cited?

The whole purpose of Reddit is to be a source of first-person user-generated content based on individuals’ own experiences and/or expertise. With subreddits dedicated to specific (at times very niche) topics, it’s the perfect source of information for LLMs. Perhaps less directly-manipulated than brand review platforms (depending on how optimistic you are..!), and combined with the official partnership Reddit signed with Google, it’s perhaps no surprise that Reddit has taken an early lead in citations for AI search.

My personal prediction is that over time, Reddit will become less highly cited as AI models learn more and diversify sources (and has already seen a drop). Although, as long as it remains a source of rich (and real) user-generated content, it should always remain valuable for LLMs to cite.

Automating Reddit Responses - The Temptation & Risks

The Temptation

With Reddit being so highly cited, the obvious desire is to ensure that the sentiment being shown across threads is a positive one, to ensure that this then gets reflected in LLMs and AI Search responses about your brand.

The quickest and easiest way to do this is to manufacture the comments and highest-rated comments within these threads - essentially manipulating the sentiment of these threads and comments to ensure a positive response.

The relative ease of this action, coupled with the potential rewards, makes it a tempting action for brands. However, this is an approach we highly recommend avoiding.

What Does This Look Like?

So, what things would be done to manipulate Reddit citations? This falls into a few different actions:

• Seeding positive posts in relevant subreddits

• Seeding positive comments in threads mentioning the brand

• Upvoting positive comments/posts

• Downvoting negative comments/posts

• Creating/buying accounts en masse to facilitate the above

Often referred to as “astro-turfing”, this is often obvious to Reddit users and has been called out in the past, which can lead to serious damage to brand reputation.

The Risks

1. Brand Damage

A reputation takes a lifetime to build and a second to destroy. The same is true with the trust that customers have in your brand. The value Reddit has as a source comes with a general feeling of transparency and legitimacy.

Being caught (or suspected of) trying to manipulate Reddit threads, or automate large-scale Reddit posts to promote your brand, can obliterate brand trust built over decades.

Often, advice is given to infiltrate niche subreddits since these are the most relevant. However, Redditors within these communities are also the most engaged and clued up on maintaining authenticity and honesty. If you are caught manipulating, you will get disowned and criticised by the very people you are trying to get to buy into your brand. Reddit’s anti-marketing radar is VERY strong.

One recent example from a marketing agency using fake Reddit accounts to promote a new game shows how Redditors can pick up on this manipulation, leading to further press pick up and brand damage.

2. The “Penguin” Impact

Manipulating Reddit threads for improved brand sentiment or AI visibility may indeed work in the short term. However, it is logically something that Google and AI tools such as ChatGPT are going to want to stamp out. The very purpose of manipulation makes this clearly a “black-hat” tactic that Google and AI search tools will want to prevent.

Much like when the Penguin algorithm update punished websites for spammy and manipulative link-building practices, we cannot rule out the possibility of an equivalent action for Reddit manipulation if detected.

Long-Term Brand Building Using Reddit

• Use official Reddit accounts to give real and helpful feedback

Whilst faking customer replies is inherently negative, having an “official” stance within these threads from somebody within your company (who is officially representing the company) can, in fact, increase trust and brand perception whilst simultaneously potentially allowing you to influence the sentiment within key threads. In this sense, we can think of Reddit as an “Owned” channel.

As shown below, Reddit itself should be seen as social media, but your account on Reddit should be seen as owned media. Neither sits in earned media.

Do not outreach. We don’t want to manipulate.

We will use this sentiment to inform our PR strategy narrative or CRM.

Why aren’t more brands jumping at this? Because it’s hard. It takes a long time to provide useful information to build your community. ROI is hard to measure. But starting now can pay dividends in the future.

Hevy Fitness App - Example

The Co-Founder of fitness app Hevy is very active within the Hevy subreddit and proactively posts polls, fixes, and feature updates. This is a USP for a lot of users, knowing that their thoughts are listened to and things are actively acknowledged and worked on.

This concept can work for bigger brands as well to create a community of active customers on Reddit, which in turn can help AI visibility and sentiment naturally.

Philadelphia - Example

Not quite in the same remit, but a nice example of brands understanding Reddit is from Philadelphia cream cheese, using a paid ad to tap into a trend from subreddit /r/kitchenconfidential in which a user is posting daily with the challenge of perfectly cutting chives.

• Research any pain points

Reddit as a data source, particularly helping to understand the pain points customers are experiencing about your brand and products, and the elements they are enjoying, can allow us to craft the customer experience onsite and at a product level.

We can use this information to double down on the positives and lean into these within press releases, whilst also working to combat the negatives. In this sense, we’re using Reddit to indirectly influence future comments and reviews about the brand.

This is nothing new, but it becomes even more prominent with the rise of AI. Back in 2019, a Reddit thread influenced a strategy for one of our clients around common sizing issues with Common Project trainers across the U.S., UK and EU - seeing this, we wrote a guide and became a very successful piece for the client, influencing their rankings for the head term ‘Common Projects’.

How Can eCom Brands Actively Respond to Reddit (The Right Way)

So how can brands respond on Reddit as part of a wider community management project? We’ve given some common scenarios and how eCom brands could respond.

Brand not included in posts about brand/product recommendations, “i.e. what is a good, affordable hiking shoe”

If your brand isn’t being included by people in relevant subreddits and posts, general brand visibility may be the issue. From an SEO perspective, Digital PR is a good route to help get into relevant publications and “best” listicles, which help overall awareness of people looking for your product, which then indirectly feed into this type of user-generated recommendations. Definitely not the time to recommend your own brand.

Posts/comments mention negative experiences with products/brand

Longer term, you can feed this back to your product and marketing teams to help produce products/services that don’t have features that customers find negative, or help to allay concerns.

In the short term, responding in a genuine way from the brand perspective, recognising the issue and providing information about how this is/has been fixed or has been noticed by the brand can do wonders to show customers you are engaged and aware.

Posts giving a genuine and balanced review of the product/brand with pros/cons

Just because you can respond, doesn’t mean you have to! It can be worth keeping tabs on these types of posts, however, since they can give invaluable insight into dedicated and loyal customers to help your marketing and product teams lean into the positives and help combat the negatives.

Conclusion

Reddit remains one of the most highly cited sources in LLMs and AI search. To take advantage of Reddit in the long term, we think this should become a community management opportunity to help fulfil brand building and customer satisfaction.

Manipulative SEO activities (which try to unnaturally game Reddit comments and sentiment) are highly risky both for your brand and long-term performance. Instead, use existing Reddit comments as a research platform to influence and support your content strategy.

Now is when eCommerce brands start thinking about 2026. As part of your New Year’s resolutions, how about beginning to see your SEO investment and paid search investment as “Search investment”? Let me explain.

💡 The Core Shift

All eCommerce brands run Google Ads, but the game is fundamentally changing.

When Google launched AI Max for Search, we got a glimpse into the future of how Google sees its advertising targeting for AI mode and beyond. Your keywords are no longer the primary instruction manual for Google. Your website content is.

AI Max is not just an ad setting; it is a mirror of your website's quality. If your SEO and content strategy targets specific, long-tail user needs (AEO/GEO), AI Max can be a powerful discovery engine. If your content is generic, your ad spend will be too.

Keyword-less technology

AI Max, PMax, and Dynamic Search Ads are all part of Google’s “keywordless technology.” This means your product descriptions, blog posts, and landing pages now serve as direct targeting parameters for your paid ads. It merges the worlds of SEO and Paid Search - your Google paid media now treats your website’s content as its key instruction manual for targeting. (I know, it’s painful for an SEO to write this.)

AI max not working? Maybe it’s your website.

There have been critics of AI Max’s performance due to poor targeting, but could this be because e-commerce websites aren’t GEO/AEO-optimised, as they don’t invest enough in improving their content targeting? 🤔

Google’s case study cites L’Oréal’s example of how it used AI Max to capture new queries it hadn’t previously targeted, such as “what is the best cream for facial dark spots?”

This is a need-based query, not a product-name query. This is the modern world of SEO (GEO/AEO/AI SEO), which we’ve written about many times on this substack. Extract below from the case study:

https://business.google.com/uk/think/ai-excellence/loreal-ai-max-search-marketing-strategy/

Google’s done this before

For context, I’m not a paid search expert, but I’m very interested in Google’s relationship with SEOs and their broader intentions.

They have a track record of this. Back in 2015, Google wanted a faster internet for its users - what did Google do?

It introduced the Core Web Vitals and said it's now a big ranking factor (it wasn’t big) - suddenly, every SEO was pushing to speed up websites to hit this ranking factor and ultimately achieve Google’s broader goal.

I believe this is what they are doing again; it’s impossible to target the hyper-personalised, ultra-long-tail searches of Gen AI, so why not get SEOs to do the work for them?

SEOs define the content strategy, leveraging the brand's personas and the on-site changes to be made. The ads algorithm takes this, says thank you very much, and suddenly you see Google ads appearing for super long-tail, commercially led terms that would have previously gone to SEO - Google makes more money. Genius really isn’t it … fair play.

How AI Search can help Performance Max (PMax)

Performance Max is the “older sibling” to AI Max and part of Google’s “AI Power Pack” (alongside AI Max and Demand Gen). It relies heavily on your website’s content to find customers across YouTube, Display, Search, Discover, Gmail, and Maps.

• Asset Generation: PMax uses your landing pages to automatically generate headlines and descriptions, similar to AI Max’s “Text Customisation”. If your content is optimised for specific personas (e.g., addressing specific pain points rather than just listing features), the AI can generate more persuasive, human-sounding ad copy.

• Audience Signals: PMax uses your content to interpret who should see your ads. By using GEO/AEO strategies - writing content that answers complex, conversational queries, you help PMax’s algorithms match your ads to users exhibiting those specific high-intent behaviours, even if you haven’t explicitly targeted them.

What is AI Max (and Why Should eCommerce Care)?

Unlike Performance Max (PMax), which is a standalone campaign type, AI Max is a layer of automation you toggle on within your existing Search campaigns.

It uses Google’s AI to find customers using three specific mechanisms:

1. Search Term Matching: Using “keywordless technology” to find queries that are semantically related to your business.

2. Final URL Expansion: Dynamic Search Ads (DSA) technology that swaps your landing page if the AI finds a better match on your site.

3. Text Customisation: Generative AI that writes headlines in real-time based on your landing page copy.

⚠️ The “Black Box” Warning: While Google claims AI Max drives 14% more conversions, independent tests from over 250 retailers found it delivered conversions at a 35% lower ROAS compared to traditional match types.

🤔As referenced previously, maybe this low ROAS is because most websites aren’t investing enough in this new world of intent targeting?

GEO content = Better ad targeting.

Because AI Max relies on Large Language Models (LLMs) to understand user intent, it doesn’t just look for “keywords” - it understands needs.

This is where GEO/AEO/ AI SEO comes in.

If your eCommerce site is optimised only for head terms (e.g., “Running Shoes”), AI Max has minimal context to work with. It might match you to broad, low-intent traffic.

Here are examples of why you must optimise for ultra-long-tail, need-based searches.

1. Optimise for “Problem-Solution” Syntax

AI Max’s Search Term Matching analyses your landing page to decide “when and to whom an ad should appear.”

• Old SEO approach: Optimise a Product Detail Page (PDP) for “Waterproof Hiking Boots.”

• AI Max / GEO approach: Update the PDP description to answer who this is for and why explicitly. Eg Adding copy like: “Ideal for hikers with flat feet needing ankle support in muddy terrains.”

Why this works: When a user searches “best boots for muddy trails flat feet,” AI Max sees the semantic match on your page and serves the ad - even if you didn’t bid on “flat feet” keywords.

The SEO/GEO Angle: If you optimise a page for specific long-tail searches (e.g., “ergonomic standing desk for small apartments” vs. just “desks”), you provide the AI with more precise semantic boundaries. This helps the “keywordless technology” identify relevant, high-intent traffic rather than wasting budget on “remotely similar” or irrelevant queries.

The L’Oréal example referenced at the start used AI Max to capture queries they hadn’t thought to target, such as “what is the best cream for facial dark spots?” This is a need-based query, not a product-name query.

We’ve written about this in this ‘situational search’ post > https://ecommerce.thisisnovos.com/p/situational-search-how-to-get-your?r=2qo9mq

2. Findability and understanding of content

If you enable AI Max, Final URL Expansion is on by default. This means Google can ignore your specified ad URL and direct users to any page on your site it deems relevant.

A messy site structure sends traffic to irrelevant pages. A well-organised site turns this into an advantage.

• The Fix: Create distinct landing pages for specific attributes—for example, separate collections for “Sulfate-Free Shampoo” and “Colour-Safe Shampoo.”

• The Benefit: When users search for niche attributes, AI Max can dynamically route them to the exact collection page instead of a generic homepage, boosting conversion rates.

The SEO Effect: If your site architecture is SEO-friendly - meaning you have distinct, well-optimised pages for specific sub-topics or products - AI Max has a better “menu” of options to choose from. It can direct a user searching for a particular long-tail niche to the exact page that answers that need, rather than a generic home page, potentially improving conversion rates.

The example here is basic, but imagine if you had a large eCommerce site selling multiple products across multiple brands - this need becomes even more critical for fundamental findability and understanding.

3. Targeted content feeds the ad copy

AI Max uses Generative AI to write headlines in real-time based on your landing page content.

• Garbage In: If your product page says “High quality, best price, buy now,” your AI-generated ads will look generic and spammy.

• Gold In: If your product page highlights unique value propositions like “Sustainably sourced cotton”, “water resistant to 300m,” or “ideal for marathon pros”, the AI will pull these into the ad copy, making your ads more relevant to specific queries.

The SEO Angle: The AI generates ads using the text it finds on your page. If your on-page content is rich, persuasive, and full of core attributes, the AI-generated headlines will likely be more relevant and compelling. Conversely, if your landing page is vague, the AI may generate generic ads.

This is the second E in our SEED framework, which is all about enhanced PDPs, which we discuss and give an example of in this post > https://ecommerce.thisisnovos.com/p/a-cmos-guide-to-geoaeo

AI Max can still go mad.

Even if AI Max is still a bit aggressive or goes too broad with good GEO content, this is a clear signal from Google about how ad targeting will work in the future.

Google needs to monetise AI mode and AIOs; 90% of Search clicks went to SEO - the long tail, which Google’s never been able to monetise. AI mode will make this even harder unless it leverages the website's content and long-tail targeting, which is SEO’s role to build and define.

Summary

The key consideration here is that better GEO/AEO targeting will impact your paid ad performance.

How should paid and organic teams work together

I believe that, as paid search has become less tactical and more strategic, paid search teams should bid where SEO isn’t and understand how their spend affects the business as a whole. SEOs should focus on content strategy to position the website toward customer needs, embrace situational search, and work closely with brand teams to align messaging and target relevant offsite coverage. This way, both channels can help each other:

• Paid Search helps avoid cannibalising SEO by constantly reviewing ad data vs. SEO performance, ensuring updated content is used to boost paid numbers without harming the wider business.

• In return, SEOs help the paid team by making the website better targeted towards need-based searches and a much more content strategy-led approach.

For marketing leaders overseeing the entire marketing mix and budget, this post is intended to show that when you invest in “GEO or AI Search,” you aren’t just investing in SEO- you’re investing in better content targeting that addresses your customer personas’ needs and pain points. This fuels all marketing channels and yes… even Google Ads.

Sources

https://smarter-ecommerce.com/blog/en/google-ads/marketers-guide-to-googles-power-pack-ai-max-pmax-demand-gen/

https://business.google.com/uk/think/ai-excellence/loreal-ai-max-search-marketing-strategy/

https://ppc.land/independent-tests-show-ai-max-underperforms-traditional-match-types/

https://searchengineland.com/ai-max-for-search-everything-you-need-to-know-462923

https://support.google.com/google-ads/answer/15910187?hl=en

https://ppc.land/independent-tests-show-ai-max-underperforms-traditional-match-types/

For years, measurement in PR has been about the number of pieces of coverage and clicks.

You’d create a strategy that earned coverage, which in turn drove consumers to your site and watch as awareness of your brand, traffic to your site and hopefully conversions poured in.

But that model has collapsed in the face of AI search.

AI for us all is changing how we discover, research, and ultimately trust a brand. This is forcing PR to evolve more than it has in the past decade.

The fight now is not just about securing coverage, it’s about building an understanding of your brand with AI systems, like ChatGPT and Perplexity.

The Old Funnel Has Collapsed

Traditionally, visibility has led to clicks. For eCommerce brands, all PR and SEO strategies hinged on driving people directly to their site.

There was more of an element of control with a PR strategy, you could tell a story, secure coverage & a link and build your pipeline from awareness through to conversion.

With AI search, that is essentially gone now.

Large Language Models (LLMs), like ChatGPT and Perplexity, are designed to give consumers the answers they need without needing to click through to a site.

This means that visibility now isn’t about how many people visit your site; it’s about whether AI understands and trusts your brand enough to surface your brand as part of their answers.

For PR, this fundamentally changes things.

AI Visibility Is The New Focus For PR

Currently, LLMs lean on a surprisingly small set of “trusted domains”. These domains are what you would typically expect if someone talked about a ‘high authority’ site, BBC, The Guardian, Forbes, to name a few.

One thing that is critical for PRs to understand is that strong coverage doesn’t automatically mean that you’ll be surfaced in AI search results, particularly if there’s no consistency behind the coverage you’re securing.

This is because LLMs don’t simply crawl for links; they learn patterns across the web. Who talks about your brand? How consistently is that conversation happening, and in what context is all of this happening? LLMs study tone, brand sentiment and context to decide whether your brand should be surfaced, whether it’s relevant and most importantly, if it’s credible for a given topic.

Put simply, AI learns who you are based on all of the stories about you online.

So what does that mean for PR? We’re no longer just a channel for awareness; we’re a channel that drives AI understanding.

The Jump PR Must Make

The jump to AI search is here, even as consumers now recognise it as part of how we source information, with ChatGPT alone enjoying around 800 million users per month.

In terms of PR, for years our impact has been measured in specific ways - volume of coverage, coverage quality and more SEO-led metrics like traffic. Those things do still matter, but they need to be assessed through a different lens now.

Now, visibility in LLMs is earned through a combination of:

1. Relevancy - Are you consistently being mentioned in the right context within your niche?

2. Credibility - Are the mentions you’re securing coming from trusted sources?

3. Consistency - Are you appearing often enough so that AI can confidently connect your brand to key topics and conversations in your niche?

It’s no longer just about moments, PR rewards patterns and consistency. Brands must understand this and take seriously how it can positively impact visibility.

You can’t just go viral once with a single campaign and expect AI to remember who your brand is and what it stands for. It’s the consistency of repeated mentions that teaches LLMs who you are - this is what begins to drive long-term visibility.

How PR Needs To Evolve: The SEED Framework

Our proprietary SEED framework offers clarity to eCommerce brands, helping them navigate the constantly evolving landscape of AI search by focusing on key areas.

But how can we incorporate the SEED framework into our PR strategies?

S - Situational Relevance

PR earns coverage that builds topical authority in the correct context for your brand.

AI understands topics by category, so your PR strategy needs to build authority across different publication types depending on the personas you’re targeting.

It’s important to remember that your audience will differ, so understanding the different personas you’re targeting is crucial. The sources cited for one persona will vary from another, as different people consume various types of media; therefore, the sources that hold the most influence will also differ, and cited sources will vary depending on the platform.

Below shows two different anonymised personas for a brand in the interiors space, which showcases the differences between the types of cited sources depending on the given persona.

Persona A

Persona B

The domains being used as citations across the different platforms demonstrate the importance of having a varied PR strategy that targets a multitude of different publications. In short, AI understands the nuances of different personas by category, so should you.

E - Expert Led Content

PR offers the natural opportunity to showcase to the world, and to AI, your expertise and credibility as a brand

Leveraging expert commentary and combining it with real-life stories, authenticity, and trust offers AI the most valuable signals for your brand. Over time, consistent expertise helps AI differentiate your brand from your competitors.

E - Enriched Product Coverage

Strong product-led coverage, whether affiliate or not, helps AI understand what you sell.

Securing coverage in top-tier product round-ups allows the opportunity to share key product information - price points, key features and the benefits of your product - all of these things send the right signals and reinforce your brand to purchasing intent from your audience.

Product coverage via a PR strategy not only has the benefit of teaching AI who you are, but it also gives the opportunity to directly tell your audience about your products.

This will help your products to gain the visibility needed for the bottom of the funnel, more commercially led terms.

D - Distinct Brand Message

Consistency of coverage here allows you to tell your audience and AI what values your brand has and exactly what you stand for

Your PR strategy should tell a clear story about who you are and what defines you.

Securing coverage in trusted sources consistently builds a consensus of your brand messaging with AI, and it’s clear that earned media is what is driving the large majority of citations.

AI, though, is not one single platform. It’s becoming increasingly evident over time that each model cites sources differently. As an example, ChatGPT and Perplexity do not prioritise sources in the same way. See below an example from a British nutraceutical brand:

Perplexity leads with sources from BBC Good Food, whereas ChatGPT takes a far more formalised approach to the authority it prioritises:

No brands in sight, they see the NHS website as their main source of truth, alongside two hugely reputable news publications.

This is evidence that two models source their information differently, which showcases clearly how a well-rounded, consistent strategy needs to be in place.

Typically, traditional PRs depend on strong contacts, which eventually lead to repeated coverage - all the examples cited throughout the SEED framework demonstrate that this approach isn’t effective with LLMs - you need to adopt a content-led strategy for diversified coverage.

Where Does Brand Sentiment Fit In?

Brand sentiment has often been seen as a softer metric - particularly for SEOs - something to monitor and be aware of, but not something that could necessarily be optimised in search for performance.

But as LLMs evolve, it is clear that brand sentiment is becoming a strong signal.

A positive, consistent brand narrative reinforces not only who you are as a brand, but why you can be seen as trustworthy and reliable.

Anything negative or inconsistent creates a level of ambiguity with LLMs, and that puts up a barrier for your brand being a cited source.

This is why sentiment now underpins relevancy and credibility. As an industry, PRs used to take the line of ‘all press is good press’; this is now fundamentally not the case. Negative brand sentiment will impact your brand’s ability to be surfaced for answers with AI.

The good news? How humans view brand sentiment is the same as an AI algorithm:

• Positive sentiment builds a level of trust

• Trust drives authority around key topics

• Authority then fuels visibility

What Does Success Look Like For PR In An AI-First World?

Success for PR in a changing PR driven landscape is going to look different to the traditional report PRs have engaged in for many years.

It’s no longer going to be about the Domain Rating (DR) of coverage or the number of backlinks you build for a specific campaign; it’s about whether your brand is being consistently cited and understood by LLMs.

The key questions to ask yourself when trying to understand whether your PR strategy is working:

1. Is the coverage you’re securing relevant to your key target categories?

2. Are we appearing in the same places as our competitors? Or, are you dominating the conversation in your industry?

3. Is your brand messaging consistent across all of your owned, earned and social channels?

4. Does the overall strategy offer AI the opportunity to build consensus around your brand, your brand values and the products you sell?

When you begin to achieve the above and have confidence around these questions, that’s when your brand will start to be understood by both your human audience and by AI.

The Opportunity for PR

This shift isn’t a threat to PR; it’s the biggest opportunity we’ve had as a channel for years.

We know that a brilliant PR strategy can directly impact how AI understands a brand. That means that PR doesn’t just sit at the top of the funnel trying to drive awareness admirably; we’re an integral part of how consumers make their purchasing decisions.

With any change comes a challenge; in theory, this is a massive opportunity for PR as a channel and one that needs to be grasped, but the challenge must be understood in equal measure. Patience is required as building consistency isn’t easy, but following the right processes for your brand will help you perform brilliantly in an AI-driven search world.

Aligning key campaigns and your overall strategy with key categories, tracking the right metrics and communicating your brand’s values are all going to be key in helping AI understand who you are as a brand.

AI is changing how people search and how people purchase, but it does not change the fundamentals of good marketing. Relevance, your expertise and positive brand sentiment have always been the key to building a high-performing brand, and they will be the key to doing the same for AI.

Agentic commerce is feed-based but AI citations are PDP copy-based

I really liked how this visual was simplified and broken down & by far the biggest point to address/summarise.

We’ve done so much PDP optimisation for AI visibility growth this year, see the chart below where we’ve taken AI visibility from est. 40% to 62% while all other competitors are flatlining - largely from PDP optimisations:

For agentic commerce, we’ve already been doing work with clients this year on owning their feeds.

We made this move at the end of 2024 mainly due to the new Google Shopping rollout in the US and the high volume of organic results now appearing in Shopping results.

When it’s eventually released for GPT, it’ll become even more interesting and will naturally be a significant focus for us next year.

Personally, I don’t think a paid search should own this; there’s no CPC or budget involved - organic search is fundamentally different, and our focus is on longer-tail, content-led approaches rather than paid search strategies.

PDP optimisation weighting

FAQs have a significant influence and are something all e-commerce brands can implement. Some of these other points require more resources.

90% of AI visibility is driven by Digital PR activity

You’ve had your tier 1 media list; now you need your tier 1 GenAI list. We’ll be talking about this more in the coming weeks, showing how much of an influence earned media has on your AI search performance.

The last Substack post ended with the following comment, which is further supported by this slide.

“Digital PR is essential at stage 1 for SEO fundamentals through link building. It becomes valuable again at stage 3, where you need citations on publications that LLM models deem authoritative. There will be overlap, but if you aren’t tracking and adding these sites to your prospect list, you’ll miss out on that competitive advantage and fail to get your brand visible.”

CMO Guide to AEO

How Chat GPT has changed shopping behaviour

I loved this image, previously you’d hunt around but now you get anchored towards the top recommendation from AI - you may end up doing your own additional research around that anchor but it still has a massive impact on behaviour and purchasing decisions.

The era of non-human transactions has begun

Partnerships continue to roll out. Google announced a “buy for me” feature. Examples included “buy me X product when it comes in stock under £200.” It will apparently bundle offers together—if you’re going on a trip and there’s a complementary offer, it will bundle everything and present it to you. (We’ll see if this actually happens or not.)

Coming soon: Try it on features.

Try-on features will soon be available within GPT itself - very impressive if true.

The purchasing funnel has gone dark

This is a great visual and i do agree with this, right now it’s so hard to know where AI search fits in the funnel. Particularly with Anchor bias referenced period, if you find a product you like in GPT it has the potential to take you from awareness to purchase in seconds.

Different LLMs value different sources.

This is just a summary but a key point to address and Mark our Head of PR will be sharing the next Substack which focuses all around this subject.

Each AI has different partners

No actions or commentary here - I just think this may be a useful slide for readers to share internally or reference, since partnerships are being announced so frequently.

Biggest impacts on LLM

Here are some key steps to influence LLM results: enhance FAQs across PDPs and PLPs, optimise product feeds when they launch in the UK, and focus heavily on data and PDP copy optimisation.

Numbers that matter

Here are some stats you can use to make the case for investing in AI Search internally. Note that this survey was conducted before instant checkout launched, so these numbers will likely increase even more now that users can buy directly.

AI Search is evolving rapidly, so these snippets help stay up to date. Please share this with any other eCom leaders who might find it valuable.

The number one takeaway from every speaker: try and test. That’s the first step you need to take before competitors do. Everything is changing fast, and we’re all figuring it out together.

Whether you are a CMO or report into one, this guide is for you. It’ll help to cut through the noise of what GEO/AEO is and how it differs from SEO.

I recently did a podcast where I mentioned NOVOS sits in between two worlds: the SEO world and the eCommerce world.

In the SEO world, GEO/AEO is not a thing; it’s very much seen as an activity within an SEO’s remit, which is just an adaptation of content for the AI era.

In the eCom world, GEO/AEO is very much still a thing, as is the case across broader marketing industries.

So in this post, I’ve focused more towards the eCommerce world. If GEO stays, this is how you should be viewing it as an eCommerce leader.

By the end of this post, your takeaways should be:

• ‘GEO’ is just a small but important part of getting ranking in GPT, Google AI, etc

• Without SEO rankings, there’s no GEO visibility

• A broader understanding of how LLMs retrieve information/content and give answers

Below is a screenshot from a Google rep which tells you what you need to know. Either way, let’s break it down more.

What is GEO/AEO?

In case you don’t know OR if you use one of the many other acronyms available, e.g. AIO, GXO, AEO, oLLM, or GEX (only one of these is made up…) - what exactly is GEO?

GEO stands for Generative Engine Optimisation, referring to optimising for AI-driven search results (ChatGPT, Perplexity, Gemini, etc.). The narrative you may hear is (particularly if your only ‘knowledge building’ is through LinkedIn) that ‘SEO’ is once again dead and GEO is the new channel to focus on.

This isn’t the case; the element referred to as ‘GEO’ is just an adaptation of how SEO works with this new technology.

Where Does GEO Fit in with SEO? Is There a Difference?

We’ve simplified how to visualise GEO vs SEO in this table:

This table takes influence from the RAG model that outlines how LLMs broadly collect and serve answers:

• Retrieval of information

• Augmentation of context from request to information

• Generation of an answer based on the best, relevant source

Note: There are stages before this, including the stored knowledge provided to an LLM called its parametric memory, but we don’t need to go into that level of detail for marketing.

Stage 1: Your Brand is Seen

This is retrieval. LLMs need a source to collect information; as it stands, they are using a combination of Google and Bing rankings.

Search results are already a prioritised list of the best, most relevant content online, so why would you not use it as a reference point?

GSC impressions have been a mess this year for this reason: an aggressive crawling of Google results increases impressions from these LLM bots. Google partly responded by not allowing 100+ pages to be crawled and extracted at the same time, and limited it to 10.

So, in the background, these LLMs are performing searches based on the probability of what the searcher will ask for next. See Google’s product lead Steins’ quote below:

Actions:

• If you don’t have good SEO rankings, the LLMs won’t retrieve your website

• You won’t be a source within the retrieval process, and therefore, the next 2 stages are redundant

• Focus on better SEO rankings by improving technical foundations and internal linking

• LLMs still struggle with JavaScript, so if you are headless or adapt JavaScript anywhere on the site, the LLMs will struggle to read it unless it’s server-side rendered

• You’ll need a clear content strategy to target niche terms your audience is searching for, and put a high emphasis on intent. If you are writing an informative led article, but the search results are all commercial, it won’t rank - think of this as user experience for content

• Finally, once on-site is in a good place, you need backlinks - Google still relies massively on backlinks; this is the key part that’s very difficult to do and therefore creates a much higher competitive advantage for your SEO performance, i.e., competitors can copy your content, but they can’t copy your backlinks; it takes a very long time.

Stage 2: You’re Relevant

If you are focused on the term GEO/AEO, then this is where this comes into play.

This is the stage where LLMs will match their retrieved information from stage 1 and match the sources to the prompt based on relevancy.

It can be viewed as a ‘reranking’ stage based on stage 1. LLMs refine the shortlist using context and intent.

A key tool in an SEO’s arsenal here is called query fan out mapping (which is referred to in the Google quote above).

How Query Fan-Out Works

AI Search issues dozens of background searches per query, using Google Search as a tool to fetch real-time data and evaluate quality signals. As part of our work with clients, we replicate this modelling to predict what the user would search for next.

Takeaways:

• SEO results provide the search index. LLMs provide the brain - i.e., it decides the relevancy and how to summarise/rephrase for the searcher.

• The content strategy referenced in the retrieval stage needs an extra level of depth, use traditional SEO data to inform priorities of where your brand has existing success and then match this with your personas.

• AI search long tail brings out clear contextual needs of each persona in their prompts/search terms (a key differentiator to traditional keywords from stage 1).

• This persona layer is what satisfies the LLM’s need for intent matching, providing content for the persona’s need, which gives the LLM content to match to their desired intent

• Example: Traditionally, an SEO would focus on a “Sofa buying guide.” Now you need greater depth, matched to your personas’ specific needs. This takes content to a new level - such as buying guides tailored for families with toddlers, first-time renters, apartment dwellers, hosts who want a statement piece, or design enthusiasts seeking niche, trend-defying furniture from influential designers.

Actions:

• Use contextual cues, e.g. “for busy families”, to help LLMs match relevance. This includes your audience for content as well as products, e.g. “for beginners”, “for CMOs”, “for families with toddlers”

• Feed models structured data, e.g. FAQs and summaries

• You win at this stage by being the clearest, most complete and most contextually relevant answer - not by keyword tricks.

• Cover the follow-up questions - we call this query fan out mapping. What would the readers’ next questions be, e.g. costs, timelines, pitfalls, pros/cons, and how it works?

• Cite your own sources next to any claims

• Add Schema data around how people ask, e.g. on PDPs: products + offer + return policy + offer shipping details

• Focus on 1 intent per URL

• Use internal linking for additional context, e.g. not ‘here’ but ‘our sofa buying guide here’

PDP Example: Hiking backpack for train travel across Europe

1. Answer capsule: who’s it for, capacity (20-60L), weight, materials, access, fits overhead racks on EU trains

2. Spec table: litres, weight, torso fit, laptop sleeve size, rain cover, warranty

3. Fit/not fit: commuters & weekend hikes vs multi-day trekking

4. Comparison table: your model vs 2 close alternatives

5. FAQs: Cabin size on Eurostar / Waterproof / Back panel venting / carry on rules

6. Schema: product, offer-shipping-details, merchant-return-policy, aggregate-rating

Stage 3: Your brand is mentioned

The ultimate goal of AI search is to be cited as a source in the answer. This builds brand visibility but more importantly credibility, and trust (which ads can’t do).

It eventually pulls users through the funnel. If you’ve completed stage 2 correctly, you’ll be mentioned again when users ask follow-up questions mapped in your query fan-out activity.

But here’s what most people miss about LLM ranking.

At this stage, the LLM has still retrieved and evaluated hundreds of relevant results.

Your direct competitor could read this and complete stages 1 and 2 just as well as you.

The true differentiator and competitive advantage comes from offsite work - digital PR and brand building.

At this stage, it’s called citation building rather than link building.

Each LLM model has a set of sites it considers authoritative sources. Over time, these lists will likely diverge more.

What does this mean for you?

To improve Perplexity rankings for relevant prompts, you need coverage on publications it deems relevant and authoritative.

This prospect list will differ from ChatGPT, Gemini, or Grok (if that’s your thing).

For example, when looking at the topic of Children’s Carriers, notice the difference between sites ChatGPT views as authoritative versus those Perplexity prioritises. As I mentioned, I expect this divergence to grow over time as each platform advances and develops its own quality guidelines.

GPT

Perplexity

Summary

AI has changed the search interface and encouraged more complex queries, but the core ranking factors remain the same—expertise and authority.

Google calls this an “expansionary moment.” People are asking far more questions now, particularly around advice, how-tos, and complex needs rather than simple queries.

Instead of optimising for isolated keywords, brands should anticipate the full intent and informational journey behind the conversational questions their personas ask.

Foundational SEO gets you seen. Then adapt your content strategy for AI search (call it GEO or AEO if you want). But the true differentiator comes from offsite coverage—and this won’t go away because it’s considerably harder to achieve than making changes on your own website.

Digital PR is essential at stage 1 for SEO fundamentals through link building. It becomes valuable again at stage 3, where you need citations on publications that LLM models deem authoritative. There will be overlap, but if you aren’t tracking and adding these sites to your prospect list, you’ll miss out on that competitive advantage and fail to get your brand visible.

Last week, Google outlined what it sees as the next era of search.

When people hear “GEO,” they immediately think of ChatGPT or Perplexity. But as we keep reminding our clients and leads, Google AIOs are already here (with AI mode recently in the UK), with a far more sophisticated algorithm. And that’s where the eyeballs and money are right now.

Google is not just a keyword engine. It’s evolving into an AI-powered conversational brain, combining natural language, visual search, and deeper contextual understanding. Robby Stein, Google’s VP of Product for Search, recently laid out this vision - and it has big implications for eCommerce brands, retailers, and creators.

Below is a breakdown of Stein’s framework, followed by what it means in practice for brands in the eCom space and how to prepare.

Quick quote from the podcast:

“The core of Google search isn’t changing… AI is expansionary”

Stein’s Framework: The Three Pillars of AI Search

Robby Stein describes Google’s next-gen search architecture as built on three interlocking pillars.

Stein envisions these three modes merging fluidly — users won’t need to choose “search vs. AI” manually. The system will detect when to deliver a simple AI Overview, or when to launch into AI Mode.

Importantly, this isn’t about replacing “search results” entirely, but evolving how those results are generated and presented. Stein emphasises Google is moving beyond serving just web pages - it wants to generate responses with context, structure, and conversational depth.

What This Means for eCommerce Brands

What does it mean for an eCommerce brand? Below are key implications and action areas:

1. Think beyond “keywords” - align with questions & decisions

In an AI-first interface, users will ask complex, multi-step questions (“Which vacuum works best on pet hair and hardwood floors within £300?”). The system will parse that, fetch data, compare, and answer.

Action: Audit your content and product pages for decision-stage queries (vs. simple product names). For example:

• “Compare X vs Y for [use case]”

• “Which is better for [condition], X or Y?”

• “How to maintain [product] over time”

• “Best X for under £XYZ”

Align content depth with filter/attribute depth, which aligns with situations/issues/solutions your customer may be looking for, for example, rather than just focusing on content around the topic of ‘leggings’, think about the subcategories and attributes below these, e.g.:

• Leggings for the gym

• High-waisted leggings

• High-waisted leggings for the gym with pockets

2. Make your structured data & product feeds airtight

The “brain” Stein describes leans heavily on Google’s internal graphs: e.g. the Shopping Graph (50 billion products updated continuously).

If your product feed is inaccurate, missing attributes (size, colour, stock, warranties), or out of sync, you risk being misinterpreted, omitted or misrepresented.

Action: Ensure your product schema (JSON-LD / schema.org markup) is clean, complete and updated in real time. Monitor feed errors in Merchant Center constantly.

Ensure your PDP attributes and hard data are as comprehensive as possible. Tools like Emfas can help to research and pull these in on mass across PDPs, e.g., see it in action here.

https://www.ellis-brigham.com/scarpa-mens-mescalito-trk-pro-gore-tex-walking-boots-244185903

3. Optimise for citation, not just ranking

In AI Overviews or AI Mode answers, Google will often cite or “source” content. That means even if your page doesn’t appear in the classic top-10, it might still be referenced in an AI response.

Action:

• Structure your content to be clear, modular (e.g. short definitions, bullet points)

• Use headers that directly match likely question phrases

• Cite your claims with sources, data, or internal/external references

• Consider including “answer boxes” or summary modules that are easily digestible

• Invest in offsite citation building to maintain a competitive advantage and get coverage on sites LLMs, and Google see as reputable

4. Embrace visual & video content as part of search

Because search is becoming multimodal, visuals carry more meaning. Image search, Lens, and AI Mode could surface your product images or how-to visuals directly.

Action:

• Invest in high-quality product visuals, annotated images, and explainer videos

• Use descriptive alt text, structured captions, and markups to help Google index image content (AI tools can generate alt text on mass, too)

• Include “how-to / unboxing / comparison” videos embedded in product or category pages

• Source on YouTube and leverage product tagging

5. Support conversational paths & follow-ups

AI Mode isn’t just a static answer — it expects to allow follow-up queries (“Wait, what about price difference?” or “Show me reviews?”) If your content supports that “conversation,” it’s more likely to be surfaced.

Action: Map out the likely “threads” a user might take (first question → follow-up → deeper drill-down). Then structure your content (or “related questions / internal links”) to accommodate those paths.

In the SEO world, we call this query fan out analysis https://www.semrush.com/blog/query-fan-out/

6. Monitor “AEO / GEO / IEO” but don’t abandon SEO fundamentals

When it comes to GEO, AEO, GXO or whatever you want to call it this week, Stein suggests that while the interface is changing, the fundamentals still matter - authority, intent matching, originality, trust.

Action:

• Don’t ditch keyword strategy — continue mapping high-intent keywords, but consider them as part of the overall customer journey

• Invest in brand authority, backlinks, and domain strength

• Continually test and iterate content for usefulness, clarity, and freshness

This is the topic of an upcoming Substack post. In the SEO world, “GEO” isn’t really a thing / it’s accepted, it’s just SEO. However, in the eCommerce world, GEO is sticky and won’t go away. Join us as we conduct a deep dive to give you an idea of how we see this working together or where “GEO” fits in vs. “SEO”.

Sample Structure for a Product Page in the AI Era

Here’s a suggested layout to better integrate into AI-driven search:

1. Headline / question

2. Short summary answer

3. Key comparison table

4. Sections for likely follow-ups

5. User FAQs (phrased as natural questions)

6. Visuals / annotated images / short video - including UGC

7. Product reviews

8. Calls to action or comparison to alternatives

If your content frequently aligns with how AI will parse and respond, there’s a better chance it’s surfaced within the AI responses, even when it might not dominate traditional SERPs.

Final Thoughts & Next Steps

The shift Stein describes is structural - not just aesthetic. Search is transforming from a list of links into a conversational information layer layered over Google’s knowledge graph, shopping data, and web content.

For eCommerce brands, that means being proactive: aligning content with queries and decisions, enriching structural data and visuals, and supporting deeper conversational threads. We outlined this a few weeks back here, where we are using personas more than ever for AI strategies: https://ecommerce.thisisnovos.com/p/situational-search-how-to-get-your?r=2qo9mq.

Sources and deeper reading:

• SEJ source and reference

• Longer podcast recording here

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