Happy Friday!

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

01 · AI Overviews now show on 43% of searches, up from 15% a year ago

What it means for you: Close to half your category's searches now resolve inside Google, which means the organic session count you are likely still reporting on every month is measuring a shrinking slice of demand, not shrinking demand. Slowly start to move your metrics to focus on share of voice/citation score, showing whether you are cited, on which prompts, and against whom, before your board reads a traffic dip as a market dip and cuts the budget that's still working.

02 · Google is running ads on nearly a third of commercial keywords in AI Mode, and the advertisers buying them are rarely cited in the answer

What it means for you: In SE Ranking's sample, the advertiser's domain appeared in AI Mode's cited sources around 11% of the time and the exact landing page under 2%, so paying to sit next to the answer buys you precisely nothing inside it. Budget and measure them as two separate things… media buys the slot, digital PR and GEO buy the citation, and the brands winning this quarter are holding both at once rather than assuming one funds the other.

03 · Google changes how budget-capped bidding works on 17 August - if your campaigns beat their targets, your costs are about to go up

What it means for you: Any Target CPA or Target ROAS campaign flagged "Limited by budget" has been outperforming because the cap, not the target, was doing the work but from 17 August the algorithm spends that headroom to hit the number you actually typed in, so a campaign running at 600% ROAS against a 400% target drifts down to 400%. Look at every budget-limited campaign this week and either reset targets to reflect real 90-day performance or deliberately raise the budget. On a thin-margin catalogue, that drift is going to be your profit.

To read more:

1. The 2026 Generative AI Landscape — Similarweb

2. Google AI Mode Ads: What the data shows — SE Ranking

3. Google Ads’ Target-Based Bidding Update For Ecommerce — Search Engine Journal (Google’s own note on the change)

Worth your time

AI Search Has Killed the Channel Silo: our Paid Media Director Benoit Legendre on why SEO, GEO, paid, creative and product feed have stopped being separate channels, plus the AI-ready feed blueprint and the Clarins numbers.

Coming up: THRIVE Fashion: Be the answer™

Wednesday 16 September · The Line, 58 Victoria Embankment, London

A morning of keynotes, panels and live demos on how fashion brands are being discovered now across Google, AI search, social, marketplaces and communities. Sessions on why fashion discoverability is breaking, the tech foundation of discovery, the paid and creative engine, digital PR for LLM presence, and retention and returns. Places are limited, and registration is subject to approval.

Request Your Free Place

Happy Friday!

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

01 · Google adds Category and Sale Duration markup: your PDPs can now speak Google's product taxonomy directly

What it means for you: You can finally put Google's own product category (GPC) and exact sale start/end dates into your on-page schema instead of leaning solely on the Merchant Center feed, which kills feed/page mismatches and stops a "20% off" showing in search after the sale's ended. Get your dev or Shopify app to add the <CategoryCode> and <validFrom/validThrough> properties before your next promo, or you're leaving accurate, higher-CTR listings on the table.

02 · Mass-produced AI content keeps collapsing in Google, and it's crawl economics - not a vibe

What it means for you: Spinning up thousands of near-identical AI pages buys a freshness spike then dies, because Google throttles crawl budget on sites whose demand and authority don't justify the page count and Scaled Content Abuse penalties are landing hard. If your growth plan leans on programmatic AI pages, cap the volume and focus on genuine information gain, or you'll be rebuilding the whole site after a manual action.

03 · Reddit and major publishers are threatening to pull their content from Google as AI Overviews eat the click

What it means for you: The publishers who feed Google's answers are revolting because AI Overviews now resolve the query on the page. The move isn't to chase the vanishing click, but it's to become the source cited inside the answer, which means quotable, authoritative content and getting mentioned on the publications LLMs still trust.

To read more:

01: Google's New Merchant Listing Structured Data (Category + Sale Duration) — Google Search Central
02: Scaled AI Content Often Fails & Google's Crawl Economics Explain Why — Search Engine Journal
03: Reddit and publishers weigh restricting Google's AI access as search traffic dries up — The Wall Street Journal

From NOVOS

Want more? Here are recent reads of our clients:

1. The Google ads brand trap

2. How your CRO tests impact agents

3. AI Search has killed the channel silo

Your Invite to The Ivy Victoria ✨

Following our Paid Media webinar last week where almost 100 eCommerce leaders were in attendance, we’d love to invite you to join us at The Ivy Victoria for a morning with Benoit, our Paid Media Director, as we continue the conversation around scaling eCommerce performance in 2026.

🗓️: Thursday 6th September

🕗: 8:00am - 10:30am

📍: The Ivy Victoria

🎟️: Free - enjoy breakfast on us!

Interested in joining us? Places are limited.

Request Your Seat →

Last week, we hosted a webinar attended by nearly 100 eCommerce leaders to unpack how AI is fundamentally changing digital marketing.

This Substack article distils the discussion into a practical guide, with the key insights and frameworks you can apply immediately. As a subscriber, you also have direct access to the full webinar recording below.

Watch the full YouTube video replay →

For years, eCommerce brands have treated their marketing departments like separate, disconnected kingdoms. The creative team works in a visual vacuum, the paid search agency manages keywords on an isolated spreadsheet, the SEO team focuses strictly on blogs, and product data is left as a dry afterthought for tech operations.

If you are running that playbook today, you are actively burning your margins.

In this era of algorithmic, content-driven ad platforms and conversation-based search engines, the silos between SEO, GEO, Paid Search, Creative, and Product Data must be entirely demolished. They are no longer separate, independent channels. They are tightly dependent components of the same system. If your asset production and your backend product data are not speaking the same persona language, the loop breaks, and your brand quietly disappears from search results.

Here is the strategic blueprint to building a closed-loop growth engine that dominates modern AI search, based on the frameworks from our recent webinar.

The brands winning in 2026 are spending more are running better systems

Generating Demand with Meta Andromeda

To understand why traditional funnels are fracturing, you have to look at how top-of-funnel discovery has evolved over the last year. Audience demographic configuration and manual interest selection are becoming obsolete. With the rollout of Meta’s Andromeda algorithm, the platform utilises advanced probabilistic AI models to analyse your actual ad creative and decide who to dynamically serve it to.

The new reality: Your creative asset is your targeting mechanism.

If your team uploads the same core creative concept 20 times with minor, surface-level text changes, Meta’s algorithm recognises it as a single identity. It triggers immediate creative fatigue, fragments your spend, constricts your audience reach, and sends your CPAs soaring.

Winning the top-of-funnel today requires true Creative Diversity. Brands must implement a systemised creative loop (Plan, Produce, Test, Learn) that feeds Meta a constant stream of distinct visual angles.

Read more about the creative loop

This means blending authentic creator content (to build consumer trust) with AI production tools to scale vertical variations and unique hooks cleanly without an agency-sized production bill.

Looking at the Forthglade cold-pressed salmon SKU examples above, generated with NOVOS Studio’s proprietary static ad generation tool, you can see how a single product can be placed into a range of distinct visual contexts. For Meta’s Andromeda algorithm, each variation may function as a different creative entry point, helping the same item reach different audience pockets.

Capturing Demand via Google AI Max & GEO

Generating high-intent demand on paid social media is only half the battle. If your capture mechanisms are siloed, you are simply paying to send traffic away.

Modern search has fundamentally transformed. Consumers are no longer typing simple, broad keywords into a box; they are asking highly granular, natural-language, long-tail questions.

To capture these high-intent buyers, Google relies heavily on AI Max and Performance Max (PMax) to algorithmically route conversational outcomes. But AI Max does not work without context. It acts as a matchmaking engine that requires an integrated foundational architecture of traditional SEO, GEO (Generative Engine Optimisation), Site Content (PDPs), and your Product Feed.

If your SEO team optimises your website PDPs, but your product feed is generic, Google’s AI cannot reliably bridge the gap. You will quietly lose conversions to competitors whose data ecosystem is entirely unified.

(Performance Tip: Stop bleeding margin by over-spending to protect your own brand keywords on Google Search. Manage your brand campaigns down to a clean 70% to 80% Search Impression Share. Anything above 80% yields heavy diminishing returns, and you can never reach 100% anyway. Reinvest that freed-up capital into other areas like your Non Brand campaigns or creative engine instead.)

Read more about how to save budget from brand traffic →

The AI-Ready Feed Blueprint

A basic product feed with only a title, price, and stock status is no longer just an operational limitation. It creates a major data gap.

Poor product data leads to “silent drops.” When a feed relies on thin descriptions, missing attributes, and generic copy, products can quietly disappear from next-generation conversational auctions in AI Mode and Search, including the new ad formats expected later this year.

To fix this, your product data must match the semantic depth of your creative strategy.

Following the monumental Google Merchant Center updates, merchants can pass deeply complex, structured contextual information directly into their backend data schemas.

If you want your brand to be cleanly indexed by modern conversational AI search assistants, you need to map these exact fields at a SKU level:

The Rule of Coherence

Modern AI search systems do not look at just your website or just your product list by themselves. They check both at the same time to make sure they match.

True optimisation in 2026 requires coherence. The hook running on your Meta ad, the text on your PDP, the search optimisation on your site, and the conversational attributes in your backend product feed should all communicate the same persona.

When your entire data stack is unified, the results are undeniable. Take a look at the Clarins Case Study from the webinar: by utilising advanced feed optimisation and automated product attribute mapping, they eliminated repetitive tasks and unlocked search volume. Athos Commerce is the feed management platform that helped them achieve these results. This framework resulted in a +168% increase in impressions, a +155% increase in clicks, and an explosive +322% increase in revenue.

Join Us In Person

London eCommerce Growth Roundtable Breakfast: August 6th, 2026

Ready to roll up your sleeves, break down the channel silos, and map out your actual execution methods? We are hosting an exclusive, closed-door eCommerce Growth Roundtable Breakfast on Thursday, August 6th, 2026 at The Ivy Victoria, London.

This morning session is designed specifically to look past surface theories and dive straight into the practical methodology. Over breakfast, we will run an extended, technical Q&A session with fellow brand founders and industry growth experts. Space is strictly capped to ensure every attendee gets direct answers to their funnel constraints.

Click here to register your interest →

Get Your Free Paid Media & Feed Audit

If you suspect your paid media setup, product feed, creative, or tracking may not be fully aligned for AI-led search and performance marketing, let us take a look.

We are offering a free Paid Media & Feed Audit covering Google Ads, Meta, Product Feed, Creative, Tracking, and other areas relevant to your setup. We will map your current ecosystem, identify the biggest gaps, and show you where to improve efficiency across the funnel.

Claim your Free Paid Media & Feed Audit here →

Watch the full YouTube video replay →

Happy Friday!

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

01 · LLM ads mention your competitors

What it means for you: A recent webinar we attended shared that 17% of GPT ads also mention a direct competitor. GPT ads won’t just be expensive; they will also be highly competitive.

02 · Gemini drives 140% increase in downstream traffic

What it means for you: AI mentions drive real downstream traffic. After an AI assistant mentions a brand, users visit that brand’s website at 1.5–2.5x their forecasted baseline rate over the following 7 days. eCommerce is one of the highest-impact industries. Measuring referral traffic is important, but measuring in isolation completely misses the impact LLM platforms have on your brand (we discuss this topic in our next Substack deep dive).

03 · Changes to target-based bid strategies

What it means for you: If your Google Ads bidding is being passively managed, you could see a major drop in performance from the 17th of August. Action: If your actual CPA is lower (or ROAS higher) than your target today, update your tCPA/tROAS before 17 August, or Google will optimise back towards the less efficient configured target.

To read more:

1. https://www.tryprofound.com/blog/the-ai-mention-effect#over-7-days-ai-mentions-drive-up-to-1.5-2.5x-brand-site-visits

2. https://support.google.com/google-ads/answer/17061251?hl=en

3. Bonus reading: if you are thinking of scaling content with AI, read this first: https://www.searchenginejournal.com/scaled-ai-content-often-fails-googles-crawl-economics-explain-why/581325/

From NOVOS

Want more? Here are recent reads of our clients:

1. The Google ads brand trap

2. How your CRO tests impact agents

3. Creative is the new targeting

Your Invite to The Ivy Victoria ✨

Following our Paid Media webinar this week, we’d love to invite you to join us at The Ivy Victoria for a morning with Benoit, our Paid Media Director, as we continue the conversation around scaling eCommerce performance in 2026.

🗓️: Thursday 6th September

🕗: 8:00am - 10:30am

📍: The Ivy Victoria

🎟️: Free - enjoy breakfast on us!

Interested in joining us? Places are limited.

More Details & Request Your Invite →

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 now shows how your social and video posts perform in Search

What it means for you: Google's new "platform properties" let you verify your Instagram, TikTok, X and YouTube profiles inside Search Console and see the exact queries pulling people to that content, essentially meaning that your social posts just became measurable search real estate, not a black box. Verify your profiles and start treating social as a discovery channel you can actually report on, because for the first time you can prove (or disprove) that your TikTok content is doing real search work.

02 · Google's John Mueller: stop building separate "files for AI" - the crawlers ignore them

What it means for you: llms.txt, llms-author.txt, Cloudflare's Content-Signal header and mirrored markdown pages have been further confirmed by Mueller that Google and the major crawlers won’t prioritise if a site has it. What actually gets you into LLMs hasn't changed: being genuinely cited/spoken about across authoritative sites and building a really strong store are the core priorities.

03 · ChatGPT Ads add audience targeting, so the answer engine is now a real ad platform

What it means for you: OpenAI is rolling out audience uploads (hashed emails and phone numbers) inside ChatGPT Ads, on top of AI-generated ad creative, meaning discovery on ChatGPT specifically is splitting into earned (get recommended in the answer) and paid (buy your way in), exactly like Google did fifteen years ago. This is being rolled out one by one alongside Creative & Tracking. Get your first-party data house in order now because it may mean running the same dual play you run on Google - be the organic answer and own the paid slot above it.

To read more:

1. Search Console now reports social & video performance for Instagram, TikTok, X and YouTube — Google Search Central Blog

2. Google on using markdown (and other “for AI” files) for AI SEO — Search Engine Journal

3. ChatGPT Ads gain new audiences targeting — Search Engine Roundtable

From NOVOS

If you haven’t yet, we’d love it if you signed up to our next THRIVE Webinar. Benoit, our Paid Media Director, and John Brolly from Athos Commerce will discuss why creative is the new targeting and how SEO foundations, PDPs and product data now feed your paid performance as one connected system.

Register Now

Auditing Google Ads accounts, I keep finding the same issue across every industry and budget size: brand traffic mixed with acquisition traffic, hidden inside Performance Max or buried in Search campaigns. The numbers look fantastic but the business isn’t achieving incremental growth.

That’s the real question behind every account review: do we want Google Ads to look good in-platform, or do we want to grow the business? They’re not always the same thing.

What “Branded Traffic” Actually Means

Branded traffic is any search containing your company or product name. Someone searching “Nike Pegasus” or your brand name directly already knows you exist; they’re finding you, not discovering you.

That distinction matters because branded and acquisition campaigns have different jobs. One protects demand, the other creates it. Google doesn’t separate them. It just maximises whatever goal you give it, and if that’s ROAS, it will default to the easiest conversions available: branded search.

Should You Bid On Your Own Brand?

It depends. If you’re a global brand selling through your own site, Amazon, retailers, and resellers, competitors and marketplaces are likely bidding on your name too. Protecting that space is worth it.

If you’re a growing ecommerce brand selling only direct, check Auction Insights. If nobody else shows up on your brand terms, you’re likely paying for clicks that would have reached you organically anyway.

Test it: reduce branded budget significantly, or pause it briefly. Watch organic traffic, total revenue, and whether conversions simply shift from Paid to Organic Search. If those hold steady, you’ve freed up some budget for actual customer acquisition.

We recently did this for one of our fashion clients, saving an estimated £15K per month, a budget that can either fund SEO investment, be reinvested into non-brand or upper funnel channels.

The Biggest Mistake I See

It’s not running branded campaigns; it’s mixing branded and non-branded traffic, usually inside Performance Max.

Example: your PMax campaign has branded searches converting at 30x ROAS and generic at 6x. Google flags it as “Limited by budget,” you increase spend, conversions rise, ROAS looks great. But that extra budget went to branded traffic, the path of least resistance for the algorithm. The dashboard improves. The business doesn’t.

What Marketing Mix Modelling (MMM) Tells Us

MMM looks beyond platform attribution, and the pattern repeats: branded search has relatively low incrementality. It still protects demand and market share, but much of that traffic would have converted anyway.

The campaigns driving real incremental growth- generic Search, generic Shopping, competitor campaigns- usually show the weakest platform metrics: lower ROAS, higher CPA. That’s because they’re finding customers who weren’t already looking for you.

The Account Structure I Recommend

One principle: separate intent.

• Competitor Search

This lets you report Brand and Non-Brand separately, see exactly where budget goes, monitor Impression Share independently, and stop branded traffic from masking acquisition performance.

How I Manage Branded Search & Shopping

How I manage branded Search and Shopping

Branded Search is a profitability and defence campaign, not a scaling one. Spend only what’s needed to own the results.

Impression Share targets

• Business as usual: target 70-80%. Above 80%, returns diminish fast, and pushing toward 95-100% is self-sabotage. You can’t own every auction, so stop trying and redirect that budget where it works harder.

• Peak periods (e.g. Black Friday): allow Search IS up to around 85%, to capture demand with an irresistible offer directly in the ad, something organic can’t do.

Match type

• Search Brand: prioritise exact match. Phrase or broad match expands reach exactly where you don’t want it, brand is about lowering CPC and protecting visibility, not maximising reach.

• AI Max: avoid it on brand traffic, since it’s built to widen reach and match. Use it on Search Non-Brand only, where expanding reach is the actual goal.

Bidding

Run Search Brand on Manual CPC, not automated strategies, since the goal is profitability and control, not scale. Two rules keep it simple:

• If Impression Share falls below 80-85%, increase bids by around £0.03.

• If it climbs above 95%, decrease bids by around £0.04.

Done consistently, this keeps the campaign lean and defends the range above, rather than letting Google chase 100% coverage.

What To Expect

Managed this way, branded Search should run at 70-100 ROAS consistently. It’s your profit engine and defence layer, not a growth lever, so keep it efficient and leave the acquisition budget for campaigns actually built to find new customers.

Shopping

Run a dedicated branded campaign with aggressive negative keywords to keep generic queries out (scripts can automate this daily). Shopping has no Manual CPC, so manage profitability through tROAS:

• If Impression Share falls, relax the tROAS target by 5 or 10%.

• If Impression Share holds, tighten the tROAS target by 5 or 10%.

Neither campaign is about volume. Both are about efficient protection.

Existing Customers Don’t Need A Branded Ad

This is most obvious with subscription businesses, but it applies to any brand with repeat purchasers. Brand protection still matters for prospects comparing you to competitors pre-signup or pre-purchase. Existing customers are different: someone who’s bought from you repeatedly doesn’t need a paid ad every time they type your name into Google. They’re logging in, reordering, or coming straight back to a site they already know.

If your CRM is solid and customer lists are refreshed regularly, exclude existing customers from branded campaigns. You still defend the brand for prospects while cutting spend on people who’d find you regardless. It’s another confident, low-risk way to reduce brand budget: for subscription businesses with high repeat purchase rates it can free up a meaningful acquisition budget, but the same logic holds for any brand with a loyal repeat customer base.

Where to Reinvest What You Save

Say you currently spend 25% of your budget on Brand and manage to cut it back to 15%. That’s 10% freed up. Where you put it matters more than the cut itself, and here you’ve really got two directions to choose from.

Reinvesting it into an unoptimised PMax Non-Brand or Search Non-Brand campaign probably won’t move the needle; those channels capture existing demand; they don’t create it. So the real decision is this: are you playing for the long game or the short one?

If you’re playing the long game, pull that budget out of paid entirely and put it into SEO. It takes longer to show up, but it builds an asset that keeps paying you back long after the campaign ends, unlike paid spend, which stops working the moment you stop paying for it.

If you want results sooner, keep the budget in paid media but move it upper funnel, into Demand Gen and YouTube. This is where you build brand equity and create the demand that Search and Shopping will capture later. It won’t compound the way SEO does, but you’ll see the effect faster.

The catch with the upper funnel route is that it only works with strong creative, ideally a proper creative system rather than one-off assets. If you want to go deeper on building a creative system that scales for 2026, we’re running a webinar on this on 15 July: luma.com/orsd3ywm.

SIGN UP HERE →

Five Rules To Remember

1. Separate intent, always. Brand and Non-Brand should never share a campaign. If Google can find the easy conversion, it will take it every time, so don’t give it the chance.

2. Branded Search defends; it doesn’t grow. Keep Impression Share at 70-80% in business as usual, manual CPC, exact match only. A 70-100 ROAS here is expected, not impressive- it’s a profit engine, not proof of growth.

3. Test before you assume you need brand spend. Check Auction Insights, briefly pause the branded budget, and watch what happens to organic traffic and total revenue. If conversions shift from Paid to Organic, you were paying for clicks you’d have gotten anyway.

4. Exclude existing customers from branded campaigns. They don’t need a paid ad to find you; they’re already coming back. This is one of the lowest-risk ways to free up budget.

5. Reinvest with intent, not by default. Don’t let freed-up budget drift into unoptimised Non-Brand campaigns. Choose SEO for compounding, long-term growth, or upper-funnel paid media (Demand Gen, YouTube) if you need results sooner. Both beat leaving it in Brand.

One Final Thought

Google isn’t trying to grow your business; it’s maximising whatever objective you’ve set. If that’s ROAS, it will always favour the easiest conversions, and branded traffic is usually the easiest conversion you’ll ever buy.

The question isn’t whether your account delivered a 20x ROAS. It’s whether your marketing created incremental growth. Optimise for one or the other, they’re rarely the same choice.

Happy Friday!

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

01 · LLMs are driving discovery your analytics can't see and your post-purchase survey is the only place to catch it

What it means for you: Our good friends Vervaunt and their founder Paul Rogers has shared data showing LLM-sourced discovery rising sharply, yet almost none of it lands in analytics. Shoppers meet you in ChatGPT, then convert via search or direct, so the LLM never gets the credit. Add an "AI assistant / ChatGPT" option to your post-purchase and NPS "how did you hear about us?" questions now, so you hold first-party proof of AI-driven demand before anyone asks you to justify AEO/GEO spend.

02 · ICYMI: Shopify's Spring '26 Edition puts AI-channel sales, and the searches you're invisible for, into your Admin

What it means for you: Genuinely useful, but Catalog only ranks what it can read, so if your feed calls one colour e.g. "Sage," "Eucalyptus" and "Soft Green", then the dashboard tells you you're invisible without telling you why. Clean, structured product data stops being a nice-to-have and becomes the price of entry to agentic commerce. The panel only counts purchases on Shopify's own surfaces, not whether the model recommends you in the first place.

03 · Google Merchant Center's conversational attributes are a first-mover feed play for AI shopping answers

What it means for you: Google now wants conversational feed data, i.e. FAQs, product highlights and richer detail to match products to intent like "a boot for muddy hikes" instead of head-term keywords, and so many brands aren’t filling them in. The six globally available attributes are a feed job you can roll out this quarter, and they're how you get surfaced in AI Mode and Gemini answers before your category catches on.

To read more:

1. Paul Rogers on LLM attribution & Census — Vervaunt

2. Selling everything, everywhere, all at once: The Spring '26 Edition — Shopify

3. How to use conversational attributes — Google Merchant Center Help

From NOVOS

If you haven’t yet, we’d love it if you signed up to our next THRIVE Webinar. Benoit, our Paid Media Director, and John Brolly from Athos Commerce will discuss why creative is the new targeting, and how SEO foundations, PDPs and product data now feed your paid performance as one connected system.

Register now

A creative system sounds like a large, complicated build. It isn’t. Audit what you already do against the method below, and the blind spots get obvious: the places where your creative quietly leaks performance.

On Meta, and most creative-led channels, creative is now the lever. Delivery is AI-decided, so the auction reads intent from the creative itself, not the audience you pick. Your ad is your targeting. Building a system to produce, test and learn from creative isn’t just a nice-to-have; it is the job.

Most brands already run part of one. They plan, produce, test and report. The gap is rarely worth the effort. It is that the loop doesn’t close, and they can’t see where. The most common blind spot: insights get written into a monthly report or a QBR deck, and die there. They never reach the next batch of creative, so nothing compounds.

Now the practical challenge. No single product runs the whole loop yet. The platforms offer strong production tools (Meta Ads Manager, Google Asset Studio, TikTok’s suite), but they operate in silos, lacking shared memory or feedback into your next brief. Third-party tools connect more, but they cost money and rarely integrate cleanly. So you build the loop with tools, with custom systems, or a mix, to fit your budget. It runs manually, semi-automated or fully automated, and the method holds at every level.

This is the map. We walk it with live examples at our THRIVE Paid Media webinar on 15 July. Save your seat here: https://luma.com/orsd3ywm

The four stages are Plan, Produce, Test, Learn. The point isn’t the boxes; it is that they feed each other: closing the loop is where the value lies. Volume alone won’t do it; the platforms read twenty headline-swaps as one ad and reward genuine diversity instead.

1. Plan

Without a plan, you can’t learn, because you can’t isolate what you tested.

The first question brands ask is how many new creatives they need a month. The honest answer is that it depends, and the best starting point is your current creative-led budget: the more you spend on channels like Meta, the more distinct creative you need to feed them.

You can read more on sizing creative volume in this piece:

AI Search Knowledge for eCommerce Leaders: SEO + Digital PR.

Creative Is Your Targeting Now. Are You Producing Enough of It?

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…

Read more

3 months ago · 5 likes · 1 comment · Benoit Legendre

From there, study what is already working in your market, then write a brief grounded in your brand and your personas.

Manual method: browse the Meta Ad Library, TikTok Creative Center and Google Ads Transparency Center, keep a swipe file, and write briefs from your brand and persona notes.

Tool-assisted method: platforms like Foreplay, Atria, Gethook and Kalodata speed up the research, and an AI assistant can draft briefs from your brand context.

Blind spot: a brief that is a vibe, not a testable hypothesis.

2. Produce

Diversity is the game, not the same ad twenty times. One ad with five headlines isn’t five ads; the algorithm reads it as one.

But not every change counts as new. Big swings, like format, concept, a new product category or the offer, unlock genuinely new reach. Smaller refinements, like the message, headline or call to action, sharpen a winner without reaching new people. The hook is the exception: the highest-impact single element on any ad. So produce across the big swings, rather than twenty versions of one idea. AI fills the volume gap; creators fill the trust gap.

Manual method: brief creators directly, edit in Canva or CapCut, and source creators through outreach.

Tool-assisted method: AI video and UGC generators like Arcads or makeugc, alongside creator marketplaces like TRIBE or Insense.

Blind spot: making more of what’s comfortable, rather than what’s genuinely different.

3. Test

You have produced different assets; testing is how you discover which ones work. This depends on one unglamorous requirement: a naming convention. Tag every asset by collection, concept, hook, format, creator and version, so you can slice the performance data later and answer the only question that matters: which worked, and why? Run tests always-on, with a fixed share of spend going into validation.

You don’t have to tag by hand. Build the naming convention into a spreadsheet that generates the names for you, and apply them with the same tool you use to upload creative to Meta in bulk, rather than adding each asset one by one. Set the convention up once, and every batch stays consistent and sliceable.

Manual method: a naming-convention spreadsheet & manual upload.

Tool-assisted method: a bulk-upload tool that carries your naming convention into Meta, plus creative analytics that auto-tag and report (e.g. Motion, Markifact).

Blind spot: changing several things at once, or failing to tag assets at all, which leaves you with results that are impossible to read.

4. Learn

This stage separates a system from a treadmill, and it is the one most brands get wrong. Set rules for signal versus noise: decide when an ad is still learning, when it is a winner to scale, when to watch, and when to kill. Then, feed what you learned into the next brief. This connects back to the blind spot mentioned at the start. The insight lives in a deck and never reaches the creative. Reporting is not learning. A report tells you what happened, but learning changes what you do next.

Manual method: create a one-page rules document and conduct a weekly review of an Ads Manager export. Decide what to scale or kill, then write the next brief.

Tool-assisted method: use creative-analytics dashboards like Motion or a custom build that reads the Meta API.

Build It Block by Block

You won’t have this overnight, and you don’t need to. Each stage is a block you can stand up on its own. Start where it hurts most, usually the intelligence in Plan or the feedback loop in Learn, because those are the two most often missing. Get one working, then connect the next.

For each block, choose the level that fits: do it manually, buy a third-party tool, or build something custom. A small team running the loop by hand can beat a bigger competitor whose loop stays open.

Then it compounds. A one-off winner is a good week; a system that learns is a widening lead. Every cycle sharpens the next brief, while brands with open loops keep starting over. That is the one edge in paid media you build rather than rent.

Creative is the lever. The system is how you pull it, again and again, a little better each time.

Want to Know More?

We are walking through this whole method, end-to-end, with live examples, at our THRIVE Paid Media webinar on 15 July. We will also cover the half we didn’t get to here: capturing the demand your creative generates, through AI Max, product data and your site alongside Athos Commerce

SAVE YOUR SEAT HERE →

Happy Friday!

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

01 · Shopify’s own data: shoppers from AI convert est 50% higher and spend 14% more than traditional organic search

What it means for you: Shoppers from LLMs convert est 50% higher and carry 14% bigger orders because the research now happens inside the chat, so the LLM compresses the whole journey and drops a ready-to-buy customer straight onto your product page (over half of LLM sessions start on a PDP vs 20% for organic). The buying decision is made before they ever reach your site, so being the LLMs recommended pick is the entire game plan and that’s heavily steered by your offsite authority (reviews, editorial, forums etc), not your homepage authority.

02 · Google’s CEO calls falling search referrals “evolution”, while the world’s biggest publishers plan as if search is zero

What it means for you: When Condé Nast tells its teams to assume search traffic is zero and Google’s CEO calls the same trend a natural evolution, I’d back the publisher’s read, not the platforms. Don’t wait for Google to admit the decline and reduce your dependence on its referrals now by building your owned audience and LLM visibility, so your demand isn’t hostage to a channel its owner won’t call “shrinking”.

03 · Google quietly switches on server-side GA4 tracking for Shopify in July and skips the bigger prize

What it means for you: It’s a welcome fix for the 10–20% of purchases that browser tracking loses, but it’s narrow as it’s purchase events only, Google’s ecosystem only, and your YoY numbers will look inflated once recovered revenue lands, so don’t rip out your existing setup. The real tell is the omission as there’s nothing for server-side Google Ads, which is the far bigger opportunity, because that’s what unlocks view-through attribution and proper use of Google’s device graph for your paid performance.

To read more:

1. AI-referred shoppers convert better and spend more — Shopify

2. Google CEO Sundar Pichai downplays Google Zero concerns — Search Engine Journal

3. Google silently routes Shopify purchases into GA4 — attribution gaps remain — PPC Land

From NOVOS

This is exactly the shift we’re running our next THRIVE webinar on. Benoit, our Paid Media Director, and John Brolly from Athos Commerce will discuss why creative is the new targeting, and how SEO foundations, PDPs and product data now feed your paid performance as one connected system. It’s the same theme as items 01 and 03 above when you think about it: the brands winning are wiring organic, paid and LLM visibility into one strategic engine.

Register for our webinar

This post covers two topics:

1. The LLMs.txt file has been around for a long time. Google guidelines say don’t worry about it, but now they’ve said it can help.

2. CRO tactics and their impact on agents. HBR research breaks down positive change, no change and negative impact on agents.

WTF Is an llms.txt & Why Won’t It Go Away?

This is a topic that has been around for over 12 months. Originally, it felt like a hack or hype tactic to boost performance in ChatGPT. There was little evidence to support LLMs actually engaging with the file or sourcing it in answers, which is why we’ve never recommended it as a priority for clients.

It even says in Google’s guidelines, directly referencing ignored hacks like llms.txt:

However, over the past few weeks, Google released having llms.txt as a test to be agentic-ready in their Lighthouse audit, and John Muller confirmed it is a thing, but not how the industry (or LinkedIn) was claiming.

Correct - It’s not a discovery tool to hack your way to success.

Instead, he says it is more of a call to action for agents to read and reference when they visit the site.

For Shopify brands, Shopify is creating this as a default for you.

Just add the following URLs at the end of your Shopify domain to see it > /llms.txt

e.g., https://www.wearetala.com/llms.txt and https://yumove.co.uk/llms.txt

To make this slightly more confusing, Shopify has recently been endorsing agents.md. All Shopify sites also have this file live here > /agents.md

e.g. https://www.wearetala.com/agents.md and https://yumove.co.uk/agents.md

What’s the Difference Between agents.md and llms.txt?

Both files are identical, minus this line of text:

Agent discovery: the canonical agent-facing description of the store is at \/agents.md. You're reading /llms.txt, which mirrors that content.

This means that Shopify is referencing the agents.md file as the main original source. Likely just keeping LLMs.txt live as a fallback or backup just in case.

What If I Had a Custom llms.txt File?

Shopify would have overwritten this now. If you still want to maintain a custom LLMs.txt file, there is a workaround - you can see my social post here.

What If I’m Not on Shopify?

Non-Shopify sites, we’d recommend creating a file yourself and replicating a similar logic to the Shopify files linked above. Any clients of ours, we’ll be briefing these in and liaising with the developers when it fits in the priority queue.

Is It Going to Make a Difference?

Dan, our Delivery Director, phrased it as “It’s probs something you should be doing “just in case” anyway. If it could feasibly be good for AI, you should probably do it, although it’s not a super high priority,” - ever the poet.

Want More on This Topic?

This topic and more were discussed on our AI Search webinar recently, the link is here if you are interested in watching back:

Dan led this webinar with our friends at Glara and our client Abhishek from YuMove.

How Does CRO Impact Agents?

HBR research suggests that many eCommerce CRO tactics designed for people do not work reliably on AI shopping agents.

As agents shape more purchase decisions, tactics like scarcity, countdown timers, vouchers, strike-through pricing, and bundles can be ignored or even reduce selection depending on the model and category.

Here’s a summary of their research findings:

POSITIVE IMPACT (Reliably worked)

• Star ratings - the only tactic that consistently increased selection across all 4 AI models and product categories

• Social proof - purchase counts showed relatively stable effects (though not as consistent as ratings)

NO IMPACT / UNSTABLE (Unreliable)

• Strike-through pricing - produced different effects depending on model and product category

• Countdown timers - no stable pattern

• Vouchers - variable effects across models

• Bundling - showed mixed/unstable results; in at least one case, it reduced selection

NEGATIVE IMPACT (Backfired)

• Advanced reasoning models (GPT-5 and Gemini 2.5 Pro) appeared to penalise overt persuasion tactics, interpreting them as low-quality or manipulative signals

• More aggressive promotional tactics are increasingly becoming counterproductive with advanced models

• Scarcity cues (”Only 2 left!”) - more advanced models like GPT-5 actually reacted negatively in some categories

• Price - higher prices reliably reduced selection (as expected for both humans and AI), caveat being relative to the category being shopped, e.g., luxury comes with higher prices relative to competitors

Takeaway: The fundamentals matter most (competitive pricing + authentic reviews). Everything else requires model-specific testing and ongoing monitoring.

Across thousands of simulations, only star ratings consistently increased choice, and price consistently reduced it; other persuasion cues were unstable and often backfired in advanced models. Treat each model as a distinct segment, focus on fundamentals (competitive pricing and authentic reviews), and keep testing as models evolve.

Article here for additional reading > https://hbr.org/2026/05/research-traditional-marketing-doesnt-work-on-ai-shopping-agents?autocomplete=true&giftToken=1938012211781628298053

Thanks for reading. As always, we focus on non-algorithm hacking content, so we only grow through word of mouth. Please share this with anyone you think it will add value to.

And to address the elephant in the room: yes, we’ve had a glow-up ✨

You’ll have noticed things looking different around here this month as we’ve rebranded. Same team, same mission, sharper focus: NOVOS has grown from an SEO agency into an eCommerce growth agency, helping brands get found across search, AI, and paid channels.

More on what’s coming across events, roundtables, merch and new THRIVE drops in the weeks ahead. Be the answer™.

Speaking of which… you’re invited 🎟️

Join our Paid Media Director, Benoit Legendre, and John Brolly, Head of Partnerships at Athos Commerce, on 15 July as they unpack what it takes to scale eCommerce performance in 2026:

✅ How Meta’s evolving algorithm is changing the way eCommerce brands scale
✅ Why creative is no longer a volume challenge, but a systems challenge
✅ A practical framework for a creative system that drives weekly learnings — and faster iteration than your competitors
✅ How to get more from AI Max by connecting SEO, product data, PDPs and onsite content as one growth system

Sign up here →

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.