Enterprise Generative Engine Optimization for Retail Giants: Getting Recommended in ChatGPT + Google AI Shopping

Retailers can’t afford to treat generative AI as “another referrer” bolted onto the same keyword playbook: the interface itself is changing. We’re moving from search terms to conversational UIs and, increasingly, agentic shopping experiences where the assistant doesn’t just rank options: it recommends, builds the cart, and can even complete checkout inside the chat. Walmart is already leaning into that future on two fronts: an OpenAI partnership that brings Walmart (and Sam’s Club) shopping into ChatGPT with Instant Checkout, and a Google partnership that connects Walmart’s catalog into Gemini via Google’s Universal Commerce Protocol, designed to let AI agents handle discovery-to-checkout natively in chat.

The headline for marketers is clear: when the “storefront” becomes the conversation, the winners won’t be the brands with the best keyword coverage, they’ll be the brands whose product and brand knowledge is most retrievable, interpretable, and recommendable by these agents. And while Walmart’s ChatGPT program launched publicly on October 14, 2025, Walmart has not yet publicly disclosed results (e.g., incremental conversion, GMV, CAC, retention) attributable to these new AI shopping experiences. However, this is a key indicator and shift that Chief Marketing Officers need to start considering and preparing for in 2026 and 2027.

Key Takeaways

From Keywords to Conversations: Why “AI Shelf Space” Is the New SERP Real Estate

Search visibility used to be a contest over query coverage: own the right keywords, win the click, then convince the user on-site. But in conversational and agentic interfaces, the most valuable “real estate” is now AI shelf space: being one of the few products or brands the assistant chooses to surface when it answers, compares, and increasingly completes checkout on the user’s behalf. That shift changes what it means to “rank,” and it changes where marketing influence begins.

What Changes When the Assistant Owns Discovery and Checkout

In classic search, the “battle” happens after the query: users scan results, open tabs, and self-navigate to a decision. In conversational UI, the assistant becomes the interface layer between the customer and the catalog: it interprets intent, filters options, summarizes tradeoffs, and increasingly executes the transaction without sending the user to ten different pages. Google is already pushing in this direction with in-chat shopping flows in Gemini (browse → recommendations → purchase), reducing the need to “click out” to a retailer site at all.

This shift is happening at scale because user adoption and usage volume are massive. By July 2025, OpenAI research reported ChatGPT had been adopted by ~700 million users (about 10% of the world’s adult population) and users were sending ~18 billion messages per week. And OpenAI disclosed that ChatGPT was already processing 2.5 billion prompts per day by mid-2025, meaning consumers are training themselves to express needs as natural-language problems, not keyword strings.

The New Funnel: Ask → Compare → Decide → Buy (without a click)

The practical implication is that precision targeting now starts at the discovery layer. Users don’t begin with “best running shoes” as a keyword, they begin with a problem-shaped prompt like: “I have flat feet, knee pain, and need shoes for standing all day under $150, what should I buy and why?” That prompt already contains segmentation, constraints, and intent and the assistant can carry those constraints through comparison and checkout.

You can see the behavioral change showing up in early commerce signals:

For CMOs, this reframes the goal: it’s no longer just “rank for keywords.” It’s earn recommendation eligibility inside the assistant’s reasoning step, before a brand short-list forms. In that world, the most valuable real estate isn’t position #1 on a SERP, it’s being one of the few options the model trusts enough to recommend when a customer describes their situation in full.

What LLMs Actually Need to Recommend a Product

When a generative engine recommends a product, it’s rarely “just generating.” Under the hood, most production-grade shopping experiences behave like a RAG (retrieval-augmented generation) system: the model uses its intrinsic (parametric) knowledge learned during training, plus extrinsic (retrieved) knowledge pulled at query time from catalogs, feeds, policy pages, reviews, and other sources that can be searched and injected into the model’s context. This architecture exists because models can’t reliably “know” your current price, inventory, shipping constraints, or variant availability from training alone and because grounding answers in retrievable sources improves factuality and reduces hallucinations.

That means recommendation eligibility is less about “having a product page” and more about whether your product knowledge can be retrieved, verified, and stitched to a user’s problem in the few seconds the system has to answer.

Verifiable Attributes: SKU, price, availability, shipping, location

For commerce, the first gate is truth. If an agent can’t confidently resolve core facts, it either won’t recommend or it will hedge with vague options. The minimum viable attribute set typically includes:

Why this matters in RAG terms: retrieval systems match the user’s prompt to candidate documents via semantic similarity and metadata. If your “offer truth” is incomplete or inconsistent across feeds/pages, the system can retrieve conflicting evidence and modern grounding approaches explicitly exist to tether outputs to verifiable data sources.

“Use-case Evidence” (spec sheets, how-to content, compatibility, safety)

Once the agent trusts the offer, it has to answer the question the user is actually asking: “Will this solve my problem?”

This is where retailers and brands often under-invest, because their catalogs are spec-heavy but context-light. RAG systems perform best when they can retrieve authoritative, structured, problem-shaped evidence such as:

This isn’t “content marketing” for its own sake: it’s retrieval fuel. The original RAG framing is literally about combining parametric knowledge with a non-parametric external memory that can be updated and cited without retraining the model.

“Decision Support” (pros/cons, alternatives, sizing, edge cases)

In conversational commerce, users don’t want 40 blue links, they want a shortlist with reasoning. So the winning product knowledge includes the pieces an agent needs to explain a recommendation, not just name it:

RAG is designed for “knowledge-intensive” questions where the model must pull the right facts and apply them in context. If you don’t publish decision-support artifacts, the system either (a) can’t retrieve them or (b) has to improvise, which is exactly the failure mode grounding is meant to reduce.

UGC that Matters: Reviews Written Like Mini Case Studies

Most reviews are sentiment (“love it!”). Agents need evidence: what problem the buyer had, what they tried, what worked, and what didn’t. High-signal UGC tends to include:

In retrieval terms, these reviews create query-matchable language that mirrors how people prompt (“I need X for Y under Z constraints”). And because RAG “injects external context at runtime,” review corpora can become a live, continuously updating knowledge layer that improves recommendation confidence without retraining the base model.

The Catalog Is the Content: Turning 10M+ SKUs Into Machine-Readable Knowledge

At enterprise scale, “content” isn’t a blog program, it’s your catalog. For generative engines, the catalog becomes a knowledge base that must be retrievable, consistent, and grounded. The challenge: most 10M+ SKU ecosystems were designed for browsing and faceted filters, not for reasoning systems that need clean entities, verifiable attributes, and canonical truth.

SKU-level Entity Modeling (attributes, variants, bundles, substitutions)

Common problems: what breaks recommendations

Simple solutions: what generative teams can implement

How to sequence it (so it’s feasible at 10M+ SKUs)

Normalizing Messy Attributes (units, naming, missing fields)

Common problems: why catalogs fail under conversational prompts

Simple solutions: how to normalize without boiling the ocean

What to optimize for (LLM reality check)

Avoiding “Attribute Hallucinations” with Canonical Sources

Common problems: where misinformation comes from

Simple solutions: how to create trustable “truth layers”

90-day Rollout Plan for Generative & Search Teams

Phase 1: pick where AI recommendations matter most

Phase 2: make “recommendation eligibility” measurable

Phase 3: fix the blockers first

Merchant Feed Optimization for LLM Retrieval (Not Just Google Shopping)

Merchant feeds matter in generative commerce for the same reason they matter in Shopping: they’re a clean, structured, “light payload” representation of your catalog (IDs, attributes, offers, availability) designed to be parsed deterministically and kept fresh. 

Google’s own Merchant Center spec frames this product data as the foundation for matching products to queries and notes that missing/inaccurate data can create issues that prevent products from serving. And as Google pushes agentic commerce via the Universal Commerce Protocol (UCP), explicitly positioned to enable AI interactions to become purchases inside Gemini/AI Mode, the “feed layer” becomes even more central.

The catch: feeds are typically optimized for transactional correctness, not semantic persuasion. They often lack the same use-case language, edge-case handling, and decision support that product pages (and supporting content) provide. So the strategy is “both/and”: feed = fast retrieval + grounded facts, pages/AXP/content = meaning + reasons to buy.

Feed Completeness as a Ranking Lever (coverage, freshness, consistency)

Here is why LLM shopping systems care about this.

Google’s Merchant Center spec is explicit that price and availability are shown in ads and free listings, and if they change often “you’ll need to let us know” to keep products accurate. Google also recommends using the Content API when price/availability updates frequently. 

And for local inventory feeds, Google notes you must update at least once per day to keep information accurate.

Common problems generative teams run into

Simple solutions (enterprise-friendly)

Attribute Enrichment: Turning “Specs” into “Reasons to Buy”

Merchant feeds are excellent at “what it is” and “can I buy it,” but weak at “why it’s the right choice for this prompt.”

Generative discovery starts with a problem statement (“best stroller for tight trunk space,” “air purifier for wildfire smoke + pets,” “drill bits that won’t snap on stainless”). If your feed only contains raw specs, you force the model to infer the match. That’s where semantic alignment breaks.

Common problems

Simple solutions

Important caveat: Feeds rarely carry the full semantic depth that pages do. That’s why you pair feed enrichment with page-level evidence (spec sheets, how-to, UGC) and/or an agent layer (AXP/UCP endpoints) that can supply richer context when the assistant needs reasoning support.

Variant Strategy: When to Split vs Consolidate (color/size/pack count)

In chat, users often specify the variant in the prompt (“size 11 wide,” “black, 2-pack,” “left-handed”). If your variant structure is messy, the agent struggles to (a) retrieve the right offer, and (b) explain the choice.

Google’s spec even calls out variant handling in guidance like including distinguishing features (color/size) and references item group ID behavior for variants.

Common problems

Simple rules that scale

Operational safeguards

Structured Data Isn’t Optional Anymore: It’s Your Product’s Resume

In agentic shopping, structured data is how you turn a product page into a machine-readable contract of sale: what the item is, what it costs, whether it’s available, how it ships, and what happens if it’s returned. Google is explicit that Product + Offer markup can make pages eligible for merchant listing experiences and can surface key details like price, availability, shipping, and returns directly in Search surfaces. 

Even beyond Google, this same “resume” effect matters for LLM retrieval: structured data is fast to parse, unambiguous, and easy to reconcile against feeds.

Product, Offer, AggregateRating, ShippingDetails, ReturnPolicy

What to include (minimum viable “recommendable” markup):

Simple implementation principle: If your feed says “in stock / $49.99 / delivers in 2 days,” your schema should match. Misalignment creates trust issues for both search features and agentic systems.

How-to + FAQ for Product Usage (and when not to use FAQ)

How-to content: still valuable as retrieval evidence for “how do I use this?” moments (setup, sizing, care, compatibility). The key is to keep it anchored to the product and avoid fluff.

FAQ: use it selectively

When not to use FAQ markup

“Source of Truth” Markup Strategy for Enterprise Catalogs

At 10M+ SKUs, the goal isn’t perfection, it’s consistency and governance:

Agentic Shopping Changes the Rules of Conversion and Attribution

When product discovery, comparison, and checkout can happen inside ChatGPT or Gemini, the customer journey stops behaving like a clickstream and starts behaving like an inference stream: a sequence of questions, constraints, shortlists, and decisions mediated by an assistant. The result is a measurement reset: attribution models that assume “session → product page → checkout” will undercount influence.

When Checkout Happens in-chat, What is a “Visit”?

In agentic flows, the “visit” may never occur or it may occur after the decision is already made. A user can ask for recommendations, refine constraints, build a cart, and complete purchase in the chat interface.

What changes for marketers:

Measuring Influence: Assisted conversions, View-through, Incrementality

If the assistant is increasingly the decision layer, you need measurement that captures influence as well as last-click outcomes:

This is the same conceptual shift that happened in paid social: attribution moved from “clicks” to “incremental lift.” Agentic commerce pushes that shift into organic discovery.

New KPIs: recommendation rate, inclusion rate, AI share-of-shelf

To manage AI visibility like a channel, teams need a few simple, repeatable metrics:

Why these are practical: they map directly to the new surfaces being created (in-chat shopping, agentic protocols, and retailer integrations) and give CMOs a way to measure progress before “sessions” catch up.

Personalization Is the Quiet Disruptor: The Rise of AI “Retargeting” Without Ads

The biggest change in AI shopping isn’t just that the interface is conversational, it’s that the interface is personal. As assistants gain permission to use account context, preferences, and history, recommendations shift from “best overall” to “best for you.” This isn’t speculative: Google has long described and patented personalization in search results based on prior behavior, and newer filings extend that idea into LLM-powered responses using user features and privacy-preserving approaches. The outcome looks a lot like retargeting, except it happens organically inside the assistant’s recommendation step, often before a user ever reaches a retailer site.

Purchase History + Preferences → Precision Recommendations

In classic SEO, relevance is inferred from the query. In conversational shopping, relevance is increasingly inferred from the query + the user. A prompt like “best running shoes” becomes far more precise if the assistant can incorporate signals such as prior purchases (brand affinity), size, budget bands, delivery expectations, or even category sensitivities (e.g., “latex-free,” “pet-safe,” “fragrance-free”).

Google’s patent filings (US20240403564A1) describe approaches for producing personalized responses to textual prompts using user features (with an emphasis on privacy-preserving methods), and related work on user embedding models signals how systems can represent user context in a way that’s usable for personalization at inference time.

What this means for GEO and search teams: recommendation eligibility becomes conditional. You’re not just optimizing to be “best,” you’re optimizing to be the best match across many micro-contexts (budget, location, urgency, compatibility, prior behavior).

Implications for Brand vs Retailer Private Label Visibility

Personalization tends to reward whoever has the strongest first-party signals and the tightest feedback loop between intent → purchase → learning. Retail giants naturally have an advantage here: they can connect transaction history, on-site behavior, store behavior, and fulfillment preferences to the assistant experience. 

That creates a real risk for national brands: if an agent is optimizing for “best outcome for this shopper,” retailer private labels can win by default when they’re (a) in-stock, (b) fast to ship, (c) competitively priced, and (d) historically “worked” for similar users.

Brand-side counterweights you can control:

Privacy, Consent, and What Marketers Can Control (and can’t)

Personalized AI shopping will be constrained by what users consent to share and what platforms permit. Marketers don’t control the assistant’s personalization logic, but you can control the inputs that make your products “safe to recommend” when personalization is applied.

Control (practical levers):

Less control (realities):

Net outcome: as assistants become more personalized, “visibility” isn’t just about ranking, it’s about being the most compatible option for a user’s context and being grounded in data the system can trust. Google’s own personalization patents, spanning classic personalized search to LLM-personalized responses, make the direction of travel hard to ignore.

AXP Files as an “Agent Interface Layer” Between Your Site and LLMs

One emerging pattern in GEO for enterprise retailers is adding an “agent interface layer,” a lighter, more structured representation of your site that’s designed for AI retrieval bots and agentic systems. Scrunch’s Agent Experience Platform (AXP) is a well-known example of this approach: it creates a parallel AI-ready version of your site that’s served to AI agents (not human visitors), mapped to sources of truth, and optimized for LLM consumption.

Why Pages are Slow: Crawl, Parse, Interpret, Resolve Conflicts

Even when your content is “indexable,” modern product pages are often a difficult environment for AI agents to consume quickly:

AXP’s pitch is essentially: strip away what AI doesn’t value and restructure pages into AI-friendly formats when AI traffic is detected. (This aligns with the broader “Agent Experience” movement: agents need content and rules that are clear, predictable, and unambiguous to operate reliably. )

Why AXP Can Accelerate Reasoning + Inclusion

In practice, an agent layer can help in three lightweight ways:

1) Faster “first-pass” understanding

Instead of making the model wade through a full webpage, AXP-like layers provide compressed, structured content that’s easier to parse and reason over.

2) More consistent grounding (less contradiction)

If the agent layer is explicitly mapped to your sources of truth (PIM, offer/inventory APIs, policy pages), it can reduce the chance the assistant retrieves stale or conflicting facts.

3) Better “answer packaging” for agents

AXP describes using rules and restructuring to make content more “LLM legible,” which can increase the likelihood of being included when the system is assembling a shortlist quickly.

Where AXP fits with feeds, schema, APIs, and content hubs

Think of this as a layered stack (each layer does a different job):

The Enterprise Scaling Problem: Optimizing 10M+ SKUs Without Burning the House Down

At 10M+ SKUs, GEO work stops being “SEO + new channel” and becomes data engineering + merchandising + governance. RAG-style systems work best when they can retrieve authoritative, current, consistent product truth from external knowledge sources (not just what the base model learned during training). 

But the catalog reality inside most retail giants is fragmented: multiple PIMs, vendor feeds, regional inventory systems, marketplace imports, and legacy content, each capable of publishing conflicting “truth.”

Prioritization Models (revenue, margin, prompt demand, LTV cohorts)

The most common workflow failure is trying to “fix the catalog” broadly and ending up with nothing shipped.

A workable prioritization model combines:

Why this matters for generative systems: RAG pipelines are only as good as the retrievable store, so you want to improve the “agent-visible” surface area where it will actually move the needle, first.

Where agencies can help: an agency can act as a specialized program office (standing up prompt taxonomies, measurement, category scoring models, and a “recommendation readiness” backlog) without pulling internal teams into a multi-quarter replatform.

Automated Enrichment Pipelines + Human QA Loops

At scale, enrichment has to be automated, but retail data is messy enough that fully automated pipelines will create new errors (especially in compatibility, safety, and variant logic). Human-in-the-loop QA is a standard pattern for maintaining data integrity in AI pipelines because humans catch edge cases and contextual failures that automated checks miss.

A simple, scalable pattern:

This aligns with common PIM best-practice guidance: prioritize accuracy, completeness, and continuous auditing so product data stays reliable across channels.

Where agencies can help: agencies are often better positioned to run the “middle layer” (enrichment logic, prompt-driven attribute requirements, QA sampling plans, weekly readiness reporting) while internal teams own source systems and approvals.

Governance: Preventing Drift Across Taxonomy, Attributes, and Pricing

Even after you “fix” catalog readiness, it will drift (new vendors, new attributes, renamed taxonomies, promo pricing chaos, regional availability logic).

Minimum viable governance for Generative Engine Optimization:

This mirrors modern data governance thinking: governance exists to protect data integrity and keep systems scalable as complexity grows.

Trust Signals LLMs Reuse: Policies, Proof, and Post-Purchase Clarity

RAG improves accuracy by retrieving from an authoritative knowledge base at inference time. But beyond “grounding,” trust signals influence the reasoning layer: when an assistant chooses what to recommend, it has to weigh risk (returns, failures, compliance issues) alongside relevance. In practice, policies and proof act like “decision stabilizers,” they make a recommendation easier to justify.

Returns, Warranties, Availability, and Fulfillment Reliability

These aren’t just conversion details; they’re recommendation confidence inputs:

When RAG systems pull external info to improve reliability, the most “useful” sources are often the ones that reduce downside risk for the user. If two products are similar, the assistant’s reasoning tends to favor the option with fewer unknowns and clearer post-purchase expectations.

Safety, Compliance, and Documentation for Regulated Categories

For categories like baby, health, electrical, hazmat, supplements, or tools:

In RAG terms, these documents are high-authority retrieval targets that reduce hallucination risk and improve response faithfulness, especially in “knowledge-intensive” questions. The assistant can’t responsibly recommend without them.

Brand Credibility and Customer Support as Ranking Inputs

There’s a quiet shift happening: supportability becomes part of “best.” Assistants can (and increasingly will) prefer brands/retailers that are:

Because RAG is fundamentally about retrieving from trusted sources to generate more reliable outputs, the sources that are repeatedly consistent and authoritative become “safer” to reuse.

Where agencies can help: trust signals often live across silos (legal policy pages, CX knowledge bases, merchant ops, PDP templates). An agency can inventory and standardize these “trust artifacts,” align them to category prompts, and create a publish-and-govern workflow so the reasoning layer has the materials it needs to confidently recommend, especially in high-consideration or regulated categories.

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