How Retailers Can Improve Their Visibility in ChatGPT, Google Gemini & Co

AI assistants like ChatGPT, Perplexity, and Gemini are becoming “research engines” that shape what people buy long before they visit a website. For retailers, visibility now means: when a shopper describes their needs in natural language, your products and your store should be among the options the AI recommends.^1^3

This article explains how online retailers can improve that visibility, with a special focus on product data enrichment.

1. How ChatGPT‑style shopping actually works

When a user types “I need a waterproof hiking jacket for winter in the Alps, under 250 CHF,” ChatGPT:

• Interprets the intent and constraints (use case, climate, budget).^1
• Scans structured product feeds and the open web for products that match.^2
• Cross‑checks your product pages, reviews, and other signals to verify details, trade‑offs, and trustworthiness.^3
• Returns a small consideration set with descriptions, comparisons, and links to merchants.^1

Research on ChatGPT Shopping and similar features highlights three main input layers:^1

• Merchant and product feeds (ID, title, price, availability, attributes, reviews, media).
• Your website pages (PDPs and categories) for context and verification.
• The wider web footprint: reviews, expert articles, user discussions, brand and retailer authority.

For retailers, the implication is clear: you must optimize both the technical data you send and the semantic story your site and the web tell about your products and your store.^2

2. Retailers vs. brands: different roles in AI recommendations

Brands (manufacturers) and multi‑brand retailers both care about AI visibility, but they influence different parts of the decision.^5

Roles in the AI shopping funnel

• Brands try to get their models into the AI’s shortlist (“Which air purifier should I consider?”).
• Retailers try to be the place to buy once a product is picked (“Where should I buy this air purifier?”).^5

Some industry practitioners have observed that ChatGPT often prioritizes retailer listings first as sources, then manufacturer sites, then community forums. For retailers, that’s a major opportunity—if your digital shelf is clean and rich, you can win a disproportionate share of AI‑driven traffic.^1

General strategic differences

• Brands focus on: product authority, canonical specs, comparisons vs. alternatives, and expert content.^6
• Retailers focus on: catalog completeness, price and availability accuracy, structured feeds, and strong merchant‑level trust signals.^7^1

The rest of this article focuses on what retailers specifically should do.

3. Foundations: technical discoverability for AI assistants

Before enrichment and content, AI systems must be able to crawl and parse your store.^4

3.1 Make your site accessible to AI crawlers

Guides on LLM optimization for ecommerce consistently stress that blocking AI‑related crawlers makes you invisible to next‑generation shopping features.^1

Key actions:

• Check robots.txt so it does not unintentionally block major AI/LLM crawlers (e.g. those used by ChatGPT, Perplexity, and others).^1
• Ensure product and category pages are not behind logins or paywalls.
• Avoid hiding critical product details behind client‑side rendering only; prerender or server‑render important pages so crawlers see full HTML.^1

3.2 Align website pages with feeds

ChatGPT Shopping and similar systems use merchant feeds together with your website pages to validate data.^4

They cross‑check:

• Titles, prices, images.
• Availability and variants.
• Key attributes and who the product is for.^1

If your feed says one thing and your PDP says another (different price, specs, or availability), your products may be down‑ranked or skipped.^2

4. Merchant feeds and LLM‑ready product data

Merchant and product feeds are emerging as a core input into AI shopping and agentic commerce protocols.^4

4.1 What a strong AI‑ready feed contains

Analysis of ChatGPT Shopping and related features shows they rely on feeds that include at least:^2

• Product ID and canonical title.
• Rich description.
• Price and currency.
• Availability/stock status.
• Images (multiple if possible).
• Attributes (size, color, material, technical specs).
• Performance and trust signals (ratings, reviews count, returns rate where available).^1
• Rich media where supported (video, 3D models).^1

AI‑optimization guides emphasize feed completeness and consistency as one of the most important ranking levers for ChatGPT Shopping.^4

4.2 Feed quality vs. classic SEO

Unlike traditional SEO, where you can sometimes rank with imperfect structured data, AI shopping features are much more feed‑driven and data‑sensitive.^4

Retailers should:

• Fill every required and recommended field in feed specs (for OpenAI/ChatGPT, Google Merchant Center, and any partner networks).^4
• Keep prices and availability updated frequently (hourly or at least daily).
• Ensure attribute names and values are consistent across categories.

5. Product data enrichment: why it matters more for retailers

Product data enrichment—making product records more complete, accurate, and semantically rich—is now a central lever for AI visibility.^8

5.1 What product data enrichment is

In ecommerce, enrichment means you take a basic record (SKU, short title, price) and add:^8

• Detailed attributes (size, material, performance metrics, compatibility).
• Clean, descriptive titles and bullet points.
• Use‑case and persona descriptions (“best for small apartments,” “for sensitive skin”).
• Identifiers like GTIN/EAN/UPC, MPN, brand, category codes.
• Rich media: multiple images, videos, manuals.

This allows AI systems to match your products to long, conversational prompts instead of simple keywords.^8

Performing this enrichment manually across thousands of SKUs is rarely feasible. Specialized product data enrichment services such as Aramis use AI to standardize and enrich supplier data at scale, transforming raw catalog records into structured, AI‑ready product information.^9

5.2 Why retailers, specifically, need deep enrichment

Brands typically enrich their own catalogs, but retailers aggregate many brands with wildly different data quality.

Retailers need to:

• Normalize attributes across brands so AIs can compare products meaningfully (e.g. one “energy efficiency” scale, one “fit” taxonomy).
• Fill gaps when brand data is missing or poor (no composition, no exact dimensions, no local compliance info).
• Add shopper‑centric attributes derived from behavior and returns (e.g. “runs small,” “quiet,” “easy assembly”).

AI shopping guides explain that ChatGPT looks for attributes that align with real prompts—audience, use case, constraints, and benefits. Retailers are uniquely positioned to enrich data with these because they see cross‑brand behavior at scale.^5^1

6. How brands vs. retailers should enrich data (and what retailers should prioritize)

Brands and retailers both enrich data, but with different priorities. For a retailer, understanding the split helps you decide what to do in‑house vs. what to demand from suppliers.

6.1 Brand‑side enrichment (for context)

Brands aim to make each product universally intelligible across all retailers:

• Very deep technical specs and performance attributes.
• Canonical identifiers (GTIN, MPN, official naming).
• Positioning content (“who it’s for,” “how it compares to previous model X”).^6
• Rich semantic descriptions and FAQs in brand voice.
• PIM‑driven distribution of this enriched data to retailers and marketplaces.

6.2 Retailer‑side enrichment (your real lever)

Retailers need to merge and extend brand data into a coherent, AI‑ready catalog:^1

• Harmonize attributes
  • Define internal attribute taxonomies (e.g. “fit,” “warmth,” “noise level,” “sustainability”).
  • Map every brand’s fields into those dimensions (even if they use different terminology).
• Fill missing data
  • Scrape or import specs from manufacturer sites or PDFs where allowed.
  • Use AI tools to extract attributes from text and images (e.g. sleeve length, heel height, room size coverage). Platforms like Aramis can ingest supplier files in formats ranging from Excel and PDF to supplier portals, automating this extraction and normalization step.^9
• Add shopper‑centric & contextual attributes
  • “Best for” tags (commuting, camping, home office, studio flats).
  • Climate, room size, skin/hair type, experience level where relevant.
  • Label products based on behavior data (high repeat purchase, low return, popular for gifting).^5
• Enrich content for intent
  • Ensure each PDP explains audience, use case, constraints (budget, size, environment), and benefits in natural language.^1
  • Add FAQs that mirror how shoppers ask questions in ChatGPT.^1

6.3 Side‑by‑side: enrichment priorities

7. Structuring product pages for conversational intent

Multiple analyses show that ChatGPT is not just answering “buy now” queries; a large share of prompts are upper‑funnel research questions. This means your PDPs and category pages must read like helpful sales advisors, not just database extracts.^3

7.1 Optimize for intent, not SKU codes

Guides on optimizing for ChatGPT Shopping recommend structuring PDPs around the same elements ChatGPT uses in its own product research answers:^2

• Audience – who this is for (persona, level, body type, household type).
• Use case – what situations it fits (commuting, winter hiking, home office, studio flat).
• Constraints – budget, space, climate, voltage, regulation, fabric care, allergies.^1
• Benefits & trade‑offs – what it does well and where it’s not ideal.^2

These are exactly the things shoppers describe to AI assistants; if your product pages speak that language, they are more likely to be selected and even quoted.^1

7.2 On‑page structure and schema

AI visibility guides stress having clear content structure and structured data:^1

• Use headings, bullet lists, and comparison tables on PDPs.
• Add FAQs addressing common questions and objections, ideally with structured markup where supported.
• Implement schema.org Product and Offer (and where appropriate, Review/AggregateRating) with fields aligned to your enriched attributes.^4

8. Reviews, trust, and external authority

LLM‑based shopping systems incorporate reviews, ratings, and external authority into their ranking and narrative.^3

8.1 On‑site reviews as structured signals

Retailers should:

• Encourage detailed, authentic reviews that describe use cases and trade‑offs (not just star ratings).^3
• Display and mark them up so crawlers can parse ratings, counts, and snippets.
• Reduce friction for reviews after purchase, and capture photos and Q&A where possible.

These become both data points (scores, volume) and text corpora that LLMs can mine for themes (“great for small spaces,” “too noisy for bedrooms”).^3

8.2 Off‑site citations and expertise

LLM optimization research emphasizes that AI assistants prefer to cite trusted domains and cross‑reference multiple sources.^5

Retailers can:

• Build presence on review platforms and comparison sites relevant to their vertical.
• Encourage customers to leave reviews on Google, Trustpilot, niche platforms.
• Pitch their guides and comparison content to be referenced in “best of” lists and expert articles.^1

This helps AI systems see your store as a reliable, widely recognized merchant, not just a lone website.

9. Practical enrichment blueprint for retailers

Putting it all together, here is a pragmatic, retailer‑centric blueprint that combines technical and semantic enrichment.

9.1 Step‑by‑step process

Drawing from ecommerce data‑enrichment and AI‑shopping guidelines:^8

1. Audit your current state
   • Export your catalog and flag missing fields (identifiers, attributes, images, descriptions).
   • Identify your top categories and top‑value SKUs to prioritize.
2. Define your internal attribute model
   • Decide the attributes that matter most for your categories (e.g., warmth rating, noise level, energy class, fit, skin type).
   • Map existing brand data into this model and list the gaps.
3. Enrich the data
   • Pull specs from manufacturers where possible.
   • Use AI tools to extract attributes from existing text and images at scale. Product data enrichment services (e.g. Aramis) can automate much of this process, reducing manual effort by up to 90% and accelerating time‑to‑market for new SKUs.^9
   • Manually refine top SKUs, especially in high‑consideration categories.
4. Rewrite PDP content around intent
   • Add audience, use case, constraints, and benefit sections to your descriptions.^1
   • Include FAQs phrased like real ChatGPT queries (“Is this too loud for a small bedroom?”).
5. Align feeds and on‑site data
   • Ensure your feeds contain the same enriched attributes, prices, and availability.^4
   • Keep them updated and monitor feed errors.
6. Implement and validate structured data
   • Add or improve Product and Offer schema with enriched attributes.^4
   • Validate regularly to avoid errors and inconsistencies.
7. Monitor AI visibility and iterate
   • Regularly test key prompts in ChatGPT (“best X for Y in [country]”) and see which products and merchants appear.^1
   • Compare against your analytics (AI‑referral reports where available) and adjust titles, attributes, and content.^3

9.2 Example: retailer enrichment vs. brand data

10. Continuous optimization in an evolving AI landscape

AI recommendation systems are not static. Studies of ChatGPT Shopping stress the need for a continuous optimization loop:^3

• Monthly or quarterly, test a set of core prompts in ChatGPT and other assistants.
• Track which of your products and pages are being surfaced.
• Update titles, bullets, FAQs, and enrichment fields on underperforming SKUs.
• Expand successful patterns to adjacent categories and products.^1

Retailers that treat LLM‑driven discovery as a measurable channel—rather than a black box—will be best positioned as conversational and agentic commerce become mainstream. For teams looking to operationalize this at scale, working with a dedicated product data enrichment partner such as Aramis can help maintain catalog quality continuously as supplier data changes and AI ranking signals evolve.^7^1

If you share your main categories and approximate catalog size, we can turn this into a concrete, category‑specific attribute matrix and a prioritized 90‑day roadmap tailored to your situation as a retailer.