GEO for Ecommerce in 2026: How to Get Mentioned by AI Shopping Engines

GEO for ecommerce: what it means and why it matters now

GEO for ecommerce — Generative Engine Optimization — is the practice of making your products visible inside AI-generated answers. When a shopper asks ChatGPT "what's the best waterproof hiking boot under $200" or uses Google AI Mode to compare running shoes, the AI constructs an answer from sources it trusts. If your product data is not structured for retrieval, you are invisible in these conversations.

This is not a hypothetical shift. Around one in five American shoppers already use AI platforms to search for products. ChatGPT processes over 72 billion messages per month. Google AI Mode is expanding from labs into mainstream search. Perplexity, Amazon Rufus, and Microsoft Copilot are all building shopping experiences where the AI recommends, compares, and sometimes even transacts on behalf of the user.

The brands that win in this environment are the ones whose product data is complete, structured, and consistent across every surface the AI might crawl. That starts with your catalog.

For a look at how Amazon's AI assistant is already changing the funnel, see our coverage of Amazon Rufus and agentic shopping.

What AI engines actually read from your catalog

AI shopping engines do not browse your site the way a human does. They extract structured signals from multiple sources and synthesize them into a response. Understanding what they read helps you prioritize what to optimize.

Primary data sources

• Product pages (crawled HTML) — the title, description, specs table, and schema markup on your PDP. If your site renders content with JavaScript and the AI crawler cannot execute it, the content does not exist for that engine.
• Structured data (JSON-LD) — Product, Offer, AggregateRating, and BreadcrumbList schema give AI engines machine-readable facts. Price, availability, brand, SKU, and review data in schema markup are high-signal fields.
• Product feeds — Google AI Mode draws from Merchant Center feed data. If your feed is complete and accurate, your products are more likely to appear in AI-generated shopping results.
• Third-party sources — review sites, comparison platforms, Reddit threads, and forum posts. AI engines cross-reference your product data against what others say about it.

What fields matter most

Not all product data is created equal for GEO. Prioritize:

• Structured attributes — material, dimensions, weight, compatibility, certifications, energy class. These are the facts AI uses to answer comparison queries.
• Concise, specific descriptions — AI engines favor content that makes precise claims ("650-fill-power down, waterproof to 10,000mm") over vague marketing ("premium quality, ultimate comfort").
• Consistent pricing and availability — if your price on the PDP, in the feed, and on a marketplace listing differs, the AI loses trust in your data.
• Reviews and ratings — aggregated review data (especially via schema markup) helps AI engines assess product quality and surface your products for "best of" queries.

How to structure your catalog for AI retrieval

Optimizing for GEO is not a separate project from good product data management — it is the same work, done with more precision. Here are the structural changes that make the biggest difference.

Make every PDP a self-contained answer

Each product page should contain everything an AI engine needs to evaluate and recommend the product, without requiring the model to visit multiple pages:

• Full spec table with key attributes
• Clear, factual description (not just a marketing paragraph)
• Price and availability in both visible text and schema markup
• Shipping and return policy summary (or link)
• Review data with aggregate rating

Think of your PDP as a data sheet that a research assistant can scan in seconds. If the key specs are buried in a PDF, hidden behind a tab, or rendered only via JavaScript, the AI will not find them.

Invest in attribute completeness

AI engines handle comparison queries by matching structured attributes. "Best noise-cancelling headphones with USB-C under $300" requires the AI to filter on: noise cancellation (yes/no), connector type (USB-C), and price (under $300). If your listing is missing the connector type, your headphones are excluded.

Tools like Lasso can enrich missing attributes by pulling specs from manufacturer data, supplier feeds, and web sources — filling the gaps that make your products invisible to AI matching.

For a deeper dive into which attributes to prioritize, see our guide on attribute enrichment for sellable listings.

Maintain cross-source consistency

AI engines aggregate data from multiple sources. If your Google Shopping feed says "in stock" but your PDP says "pre-order", the engine may flag the inconsistency and deprioritize you. Ensure that:

• Prices match across your site, feeds, and marketplace listings
• Availability is synchronized in near real-time
• Product names and brand names are consistent
• Images are the same across channels

Building brand authority for AI citations

GEO is not just about structured data on your own site. AI engines also decide which brands to cite based on authority signals across the web.

Get mentioned in trusted sources

AI models weight citations from authoritative sources. For ecommerce, that means:

• Review sites and comparison platforms — getting your products reviewed on Wirecutter, RTINGS, or niche comparison blogs increases the chance of being cited.
• Forum and community mentions — Reddit, specialized forums, and Q&A sites are heavily indexed by AI engines. Organic discussion of your products feeds the model's knowledge.
• Press coverage — product launches, industry awards, and feature coverage in trade publications build entity recognition.

Publish expert content on your own site

Beyond product pages, your blog and resource content contributes to GEO. Content that is structured, factual, and answers specific questions performs best:

• Buying guides with clear comparison tables
• How-to content with specific product recommendations
• FAQ pages that directly answer common shopping queries

This content does double duty: it ranks in traditional search and gets cited in AI-generated answers. For more on how product data powers both search types, see our article on on-site search vs SEO.

Measuring GEO performance

Unlike traditional SEO, GEO does not have a universally adopted analytics framework yet. But you can track meaningful signals:

• Brand mention monitoring — tools like Ahrefs Brand Radar, Otterly.ai, and manual spot-checks on ChatGPT, Perplexity, and Google AI Mode can show whether your products are being cited.
• Referral traffic from AI platforms — Google Analytics can segment traffic from AI sources. Look for referrers like chatgpt.com, perplexity.ai, or traffic from Google AI Mode (which may appear as organic with different engagement patterns).
• Feed performance correlation — if your Merchant Center feed is strong and your products start appearing in AI-generated shopping results, you should see it in impression data.
• Conversion rate by source — early data suggests that AI-assisted sessions convert at 2-3x higher rates because the AI has already addressed the shopper's objections before they arrive at your site.

Start with qualitative monitoring (are your products mentioned?) before investing in advanced tooling. The measurement ecosystem is still evolving.

Getting started with GEO for your catalog

GEO for ecommerce is not a one-time project — it is a continuous operating layer. Start with these priorities:

1. Audit attribute completeness across your top 20% of SKUs (by revenue). Fill gaps in specs, materials, compatibility, and certifications.
2. Implement Product schema markup on every PDP with price, availability, brand, SKU, and review data.
3. Align your feeds — ensure pricing, availability, and product names are consistent between your site, Google Merchant Center, and any marketplace listings.
4. Test your crawlability — verify that your product pages render correctly for AI crawlers that do not execute JavaScript.
5. Monitor brand mentions — set up basic tracking to see whether your products appear in AI-generated answers.

Lasso helps teams accelerate steps 1-3 by enriching product attributes, generating structured descriptions, and standardizing catalog data across channels. Explore pricing or book a demo to see how it fits your workflow.

For broader context on how product discovery is evolving, read our guide on product discovery in 2026.