Over the past decade, retail marketers have become fluent in SEO, structuring content so it’s discoverable in search engines like Google. But the way people search for information is changing. Increasingly, shoppers (and B2B buyers alike) are turning to AI search through generative platforms like ChatGPT, Gemini, Claude and Perplexity to answer questions, compare options, and make recommendations.

These AI assistants are powered by large language models (LLMs), the underlying AI technology that ingests vast amounts of online content and generates natural, conversational answers that we receive in the chat-like interfaces. This shift introduces a new challenge: how do you ensure your brand shows up in these AI-driven results? The answer lies in Generative Engine Optimization (GEO).

If you’re a retail CRM or marketing leader, here’s what you need to know.

Contents

What is Generative Engine Optimization?

GEO vs AEO vs SEO

Why GEO matters for retail

Test how you show up in chatbots

5 ways to optimize retail content for GEO

1 - Structure content for machines and humans

2 - Write how customers ask

3 - Build trust and authority

4 - Prioritize content that's easy for AI to reuse

5 - Keep content fresh and discoverable

Your GEO checklist

What this means for CRM

What is Generative Engine Optimization?

Generative Engine Optimization (GEO) is the practice of preparing your content so that AI-powered assistants like ChatGPT, Gemini, Claude, and Perplexity can understand, trust, and surface it in their answers.

You may also see this discipline referred to as Answer Engine Optimization (AEO) or Large Language Model Optimization (LLMO). While the terms are often used interchangeably, they all describe the same idea: ensuring your brand appears in AI-generated responses when customers are searching for advice, recommendations, or product options.

GEO vs AEO vs SEO vs LLMO

Marketers are beginning to ask how these terms relate to each other, and to the SEO strategies they already know. Here’s a quick comparison:

• SEO (Search Engine Optimization): Optimizing your website so it ranks highly on search engines like Google. Focuses on keywords, backlinks, and page structure.
• GEO (or AEO / LLMO): Optimizing content so it shows up in AI-driven chat and discovery experiences. Instead of ranking pages, the goal is to have your content cited, summarized, or recommended in conversational answers.

Both approaches depend on clear, structured, trustworthy content. But while SEO can take months or years to show results, GEO can deliver visibility more quickly. In many ways, GEO is the “new SEO.” While SEO was about making content discoverable in Google, GEO is about making content discoverable in the emerging world of AI-driven search and chat interfaces. 

Why GEO matters for retail

When a shopper asks an AI assistant (like ChatGPT or Gemini)... 

• “What’s the best skincare brand with sustainable packaging?”
• “Where can I find limited-edition sportswear drops?”
• “Which brands offer the most reliable rainproof outerwear for kids?”

The AI’s answer may not come from a single search ad or even a website. Instead, it will pull together information it has ingested across a vast set of sources, often blending product descriptions, reviews and press mentions.

And it’s not just about visibility, it’s about conversions. Ethan Smith, CEO of SEO growth agency Graphite, has found that traffic from ChatGPT converts six times better than Google search traffic. In other words, showing up in generative results isn’t only a brand awareness play, it can directly impact revenue.

This opportunity is accelerating fast. OpenAI just announced “Instant Checkout” in ChatGPT, powered by the new Agentic Commerce Protocol. That means when shoppers ask product discovery questions like “best running shoes under $100,” they won’t just see recommendations, they’ll be able to buy directly in the chat. For retail brands, this makes GEO even more critical: if your product content isn’t optimized to be understood and surfaced by generative engines, you won’t just miss visibility, you could miss sales happening in real time.

If your brand’s content isn’t structured and written in a way that generative engines can use, you risk invisibility and potentially sales, in this new discovery channel.

Test how you show up in chatbots

Before you look to optimize your product content for generative AI assistants, try asking ChatGPT or Gemini questions your customers might ask:

• “Which brands have the best waterproof jackets for kids?”
• “Where can I find eco-friendly skincare?”
• “What are the best hiking boots for a first-time hiker going to Snowdonia?”

If your brand isn’t appearing, review how your content is framed. Are you matching the language of the query? Are your differentiators clear?

5 ways to optimize retail content for GEO

The good news is that this is not a new skill set, but an extension of the customer-centric mindset you already use in CRM. While generative engines don’t usually access private content like emails, the same assets you create for campaigns, such as structured product data, FAQs, reviews and guides, can also be surfaced on your website. This way, you keep consistency across channels and ensure your brand is visible when customers turn to AI for advice.

1. Structure content for machines and humans

AI assistants need content that’s organized in a way they can easily interpret, just like shoppers do. That means making sure product details are clear, consistent, and machine-friendly:

• Use schema markup tags for product details like price, availability, and reviews. Schema is background code that tells AI: “£79.99 = Price” or “In stock = Availability.” Most ecommerce platforms (Shopify, BigCommerce, Magento) already support this.
• Present product specs in bullet points or tables so information like sizing, materials, or shipping times is easy for AI to pull into an answer.
• Add FAQs to your pages that address common shopper questions (e.g. “Is this product waterproof?” or “Does it come in kids’ sizes?”). AI assistants often lift directly from this type of Q&A format.
• Create help-center content around troubleshooting and “how-to” topics. These articles often rank highly with AI models because they directly answer practical customer questions.
• Build landing pages targeting clusters of related questions. For example, instead of just “hiking boots,” create a resource that covers “best hiking boots for beginners,” “waterproof hiking boots,” and “lightweight boots for summer.” This increases the chances your brand shows up across a variety of AI-driven queries.

The clearer and more structured your content is, the more likely AI assistants are to present your brand accurately when shoppers ask about your category.

How Ometria helps: Ometria’s product attribute system keeps product data complete and consistent across campaigns, powering personalized recommendations that are machine-readable and discoverable. Our accessibility features also ensure images include descriptive alt text, helping AI assistants understand and use your visual content.

2. Write how customers ask

Think about how someone might ask a chatbot for help. Shoppers don’t use brand jargon; they use everyday phrases.

Instead of only saying:

“Our jacket uses hydrophobic technology.”

Try:

“This jacket is fully waterproof and keeps you dry in heavy rain.”

Natural, conversational language makes your content more likely to match real customer queries.

How Ometria helps: Natural, conversational language in each market improves generative engine understanding. With multi-language templates and custom fields, Ometria makes it easy to maintain natural, conversational phrasing across both campaigns and your website. This consistency ensures shoppers, and AI assistants, encounter the same customer-friendly language wherever they interact with your brand.

3. Build trust and authority

AI assistants are more likely to surface content from brands that look credible and reliable. For retail brands, that means leaning into signals that shoppers already use to make decisions:

• Collect and showcase reviews and ratings: star ratings and customer quotes are easy for AI assistants to recognize and reference.
• Highlight press mentions or awards: e.g. “Featured in Vogue’s Summer Edit.”
• Be transparent with product information: clear sizing guides, sustainability credentials, or care instructions all add to your credibility.
• Use authentic imagery and stories: UGC (user-generated content) or influencer mentions show your products in real life, which AI tools can also pick up on.
• Engage authentically in Reddit forums: AI assistants frequently cite Reddit threads because they represent authentic, conversational advice. The key is to participate genuinely, share useful insights and avoid spamming with brand-heavy posts.

The stronger your signals of trust, the more likely an AI assistant is to “believe” your brand is worth surfacing when shoppers are exploring options.

How Ometria helps: Through integrations with Yotpo, Trustpilot, and Feefo, reviews and ratings can be systematically pulled into multi-channel campaigns or website content, whilst custom fields let you display sustainability credentials or awards, providing social proof that generative engines prioritize.

4. Prioritize content that’s easy for AI to reuse

AI assistants work best when your content is easy to understand and predictable in structure. The more organized and repeatable your content, the more likely it is to be lifted directly into answers for shoppers. Prioritize formats including:

• YouTube videos: AI models are increasingly trained on and reference YouTube content. Well-structured video guides, reviews, or tutorials not only reach human audiences but also boost the likelihood your brand is cited in AI answers.
• Comparison tables: Side-by-side breakdowns of products, like “Compare our three winter coats: warmth rating, waterproofing, and price.”
• Size and fit guides: Charts or instructions to help shoppers pick the right size, e.g., “Find your perfect fit in our jeans.”
• How-to guides: Provide step-by-step advice, such as “How to choose the right size in our trainers” or “How to pack this suitcase for a weekend away.”
• Product guides:  Step-by-step or comparison content, e.g., “Which hiking boots are best for wet trails?”, “Backpack comparison: 20L vs 30L vs 40L for weekend trips.”
• Benefit-led lists: Highlight key reasons to buy a product: “5 reasons our trainers are perfect for everyday wear.”
• Style inspiration/lookbooks: Seasonal suggestions:“How to layer for a weekend camping trip.”

By using these repeatable formats, you’re making it simple for AI assistants to understand your content and pull it into answers when shoppers ask questions. 

How Ometria helps: Dynamic content blocks and template logic make it possible to automate these formats at scale. For example, you can set up FAQ blocks that only appear for certain segments, or comparison tables that update automatically with current product data across your website as well as in campaigns.

5. Keep content fresh and discoverable

AI platforms are increasingly connected to real-time web browsing. Make sure your product pages are:

• Frequently updated with new products, features, or campaigns
• Crawlable, with clear sitemaps and metadata.
• Written in plain language that avoids jargon.

How Ometria helps: Ometria’s product recommendation engines (“Recently added,” “Most popular,” “Trending”) automatically refresh with new stock and campaign updates. This keeps your site and campaigns fresh, signaling to AI that your brand is active and relevant.

What this means for CRM

As a CRM marketer, you’re already skilled at thinking from the customer’s perspective. GEO extends that mindset into your content. It’s about making sure that when a future or current customer turns to AI for advice, your brand is visible, credible, and relevant.

In the same way that SEO became a core marketing discipline, expect GEO to become just as important for AI search. Start small, audit your existing content through this new lens and you’ll be better positioned to capture visibility in the AI era.