GEO vs SEO: What’s the Difference? How to Optimize for AI?

The debate over GEO vs SEO has quickly become one of the most important conversations in digital marketing. Traditional SEO focuses on ranking web pages on search engines like Google, while Generative Engine Optimization (GEO) is designed for AI-driven platforms that deliver direct answers. For e-commerce brands, this shift changes the rules of visibility and requires a new approach. 

Consumers no longer rely solely on Google to make purchase decisions. Instead, they’re turning to generative AI tools such as ChatGPT, Perplexity, and Amazon Rufus that provide instant answers, often without links.  

In this article, we’ll explore how SEO and GEO compare, why both matter for e-commerce brands, and how SellerMetrics can help businesses succeed in the evolving search landscape.

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the practice of shaping content so that it can be found, understood, and cited by generative AI systems. Unlike search engines such as Google, which crawl and rank web pages, generative engines rely on large language models (LLMs) like GPT-4, Claude, or Gemini.

These systems don’t simply list results—they synthesize content into ready-made answers. To be included, a brand’s content must be structured to be accessible to AI retrieval pipelines. 

How GEO Works

Instead of indexing and ranking pages, AI engines rely on semantic embeddings, retrieval-augmented generation (RAG), and prompt interpretation. When a user asks a question, the model searches its stored knowledge and external databases for passages that match the query.

It then pulls short, modular content “chunks” and reassembles them into a natural answer. If your content isn’t formatted to be retrieved in these small, stand-alone sections, your brand risks being invisible in AI-driven search. 

Why GEO Matters for E-commerce

Consumers increasingly rely on AI-powered assistants rather than clicking through a list of search results. Imagine a shopper asking, “What’s the best ergonomic office chair under $200?” Instead of scrolling Google, they might turn to ChatGPT or Perplexity, which instantly provides a short list of product names and features.  

If your brand’s product details aren’t included in that answer, you’ve already lost visibility. This is why adopting GEO is critical—not only for building brand awareness but also for how to increase traffic to your Amazon listing through smarter optimisation. 

What is Traditional SEO?

Search Engine Optimization (SEO) is the process of improving your website’s visibility on search engines like Google and Bing. It focuses on making content discoverable and rankable through a mix of keyword targeting, site performance, backlink building, and user experience.  

When done effectively, SEO ensures that potential buyers see your content at the very moment they’re searching for solutions, products, or services.

Why SEO Still Matters

Although the landscape is shifting toward generative engines, traditional SEO remains essential. Most digital journeys still begin with Google, and a strong SEO foundation helps businesses earn trust, attract clicks, and drive conversions.  

For e-commerce brands, this means that ignoring SEO in favor of GEO alone would be a costly mistake. Instead, both must work together to create a comprehensive optimisation plan.

Core SEO Strategies Still Relevant

Even as AI-driven search grows, several SEO strategies continue to be critical for e-commerce brands:

1. Keyword Optimisation – Identifying and using the terms your target customers are searching for remains a core pillar. A strong Amazon SEO Strategy also builds on this, ensuring your listings reflect the exact phrases buyers use on the platform. 
    
2. Backlinks and Authority Building – Links from reputable sites are still a powerful ranking signal. The stronger your domain authority, the more likely your content will appear at the top of SERPs. Many brands now combine this with professional Amazon SEO Services to ensure their presence is optimised both on and off Amazon. 
    
3. Content Freshness and Technical Health – Regular updates, mobile responsiveness, and fast-loading pages are non-negotiables for ranking well. To make these efforts more measurable, businesses increasingly rely on Amazon SEO Tools to track listing performance, monitor keyword trends, and identify optimization gaps. 
    
4. User Experience (UX) – Google increasingly prioritises websites that are easy to navigate, readable, and valuable to visitors. A good SEO strategy considers not just rankings, but also how long visitors stay engaged. 

GEO vs SEO – Key Differences Ecommerce Brands Must Know

The conversation around GEO vs SEO is not about replacement but adaptation. Both play vital roles in how brands are discovered online, but they function very differently. Where SEO relies on crawling, indexing, and ranking, GEO uses semantic embeddings and AI-driven retrieval. 

For e-commerce brands, understanding these differences is key to future-proofing visibility and conversions.

Comparison Table: GEO vs SEO at a Glance 

Aspect	Traditional SEO	Generative Engine Optimization (GEO)
Discovery Mechanism	Crawling, indexing, ranking	Semantic embedding, retrieval-augmented generation
Content Format	Long-form, context-rich pages	Modular, answer-style content chunks
Output Surface	Search Engine Results Pages (SERPs) with links	LLM-generated answers, often without links
Optimisation Target	Search algorithms	AI prompt relevance and answer inclusion
Trust Signals	Backlinks, domain authority	Factual consistency, repetition, and brand accuracy
Measurement	Rank tracking, CTR, GA4	Mentions in AI outputs, brand recall, and untagged conversions
Conversion Path	SERP → Click → Product Page	Inline brand suggestion → recall → direct purchase

This makes it clear that while SEO is about ranking on search engines, GEO is about becoming part of the AI’s recommended answer. That distinction is what separates SEO vs GEO as two complementary, not competing, disciplines.

Discovery Mechanism and Ranking

Traditional SEO depends on search engines crawling your website and ranking it based on signals like keyword relevance, backlinks, and domain authority. GEO, by contrast, relies on how well your content is embedded in AI training data and retrieval systems.

On Amazon, for example, the Amazon A9 algorithm serves a similar purpose—it decides which listings show up when a customer searches. GEO works the same way for AI models, but instead of rankings, the goal is answer inclusion.

Content Format and Structure

SEO content often stretches across long-form blogs, technical pages, and resource hubs. GEO requires a different approach: modular content chunks of 40–120 words that can stand alone without prior context. This chunk-based structure helps AI retrieve passages efficiently and include them in its responses.

Outputs and Conversion Pathways 

With SEO, the path is familiar: appear in the SERP, win the click, and convert on the product page. With GEO, the journey is shorter but less direct—your brand may be mentioned in an AI-generated answer, which builds recall and drives the user to search for you later.

For e-commerce brands, knowing GEO vs SEO and combining both strategies ensures you capture visibility across every search surface.

How Generative AI Search Works (Behind the Scenes) 

Generative AI search isn’t a mystery—it follows a systematic process to interpret queries, retrieve data, and generate answers. For e-commerce brands, knowing how this process works can highlight exactly why Generative Engine Optimization (GEO) is needed.

Unlike traditional SEO, which waits for search engines to crawl and rank pages, generative engines actively assemble content into fluid, conversational responses.

Step 1: Prompt Interpretation 

When a user types a question, the large language model (LLM) first interprets the meaning of the query. For example, a prompt like “best running shoes under $120” isn’t taken literally—it’s expanded semantically to include ideas such as comfort, durability, and breathability. This stage determines the scope of content the AI will look for.

Step 2: Embedding Matching 

The AI then compares the query against its vast library of semantic embeddings—mathematical representations of words, concepts, and contexts. This allows it to match content not just by keywords but by intent. Unlike SEO keyword matching, this is about finding meaning rather than exact phrases.

Step 3: Chunk Retrieval

Generative engines prefer modular “chunks” of text, usually between 40 and 120 words. These are short, self-contained passages that directly answer a user’s question. Content structured in this way is far more likely to be surfaced than long paragraphs or context-heavy sections.

Step 4: Response Synthesis 

The AI assembles the retrieved chunks into a fluent, human-like answer. Sometimes it will cite sources with links, but often it simply delivers the content without attribution. This is where brands risk invisibility—if their content isn’t retrieved, they don’t get mentioned at all.

Step 5: Answer Display 

Finally, the answer is presented instantly, often replacing the need for the user to visit a search engine results page. Instead of browsing a list of ten links, the user walks away with a complete response. This makes GEO essential since it is mentioned that generated output may be the only way your brand reaches the buyer.

How to Optimize for GEO: A Practical Playbook for Ecommerce

Knowing what GEO is won’t make a difference unless brands adapt their content strategy to match how AI engines retrieve and generate answers. The following steps are a practical framework that e-commerce businesses can implement to ensure their content is ready for the AI-driven search era. 

Create “Answer Blocks” for AI Retrieval

AI engines prefer modular content that directly answers specific questions. On product pages or blogs, structure sections into short 40–120-word blocks that could stand alone without context. 

For example, instead of simply writing “Our jacket is lightweight and water-resistant,” reframe it as: “Looking for a lightweight, water-resistant jacket under $100? The AlpineCore Pro delivers warmth with a durable water-repellent finish, making it ideal for winter hikes.”  This style gives the AI a complete answer that it can pull directly into its output. 

Write in Prompt-Compatible Language 

Buyers increasingly phrase queries like they’re asking an assistant: 

• “What’s the best blender for smoothies under $50?” 
• “Which sunscreen works for sensitive skin?” 
  Your headings and paragraphs should reflect these natural patterns. Aligning content with these conversational prompts also reinforces Amazon Search Term Optimization, since Amazon’s own ranking systems rely on matching buyer intent with structured keywords. 

Make Content Self-Contained

Generative engines don’t scroll. Each passage should hold enough information to provide value without relying on previous text. For ecommerce brands, this means rewriting product descriptions and category content so every section delivers standalone clarity. 

Unify Brand Data Across Platforms 

Consistency is critical. If your website lists a product as “$99 with Bluetooth 5.1” and your Amazon page says “$89 with Bluetooth 5.2,” AI engines may exclude you altogether. This is where Amazon Listing Optimization Services become invaluable—ensuring all details across marketplaces, ads, and third-party listings are aligned and trustworthy. 

Use Schema and Structured Data 

While most LLMs don’t parse schema directly, search engines that power hybrid AI experiences—like Google SGE—do. Adding FAQ schema, product schema, and how-to schema increases your chances of being pulled into generative responses. 

Simulate Buyer Prompts Weekly

Don’t guess how your brand appears in AI outputs—test it. Use tools like Perplexity, Claude, ChatGPT, or Google SGE Labs to type in buyer-style prompts and see if your content surfaces. This is where techniques like reverse keyword search Amazon also help, because they allow you to discover what buyers are already typing and reframe your content to align with those prompts. 

Case Studies – GEO in Action 

Case studies show how GEO, when applied alongside SEO, creates measurable results for e-commerce brands. Below are two examples that illustrate the impact of adopting a GEO-first mindset. 

Case Study 1: GEO-Bench Experiment (Aggarwal et al., 2023) 

Researchers from the University of Washington and Allen Institute developed a concept of Generative Engine Optimization (GEO) through a benchmark called GEO-Bench. They tested 10,000 queries across domains like health, education, and ecommerce to see how content appeared in generative search engines.  

By restructuring content into concise, answer-ready snippets, they increased inclusion in ChatGPT and Perplexity answers. The study showed that GEO principles improved AI visibility by up to 40% compared to traditional formats. This experiment proved that GEO could directly influence how brands appear in AI-driven search. 

Case Study 2: Brand vs. Earned Media in GEO (Mahe Chen et al., 2025) 

Mahe Chen and colleagues studied how AI search engines prioritise content sources. They discovered that earned media such as reviews, blogs, and third-party articles were cited more often than brand-owned websites. Even a strong SEO authority was sometimes overlooked in favour of external credibility.  

Their findings suggested that GEO success requires both on-site optimisation and high-quality off-site coverage. For brands, this means balancing content control with broader media visibility to dominate AI mentions. 

GEO and SEO – Why You Need Both 

Some marketers frame the debate as SEO vs GEO, as if one will replace the other. The truth is more balanced: SEO and GEO work best together.  

SEO ensures that your website, blog posts, and product pages appear in traditional search results, while GEO helps your brand show up in the conversational answers delivered by generative AI platforms. When combined, they create a full-spectrum visibility strategy across every search surface. 

How SEO Supports GEO 

Traditional SEO tactics—such as keyword research, link building, and technical optimisation—create a strong base for content. Without this foundation, even GEO-friendly passages may struggle to gain authority.  

Optimised, crawlable, and authoritative websites are more likely to be referenced by hybrid engines like Google’s SGE, which bridges classic SEO with generative answers. 

How GEO Enhances SEO 

GEO-ready content reinforces SEO by creating modular, answer-focused sections that can also rank well in SERPs. Short, self-contained passages not only increase the chance of being pulled into AI responses but can also serve as featured snippets in Google search. This dual optimisation helps brands capture both link-driven traffic and AI-driven mentions. 

The Amazon Example 

On Amazon, success requires a mix of organic and paid visibility. The same logic applies here. Leveraging Amazon SEO and PPC together ensures your products are optimised for discovery through organic ranking while also gaining exposure through targeted advertising.  

Many sellers further enhance performance by partnering with Amazon PPC Services, which helps them manage bids, campaigns, and ad spend efficiently for maximum ROI. GEO takes this further by positioning your brand in AI-generated answers outside the Amazon ecosystem, reinforcing recall and driving cross-channel conversions. 

The Future of GEO vs SEO in E-commerce 

Search isn’t disappearing; it’s evolving. Google, Bing, and other engines still serve billions of queries every day, but generative platforms are reshaping how people discover products and make decisions.  

Tools like ChatGPT, Perplexity, and Gemini have normalised conversational search, while Amazon’s Rufus is already changing how shoppers interact directly within the marketplace. For e-commerce brands, this signals a future where visibility depends on balancing both GEO and SEO. 

Google’s Search Generative Experience (SGE) 

Google’s rollout of SGE blends traditional search with generative AI responses. Instead of only showing a list of links, SGE highlights summaries and product suggestions directly in the results page. This hybrid approach rewards brands that invest in both SEO (to be indexed and ranked) and GEO (to structure content for inclusion in AI summaries). 

Amazon Rufus and Marketplace AI 

Amazon has begun experimenting with its own generative search assistant, Rufus. This feature answers shopper queries inside Amazon’s ecosystem by pulling from product listings, reviews, and brand content.  

Sellers who fail to adapt their content for generative retrieval risk losing visibility in the very marketplace they depend on. Ensuring consistent optimisation across listings, ads, and websites will become more important than ever. 

Why Brands Must Act Now 

The future of GEO vs SEO is not a choice between two paths but a merging of strategies. As AI assistants become more common, e-commerce businesses that wait to adapt will fall behind competitors who are already optimising for answerability. 

Preparing your content today—through modular passages, unified brand data, and consistent product messaging—ensures you’re visible tomorrow, no matter which search surface buyers use. 

Conclusion: The New Game is Answerability 

The distinction between GEO vs SEO is reshaping how e-commerce brands compete for visibility. SEO continues to secure rankings in traditional search engines, while GEO ensures your brand is surfaced in AI-driven answers that buyers increasingly rely on.  

Together, they create a complete strategy for discovery, authority, and conversions in today’s fragmented search landscape. 

At SellerMetrics, we help brands master both. By combining expert Amazon PPC management, listing optimisation, and GEO-focused strategies, we make sure your products aren’t just ranked—they’re recommended.  

Contact SellerMetrics today to future-proof your brand and become the answer customers are already searching for. 

FAQ: GEO vs SEO