GEO vs SEO: what changes for fashion brands when answers replace search results?
Generative engine optimisation builds on SEO rather than replacing it. What changes is the unit of success: being cited and described correctly inside an answer, not only ranking on a page.
KEY TAKEAWAYS Summary by the editors
- Generative engine optimisation (GEO) aims to make content visible and correctly represented inside AI-generated answers, while SEO aims to rank pages in lists of search results.
- The term GEO was introduced in a 2023 research paper by Aggarwal and colleagues, which reported that optimisation methods could raise visibility in generative engine responses by up to 40% in its test setting.
- Pew Research Center found that in March 2025 Google users clicked a traditional result link on 8% of visits to pages with an AI summary, compared with 15% on pages without one.
- Google states that no special optimisation or schema.org markup is required for AI Overviews or AI Mode, so crawlability, indexing and clear content remain the foundation for GEO.
- For fashion brands, GEO shifts attention towards accurate product data, explicit facts on materials, sizing and policies, and the third-party sources that assistants cite.
GEO (generative engine optimisation) and SEO share the same foundation, crawlable and useful content, but they optimise for different outcomes. SEO aims to rank a page in a list of links; GEO aims to have a brand mentioned, described accurately and cited inside an answer that an AI system writes. For fashion brands, that moves the focus from keywords and rankings towards facts, product data and the reputation the brand has across other sources.
What is GEO and where does the term come from?
The term was introduced in the research paper GEO: Generative Engine Optimization by Pranjal Aggarwal and colleagues, first submitted to arXiv in November 2023. The authors defined generative engines as systems that retrieve sources and synthesise an answer, and reported that their optimisation methods could boost visibility in generative engine responses by up to 40% in their experiments, with effectiveness varying by domain.
The industry now uses GEO loosely to cover visibility in ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google's AI Overviews and AI Mode. McKinsey and The Business of Fashion, in The State of Fashion 2026, put it bluntly: AI chatbot responses are "the new SEO".
How is GEO different from SEO in practice?
| Dimension | SEO | GEO |
|---|---|---|
| Unit of success | Ranking position and click | Mention, citation and accurate description in an answer |
| Query type | Short keywords and phrases | Longer conversational questions with constraints (budget, fit, occasion) |
| Retrieval | One query, one results page | Several background searches combined into one answer |
| Main signals | Relevance, links, page experience | Same foundations plus clear factual statements and consistency across sources |
| Measurement | Rankings, impressions, clicks | Share of answers, citations, AI referrals, answer accuracy |
| Owner | SEO and content team | Marketing, e-commerce, product data and PR together |
The retrieval row matters most. Google describes AI Mode as "breaking down your question into subtopics and issuing a multitude of queries simultaneously", a technique it calls query fan-out. A shopper asking for a waterproof jacket for city cycling under a given budget triggers several searches, and the answer draws from whichever pages answer those sub-questions clearly.
Does GEO replace SEO?
No. Google states that there are "no additional requirements" to appear in AI Overviews or AI Mode and no special optimisations necessary beyond Search fundamentals: allowing crawling, internal linking, a good page experience, important content in text form and up-to-date Merchant Center and Business Profile information. It also says there is no special schema.org structured data to add for these features.
Other assistants run their own crawlers and search indexes, but the principle is similar: a page that cannot be crawled, indexed or understood will not be cited. GEO is best seen as SEO with a wider field of view, where third-party mentions and factual precision count for more.
What happens to clicks when answers replace results?
Fewer clicks reach websites from informational searches. Pew Research Center analysed the browsing of 900 US adults in March 2025 and found that users clicked a traditional search result on 8% of visits to Google results pages that showed an AI summary, compared with 15% when no summary appeared. Clicks on links inside the summary itself happened on 1% of visits.
For fashion this hits upper-funnel content hardest: style guides, "how to wear" articles and generic category explainers. Product and brand queries still lead to sites and stores, but the shopper may arrive later in the journey, already holding a shortlist that an assistant composed.
What should fashion brands change first?
- State facts explicitly. Fibre composition, weight, fit, size range, country of manufacture, care and return terms written as plain text, not only in images or accordions.
- Fix product data at the source. Inconsistent names, colours and prices between site, feeds and retailers produce inconsistent answers.
- Answer real questions. FAQ and guide content built from customer service logs and search queries, with direct answers in the first sentences.
- Earn third-party coverage. Assistants cite editorial reviews, retailers and forums; accurate information there shapes how the brand is described.
- Check crawler access. Make sure search crawlers from the major assistants are not blocked by robots.txt or bot protection.
How should success be measured?
Rankings alone no longer describe performance. Brands should combine four views: a recurring prompt audit that records share of answers and accuracy; analytics that separate referrals from AI assistants; Search Console data, where Google includes AI Overview and AI Mode traffic in the Web search figures; and commercial outcomes such as conversion rate of assistant-referred sessions. Report the metrics separately rather than inventing a single "AI visibility score" that hides the uncertainty.
Which fashion content is most at risk, and which gains?
Category pages sit in between. A listing page with a short, factual introduction (what the category covers, price range, key materials, sizes available) gives an assistant something to cite for discovery questions. A listing page with only a product grid and a generic headline does not. The same applies to brand pages: a clear statement of what the brand makes, for whom and where it is sold is more useful to an answer engine than a mood text.
Generic advice content that any brand could have written is the most exposed, because an assistant can summarise it without sending a visit. Content with unique information gains relative value: proprietary fit data, detailed material and sourcing information, original photography of garments on different body types, and verified customer reviews. Those are the elements an answer needs to cite, and they are hard for competitors to copy.
The practical conclusion for fashion teams is to audit content by usefulness rather than by volume. Pages that exist only to rank for a keyword are candidates for consolidation; pages that contain information nobody else has deserve more investment, better structure and regular updates.
Frequently asked questions
What is the difference between GEO and SEO?
SEO aims to rank pages in search results so users click through. GEO aims to make a brand appear, be described accurately and be cited inside answers generated by AI systems such as ChatGPT or Google AI Mode. Both depend on crawlable, clear and trustworthy content.
Is SEO dead because of AI search?
No. Google says AI Overviews and AI Mode rely on the same Search fundamentals and need no special optimisation. SEO remains the base layer, while GEO adds attention to factual clarity, product data and third-party sources.
Do AI Overviews reduce clicks to fashion websites?
Pew Research Center found that in March 2025 users clicked a traditional result on 8% of Google visits with an AI summary versus 15% without. The effect is strongest for informational queries; product and brand queries still drive visits.
Who coined the term generative engine optimisation?
The term comes from the paper GEO: Generative Engine Optimization by Pranjal Aggarwal and co-authors, first published on arXiv in November 2023. It reported visibility gains of up to 40% in generative engine responses in its experiments.
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