Winning the Generative Search Race

A guide to AEO and GEO for fashion and luxury brands

How AI-mediated discovery is changing what shoppers find, what they buy, and what brands need to do about it. Anchored in a proprietary Measmerize study of 60,000 reviews across 60 fashion and luxury brands.

Executive summary

Search is no longer a list of links. For a growing share of shoppers, search is a direct answer generated by an AI engine: ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, or Grok. Research by The Boston Consulting Group finds that 22% of GenAI users have already used these tools to research brands and products to purchase, up from 19% in late 2024. LLM referral traffic in fashion and sport grew roughly twentyfold year on year in 2025. The trajectory points to potentially up to five percent of total web traffic flowing through LLMs within twelve months.

The discipline that wins inside that answer is Answer Engine Optimisation (AEO) or, in its broader form, Generative Engine Optimisation (GEO). On-site work — schema, FAQs, long-form content, technical hygiene — is necessary but not sufficient. The decisive competition happens off-site. Only 38% of pages cited in AI Overviews appear in Google's top ten organic results. 96% of fashion citations during the most recent holiday cycle on Gemini went to non-retailers. Large language models systematically over-index user-generated content (UGC), editorial coverage, and forum discussion to minimise hallucination risk.

That presents a specific problem for fashion and luxury. The user review ecosystem is the same off-site graph many fashion brands have historically been least comfortable governing. To understand what AI engines are actually being told about fashion brands, Measmerize built a curated dataset of 60,000 reviews: 1,000 from each of 60 brands across mass-market, mid-market, premium, and luxury categories. The reviews were clustered into nine macro topics and analysed for sentiment.

Size and Fit is the single largest topic in the dataset. It accounts for 30% of all reviews, and 60% of those reviews are negative. In apparel, Size and Fit alone draws 45% of all reviews. Quality and Construction sits behind it at 25% of reviews overall, also 60% negative. Customer Service and Returns drives the negative discourse in luxury, where 35% of luxury footwear reviews are negative on returns and only 2% are positive. The brightest spot in the conversation is Style and Design: 83% of reviews on style across the industry are positive.

The implication for AI search visibility is sharp. The biggest single signal LLMs are picking up about fashion brands today is dissatisfaction with sizing and fit. Fixing that signal — through better size charts, AI-driven size recommendation, body measurement tooling, and virtual try-on — is the highest-return AEO investment a fashion brand can make. The work needed in luxury extends into quality storytelling and a re-engineered returns experience. Across the board, it requires actively governing the off-site conversation rather than ignoring it.

From clicks to answers: why AEO and GEO matter now

What is AEO, and how is it different from SEO?

Something fundamental has shifted in how people find products. For two decades, search was a list of ten blue links and a competition to rank inside that list. Search engine optimisation grew up around that competition: keyword research, on-page hygiene, backlinks, technical crawlability. Win the rank, win the traffic, win the sale.

That model is being replaced. When a shopper asks ChatGPT, Perplexity, Google AI Overviews, Claude, or Gemini for the best wool coats under €800 with a slim cut for a tall man, they do not get ten links. They get a recommendation. The links sit beneath the answer, or do not appear at all.

Answer Engine Optimisation, usually abbreviated AEO, and Generative Engine Optimisation, abbreviated GEO, are the disciplines that have emerged to compete inside that answer. The two terms are often used interchangeably. AEO emphasises being the source the model quotes when it answers a question. GEO emphasises shaping the generative response itself across every surface where AI now writes the verdict on a brand. Whichever label you prefer, the underlying shift is the same. The unit of competition is no longer the page rank. It is the citation inside a generated response.

How big is the shift, and is it actually happening yet?

It is happening, and it is happening fast. Research by The Boston Consulting Group (BCG Global Consumer Radar Survey, February 2025), drawing on first-party traffic data from a sample of twenty European fashion, sport, luxury, and specialty retailers, captures the moment with unusual clarity. Across eight major markets, 48% of consumers say they are excited about how generative AI can help brands deliver better products and services. 22% of GenAI users have already used these tools to research brands and products to purchase, up from 19% in September 2024.

The use cases line up directly with the decisions that determine which brand the shopper puts in the basket. 69% use GenAI to compare product features. 65% use it to learn more about a brand. 52% search for deals and promotions. 49% discover new brands they had not heard of before. 49% do a price comparison. 46% gather or summarise customer reviews. None of those behaviours are peripheral. Each one used to be a Google search. Now they are a conversation with an AI engine.

The traffic data corroborates the consumer surveys. Across the same sample, LLM referral traffic grew roughly twentyfold year on year in fashion and sport, twelvefold in luxury, and sevenfold in specialty retail through 2025. Across the same period, organic Google traffic was flat to declining in every category. LLM referrals are still under one percent of total traffic today, but the trajectory points to roughly five percent within twelve months: twice the share of display traffic and half the share of social. Google's own AI Mode and AI Overviews already appear in more than twenty percent of search results pages, often suppressing the click on the underlying links.

How does the ranking logic actually change? AEO vs SEO

The shift from SEO to AEO and GEO is not a tuning exercise. It is a different game with a different scoreboard. The right-hand column does not just reshuffle the tasks. It changes which team owns the result. SEO was a marketing function. AEO sits at the intersection of marketing, digital, PR, customer experience, and product. The brand most ready for the new paradigm is the one whose departments can actually coordinate on the off-site signal that LLMs read.

On-site AEO: what you can control

On-site AEO covers everything a brand can change inside the four walls of its own website. It splits into two workstreams that should run in parallel: an editorial workstream that improves what the site says, and a technical workstream that improves how the site can be read.

What on-site content makes a brand more citable by AI?

Large language models reward content that answers a clear question with credible specificity. Five practices matter most, and a sixth, structured data, sits underneath all of them.

  1. Authoritative long-form editorials and guides. A product detail page is not enough. Buying guides, sizing explainers, material primers, and category overviews give models the substance they need to cite. The pages should target the high-volume prompts shoppers actually ask.

  2. FAQ and Q&A content formats. Question-and-answer structure is, in effect, native to how LLMs construct responses. Branded FAQs, product FAQs, and category FAQs make the model's job easier.

  3. Clear header hierarchy and descriptive metadata. Page titles, H1s, H2s, and meta descriptions are the semantic scaffolding the model uses to understand the page.

  4. Brand entity signals. AI engines maintain an internal model of who you are, what you sell, who your customers are, and what you stand for.

  5. Content that answers the question directly. Open the paragraph with the answer. Use the rest to support it.

What on-site technical work supports AEO?

The technical layer is less glamorous but no less important. It covers updated XML sitemaps, removal of duplicate page titles and meta descriptions, faster page loads, broken-link hygiene, and the elimination of duplicate product pages.

One technical priority deserves particular attention. A surprising amount of fashion and luxury site content still relies on JavaScript-only rendering. Many crawler agents that power LLM indexing do not execute JavaScript.

Off-site AEO: where the battle is actually won

If on-site AEO is the work you can directly control, off-site AEO is the work you can only influence. The data is clear that off-site is where the real differentiation happens.

Why is off-site AEO so decisive?

Two recent findings on analysis of LLM citation behaviour truly crystallise the asymmetry.

  1. Only 38% of the pages cited in AI Overviews appear in Google's top ten organic results.

  2. When Gemini was asked product questions during the most recent holiday cycle, only 4% of citations went to direct retailers. 96% went to non-retailer sources.

What does off-site AEO actually require?

It requires governing what is said about the brand on platforms the brand does not own. Reviews on Trustpilot and retailer portals. Threads on Reddit. Video reviews on YouTube. Visual boards on Pinterest.

What sources do AI algorithms actually use?

The five major consumer-facing LLMs do not share a single index. Each pulls from its own preferred sources, and the differences are larger than most marketing teams assume.

Which sources dominate generic queries?

The table below shows the share of total citations captured by the top five sources on each engine for generic queries.

What changes for fashion-specific queries?

Fashion, as is often the case, is different. Everything tilts further toward editorial, social, and user-generated content.

What customers actually say

a. The overall picture

Across the full fashion industry, Size and Fit is the single most discussed topic in customer reviews. It is also the most negative. Size and Fit attracts 30% of all reviews. 60% of those reviews are negative.

b. The data, by segment and category

Fashion industry overall

Fashion industry: Apparel

Fashion industry: Shoes

Fashion industry: Bags

Fashion industry: Other (accessories, eyewear, jewelry)

Luxury overall

Luxury: Apparel

Luxury: Shoes

Luxury: Bags

Luxury: Other

Mass market and fast fashion overall

Mass market and fast fashion: Apparel

Mass market and fast fashion: Shoes

Mass market and fast fashion: Bags

Mass market and fast fashion: Other

Recommendations: what fashion and luxury brands should do now

Priority 1. Solve the fit problem at the source

Roughly 45% of all apparel reviews discuss Size and Fit; 25% of all apparel reviews are explicitly negative on it.

Priority 2. Make quality and craftsmanship visible

In luxury footwear, Quality and Construction draws 25% negative sentiment.

Priority 3. Streamline the returns experience

Customer Service and Returns is the worst-scoring topic across luxury. 35% of luxury footwear reviews are negative on returns.

Priority 4. Own the conversation across the off-site graph

The brands losing AEO are losing it because narratives run rampant about them on YouTube, Reddit, X, and TikTok without any brand input.

How to go further

Get the full brand-by-brand analysis

Speak with the Measmerize team

About Measmerize

Appendix: The Measmerize study — how the dataset was built

How was the dataset built?

How were the topics identified?

Table 19. The Measmerize topic taxonomy

  • Size & Fit
  • Quality/Construction
  • Fabric/Materials
  • Style/Design
  • Comfort
  • Customer Service/Returns
  • Shipping/Delivery
  • Price/Value
  • Other

Sentiment was scored per-topic, not per-review.