Farfetch × Korpora · AI mindshare measurement · luxury e-commerce
Farfetch tops AI recall in luxury.
It owns none of the citations behind it.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
farfetch.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Leads the answer on citations
it does not own.farfetch.com and seven luxury rivals all return null at the domain level. AI cites YouTube, Reddit, resale marketplaces and Wikipedia instead. Source: Google AI citation graph, farfetch-v3 run 2026-06-01.
Across 54 subject-blind cells, Farfetch surfaces in 61.1% of AI answers, the top of the cohort with NET-A-PORTER well behind at 44.4%. But look at who the models actually cite across luxury buyer-decision queries and farfetch.com returns null, as do all eight rivals. AI leans on third-party authority instead: YouTube, Reddit, resale marketplaces, Wikipedia. The lead is real, and Farfetch runs zero Meta ads, so it is pure earned presence, but it rests on borrowed supply. The opening is to become a source the models cite directly.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
farfetch.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Leads the answer on citations
it does not own.farfetch.com and seven luxury rivals all return null at the domain level. AI cites YouTube, Reddit, resale marketplaces and Wikipedia instead. Source: Google AI citation graph, farfetch-v3 run 2026-06-01.
Farfetch · public dashboard
Farfetch's AI mindshare, live
3 headline views
snapshot · 2026-06-01
Where Farfetch stands in the answers AI shopping assistants give luxury buyers, measured against its cohort: SSENSE, NET-A-PORTER, Mytheresa and MR PORTER. Tab through the findings; each number is real, pulled from the v3 run on 2026-06-01.
Action cards
What to ship before the next training corpus closes.
Ordered by qualitative lift potential (cross-stream evidence weight, ship feasibility, and time-to-corpus). The strongest cards give an AI the machine-readable proof to pick you and defend that choice to its operator, in metrics, not adjectives. Each carries a paste-ready engineer prompt; cards involving copy and image generation also carry a brand-voice growth prompt.
Active
5 cards · 0 shipped
In-flight and queued. Every card is an open recommendation today; on a paid engagement they push into the team's tools and ship against this board.
Shipped
0 cards executed
Public build history of the Farfetch engagement. Empty because this is a prospective measurement and nothing has shipped yet. The slot is wired, not decorative.
No cards executed yet
The first executed card appears here the minute it pings back through the protocol above.
How we work
Two steps. You are on step one.
Most AI-visibility vendors sell you fear, then a subscription. Korpora sends you your real number first, free. Farfetch cleared the internal fit review (scale, vertical, AI-channel relevance, execution capacity), so Korpora ran the measurement and shipped this dashboard. There was nothing to request and nothing to buy. What happens next comes down to one thing: whether the measurement in front of you earns the engagement.
- 1
Prospective measurement (you are here)
This is it. Korpora ran the full cross-channel measurement on Farfetch and shipped the result as the dashboard you are reading: the live mindshare monitor, ranked action cards (each with a paste-ready engineer prompt), the full method, and the Ask Korpora chat for follow-up questions. Run once, on us. Nothing was requested and nothing is owed. You read it and decide whether the measurement earns the engagement.
- 2
Paid engagement (what continuing looks like)
When the measurement earns it, continuing is a paid engagement scoped to Farfetch's cohort and depth: weekly re-measurement across every stream and every model, Western and Chinese, fresh action cards as the surfaces move, an alert the moment a rival accelerates into the next training corpus, and an operator who can run the moves alongside your team. There is no fixed self-serve tier; every cohort prices differently. Apply with a work email and we come back with scope and pricing, no call required to get the number.
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Methodology
How we measure, in full.
Every number on this page traces back to one of the streams below. Expand any section for the method, the headline-metric definitions, the live streams themselves, and the caveats.
1 · How we measure
How each number on this page is produced. Every stream re-runs weekly on a paid engagement:
- AI assistant recall: buyer-decision queries (subject-blind and subject-named) run through three frontier models, 108 cells this cycle, Wilson 95% confidence range on every share. Organic (unprompted) reported separately from evaluation (named).
- LLM citation graph: which domains Google AI and ChatGPT cite for luxury fashionbuyer queries, and whether any retailer's own domain is cited at all. From DataForSEO LLM Mentions.
- Creative landscape: competitive Meta ad inventory (Facebook + Instagram). Active ad volume per company, from the live GetHookd ad library.
- Instagram + TikTok organic: engagement-per-post and engagement-per-video, content-format mix. Via Apify (live), shown in the attention-footprint stream.
2 · How to read the headline metrics
- Organic owned share (61.1%, the metric that drives action)
- Farfetch was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question and recorded whether Farfetch came up. 54 subject-blind cells; Farfetch was named in 33 of them. This is unprompted recall: whether AI surfaces Farfetch to a buyer who does not already know it exists. We treat it as a leading proxy for AI-driven discovery, not a measured conversion.
- 38.9pp organic-to-evaluation spread = moderate gap classification
- The gap between organic (61.1%) and evaluation (100%) is 38.9 percentage points. A spread of 15 to 40pp is what Korpora flags as a moderate gap (our own working threshold, not an industry standard; above 40pp is cold-start): the model knows Farfetch well enough to engage when named but does not surface it unprompted at the discovery stage as often as you would want for a category-defining company.
3 · The live measurement streams
Three data streams, re-run weekly on a paid engagement. Streams 1 and 2 (AI recall, citation graph) are the model-memory readout; Stream 3 collapses the attention channels (Meta ads, Instagram, TikTok), none of which feed the training corpus, into one panel that sizes the attention-vs-memory gap.
Stream 1 of 3 · live data · Korpora 108-cell battery · 2026-06-01
AI assistant recall composition
12 buyer-decision queries × 3 frontier models (Claude Sonnet, Claude Haiku, GPT-5.5) × 3 rounds, 108 cells total. Subject-blind cells (organic) reported separately from subject-named (evaluation).
Organic mindshare composition (54 subject-blind cells)
39% of buyer-decision queries are either contested or unclaimed, the mindshare we would treat as addressable over the next two corpus cycles.
Subject-own organic AI mindshare · head-to-head (each brand’s own battery)
On each brand's own subject-blind battery, Farfetch leads luxury AI discovery outright at 61.1%, with NET-A-PORTER the only real challenger at 44.4% (the CIs touch at the edges, so read them as a near-tier, not a settled gap). SSENSE, measured in its own reference run, sits level with Farfetch at 61.1%, so the honest read is Farfetch and SSENSE co-lead, NET-A-PORTER trails close, and the rest of the cohort barely registers unprompted: Mytheresa 9.3%, everyone else at or near zero. Aggregate (named + unnamed) recall flatters the field; organic is where the real separation lives.
Across both views, Farfetch is the organic leader in luxury AI discovery, co-leading with SSENSE and ahead of NET-A-PORTER, while most of the cohort is effectively invisible unprompted. The defend-and-extend work is the citation graph (Stream 2): Farfetch leads the answer but owns none of the domains AI actually cites, so the single highest-leverage lever is becoming a directly-cited source.
Stream 2 of 3 · live data · DataForSEO LLM Mentions · 2026-06-01
LLM citation graph
For 12 luxury buyer-decision queries (US-scoped), which domains do Google AI and ChatGPT cite most as sources?
Google AI · top cited domains
ChatGPT · top cited domains
Domain-scoped LLM Mentions returns null for farfetch.com on both platforms. Same for ssense.com, mytheresa.com, net-a-porter.com, mrporter.com, highsnobiety.com, bergdorfgoodman.com and selfridges.com. No luxury retailer is cited at the domain level. AI leans entirely on third-party surfaces, video, forums, resale marketplaces and reference sites, when it answers a luxury buyer. Notably, resale (ThredUp, Alibaba) is cited where the retailers themselves are not.
All LLM Mentions counts are US-scoped (DataForSEO location 2840), Google AI and ChatGPT pulled as separate platforms, summed across 12 buyer-decision queries. The cohort pattern (no luxury retailer at domain level) is robust; individual per-domain ordering on a single query is less reliable. DFS billed about $0.10 per query.
Stream 3 of 3 · attention footprint vs AI memory
Meta ads, Instagram, and TikTok are channels models do not read. None feed the training corpus, so none move organic AI mindshare directly. They are measured together here to size one gap: the attention Farfetch buys and earns on human channels, against how little of it compounds into the model memory measured in Streams 1 and 2.
Paid signal · Meta ad inventory · GetHookd
Active Meta ad inventory
Count of unique active Meta ads (Facebook + Instagram placements combined) per brand, from the GetHookd ad library.
Farfetch runs zero active Meta ads, the only major cohort retailer absent from the ad library, while leading the cohort on organic AI mindshare. Mytheresa runs the most by far (602), NET-A-PORTER 211. That inversion is the headline: in this cohort, paid Meta volume does not track AI mindshare. Farfetch's lead is earned, not bought, which is exactly the kind of presence that compounds into model memory when paid creative does not.
Snapshot from the GetHookd ad library, 2026-06-01. Active-ad counts combine Facebook and Instagram placements. Farfetch returns no tracked active ads; for a brand of Farfetch's size this most likely reflects a deliberately low Meta presence rather than a data gap.
Organic signal · Instagram
Instagram organic engagement-per-post
Mean of (likes + comments) on the most recent 30 posts per brand. Via Apify.
Farfetch ranks 7th of 10 on Instagram engagement-per-post (840), well behind editorial-led accounts like Highsnobiety (11,728) and SSENSE (3,313). Farfetch's organic AI-mindshare lead is not coming from Instagram reach. This is the attention-vs-memory gap: the surfaces buyers see most are not the surfaces the models read.
Most-recent 30 posts per brand, via Apify, captured 2026-06-01. Bootstrap roughly ±15% on engagement averages at this sample size. The @selfridges pull resolved to a wrong handle and is excluded; MR PORTER's @mrporterlive is likely a secondary account.
Organic signal · TikTok
TikTok organic engagement-per-video
Mean of (likes + comments + shares + saves) on the most recent 30 videos per brand. Via Apify.
Farfetch ranks near the bottom on TikTok engagement-per-video (69, 0.08% ER) despite 86K followers. MR PORTER drives roughly 47× Farfetch's per-video engagement at a fraction of the followers, so the menswear-luxury TikTok format is a live opportunity. As with Instagram, Farfetch's AI-mindshare lead is not built on social attention; it is earned editorial and forum presence that the models actually read.
Data gaps: Mytheresa, Bergdorf Goodman (squatted or unverified handles).
Most-recent 30 videos per brand, via Apify, captured 2026-06-01. Engagement is volatile post-to-post. @mytheresa and @bergdorfgoodman resolve to squatted or empty handles and are excluded.
4 · Notes & caveats
- Query battery. This run uses buyer-decision queries inferred from the category, not Farfetch's real on-site search terms. A paid engagement swaps the inferred queries for the terms buyers actually use, so the organic-mindshare number and the per-query gaps get materially more representative.
- Card lift quantification. Cards give the mechanism, the hypothesis, and the re-measurement criterion, not yet a calibrated forecast. The ordering is a qualitative lift ranking.
- SSENSE's organic figure (61.1%) is from its own reference run (2026-05-28); the rest of the cohort is from the farfetch-v3 run (2026-06-01). Same battery shape, subject swapped, so the comparison stays apples-to-apples.
Re-measurement cadence: weekly across every stream. The Korpora measurement model and the channel surfaces it measures both evolve continuously; weekly captures inflections that quarterly would miss.
Methodology question or factual correction? Ask in the chat above.
Related: full methodology · data provenance · luxury leaderboard · home