NET-A-PORTER × Korpora · AI mindshare measurement · luxury fashion
NET-A-PORTER is the close challenger in luxury AI recall.
It still trails the leaders on the metric that matters.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
net-a-porter.com ········· null
+7 rivals ········· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Challenges on recall, owns
none of the citations.net-a-porter.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, net-a-porter-v3 run 2026-06-01.
Across 54 subject-blind cells, NET-A-PORTER surfaces in 44.4% of AI answers, the only real challenger to the Farfetch and SSENSE co-leaders at 61.1%. A healthy 211 active Meta ads and strong editorial social do not change the gap: when a buyer names no brand, NET-A-PORTER comes up roughly four times in nine, the leaders six. And look at who the models actually cite across luxury buyer-decision queries: net-a-porter.com returns null, as do all eight rivals. AI leans on third-party authority instead, YouTube, Reddit, resale marketplaces, Wikipedia. The challenger position is real; closing on the leaders means being recalled unprompted and being a source the models cite directly.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
net-a-porter.com ········· null
+7 rivals ········· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Challenges on recall, owns
none of the citations.net-a-porter.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, net-a-porter-v3 run 2026-06-01.
NET-A-PORTER · public dashboard
NET-A-PORTER's AI mindshare, live
3 headline views
snapshot · 2026-06-01
Where NET-A-PORTER stands in the answers AI shopping assistants give luxury buyers, measured against its cohort: Farfetch, SSENSE, 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 NET-A-PORTER 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. NET-A-PORTER 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 NET-A-PORTER 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 NET-A-PORTER'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 (44.4%, the metric that drives action)
- NET-A-PORTER was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question and recorded whether NET-A-PORTER came up. 54 subject-blind cells; NET-A-PORTER was named in 24 of them. This is unprompted recall: whether AI surfaces NET-A-PORTER 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.
- 55.6pp organic-to-evaluation spread = cold start classification
- The gap between organic (44.4%) and evaluation (100%) is 55.6 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 NET-A-PORTER 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)
56% 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, NET-A-PORTER lands at 44.4%, the only brand within reach of the leaders. Farfetch leads at 61.1% and SSENSE, measured in its own reference run, sits level at 61.1%, so the honest read is Farfetch and SSENSE co-lead and NET-A-PORTER trails close behind as the clear #2. The CIs touch at the edges (NET-A-PORTER 32.0-57.6, Farfetch 47.8-73.0), so the gap is real but not a chasm: this is a near-tier with the leaders a step ahead, not a settled rout. Behind NET-A-PORTER the cohort collapses, Mytheresa 9.3%, everyone else at or near zero. Aggregate (named + unnamed) recall flatters NET-A-PORTER to the front of the field at 72.2%, but that mixes real buyer signal with model compliance when the brand is named; organic is where the real separation lives, and there NET-A-PORTER is chasing, not leading.
Cross-battery recall
- On NET-A-PORTER's own battery, the organic-to-evaluation spread is the widest tell in the cohort: 44.4% unprompted against 100.0% when the brand is named in the query.
- The 55.6pp spread classifies as cold start: the evaluation wins are largely the models complying with a named brand, not a trained association they reach for on their own.
- By framing, the split is stark: direct-comparison 100.0% and differentiation 100.0% (subject named), but use-case 51.9% and discovery 37.0% (subject blind). Discovery, the earliest buyer moment, is the weakest.
The challenger position is genuine but fragile. NET-A-PORTER wins every framing where the buyer already names it, and roughly half of use-case prompts, but only 37.0% of open discovery prompts. That is the gap to the leaders in one number: when a buyer opens with a problem and no brand, NET-A-PORTER is recalled barely a third of the time. The work is converting a named-brand advantage into an unprompted one before the next training cycle indexes.
Across both views, NET-A-PORTER is the close #2 in luxury AI discovery, the only credible challenger to the Farfetch/SSENSE co-leaders, but it trails them on the organic metric that matters and its lead evaporates the moment you strip out subject-named framings. The defend-and-close work is twofold: lift unprompted discovery recall (Stream 1), where it sits at 37.0%, and the citation graph (Stream 2), where NET-A-PORTER, like every rival, owns none of the domains AI actually cites.
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 net-a-porter.com on both platforms. Same for farfetch.com, ssense.com, mytheresa.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, NET-A-PORTER included, 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 NET-A-PORTER 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.
NET-A-PORTER runs a healthy 211 active Meta ads, second only to Mytheresa (602) and well ahead of SSENSE (98). But the two brands NET-A-PORTER trails on organic AI mindshare tell the inversion story: SSENSE co-leads recall on a third of NET-A-PORTER's ad volume, and Farfetch co-leads it while running zero Meta ads at all. In this cohort, paid Meta volume does not buy AI mindshare. NET-A-PORTER's spend keeps it visible to shoppers in the feed, but feed impressions are not what the models read, so the paid presence is not closing the recall gap to the leaders.
Data gaps: Farfetch (no active Meta ads in the library).
Snapshot from the GetHookd ad library, 2026-06-01. Active-ad counts combine Facebook and Instagram placements. Farfetch returns no tracked active ads; for brands of their 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.
NET-A-PORTER ranks 4th of 10 on Instagram engagement-per-post (3,196, via the @portermagazine editorial handle), just behind SSENSE (3,313) and ahead of every other retailer in the cohort including Farfetch (840). This is the brand's strongest competitive surface: its editorial apparatus drives real per-post engagement. But the surfaces buyers see most are not the surfaces the models read, so this strength is not what is moving organic AI recall, where NET-A-PORTER still trails the leaders.
Most-recent posts per brand, via Apify, captured 2026-06-01. Bootstrap roughly ±15% on engagement averages at this sample size. NET-A-PORTER's engagement is measured on @portermagazine. The @selfridges pull resolved to a wrong handle and is excluded; MR PORTER's @mrporterlive is likely a secondary account. NET-A-PORTER's @portermagazine pull is unverified for ownership, so its Instagram figure is directional.
Organic signal · TikTok
TikTok organic engagement-per-video
Mean of (likes + comments + shares + saves) on the most recent 30 videos per brand. Via Apify.
NET-A-PORTER ranks 5th of 9 on TikTok engagement-per-video (2,196, 2.25% ER on 97.6K followers), the strongest of the pure luxury retailers and far ahead of both co-leaders, SSENSE (91) and Farfetch (69). Its 2.25% engagement rate also beats Highsnobiety's larger but cooler audience. As with Instagram, NET-A-PORTER's social attention is genuinely competitive, but it is not what is building AI mindshare; the recall gap to the leaders persists on a surface where NET-A-PORTER is ahead of them.
Data gaps: Mytheresa, Bergdorf Goodman (squatted or unverified handles).
Most-recent 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 NET-A-PORTER'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 net-a-porter-v3 run (2026-06-01). Same battery shape, subject swapped, so the comparison stays apples-to-apples.
- NET-A-PORTER's aggregate share (72.2%) leads the field, but it mixes subject-named and subject-blind framings and is reference-only; the cold-start spread (55.6pp) is why organic (44.4%), not aggregate, is the headline.
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