MR PORTER × Korpora · AI mindshare measurement · luxury menswear
MR PORTER owns the feeds AI does not read.
It is barely in the answers they give.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
mrporter.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Leads the feeds the models
never read.mrporter.com and seven luxury rivals all return null at the domain level. AI cites YouTube, Reddit, Instagram and Wikipedia instead. Source: Google AI citation graph, mrporter-v3 run 2026-06-01.
Across 54 subject-blind cells, MR PORTER surfaces in just 5.6% of AI answers, a cold-start position far behind Farfetch (61.1%) and NET-A-PORTER (44.4%). Yet it posts the highest engagement-per-video in the entire luxury cohort, 3,240 per TikTok at a 6.20% engagement rate, on only 52.3K followers, the leanest audience-to-attention ratio of any brand here. It runs effectively zero paid Meta (2 active ads against Mytheresa's 602). That is strong, efficient earned attention that has not converted into AI memory. And for the luxury queries AI does answer, mrporter.com is cited zero times, as are all eight rivals. The gap is the story: MR PORTER leads the surfaces the models do not ingest and trails the answers they give.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
mrporter.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Leads the feeds the models
never read.mrporter.com and seven luxury rivals all return null at the domain level. AI cites YouTube, Reddit, Instagram and Wikipedia instead. Source: Google AI citation graph, mrporter-v3 run 2026-06-01.
MR PORTER · public dashboard
MR PORTER's AI mindshare, live
3 headline views
snapshot · 2026-06-01
Where MR PORTER stands in the answers AI shopping assistants give luxury menswear buyers, measured against its cohort: Farfetch, NET-A-PORTER, SSENSE and Mytheresa. 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 MR 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. MR 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 MR 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 MR 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 fashion (menswear)buyer 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 (5.6%, the metric that drives action)
- MR PORTER was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question and recorded whether MR PORTER came up. 54 subject-blind cells; MR PORTER was named in 3 of them. This is unprompted recall: whether AI surfaces MR 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.
- 90.7pp organic-to-evaluation spread = cold start classification
- The gap between organic (5.6%) and evaluation (96%) is 90.7 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 MR 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)
94% 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, MR PORTER surfaces in just 5.6% of answers (3/54 cells, Wilson CI 1.9–15.1%), a cold-start position. Farfetch leads luxury AI discovery at 61.1%, with SSENSE level at 61.1% from its own reference run and NET-A-PORTER the only other real presence at 44.4%. MR PORTER sits in the long tail with Mytheresa (9.3%), just above Highsnobiety (1.9%) and the brands that do not register unprompted at all. The number is small but it is real and measured: MR PORTER is present in the organic answer, barely. Aggregate (named + unnamed) recall flatters it badly at 50.9%, because the subject-named framings force the models to engage; organic is where the honest separation lives, and there MR PORTER is near the floor.
Cross-battery recall
- Aggregate recall (all framings, reference only): MR PORTER 50.9% (n=55), inflated by subject-named cells.
- Evaluation-stage (subject-named framings): 96.3% (52/54). MR PORTER wins direct-comparison 92.6% and differentiation 100%.
- The 90.7pp spread between organic (5.6%) and evaluation (96.3%) is the cold-start signal: the models have thin unprompted knowledge and generate compliance responses when the brand is named for them.
Read the two numbers together and the diagnosis is unambiguous. When a buyer already holds MR PORTER's name and asks "MR PORTER or SSENSE", the models pick MR PORTER almost every time (92.6% direct-comparison, 100% differentiation). When the buyer names no brand, MR PORTER almost never comes up (5.6%). That 90.7pp gap means the evaluation-stage win is mostly query-framing artifact, not durable buyer signal. The work is to close the organic gap, not to celebrate the evaluation one.
The honest position: MR PORTER's organic AI mindshare is 5.6%, a cold-start, far behind a 61.1% Farfetch and a 44.4% NET-A-PORTER. It is present in the answer but small, and the large contested slice (75.9%) means the work is taking share from rivals who surface where MR PORTER does not, not claiming empty territory. Everything around the recall number tells the same story differently: best-in-cohort TikTok engagement, near-zero paid Meta, and a citation graph where no luxury retailer including MR PORTER owns a single domain-level mention. The earned attention is real and efficient; it simply has not reached the surfaces the models read. Closing that gap is the entire program.
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 mrporter.com on both Google AI and ChatGPT. Same for farfetch.com, ssense.com, mytheresa.com, net-a-porter.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, social and reference sites, when it answers a luxury menswear buyer. This is a cohort-wide gap, not an MR PORTER-specific one, and it means the brand that first becomes directly citable has a clear opening. 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 MR 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.
MR PORTER runs effectively no paid Meta, 2 active ads against Mytheresa's 602 and NET-A-PORTER's 211. Only Farfetch (0) is lower in the cohort. Set against its best-in-cohort TikTok engagement, the read is that MR PORTER's attention is almost entirely earned and organic, not bought. Paid Meta volume does not feed training corpora anyway, so this is not the lever that moves AI mindshare. But it sharpens the diagnosis: with organic recall at a cold-start 5.6% and no paid presence to lean on, the entire burden of moving AI memory falls on earned, citable surfaces, the exact surfaces where MR PORTER is currently absent.
Snapshot from the GetHookd ad library, 2026-06-01. Active-ad counts combine Facebook and Instagram placements. MR PORTER's 2 active ads and Farfetch's 0 most likely reflect 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.
On Instagram engagement-per-post MR PORTER lands near the bottom at 20, but treat this with caution: the captured handle (@mrporterlive) is likely a secondary account, so this is not a clean read of MR PORTER's main Instagram presence. What is clean, and the contrast that matters, is that MR PORTER's TikTok engagement is the most efficient in the cohort while its Instagram footprint here is negligible. As with Farfetch, the surfaces buyers see most are not the surfaces the models read; the attention-vs-memory gap is the recurring theme. Resolving the primary Instagram handle is a measurement fix before any Instagram conclusion is drawn.
Most-recent 30 posts per brand, via Apify, captured 2026-06-01. Bootstrap roughly ±15% on engagement averages at this sample size. MR PORTER's @mrporterlive is likely a secondary account, so the figure understates the brand's true Instagram presence; the @selfridges pull resolved to a wrong handle and is excluded.
Organic signal · TikTok
TikTok organic engagement-per-video
Mean of (likes + comments + shares + saves) on the most recent 30 videos per brand. Via Apify.
This is MR PORTER's strongest surface and the most striking number on the page. MR PORTER posts 3,240 engagement per TikTok at a 6.20% engagement rate, the highest ER in the cohort, on just 52.3K followers. StockX and Vestiaire Collective post higher absolute engagement but ride 3x to 10x the audience. Per follower, MR PORTER is the most efficient brand here: it beats Highsnobiety's 940K-follower account on raw per-video engagement and runs roughly 47x Farfetch's per-video engagement. The menswear-luxury TikTok format clearly works for MR PORTER. The unresolved tension is that this best-in-cohort attention sits next to a cold-start 5.6% organic AI recall, because TikTok is not a surface the models read. Converting that earned format into citable, indexed content is the central opportunity.
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, so read the ER ranking as directional, not a settled order. @mytheresa and @bergdorfgoodman resolve to gaps or empty handles and are excluded.
4 · Notes & caveats
- Query battery. This run uses buyer-decision queries inferred from the category, not MR 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.
- MR PORTER's organic figure (5.6%) is from its own mrporter-v3 run (2026-06-01); the rest of the cohort's own-battery figures are from their respective runs. Same battery shape, subject swapped, so the comparison stays apples-to-apples.
- SSENSE's organic figure (61.1%) is from its own reference run (2026-05-28). MR PORTER's 50.9% aggregate is reference-only and not the headline; the cold-start diagnosis rests on the 5.6% organic figure and its 90.7pp spread to evaluation-stage recall.
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