Mytheresa × Korpora · AI mindshare measurement · luxury e-commerce
Mytheresa runs the most ads in luxury.
It has the least unprompted AI recall.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
mytheresa.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Heaviest paid spend in the cohort,
near-bottom earned recall.mytheresa.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, mytheresa-v3 run 2026-06-01.
Mytheresa runs 602 active Meta ads, the most of any retailer in this cohort, roughly 3x NET-A-PORTER and 6x SSENSE. Yet across 54 subject-blind cells it surfaces in just 9.3% of AI answers, near the bottom of the field, while Farfetch and SSENSE co-lead at 61.1%. The spend is the largest in the cohort and the unprompted recall is among the smallest. That is the sharpest proof point we have measured: paid Meta volume does not buy AI mindshare. The opening is to build the earned, indexable presence the models actually read.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
mytheresa.com ············· null
+7 rivals ············· null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Heaviest paid spend in the cohort,
near-bottom earned recall.mytheresa.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, mytheresa-v3 run 2026-06-01.
Mytheresa · public dashboard
Mytheresa's AI mindshare, live
3 headline views
snapshot · 2026-06-01
Where Mytheresa stands in the answers AI shopping assistants give luxury buyers, measured against its cohort: Farfetch, NET-A-PORTER, SSENSE 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 Mytheresa 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. Mytheresa 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 Mytheresa 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 Mytheresa'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 (9.3%, the metric that drives action)
- Mytheresa was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question and recorded whether Mytheresa came up. 54 subject-blind cells; Mytheresa was named in 5 of them. This is unprompted recall: whether AI surfaces Mytheresa 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.
- 87.0pp organic-to-evaluation spread = cold start classification
- The gap between organic (9.3%) and evaluation (96%) is 87.0 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 Mytheresa 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)
91% 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, Mytheresa surfaces in 9.3% of answers, far behind Farfetch (61.1%) and NET-A-PORTER (44.4%). 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 Mytheresa sits in a distant third tier, ahead of only Highsnobiety (1.9%) and the brands at zero. Note the inversion against paid spend: Mytheresa runs the most Meta ads in the cohort and has among the least unprompted recall. Aggregate (named + unnamed) recall flatters Mytheresa to 52.8%, but that number conflates real recall with model compliance when the brand is named in the prompt; organic is where the real position shows.
Cross-battery recall
- Aggregate share (all framings, reference only): Mytheresa 52.8% (CI 43.4-61.9%), level with Farfetch and just above NET-A-PORTER 50.9%, behind SSENSE 65.7%.
- The aggregate sits 43.5pp above the organic figure because two of four framings name Mytheresa in the prompt, forcing a mention. Strip those out and 9.3% is what remains.
- Per-framing tells the story: direct-comparison 92.6% and differentiation 100% (both subject-named), against use-case 11.1% and discovery 7.4% (both subject-blind).
The 87.0pp spread between organic (9.3%) and evaluation (96.3%) classifies as cold-start: when buyers name Mytheresa, models engage favorably; when they do not, Mytheresa is mostly absent. The evaluation strength is real but it is downstream of a buyer already knowing the brand, which is exactly what paid Meta is meant to drive and AI discovery is not yet returning.
Across both views, Mytheresa is a cold-start brand in luxury AI discovery: strong when named (96.3% evaluation), weak when not (9.3% organic), a distant third behind Farfetch and SSENSE on unprompted recall. The organic composition is competitor-dominated, the contested slice (72.2%) is large, so the work is taking share from rivals on the surfaces models read, not claiming free territory. The largest lever is that the heaviest paid spend in the cohort is not translating into the earned, indexable presence that builds organic recall.
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 mytheresa.com on both platforms. Same for farfetch.com, ssense.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. For Mytheresa specifically, this means 602 active Meta ads buy reach on paid surfaces but contribute nothing to the citation graph the models actually read. 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 Mytheresa 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.
Mytheresa runs 602 active Meta ads, the most of any cohort retailer by a wide margin, roughly 3x NET-A-PORTER (211) and 6x SSENSE (98). Farfetch runs zero and still co-leads organic AI mindshare at 61.1%, while Mytheresa sits at 9.3%. That is the headline of this whole cohort: paid Meta volume runs exactly opposite to AI mindshare. The heaviest spender has the weakest unprompted recall, and the brand that spends nothing leads. Meta ads buy paid reach today; they do not enter the training corpus the next model generation reads, so none of that spend compounds into earned AI presence.
Snapshot from the GetHookd ad library, 2026-06-01. Active-ad counts combine Facebook and Instagram placements. Mytheresa's 602 is a live count, not an estimate. 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.
Mytheresa ranks 8th of 10 on Instagram engagement-per-post (138), well behind editorial-led accounts like Highsnobiety (11,728) and SSENSE (3,313), and below Farfetch (840). The 602 Meta ads are not translating into organic Instagram pull either, so the heavy paid presence is not building earned attention on the same platform. This is the attention-vs-memory gap in its sharpest form for Mytheresa: spending the most to be seen while earning little that the models will 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.
Mytheresa has no usable TikTok presence: the @mytheresa handle resolves to a squatted account, so measured engagement is zero. Meanwhile StockX (32,810 per video) and Vestiaire Collective (7,388) show the format works for product-led luxury and resale. For the cohort's heaviest Meta spender to be absent from TikTok entirely is a structural gap, not a tactical one. As with Instagram, Mytheresa's paid budget is not buying earned presence on the surfaces that feed culture or the models.
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 Mytheresa'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 mytheresa-v3 run (2026-06-01). Same battery shape, subject swapped, so the comparison stays apples-to-apples.
- Aggregate mindshare (52.8%) is reported for reference only and is not the headline. For a cold-start brand it conflates real recall with model compliance on subject-named framings; organic (9.3%) is the honest measure.
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