SSENSE × Korpora · AI mindshare measurement · luxury e-commerce
SSENSE co-leads AI mindshare in luxury.
No retailer owns it at the domain level.
WHO AI CITES FOR "best luxury
fashion online retailer"
ssense.com ·············· null
+8 rivals ·············· null
CITES INSTEAD
youtube ██████████████ 515
reddit █████████████░ 471
newsweek ██░░░░░░░░░░░░ 87
wikipedia ██░░░░░░░░░░░░ 85
Wins the answer on citations
it does not own.ssense.com and eight luxury rivals all return null at the domain level. AI cites YouTube, Reddit, Newsweek and Wikipedia instead. Source: Google AI citation graph, v3 run 2026-05-28.
Across 54 subject-blind cells, SSENSE surfaces in 61.1% of AI answers, level with Farfetch at the top of the cohort. But look at who the models actually cite for “best luxury fashion online retailer” and ssense.com returns null, as do all eight luxury and streetwear rivals. AI leans on third-party authority instead: YouTube, Reddit, Newsweek, Wikipedia. The lead rests on borrowed supply, not a domain-level moat. The opening is to become a source the models cite directly.
WHO AI CITES FOR "best luxury
fashion online retailer"
ssense.com ·············· null
+8 rivals ·············· null
CITES INSTEAD
youtube ██████████████ 515
reddit █████████████░ 471
newsweek ██░░░░░░░░░░░░ 87
wikipedia ██░░░░░░░░░░░░ 85
Wins the answer on citations
it does not own.ssense.com and eight luxury rivals all return null at the domain level. AI cites YouTube, Reddit, Newsweek and Wikipedia instead. Source: Google AI citation graph, v3 run 2026-05-28.
SSENSE · public dashboard
SSENSE's AI mindshare, live
3 headline views
v3 snapshot · 2026-05-28
Where SSENSE stands in the answers AI shopping assistants give luxury buyers, measured against its category cohort: Farfetch, Mytheresa, NET-A-PORTER and MR PORTER. Tab through the findings; each number is real, pulled from the v3 run on 2026-05-28, and re-pulled weekly on a paid engagement.
Action cards
What to ship before the next training corpus closes.
Six cards, ordered by qualitative lift potential (cross-stream evidence weight × ship feasibility × time-to-corpus). Each carries a paste-ready engineer prompt; cards involving copy and image generation also carry a brand-voice growth prompt. The battery cutoff was 2025-12-01, so content shipped now is building toward the cohort after next, roughly 6–12 months out.
Active
6 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 SSENSE 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.
Korpora does not run these for everyone. SSENSE 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 SSENSE and shipped the result as the dashboard you are reading: the live mindshare monitor, six ranked action cards (each with a paste-ready engineer prompt), the full method and v1 notes, 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. See /eight-sleep and /workwhile for the same treatment on other brands.
- 2
Paid engagement (what continuing looks like)
When the measurement earns it, continuing is a paid engagement scoped to SSENSE'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.
Ask Korpora
Tell Korpora what you need
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 v1 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 buyer queries, and whether any retailer's own domain is cited at all. From DataForSEO LLM Mentions.
- Reddit corpus velocity: earned brand mentions across eight luxury and streetwear subreddits, the third-party surface AI cites most for this category, with recent publication velocity measured against a matched pre-cutoff window. Read against the demand axis (SSENSE AI search volume, -34% from its Aug 2025 peak).
- Creative landscape: competitive Meta ad inventory (Facebook + Instagram). Active ad volume per brand, from the live GetHookd ad library.
- Instagram organic: caption + engagement signal per post, content format mix. Via Apify (live), shown in the attention-footprint stream.
- TikTok organic: engagement-per-video, format pattern, the fastest-growing upstream for next-cycle editorial coverage. Via Apify (live), shown in the attention-footprint stream.
2 · How to read the headline metrics
- Organic owned share (61.1% [Wilson 95% CI 47.8–73.0], the metric that drives action)
- SSENSE was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question (“best place to buy designer streetwear”) and recorded whether SSENSE came up. 54 subject-blind cells; SSENSE was named in 33 of them. This is unprompted recall: whether AI surfaces SSENSE to a buyer who doesn't already know it exists. We treat it as a leading proxy for AI-driven discovery, not a measured conversion (whether that surfacing routes a purchase is a step we don't observe here). The CI is ±13pp because the sample is small (54 cells); a 5× larger battery would tighten this materially.
- 38.9pp organic↔evaluation spread = moderate-gap classification
- The gap between organic (61.1%) and evaluation (100%) is 38.9 percentage points. A spread of 15–40pp is what Korpora flags as a moderate gap (our own working threshold, not an industry standard; >40pp is cold-start): the model knows SSENSE well enough to engage when named but does not surface it unprompted at the discovery stage as often as you'd want for a category-defining brand. The mindshare lead is real; on our read it is corpus-supply-dependent, see the citation graph below for the structural evidence.
3 · The live measurement streams
Four data streams, re-run weekly on a paid engagement. Streams 1 and 2 (AI recall, citation graph) are the model-memory readout; Stream 3 (Reddit) is the corpus-feeding surface models actually read; Stream 4 collapses the three 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 4 · live data · Korpora 108-cell battery · v3 · 2026-05-28
AI assistant recall composition
12 buyer-decision queries × 3 frontier models (Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5.5) × 3 rounds, 108 cells total. Codex dropped as inappropriate for luxury queries. 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.
Organic owned share 61.1% [Wilson 95% CI 47.8–73.0]. How to read this and the 38.9pp moderate-gap spread is in the method notes below.
Subject-own organic AI mindshare · v2 head-to-head (each brand’s own battery)
Pierre-lens read.On true subject-own organic ownership (each brand’s own battery, subject-blind cells only), Farfetch and SSENSE are statistically tied for category leadership, their 95% CIs heavily overlap [46.8–69.0] vs [40.1–62.6]. NET-A-PORTER and Mytheresa form a clearly separated second tier. The SSENSE v1 framing of “category leader on the AI mindshare battery” was an artifact of subject-centric measurement, the v2 honest read is “tied with Farfetch at the top, with Mytheresa and NET-A-PORTER materially behind.” Versioning note: the headline 61.1% organic figure at the top of this stream is SSENSE's latest 3-model re-run (v3, Codex dropped); this head-to-head holds the original 4-model battery where all four brands were measured identically, so the comparison stays apples-to-apples. The competitor 3-model re-runs land next cycle.
Cross-battery recall · v2 finding
The strongest evidence for SSENSE’s mindshare position isn’t the SSENSE-centric battery, it’s how often SSENSE surfaces in competitors’ batteries:
- In Mytheresa’s battery: SSENSE 68.1% [60.1–75.1], Mytheresa (subject) 59.7%
- In NET-A-PORTER’s battery: SSENSE 66.7% [58.6–73.8], NET-A-PORTER (subject) 66.7%
- In Farfetch’s battery: SSENSE 65.3% [57.2–72.6], Farfetch (subject) 79.2%
SSENSE is surfaced more often than the subject brand in Mytheresa’s own battery, and ties NET-A-PORTER in its own. That kind of cross-query-framing consistency is the strongest signal of category-shaping AI mindshare we have, stronger than a single headline number on a subject-centric battery.
Honest summary across both views: SSENSE is tied with Farfetch at the top tier on subject-own organic, and is the most consistent cross-battery competitor in the cohort. Mytheresa and NET-A-PORTER are a clearly separated second tier. The defense work is the same as before, citation graph (Stream 2) and upstream creative pipeline (Stream 4), but the positioning is no longer “lone leader”; it’s “co-leader with Farfetch, with the most consistent cross-query recall in the cohort.”
v2 data sources. mytheresa-v1-2026-05-28, net-a-porter-v1-2026-05-28, farfetch-v1-2026-05-28, each 144-cell battery (12×4×3), same config shape as the SSENSE v1 battery, subject swapped per run. All four artifacts available on request.
Stream 2 of 4 · live data · DataForSEO LLM Mentions · 2026-05-28
LLM citation graph
For the buyer query “best luxury fashion online retailer”, which domains do Google AI and ChatGPT actually cite as sources? None of the retailers' own pages.
Google AI · top cited domains
ChatGPT · top cited domains
Domain-scoped LLM Mentions returns null for ssense.com on both platforms. Same for mytheresa.com, net-a-porter.com, farfetch.com, mrporter.com, highsnobiety.com, bergdorfgoodman.com, selfridges.com, endclothing.com. Only StockX (21,621 mentions on Google AI) and Vestiaire Collective (2 mentions) register at the domain level.
Data confidence. Per-query sample is small (per-domain mention counts range 2–55 on ChatGPT, 73–515 on Google AI). The cohort pattern (no luxury retailers at domain level) is robust because 9/9 domains return null; individual per-source ordering on a single query is not statistically reliable. All LLM Mentions counts are US-scoped (DataForSEO location 2840), Google AI and ChatGPT pulled as separate platforms. DFS billed $0.10 per query.
Stream 3 of 4 · Reddit corpus velocity · paused
Corpus-feeding velocity (Reddit)
Stream 2 shows reddit.com is the #2 cited domain for luxury buyer queries on both Google AI and ChatGPT, so Reddit is a surface the models actually read, and that citation behaviour is measured there. Per-brand Reddit corpus ingestion is paused pending a commercial data license, so the footprint and velocity figures are held back here. This stream returns, with the full per-brand footprint and post-cutoff velocity, once the licensing is in place.
Stream 4 of 4 · 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 SSENSE buys and earns on human channels, against how little of it compounds into the model memory measured in Streams 1 and 2. Read this as the attention-vs-earned-memory contrast, not as a mindshare input.
Paid signal · Meta ad inventory · GetHookd · 2026-05-26
Active Meta ad inventory
Count of unique active Meta ads (Facebook + Instagram placements combined) per brand, snapshotted from the GetHookd ad library.
Mytheresa runs 6.9× SSENSE's active ad volume (712 vs 103). NET-A-PORTER 3.5×. Separately, on the Instagram panel SSENSE drives 23× Mytheresa's organic engagement-per-post. These are different metrics (ad count vs engagement-per-post), so this is not a like-for-like efficiency comparison; our hypothesis is that SSENSE's editorial register on owned channels earns attention its lower paid volume does not, and the action cards test whether shifting Meta creative toward that organic register lifts paid efficiency.
Data gaps: Farfetch, Highsnobiety, Vestiaire Collective, StockX, not in the original GetHookd pull. Re-pull scheduled for the next v2 measurement.
Data confidence. Snapshot only, no trend curve yet. Single timestamp 2026-05-26. Active-ad counts include both Facebook and Instagram placements (combined, not deduplicated). Real weekly cadence on engagement would re-pull this; v1 carries the single snapshot.
Organic signal · Instagram · captured 2026-05-27
Instagram organic engagement-per-post
Mean of (likes + comments) on the most recent 30 posts per brand. Via Apify.
SSENSE ranks 4th of 10 in this sample. Caption-length signal is informative on its own: SSENSE averages 300 characters per post (editorial-tight); Highsnobiety 758 (editorial-long); Mytheresa 623 with under-200 engagement; MR PORTER's 20-engagement / 5-char captions strongly suggest the @mrporterlive handle is a secondary surface and the primary live elsewhere (verify before next pull).
Data confidence. Most-recent 30 posts per brand. Bootstrap ±15% on engagement averages at this sample size. MR PORTER @mrporterlive flagged as likely-secondary handle; treat its 20-engagement row as unverified rather than as a category ranking. Mean is volatile to a single high-performing or viral post in the 30-post window.
Organic signal · TikTok · captured 2026-05-28
TikTok organic engagement-per-video
Mean of (likes + comments + shares + saves) on the most recent 30 videos per brand. Via Apify.
SSENSE ranks 7th of 9 here. MR PORTER drives 36× SSENSE's engagement per video with half the followers (52k vs 99k) and the highest ER% in the cohort, the menswear-luxury TikTok format reverse-engineer is Card 3.
Data gaps: Mytheresa, Bergdorf Goodman (squatted handles, alt handles TBD for v2).
Data confidence. Most-recent 30 videos per brand. Engagement is volatile post-to-post; bootstrap ±20–40% on the per-brand means at this sample size. Account-age confound: TikTok's algorithm favours warm accounts with consistent post history; the MR PORTER vs SSENSE gap could reflect format, cadence, OR account age, see Card 1 Phase 0 for the controlled test.
4 · v1 notes & caveats
- Query battery.v1 ran 12 buyer-decision queries inferred from the category, not SSENSE's real on-site search terms or demand-side analytics. 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 of what is actually losing SSENSE buyers.
- Card lift quantification. Cards give the mechanism, the hypothesis, and the re-measurement criterion, not yet a calibrated “N points over M corpus cycles” forecast. The ordering is a qualitative lift ranking; quantified estimates ship in v2+ once a backfilled measurement-to-outcome corpus exists to fit a channel-weight model.
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 · home