Bergdorf Goodman × Korpora · AI mindshare measurement · luxury department store
Bergdorf Goodman is a household luxury name.
In AI luxury discovery, it is invisible.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
bergdorfgoodman.com ········ null
+7 rivals ········ null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Absent from the answer and from
the citations behind it.bergdorfgoodman.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, bergdorf-v3 run 2026-06-01.
Across 54 subject-blind cells, Bergdorf Goodman surfaces in 0.0% of AI answers (Wilson upper bound 6.6%). When a model is handed a prompt that names the brand, it produces fluent, specific differentiation copy (94.4% evaluation-stage), so the positioning language exists in model knowledge. But when no brand is named, the brand does not come up at all. The 94.4pp gap between the two is the textbook cold-start signature: deep offline equity, near-zero presence on the surfaces models actually read. Bergdorf is not losing a close race; it is not yet in the race. The entire opportunity is to build a citable presence from a near-blank base, in the 6-to-12-month window before the next model corpus closes.
WHO AI CITES ACROSS 12 LUXURY
BUYER-DECISION QUERIES (US)
bergdorfgoodman.com ········ null
+7 rivals ········ null
CITES INSTEAD
youtube ██████████████ 7237
reddit █████████████░ 6635
instagram ██░░░░░░░░░░░░ 1286
wikipedia █░░░░░░░░░░░░░ 315
Absent from the answer and from
the citations behind it.bergdorfgoodman.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, bergdorf-v3 run 2026-06-01.
Bergdorf Goodman · public dashboard
Bergdorf Goodman's AI mindshare, live
3 headline views
snapshot · 2026-06-01
Where Bergdorf Goodman stands in the answers AI shopping assistants give luxury buyers, measured against its cohort: SSENSE, NET-A-PORTER, Farfetch and Mytheresa. Tab through the findings; each number is real, pulled from the v3 run on 2026-06-01. The honest headline is a cold-start: strong real-world brand, zero unprompted AI recall.
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 Bergdorf Goodman 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. Bergdorf Goodman 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 Bergdorf Goodman 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 Bergdorf Goodman'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 department storebuyer 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 (0.0%, the metric that drives action)
- Bergdorf Goodman was surfaced by AI without being named in the prompt. We asked the model an open buyer-decision question and recorded whether Bergdorf Goodman came up. 54 subject-blind cells; Bergdorf Goodman was named in 0 of them. This is unprompted recall: whether AI surfaces Bergdorf Goodman 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.
- 94.4pp organic-to-evaluation spread = cold-start classification
- The gap between organic (0.0%) and evaluation (94%) is 94.4 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 Bergdorf Goodman 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)
100% 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, Bergdorf Goodman sits at the floor of the cohort: 0.0% organic share (0/54 cells, Wilson CI 0.0-6.6%), tied with Selfridges at zero. Farfetch leads at 61.1% and NET-A-PORTER follows at 44.4%; even the smallest brands with any indexed discussion (Mytheresa 9.3%, Highsnobiety at 1.9%) register above Bergdorf. SSENSE, in its own reference run, sits level with Farfetch at 61.1%. The read is unambiguous and not flattering: when a luxury buyer asks AI for a recommendation without naming a brand, Bergdorf Goodman does not come up, while four rivals reliably do. This is the starting line, not a ranking to defend.
Cross-battery recall
- Evaluation-stage (subject-named framings): 94.4% (51/54 cells, CI 84.9-98.1%).
- The split by framing is the whole story: differentiation 100% (27/27), direct-comparison 88.9% (24/27), use-case 0.0% (0/27), discovery 0.0% (0/27).
- Per model the brand is identical across the line: Sonnet, Haiku and GPT-5.5 each surface it in 0 of 18 subject-blind cells, and each at 47.2% aggregate when the brand is named.
When a model is told the brand exists, it can describe Bergdorf Goodman fluently: curated luxury classics, Chanel and Hermes adjacency, Fifth Avenue service. That fluency is real positioning language that has reached model knowledge, and it is why evaluation-stage reads 94.4%. But on the two subject-blind framings (discovery and use-case), recall is an absolute zero across all three models, with no per-model variance to exploit. Treat the 94.4% as model compliance when handed the name, not unprompted demand. The gap between the two (94.4pp) is the cold-start diagnostic, and it is the largest in the cohort.
Across both views, Bergdorf Goodman is a cold-start in the AI channel: 0.0% organic recall against a 94.4% evaluation-stage figure that reflects model compliance, not unprompted demand. The brand the models will narrate when prompted is invisible when they are not. The work is not defend-and-extend (there is nothing yet to defend); it is to manufacture indexed, citable presence on the surfaces models read, starting from the contested slice where rivals already surface and Bergdorf does not.
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 bergdorfgoodman.com on both platforms. Same for ssense.com, mytheresa.com, net-a-porter.com, farfetch.com, mrporter.com, highsnobiety.com and selfridges.com. No luxury retailer is cited at the domain level. For Bergdorf this is double jeopardy: the brand is absent from the AI answer (Stream 1) and absent from the third-party surfaces AI cites to build that answer. AI leans entirely on video, forums, resale marketplaces and reference sites. Notably, resale (ThredUp, Alibaba) is cited where the first-party retailers 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. The citation graph measures where AI points, not on-site search behaviour.
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 Bergdorf Goodman 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.
Bergdorf Goodman runs 46 active Meta ads, mid-pack in the cohort, behind Mytheresa (602), NET-A-PORTER (211) and SSENSE (98). So the brand is spending on paid social while scoring zero on organic AI recall. That is the central inversion for Bergdorf: paid Meta volume is not translating into the earned, indexed presence that models read. The dollars are buying feed impressions that do not compound into model memory. The action is not more Meta spend; it is redirecting effort toward crawlable, citable editorial and structured content, which is exactly what the cold-start signal demands.
Snapshot from the GetHookd ad library, 2026-06-01. Active-ad counts combine Facebook and Instagram placements. Farfetch returns no tracked active ads.
Organic signal · Instagram
Instagram organic engagement-per-post
Mean of (likes + comments) on the most recent 30 posts per brand. Via Apify.
Bergdorf Goodman ranks last of 10 on Instagram engagement-per-post (19, @bergdorfs), two orders of magnitude below editorial-led accounts like Highsnobiety (11,728) and SSENSE (3,313). For a brand with this much real-world cachet, that is a striking under-index on the platform models read most directly for visual fashion signal. The attention surface is not feeding the corpus, because there is almost no attention to feed. This is consistent with the cold-start: the brand's equity lives offline and in the store, not in the social and editorial footprint AI ingests.
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.
Bergdorf Goodman has no measurable TikTok presence: the handle is squatted and returns no data, so the brand cannot even be ranked. It shares that gap with Mytheresa, but the rest of the cohort is active, and StockX (32,810 per video) shows how much engagement a luxury-adjacent format can pull. A squatted handle is both a brand-control problem and a missed corpus surface. As with Instagram, none of this is feeding the model, because the brand is effectively not present on the platform.
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. @bergdorfgoodman and @mytheresa resolve to squatted or empty handles and are excluded from ranking.
4 · Notes & caveats
- Query battery. This run uses buyer-decision queries inferred from the category, not Bergdorf Goodman'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 bergdorf-v3 run (2026-06-01). Same battery shape, subject swapped, so the comparison stays apples-to-apples.
- Evaluation-stage recall (94.4%) is reported but is not the headline: for a cold-start brand it conflates real buyer evaluation with model compliance and hallucination when the brand is named. Organic (subject-blind) recall is the metric that drives action.
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