Gucci went from 40% to 82% in AI answers.
We measured every week of it.
Eight months of weekly scans across 20+ engines, 219 stored analyses. Below: the curve, the split between market and brand questions, the engines, the market’s source graph, the sub-query gaps, and the exact pages that influence the answers.
The climb, week by week.
Share of voice across all tracked prompts. Monthly averages shown.
Market questions vs brand questions.
The split that most tools hide: prompts that name the brand behave completely differently from the market questions where buyers discover. We measure them separately — and exclude branded prompts from every gap metric below.
| Tracked prompt | Type | Stored answers | Own site cited | Rate |
|---|---|---|---|---|
| “Gucci stores near me in the United States” | Brand | 96 | 78 | 81% |
| “best Gucci handbags to buy online” | Brand | 140 | 85 | 61% |
| “best gucci handbags 2024” | Brand | 142 | 54 | 38% |
| “what are the best luxury shoes to buy this season?” | Market | 295 | 3 | 1% |
| “best luxury fashion brands in the US” | Market | 132 | 0 | 0% |
The citation ledger — before and after.
The raw accounting, market questions only, branded prompts excluded. Every number counted from the stored answers, not estimated.
| Metric — market questions only | Before (Nov–Jan) | After (Feb–Jun) | Change |
|---|---|---|---|
| Stored answers analysed | 228 | 389 | +71% |
| Total citations inside those answers | 2,848 | 5,035 | +77% |
| Unique domains cited | 202 | 526 | ×2.6 |
| Citations of gucci.com | 7 (0.2%) | 0 (0.0%) | flat ≈ 0 |
| gucci.com URLs appearing as sources | 3 | 0 | — |
| Citations from sources that NAME the brand | 1,521 (53.4%) | 2,684 (53.3%) | +76% volume |
“Names the brand” = Get Cited text scan of each cited domain’s content (679 domains scanned, 285 name the brand).
The market’s source graph.
The domains the AI cites when answering the category’s questions. This is the market view — not brand endorsements. The last column is a Get Cited text scan: does that domain actually name the brand in its content?
| Domain the AI cites (market view) | Before | After | Mentions the brand? |
|---|---|---|---|
| 4 | 152 | — | |
| 2 | 22 | — | |
| 3 | 17 | ✓ yes | |
| 6 | 15 | no | |
| 6 | 10 | no | |
| 3 | 9 | no | |
| — | 29 | ✓ yes | |
| — | 13 | ✓ yes | |
| — | 11 | ✓ yes | |
| — | 11 | no | |
| — | 10 | ✓ yes | |
| — | 10 | ✓ yes |
The cause is in the citations.
Same windows, every citation counted from the stored runs: 3,611 before, 9,501 after. The engines changed their diet — and the change explains the climb.
| Source | Share before | Share after | Change |
|---|---|---|---|
| 18.3% | 12.4% | -5.9 pts | |
| 9.1% | 4.9% | -4.2 pts | |
| 4.3% | 1.7% | -2.6 pts | |
| 4.5% | 9.3% | +4.8 pts | |
| 0.0% | 1.7% | +1.7 pts | |
| 0.0% | 1.4% | +1.4 pts | |
| 0.0% | 1.0% | +1.0 pts |
The proof: which pages put Gucci in the answer.
1,109 stored runs on the market questions; the brand appears in 61% of them. For every stored run we check two things in the same answer: which sources it cites, and whether the brand’s name appears in the answer text. Comparing runs where a source is present vs absent isolates each source’s effect. Correlation measured across runs — not a causal claim — sources with enough runs to matter only.
| Source cited in the run | Runs | Brand in answer when cited | When absent | Effect |
|---|---|---|---|---|
| 35 | 97% | 59% | +38 pts | |
| 81 | 95% | 58% | +37 pts | |
| 98 | 94% | 57% | +37 pts | |
| 59 | 95% | 59% | +36 pts | |
| 56 | 95% | 59% | +36 pts | |
| 71 | 4% | 64% | -60 pts | |
| 116 | 20% | 65% | -45 pts | |
| 60 | 15% | 63% | -48 pts |
The fan-out layer: where the gaps live.
Every prompt a user asks explodes into sub-queries the engines run silently. We track each one. Most appear once and vanish — nobody owns them yet. That long tail is the gap map.
| Sub-query the engines actually ran | Times seen | Engine consensus |
|---|---|---|
| “best luxury shoes spring 2026” | 63× | 1 engines |
| “best luxury shoes this season” | 57× | 1 engines |
| “best luxury shoes spring summer 2026 trends” | 56× | 1 engines |
| “best luxury shoes 2026” | 46× | 1 engines |
| “best luxury fashion brands in the us” | 23× | 1 engines |
The playbook.
Each move is one tool in the AI Rankia suite.
| The move | What it does | In the suite |
|---|---|---|
| Answer-first pages for market questions | Own the 502 unclaimed sub-queries | Content Hub |
| Pitch the sources that don’t name you yet | The ✗ rows in the source graph | Get Cited |
| Buy into the domains AI already cites | Citation graph → shopping list, measured lift | AI Links |
| Schema + entity on the pages that win | Make every page quotable | Action Center |
The schema autopsy: what the winners run under the hood.
We fetched the cited pages and extracted their structured data (JSON-LD), live. The pattern maps one-to-one to which questions each page wins.
| Cited page | Schema found | What it wins |
|---|---|---|
| laptopmag.com/best-laptops-for-graphic-designers | Article + ItemList + Breadcrumb | Market questions — the round-up recipe (349× fan-out winner) |
| apple.com/macbook-pro | Product + Organization | Product questions |
| gucci.com/store/725-fifth-avenue | Store | “Near me” questions (74+ citations) |
| gucci.com capsule & category pages | none found | Only win when the brand is already named |
| zara.com (any page) | unreachable — 403 to all bots | Nothing: the engines cannot read the site |
Method: live fetch of each cited URL with JSON-LD extraction (Aug 7, 2026). Gucci pages verified via Wayback snapshot — gucci.com blocks datacenter traffic. zara.com returned 403 to browser, GPTBot and PerplexityBot user-agents alike.
More cases, same measurement.
The earned-media case: 0 of 386 market answers cite zara.com — its visibility rides entirely on city guides, press and retailers.
The review-ecosystem case: in market questions, apple.com is cited in under 1% of answers — the tech press carries it.
The decline case: −23 points in one week when the engines dropped Yelp from the answer. Borrowed visibility, gone overnight.
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Scan my brand free → Book a demoObservational study of public AI answers, measured weekly by AI Rankia (Nov 2025 – Jun 2026). Per-engine presence rates; early-window samples are smaller. Brand names are trademarks of their respective owners, referenced for identification only; no affiliation or endorsement implied.