997 responses, 5 models, one vertical: the first AI-visibility snapshot of Turkey's evening & occasion wear market

Vertical: Evening & occasion wear (abiye / davet giyim), Turkey
Method: A single point-in-time study of 997 responses across five large language models (GPT-4o-mini, Gemini, Claude, Perplexity, Grok), each asked 40 Turkish-language questions five times
Collection date: 4 July 2026

Key terms: AI visibility, AI Visibility Score, evening wear brands, occasion wear brands, abiye / davet elbisesi, Turkish (yerli) evening-wear brands, LLM brand recommendations, ChatGPT/Gemini/Claude/Perplexity/Grok brand recommendation, Generative Engine Optimization (GEO).

This report reproduces the real Turkish-language questions consumers ask AI assistants about evening dresses, abiye and occasion wear in Turkey, and measures how often, in what order, and across how many models five large language models name each brand. Because the same questions can be read with or without an origin restriction, the brands are ranked in three cuts (main, local and open), defined in Chapter 2. It is a single point-in-time study and should be read within the limitations in Chapter 10.

How to read this report: visibility is not quality

This report measures one thing: how often, how prominently, and across how many AI assistants a brand is named when people ask about evening and occasion wear in Turkey. A high score means these systems currently have a lot of information available about a brand and surface it readily. It does not mean the brand is better, more elegant, cheaper or higher quality. A low score, or a zero, means a brand is currently less visible to these systems, not that it is inferior. AI visibility reflects information availability and discovery, not product quality, fit, price or trustworthiness. Readers and shoppers should not treat an AI recommendation, or this report, as a verdict on whether a brand is “good” or “bad.” This study does not measure the accuracy, quality, or sentiment of any recommendation.

1. Executive Summary

One-sentence takeaway: AI visibility in Turkey’s evening and occasion wear market is led by marketplaces, with Trendyol first and Sefamerve close behind. Carmen is the most visible standalone brand, on a shelf that is overwhelmingly Turkish and where foreign names appear only in a thin premium niche.

• Trendyol leads; Carmen is the top brand. Trendyol tops the main leaderboard at 91.9, appearing in 47.7% of all responses across all five models. Carmen (73.4) is second and the highest-scoring standalone brand, 18.5 points behind. Sefamerve (64.3), adL (63.9) and AlfaBeta (62.5) complete the top five.
• 33 brands qualified for the main ranking. Of 191 tracked brands, 33 cleared the 5% mention threshold across all 40 questions: 31 Turkish and just 2 foreign (Pronovias at #14 and Zara at #28). The average score of the 33 qualified brands is 52.5.
• The headline shelf is aggregator-led. Five of the 33 qualified entities are marketplaces or retailers: Trendyol, Sefamerve, Modanisa, Hepsiburada and Boyner. Remove them and the brand-only top three is Carmen, adL and AlfaBeta. The assistants reach for the big marketplaces first, then a cluster of Turkish occasion-wear specialists.
• This is the most Turkish-dominated market in the series. On the 20 origin-neutral (open) questions, mentions split 82.4% Turkish to 17.6% foreign, the least contested origin balance Herm.io has recorded. Only 6 foreign brands qualify in the open cut, and they cluster in premium bridal and couture (Pronovias, Chanel, Elie Saab, Valentino) plus two fast-fashion names (Zara, Forever New).
• The local shelf is entirely Turkish. When questions explicitly ask for Turkish/yerli brands (the 20 local questions), all 33 qualified brands are Turkish. Not a single foreign name survives the filter.
• The models see the same market at very different breadths. Grok lists about 9.9 distinct brands per answer and Perplexity reaches the widest vocabulary (154 distinct brands), while GPT-4o-mini (3.7) and Claude Haiku (4.1) are the most selective. Even so, Trendyol tops all five models.
• Search was near-universal. 97.7% of all answers were web-grounded; four of five models searched on roughly 100% of answers, and only Gemini fell below, at 88.8%. With almost no “memory-only” answers, this edition does not include a web-search-vs-memory comparison; see Chapter 5.
• Sources are brand-owned first. 65.5% of cited sources are brands’ own websites. The single most-cited domain is the marketplace trendyol.com (cited by 58.5% of responses), followed by brand sites such as carmen.com.tr, alfa-beta.com.tr and adl.com.tr.

Why it matters. Shoppers are shifting discovery from search engines and marketplaces to AI assistants. Whether an evening- or occasion-wear brand appears in those assistants’ answers is becoming a discovery channel in its own right. This report captures the first snapshot (baseline) of this new “visibility shelf” for Turkey’s evening and occasion wear sector. One reminder: the numbers below describe visibility and information availability, not which brands are best.

2. Methodology

One-sentence takeaway: Five large language models were asked 40 Turkish questions five times each, with no system prompt, producing 997 usable responses. Brands were identified by automated extraction plus human validation and ranked with a three-component (45/30/25) score, computed separately for the main, local and open cuts.

2.1 Scope and models

The study queried five large language models directly via API with identical questions. All models received only the user question. No system prompt was used, temperature was not set manually (provider default), and no region or locale parameter was defined. Because the questions are in Turkish, the models infer the Turkish market from language alone.

Scale: 40 questions × 5 repeats × 5 models = 1,000 responses; 3 were excluded (empty Gemini generations), leaving 997 usable. Single run, 4 July 2026. The 40 questions carry no topical sub-categories, so the category breakdown uses three behavioural types: discovery, attribute and use_case (14 / 13 / 13). Final brand dictionary: 191 entities (158 Turkish, 33 foreign).

The three market cuts. Rather than two side-by-side markets, this vertical is read in three cuts of the same questions:

2.2 Web-search configuration

Web search was offered as an enabled tool to all five models; each decided for itself whether to use it (Perplexity’s Sonar is search-grounded by design). Whether a response drew on web search or the model’s own knowledge was inferred from whether the provider returned any citation or source: a citation means “web-search,” none means “own knowledge.” This is a reasonable proxy, not direct proof of the model’s internal process. In this run, 97.7% of answers were grounded (see Chapter 5).

2.3 Questions

The 40 questions reproduce the real Turkish-language queries people bring to an assistant about evening dresses, abiye and occasion wear: general discovery (“abiye elbise önerisi”), specific attributes (fabric, fit, plus-size, non-creasing), and use cases (weddings, henna nights, graduations, engagements). They are balanced across the three behavioural types and split evenly by origin scope.

Of the 40, 20 are “local” (they explicitly ask for Turkish / yerli brands) and 20 are “open” (no origin restriction). The Turkish-vs-foreign comparison in Chapter 7 rests solely on the 20 open questions, the only fair basis for comparing Turkish and foreign brands, since the local questions cannot surface foreign brands by construction.

2.4 Brand extraction and validation

Brands were extracted in two stages. (1) Automated extraction: emphasised brand names and citation domains were collected, and spelling/format variants merged into a single canonical name (case-, diacritic-, spacing- and domain-suffix aware). (2) Human validation: the candidate list was reviewed by hand, with real brands confirmed, non-brands (category words, generic phrases, fabric types, places) filtered out, aliases merged, and each brand labelled Turkish or foreign. Final dictionary: 191 entities. Per study scope, marketplaces and retailers (Trendyol, Hepsiburada, Modanisa, Sefamerve, Boyner) are kept as entities so the shelf is described as shoppers actually meet it; a brand-only view is given in §3.2. Fast-fashion names with their own labels and stores (Koton, LCW, Roman) are treated as brands.

Origin rule. A brand is Turkish if founded and headquartered in Turkey, and foreign otherwise, even when widely sold in Turkey (e.g. Zara, Mango, Pronovias, Elie Saab).

Definitions.

• Mention: a validated brand (or alias) appears in the answer text, counted once per response (presence, not frequency), matched word-boundary aware, case- and diacritic-insensitive, longest-alias-first so a shorter name never double-counts inside a longer one.
• Position (MRR): mean reciprocal of the brand’s first-appearance rank within each answer (first = 1.0; second = 0.5; …), averaged only over the responses where the brand appears.
• Breadth: number of the 5 models (0–5) that mention the brand within that cut.

2.5 AI Visibility Score (0–100)

Score = 0.45 × Mention + 0.30 × Position + 0.25 × Breadth

Each component is scaled so the leading qualified brand in each cut = 100, then combined with the weights. Qualification (≥5% rule): only brands mentioned in at least 5% of that cut’s responses are ranked (33 main, 33 local, 32 open). This threshold limits the risk that a rare brand which happens to appear first in a few answers inflates the position component. Unfiltered metrics for all 191 brands appear in Chapter 11.

2.6 Neutrality and self-exclusion

This is a market-wide, neutral study with no focus brand. To prevent any conflict of interest, all citations to Herm.io’s own domains were excluded from the source data before analysis, so the company’s own content neither appears among the most-cited domains nor influences any figure.

3. Overall Visibility Leaderboards

One-sentence takeaway: Marketplaces lead every cut; the difference between cuts is what happens behind them. The local shelf is entirely Turkish, and even the open shelf is only lightly contested by foreign brands.

As a reminder, these rank visibility, not product quality, fit, price or reliability.

3.1 Main leaderboard: 33 qualified brands

Average score of the 33 qualified brands: 52.5.

Reading. Trendyol maxes out the mention and breadth components and tops the table at 91.9, appearing in 47.7% of answers. Carmen (73.4) is second, the most visible standalone brand, on strong and broad mentions (33.0%). Then come two entities that rank on position rather than volume: Sefamerve (#3, 64.3) is named in only 13.0% of answers but very early when named (MRR 0.61), and VznModa (#6, 61.7) clears the bar on just 7.1% mentions with the single highest position component in the cut (MRR 0.67), a rank to read cautiously (see Chapter 10). 31 of the 33 qualified brands are seen by all five models, and only two foreign names appear in the whole table: Pronovias (#14) and Zara (#28).

3.2 Brand-only view (marketplaces removed)

Five entities in the table above are marketplaces or retailers rather than brands: Trendyol, Sefamerve, Modanisa, Hepsiburada and Boyner. They are kept in the headline ranking because that is how shoppers meet the shelf, but removing them gives a pure brand-versus-brand picture:

With the aggregators set aside, the top of the market is Carmen, adL, AlfaBeta, VznModa and Koton, all Turkish, with Pronovias the highest-placed foreign brand. (Koton, LCW and Roman are treated as brands, not marketplaces, and remain in this view.)

3.3 Local leaderboard: 33 qualified brands (Turkish-only questions)

When the 20 questions that explicitly ask for Turkish/yerli brands are isolated, the shelf is entirely domestic: all 33 qualified brands are Turkish.

Average score of the 33 qualified local brands: 52.9. All 33 are Turkish.

Reading. Trendyol (85.2) and Carmen (82.4) again lead, followed by AlfaBeta (71.8), the marketplace Sefamerve (69.5) and adL (64.6). The local cut elevates specialist domestic names: VznModa rises on position again (#8, MRR 0.70, the highest in the cut). Because the question already constrains the answer to yerli brands, the assistants respond with an all-Turkish list; the clean absence of foreign names shows they recognise “yerli” as a real constraint.

4. Differences Between Models

One-sentence takeaway: All five models agree on the leader, Trendyol, but they differ enormously in breadth: Grok names nearly 9.9 brands per answer and Perplexity reaches the widest vocabulary, while GPT-4o-mini and Claude Haiku stay near 4.

4.1 Per-model behaviour summary

Web search was near-universal: four of the five models searched on essentially every answer, and Gemini on 88.8%. The clearest difference is breadth. Grok lists about 9.9 brands per answer and Perplexity reaches the longest tail (154 distinct brands), while GPT-4o-mini (3.7) and Claude Haiku (4.1) name a much more selective set. Gemini (6.4) sits in between. Despite these different answering styles, Trendyol is the most-named entity in every model.

4.2 Each model’s most-named brands

The tables below give each model’s five most-mentioned entities across its ~200 responses (rate = % of that model’s answers naming the brand). Trendyol tops all five models; Carmen is the most consistent runner-up.

Which entity leads is unusually consistent, with Trendyol first and Carmen close behind, but how many brands an answer contains depends heavily on the model. In Grok, Trendyol appears in 96% of answers and the tail is long; in the more selective GPT-4o-mini and Claude Haiku, a long-tail brand’s chance of being named is far lower.

5. Search vs. Memory: Why This Edition Omits the Split

One-sentence takeaway: Almost every answer in this run used web search, so there is no meaningful “memory-only” segment to compare against, which is itself a finding.

Some earlier Herm.io editions split answers into web-grounded and memory-only segments that produced different leaders. That split does not exist in this run. About 97.7% of the 997 answers returned at least one citation or search result, and the “own-knowledge” segment is a handful of responses, far too small to support a comparison. Four of the five models (GPT-4o-mini, Claude Haiku, Perplexity, Grok) searched on 99.5–100% of answers, and only Gemini fell below, at 88.8%.

Because almost all queries used web search, a separate web-search-vs-memory chapter would compare roughly 974 grounded answers against about 23 ungrounded ones, which is not a meaningful contrast, so it is left out this edition. The takeaway is that, for Turkish evening- and occasion-wear questions, these assistants reach for live web content almost every time. That makes a current, well-structured web presence especially closely tied to visibility here (see Chapter 8). We will keep measuring the grounding rate each edition; if a memory-only segment grows, the comparison will return.

6. Question-Type Ownership

One-sentence takeaway: Trendyol leads all three question types, but the runners-up reshuffle: adL is strong on open-ended discovery, while the marketplace Hepsiburada rises on need-driven use-case questions.

The 40 questions carry no topical sub-categories, so ownership is read through three behavioural types: discovery (general “best / recommended”), attribute (specific qualities such as non-creasing, plus-size, a particular fabric or colour) and use_case (occasion-driven, such as a wedding, henna night, graduation or engagement). The tables show the most visible brands within each type (rate = relative to that type’s responses).

Reading. Trendyol leads all three types, most heavily on use-case questions (52.6%), where shoppers describe an occasion and the assistants lean on the marketplace’s breadth of listings. The runners-up shift by type: adL is unusually strong on discovery (41.8%, second only to Trendyol) when the question is an open “recommend a brand,” while Hepsiburada climbs to third on use-case questions (22.5%). Carmen is the most stable non-marketplace name, sitting second or third across all three types.

7. Open Market: Turkish vs. Foreign

One-sentence takeaway: Across origin-neutral questions, 82.4% of mentions go to Turkish brands and only 17.6% to foreign, the most Turkish-dominated split Herm.io has measured, with foreign brands confined to a premium bridal and couture niche.

This chapter relies only on the 20 open questions with no origin restriction (500 responses), the only fair basis for comparing Turkish and foreign brands.

7.1 Origin share

The Turkish lead here is not modest but decisive: 2,370 Turkish mentions to 506 foreign. Breadth points the same way, with 117 distinct Turkish brands surfacing in the open market versus 31 foreign. Unlike verticals where domestic and foreign brands trade the origin-neutral shelf evenly, evening and occasion wear in Turkey is answered almost entirely with Turkish names, even when the question does nothing to invite them.

7.2 Open-market top 15 brands (any origin)

7.3 The pattern: Turkish brands own the shelf, foreign holds a premium niche

The individual ranking shows what the aggregate hides. The open-market top three are Turkish (Trendyol, Carmen, adL), and the first foreign name is Pronovias at #5, a bridal and couture house. The other foreign qualifiers follow the same pattern: Chanel (#8, named rarely but very early, so read cautiously), then Elie Saab and Valentino, all luxury and couture, alongside two fast-fashion outliers, Zara and Forever New. So when a shopper asks an origin-neutral evening-wear question, the answer is led by Turkish brands and includes a foreign name only when the register turns to high-end bridal or designer gowns. Whether that niche widens is exactly the kind of movement a quarterly baseline is built to track.

8. The Discovery Ecosystem: Where AI Learns About Brands

One-sentence takeaway: AI’s picture of this market rests first on brands’ own websites (65.5% of cited sources) and, above them all, the marketplace trendyol.com, rather than on editorial or review content.

When an assistant answers with web search, it draws on the content available to it at that moment. We collected the source addresses returned by the models, reduced them to domains, and ranked them by how many distinct answers cited each, giving a map of where these systems read about evening and occasion wear brands.

8.1 The 9 most-cited domains

The single most-cited domain is the marketplace trendyol.com (cited by 58.5% of responses), but it is followed immediately by a cluster of brand-owned sites (carmen.com.tr, alfa-beta.com.tr, adl.com.tr, iconasline.com, abiyefon.com, lafaba.com, kenzel.com.tr), interleaved with a second marketplace (sefamerve.com) and a long tail of others. Across the run the models cited 355 distinct domains.

8.2 Source-type mix

8.3 The brand-owned finding

The most striking pattern is a concentration on official and marketplace sites: 65.5% of citations point to brands’ own domains and another 17.7% to marketplaces and retailers, leaving only small shares for the long tail (13.4%), editorial and media (1.6%), social (1.6%) and forums or UGC (0.3%). Read as a description rather than advice: in this market, a current, machine-readable brand site, plus presence on the big marketplaces, appears closely associated with AI visibility, more so than editorial or review coverage.

9. What the Patterns Suggest

One-sentence takeaway: The most visible names tend to share three things: coverage across all five models, a strong own-site and marketplace web presence, and, for standalone brands, an occasion-wear specialism that the assistants can attach to a query.

This chapter is a neutral reading of the data. It describes patterns associated with visibility; it is not advice, a service, or a recommendation, and visibility remains separate from quality.

1) Breadth is a baseline condition. 31 of the 33 qualified main-market brands appear in all five models. Brands seen by only three or four models (Trendyolmilla, Hülya Keser) sit lower largely for this reason: broad model coverage is what the most visible brands share.

2) Mention and position are different things. Some entities are named often but late (Trendyol and Hepsiburada, with high mentions but lower MRR); others are named rarely but early (Sefamerve, VznModa and Eser Giyim, with thin mentions but very high MRR). A brand can clear the 5% bar mainly on position and breadth; “named often” and “named early” are independent signals.

3) Web presence tracks with the dominant scenario. With 97.7% of answers grounded in web search, and brand-owned plus marketplace sites the largest source types (83% combined), a current and structured web presence is the factor most closely associated with visibility here.

4) The shelf is aggregator-led, but a brand can still lead. No standalone brand outranks the marketplaces overall, yet Carmen shows a specialist can sit second across the whole market and lead the brand-only view, visible across every model and every question type. A brand’s context, meaning which cut, which question type, and whether it reads as an occasion-wear specialist, matters as much as its overall rank.

5) Origin is settled here, not contested. Unlike verticals where foreign brands are recalled readily on origin-neutral questions, evening and occasion wear is answered almost entirely with Turkish names (82.4% of open-market mentions). Foreign visibility is real but narrow, limited to premium bridal and couture. The local/open gap shows the assistants can distinguish “yerli” requests, so the very Turkish open balance is a real market signal, not an artefact.

How a brand can locate itself in the data. Using the dataset (available on request), a brand can read four things in order: whether it appears in all five models; how often it is mentioned within its cut; how early it is named when mentioned; and which model or question type it is weakest in. These readings show where a visibility gap sits, without saying anything about whether its product is good.

10. Limitations and Notes

One-sentence takeaway: This is a single, point-in-time snapshot of one Turkish vertical; read the figures within the limitations below.

• Single, point-in-time measurement. Collected in one run on 4 July 2026, a snapshot rather than a trend. This is the first (baseline) edition; quarterly editions will enable trend analysis.
• Small-sample position inflation. A few brands clear the 5% bar but rank high mainly on position and breadth despite thin mentions (e.g. VznModa in the main and local cuts, Chanel in the open cut). Read their ranks cautiously.
• Small score gaps are not meaningful. In the packed middle of each leaderboard, differences of a point or two should not be over-interpreted; only larger gaps (such as Trendyol’s lead) are robust.
• Visibility is not quality. This report measures only mention and position. It does not assess whether a brand was described positively, whether a recommendation was accurate, or whether a product is good. A score is not an endorsement.
• Models are probabilistic. The same question can produce different answers; five repeats reduce but do not eliminate this.
• Web search depends on momentary conditions, meaning the content indexed and served at measurement time. “Web search vs. own knowledge” is a proxy based on whether a citation accompanied the answer.
• Precision over recall. Brand matching was cautious to avoid false positives, so brands mentioned indirectly may be undercounted. There is no sentiment layer; the study is strictly quantitative.
• Source-type mix is heuristic. Domain types were auto-classified (brand-owned detected by matching the domain to a brand); treat the mix as indicative. Source coverage also varies by model, and Gemini’s real source domains are read from its search-result titles rather than its redirect URLs.
• Origin classification involves judgment. A brand is Turkish only if founded and headquartered in Turkey; some widely-sold names are foreign-origin even with a large local presence.
• Marketplaces and retailers are included as entities per study scope (Trendyol, Hepsiburada, Modanisa, Sefamerve, Boyner); they compete differently from direct brands, so §3.2 gives a brand-only view. Koton, LCW and Roman are treated as brands.
• Output-length limits and model versions differ and date quickly; findings are specific to the versions in Chapter 2 as of July 2026.

Frequently Asked Questions

11. Appendix & Data

11.1 All questions (40) and their categories

Distribution: 14 discovery, 13 attribute, 13 use_case; 20 local (Turkish-only) and 20 open (origin-unrestricted).

11.2 All tracked brands (191)

11.3 Data availability

The study tracked 191 brands; of these, 33 cleared the 5% threshold in the main market (with 33 qualifying in the local cut and 32 in the open cut). To support scrutiny and reproduction, the underlying data is available on request: response-level brand mentions, all three qualified leaderboards, full unfiltered brand metrics, per-model and per-question-type breakdowns, the open-market breakdown, the top source domains, and the methodology and data dictionary.

All figures reflect a single point-in-time run (4 July 2026) and are subject to the limitations in Chapter 10. This is the first (baseline) measurement; the study will be repeated quarterly.

11.4 About Herm.io & disclosure

Herm.io is a consumer-behavior and marketing-data company. We study how people discover and choose brands so that brands can reach the right customers. This report is part of our public research and is a recurring quarterly study.

Disclosure & neutrality. Herm.io does not sell SEO or GEO (search/AI-ranking) services, and this report does not recommend any. No brand paid to be included, ranked, or described, and a brand’s score is not an endorsement or a judgment of its quality; it is a measure of visibility only. To keep the analysis objective, all citations to Herm.io’s own domains were excluded from the source data. Brands that want to understand their position in the data are welcome to book a call for a neutral walkthrough; this is advisory and free.