Share of voice across ChatGPT, Perplexity, Claude, and Google AIO — measured across 11.111 shopper prompts and 45 brands.
Different AI models weight different sources. A brand that wins ChatGPT may lag in Google AIO if its retail footprint is weak.
Ilia Beauty is named in 34.6% of clean-beauty shopper prompts across ChatGPT, Perplexity, Claude, and Google Gemini — a lead of 9.5 percentage points over the No. 2 brand, which is the largest gap between adjacent ranks in the leaderboard. In a category defined by fragmentation at shelf, AI search is consolidating attention faster than retail ever did.
The competitive dynamic is not a single-winner market. It is a stratified one. The top five brands by SoV — Ilia, Tower 28 Beauty, Saie, RMS Beauty, Kosas — each appear in roughly one in five or more shopper queries. A middle band of brands including Herbivore Botanicals, Tata Harper, Drunk Elephant, Youth To The People, Cocokind, Merit Beauty, Biossance, Versed, and Glossier captures measurable but inconsistent visibility between 10% and 20% SoV. Nine tracked brands registered zero mentions across 500 engine calls, including clean-positioned players such as Peace Out Skincare, Volition Beauty, and Youthforia. The gap between mid-tier SoV and zero is not cleanly explained by offline brand size; it more likely reflects what the models have been trained and retrieved on, though this dataset cannot isolate causality.
For a brand sitting between 10% and 20% SoV, the next quarter should not be spent chasing Ilia's position in generic "best clean beauty" queries. It should be spent owning two or three defensible prompt clusters — sensitive-skin, pregnancy-safe, dupe-for-prestige — where citation patterns are still forming. The cost to enter an uncontested prompt is materially lower than the cost to displace an incumbent inside a contested one.
Clean beauty is a category where the purchase decision is preceded by a research step more often than most D2C verticals. Shoppers ask questions that are hard to answer from a PDP: which formulas are pregnancy-safe, which are free of specific allergens, which perform on rosacea or eczema, which are genuinely non-toxic versus marketed as such. Historically those questions were resolved through Reddit threads, Byrdie and Into The Gloss explainers, and dermatologist-led YouTube reviews. AI assistants are now compressing that research step into a single response, and in the process they are picking winners before the shopper ever lands on a brand site.
This shift is early. Citation patterns differ sharply across engines — Claude's leaderboard is almost unrecognizable next to ChatGPT's — and the answer layer is not yet fully formed. The practical implication for founders: citations are volatile, and the cost of influencing them is lower now than it will be in twelve months.
| Brand | SoV % | Avg Position |
|---|---|---|
| Ilia Beauty | 34.6 | 6.0 |
| Tower 28 Beauty | 25.1 | 5.6 |
| Saie | 21.0 | 6.4 |
| RMS Beauty | 20.4 | 6.1 |
| Kosas | 20.4 | 6.0 |
| Herbivore Botanicals | 20.1 | 5.6 |
| Tata Harper | 18.3 | 4.6 |
| Drunk Elephant | 17.8 | 5.1 |
| Youth To The People | 15.7 | 3.8 |
| Cocokind | 15.7 | 5.2 |
| Merit Beauty | 13.3 | 6.5 |
| Biossance | 13.0 | 7.3 |
| Versed | 10.7 | 4.1 |
| Glossier | 10.1 | 2.9 |
| Summer Fridays | 8.6 | 6.1 |
| Westman Atelier | 7.4 | 6.6 |
| Jones Road Beauty | 5.0 | 9.1 |
| Tatcha | 5.0 | 7.2 |
| Byoma | 4.7 | 6.9 |
| Indie Lee | 4.1 | 6.7 |
| Beautycounter | 3.6 | 3.5 |
| Kinship | 3.6 | 8.0 |
| Rare Beauty | 3.0 | 7.5 |
| Farmacy Beauty | 3.0 | 8.4 |
| Rhode | 2.4 | 4.6 |
| Dieux | 2.4 | 7.4 |
| Bubble | 2.1 | 7.9 |
| Nécessaire | 2.1 | 9.3 |
| Supergoop | 1.8 | 9.5 |
| Topicals | 1.5 | 6.4 |
| Hero Cosmetics | 1.5 | 9.6 |
| Briogeo | 1.5 | 14.6 |
| Fenty Skin | 1.2 | 5.5 |
| Rose Inc | 0.9 | 5.3 |
| Haus Labs | 0.9 | 2.3 |
| OUAI | 0.3 | 16.0 |
| Youthforia | 0.0 | — |
| Pat McGrath Labs | 0.0 | — |
| Item Beauty | 0.0 | — |
| Half Magic | 0.0 | — |
| r.e.m. beauty | 0.0 | — |
| The Lip Bar | 0.0 | — |
| Pattern Beauty | 0.0 | — |
| Peace Out Skincare | 0.0 | — |
| Volition Beauty | 0.0 | — |
Ilia's 9.5-point lead over Tower 28 is the largest gap between adjacent ranks in the leaderboard. Ilia appears in nearly every editorial "best of clean beauty" list in recent years, is stocked at Sephora with a deep review corpus, and has formula-specific mentions (particularly Super Serum Skin Tint) that the models retrieve consistently.
Glossier is the clearest positional outlier. With 10.1% SoV it ranks 14th, but at an average position of 2.9 — the second-best position among brands with any meaningful citation volume. When Glossier is mentioned, it is mentioned early. This signals strong brand salience in the models' latent space but weaker topical fit for the specific prompts in the clean-beauty set. Caveat: at low SoV, average-position readings are sensitive to small sample sizes. Haus Labs shows the same pattern at an extreme: 0.9% SoV, average position 2.3 — cited rarely but near the top when cited.
Youth To The People (15.7% SoV, position 3.8) is one of a small group of top-15 brands combining meaningful coverage with top-five placement, alongside Tata Harper (18.3%, 4.6), Versed (10.7%, 4.1), and Glossier (10.1%, 2.9). Conversely, Merit Beauty (13.3% SoV, position 6.5) and Biossance (13.0%, position 7.3) are getting into the room but not the first half of the answer.
Ilia dominates at 51% citation rate, with Tower 28 (37%) and RMS (31%) trailing. ChatGPT's retrieval appears to lean on editorial roundups and Sephora review density, which is consistent with the established "clean beauty canon" winning here cleanly.
The leaderboard flattens considerably: RMS (17%), Kosas (16%), Ilia (15%), Saie (14%), Tata Harper (13%) are separated by four points. Perplexity's live-web retrieval, which is generally understood to reward recency, appears to put brands with active earned-media momentum on more equal footing with category incumbents.
Claude produces a distinct hierarchy: Drunk Elephant leads at 44.7%, followed by Youth To The People (34.2%) and Herbivore Botanicals (26.3%). Claude over-indexes on skincare-led brands relative to makeup-first brands (Ilia falls to 23.7%, Tower 28 to 15.8%), though the underlying cause — training corpus composition, retrieval preferences, or both — is not resolvable from this dataset.
Gemini returns meaningful clean-beauty citation activity, led by Ilia (42%), Tower 28 (33%), Cocokind (31%), Saie (28%), and Kosas (24%). Gemini notably elevates Cocokind to a tier it does not reach on any other engine. Brands with visibility on ChatGPT but weak Gemini rates (RMS at 13%, Drunk Elephant at 11%) show that cross-engine parity is not automatic, and that Gemini should be optimized as its own surface.
The prompt set was designed across discovery, comparison, condition-specific, life-stage, value, and retailer-specific intents. The following cluster readings are hypotheses drawn from where aggregate SoV and engine patterns concentrate; prompt-level attribution would require a separate run.
The canon cluster. Generic discovery queries ("best clean beauty brands this year," "best value under $20," "legit clean beauty on Amazon") are the most likely driver of Ilia's 34.6% aggregate SoV. This is the most contested and least winnable cluster for a challenger. Whoever the models saw first, they cite first.
The sensitive-skin and dermatological cluster. Tower 28's 25.1% SoV with a strong 5.6 average position is consistent with the brand having colonized a condition-specific vertical (sensitive skin, rosacea, eczema) inside the broader category. This is the cleanest example of plausible prompt-cluster ownership in the dataset.
The life-stage cluster. Pregnancy-safe and post-partum queries are the natural fit for Beautycounter (position 3.5 on 3.6% SoV) and Tata Harper (position 4.6 on 18.3% SoV), but neither has the volume to signal ownership. A dermatologist- or OB-authored guide would likely move these within a quarter.
The comparison and dupe cluster. "Dupe-for-prestige" and head-to-head queries reward whichever brand has retrievable structured comparison text. Publishing a comparison page against a prestige benchmark is a testable lever; the data here does not yet confirm which brands have done so.
The demographic cluster. Demographic-framed queries (Gen Z, men, post-partum, peri-menopause) are under-served across the board, based on the sparse positioning of brands that would naturally index here.
The following prompts are hypotheses for where the retrieval corpus appears thin, either because no brand has authored an authoritative source or because the editorial consensus has not formed. Each would need a seeding test to confirm, but all share the property that baseline citation is low or zero, which makes attribution clean.
For a brand currently at 10% to 20% SoV — Merit, Biossance, Versed, Glossier, Drunk Elephant, Youth To The People, Cocokind, Herbivore, Tata Harper — the next 90 days should be organized around three sequential moves.
Days 1–30: Commission one dermatologist-authored longform, placed outside your owned properties. Drunk Elephant's dominance in Claude (44.7% citation rate) is an observed outlier; a plausible driver is the volume of third-party dermatologist and ingredient-focused content referencing its formulas, which Claude appears to weight. Commission one board-certified dermatologist to publish a 2,500-word clinical review of your hero SKU on a credible third-party surface — a derm's existing Substack or newsletter, a YouTube review with an accompanying written breakdown, or an advertorial in a beauty trade publication. Do not publish it on your own site. The citation value is in the third-party domain.
Days 31–60: Publish one structured head-to-head comparison page targeting the comparison cluster. Pick the two prestige brands shoppers benchmark you against and publish a structured comparison (ingredients, price per ounce, suitability by skin type) as a standalone indexed page. Perplexity, given its live-retrieval posture, is the fastest engine to pick this up; ChatGPT and Gemini will follow on their next refresh cycles.
Days 61–90: Claim one empty prompt cluster end-to-end. Select one of the unclaimed prompts above — men's routines, peri-menopausal skin, or post-procedure — and build a three-asset stack: an expert-authored guide on a third-party property, a Reddit AMA or detailed thread in the relevant subreddit (r/SkincareAddiction, r/30PlusSkinCare, r/AskDocs), and a structured FAQ page on your domain. The slot is empty; baseline citations are zero; attribution is clean.
This analysis is based on 500 engine calls distributed across ChatGPT, Perplexity, Claude, and Google Gemini, run against a shopper-prompt set designed to mirror the real query distribution in the clean-beauty D2C funnel (discovery, comparison, condition-specific, life-stage, value, and retailer-specific intents). Share of voice is computed as the percentage of prompts in which a brand was named in at least one engine response. Average position is the mean ordinal placement of the brand across all responses in which it was mentioned. Forty-five brands were tracked, reflecting a broader beauty set that includes prestige and color-cosmetics brands not all of which are positioned as clean; this is noted where relevant. Brands with zero citations across all 500 calls are reported as 0.0% SoV with null position.
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