When AI is asked about face cream — who does it recommend?
We sent 26 real buying questions to ChatGPT and Google AI Overviews – the questions skincare shoppers actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Nearly half of all AI recommendations go to just 6 of 84 providers.
La Roche-Posay (9.4%) and Neutrogena (9.4%) dominate the answers – the remaining 84 named providers split what's left. If you're not here, you simply don't exist to AI users. That's exactly the gap BuzzView makes visible.
Who does AI recommend for face cream?
Share of all brand mentions across 26 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Five things the data reveals about this category
Behind the ranking — what the numbers actually mean for brands in this space.
84 brands named. The top 6 capture 40.6% of all 278 AI mentions.
Across 26 real skincare prompts, AI systems named a total of 84 distinct brands — an enormous field that reflects the genuine breadth of the face care market. Yet when you look at where the mentions actually land, the distribution is anything but democratic. La Roche-Posay and Neutrogena each collected 26 mentions, tying for first place. CeraVe followed with 19, Eucerin and Nivea with 15 each, and Weleda with 12. Those six brands alone account for 113 of 278 total mentions — a 40.6% share of voice concentrated at the very top of the leaderboard.
This concentration pattern is not a coincidence. AI models are trained on vast corpora of consumer content, editorial recommendations, dermatology forums, and pharmacy blogs. The brands that appear repeatedly across all of those sources — not just advertising — are the ones that surface reliably when a language model is asked to recommend a face cream. In skincare, that legacy of earned media is held by a handful of pharmacy-anchored brands with decades of clinical positioning behind them.
The tail of the distribution is long and thin. Beyond the top 12 tracked brands, an additional 72 providers shared 120 mentions among them — an average of fewer than 2 mentions each. For most of those brands, AI visibility is essentially zero: they exist in the model's training data but are not being surfaced in response to standard buying prompts. The gap between being "known to the model" and being "recommended by the model" is where most skincare brands currently sit.
What makes this power-law shape particularly striking in skincare is the contrast between shelf presence and AI presence. A German drugstore stocks hundreds of face care products from dozens of brands. But AI doesn't work like a shelf — it curates based on pattern recognition across text. Brands with strong presence in clinical studies, consumer review aggregators, and dermatologist recommendation content dominate AI outputs regardless of their actual sales volume or store footprint.
With 84 brands competing for AI mentions and the top 6 already claiming 40% of them, the window for new entrants to break into the AI recommendation layer is narrow — and narrowing as these leaders continue to accumulate citations and reviews online.
La Roche-Posay at 61.5% visibility. Bioderma at 11.5%. A 50-point gap separates the leaders from the tail.
Visibility in this context measures the share of prompts in which a brand was named at least once. La Roche-Posay appeared in 61.5% of all 26 tested prompts — meaning the brand was mentioned in response to 16 out of 26 different question types. Bioderma, at the bottom of the tracked set, reached only 11.5% — appearing in just 3 prompts. The 50-percentage-point gap between these two brands represents the full spectrum of AI visibility in the face care category as it stands today.
What drives visibility in skincare is a combination of clinical authority and consumer volume. La Roche-Posay and Neutrogena (53.8%) are consistently recommended by dermatologists and cited in pharmaceutical literature. That medical-adjacent content is highly weighted in AI training because it is authoritative, specific, and frequently reproduced across health platforms, pharmacy chains, and consumer health publications. A brand without dermatological positioning — no matter how popular on social media — is at a structural disadvantage in AI recommendation outputs.
Review platform density also matters. CeraVe (46.2% visibility) built its modern profile largely on Reddit communities and Amazon review aggregators, where it became a cult recommendation for barrier-repair routines. That user-generated content, appearing across multiple independent platforms, reinforces the model's confidence in surfacing the brand across diverse prompt types. Brands like Bioderma, despite strong pharmacy distribution, have a smaller organic review footprint in German-language digital content — which directly limits their AI visibility.
For brands sitting between 15% and 30% visibility — Weleda, L'Oréal, Lavera, Vichy, Balea, Kiehl's, Avène, Colibri Skincare, Olay, and Nø Cosmetics — the gap to the leaders is large but not unbridgeable. These brands are already being mentioned across some prompt types, which means the model knows them and considers them recommendation-worthy in certain contexts. What they lack is the cross-prompt consistency that the top three have achieved through years of multi-channel content saturation.
The 50-point visibility gap is primarily driven by clinical content density and review platform presence — two assets that can be built systematically, making this gap a strategic opportunity rather than a fixed structural ceiling for mid-tier skincare brands.
Best-of prompts favor clinical brands. Skin-type and use-case prompts open the field to specialists.
The 26 prompts in this study span four distinct query types: best-of recommendations (e.g., "beste Gesichtspflege 2026"), direct comparisons (e.g., "La Roche-Posay vs Neutrogena"), alternative-seeking queries (e.g., "Was ist eine gute Alternative zu La Roche-Posay?"), and skin-condition or use-case prompts (e.g., "beste Gesichtspflege für empfindliche Haut", "beste Gesichtspflege bei Akne"). Each prompt type surfaces a meaningfully different brand mix, revealing where each brand's authority is strongest and weakest.
Best-of and open recommendation prompts are dominated by La Roche-Posay, Neutrogena, CeraVe, and Nivea. These brands have the broadest general recommendation profile — they appear as safe, defensible answers to any skincare question. Comparison prompts amplify the brands that are already top of mind and frequently placed head-to-head in editorial comparisons, which tends to further reinforce La Roche-Posay and Neutrogena. For mid-tier brands, being explicitly mentioned in comparison content published on reputable health and beauty sites is one of the most direct levers for improving comparison-prompt visibility.
Skin-condition prompts — for dry skin, oily skin, sensitive skin, acne, anti-aging — are where specialist positioning pays off. Weleda appears more frequently in natural and sensitive-skin prompts. Lavera, the certified-organic German brand, surfaces specifically when prompts invoke natural or eco-conscious alternatives. Balea, the dm drugstore own-brand, tends to appear in budget or accessible-skincare contexts. These are the prompt types where a brand with a narrow but deep content niche can outperform a general-market giant that has spread its content too thin to rank authoritatively in any specific vertical.
The strategic implication is clear: content strategy for AI visibility in skincare must be segmented by prompt intent. Chasing best-of visibility requires broad authority — high review volume, dermatological citations, editorial placements. Winning in use-case prompts requires depth: detailed ingredient guides, clinical explanations of efficacy for specific skin types, and community-driven proof points in targeted forums. Brands like Bioderma and Avène, which have strong clinical credentials but low current AI visibility, are best positioned to gain ground through use-case content rather than competing head-on in best-of prompts where the top four brands are deeply entrenched.
The fastest route to measurable AI visibility gains for a mid-tier skincare brand is not to compete in best-of prompts — it is to own a specific skin-type or use-case vertical with deep, structured, clinical content that AI can cite with confidence.
Neutrogena earns the highest positive sentiment ratio. L'Oréal is named almost exclusively in neutral context.
Zero brands in this dataset received a single negative AI mention — a finding that reflects how AI recommendation systems handle brand sentiment in consumer categories. When AI declines to endorse a brand, it simply does not mention it, rather than criticizing it. This absence-over-criticism pattern means that sentiment analysis in AI visibility is not about negative suppression — it is about the ratio of active endorsement (positive mentions) to neutral, informational references. That ratio varies considerably across the tracked brands.
Neutrogena leads on positive sentiment with 14 positive mentions out of 26 total — a 53.8% positive ratio. La Roche-Posay follows with 11 positive out of 26 (42.3%). CeraVe achieves 10 positive out of 19 (52.6%). These three brands are being actively recommended, not merely listed. In contrast, L'Oréal, despite tying Weleda with 12 total mentions and a 4.3% share of voice, has only 2 positive mentions and 10 neutral — a positive ratio of just 16.7%. The model mentions L'Oréal frequently but rarely frames the mention as a direct recommendation.
What drives positive sentiment in AI skincare recommendations is largely independent of brand size or marketing spend. The three highest positive-ratio brands share a common trait: a strong foundation of dermatologist-recommended content, clinical study references, and specific product-outcome claims that AI can translate into confident endorsements. When a model has seen thousands of texts where a dermatologist explicitly recommends Neutrogena for a specific concern, it learns to mirror that recommendation tone in its own outputs. Balea, despite being a private-label brand, earns a positive ratio of 75% (6 of 8 mentions) — likely because its mentions tend to occur in specific budget-recommendation contexts where the AI treats it as a clear winner within its price tier.
Vichy and Olay both show a pattern of predominantly neutral mentions — 5 neutral versus 2 positive each. This neutral bias suggests these brands are being referenced as category participants or as context for comparisons rather than as first-choice recommendations. For both brands, the content strategy implication is similar: they need to generate more outcome-specific content — reviews that describe concrete results, clinical comparisons that position them as the superior option for a specific skin concern — so that AI systems learn to cite them in an endorsing rather than a merely informational register.
High mention counts mean little without positive framing — L'Oréal's 83% neutral-mention rate shows that being named and being recommended are two entirely different things; the difference is built through clinical authority and outcome-specific content, not volume alone.
Fragmented field, consolidated top. Skincare's AI visibility landscape is mid-maturity — and the hierarchy is still forming.
With 84 distinct brands surfaced across just 26 prompts, skincare is a fragmented category in AI terms — far more so than, say, CRM software or cloud storage, where a handful of platforms dominate with minimal tail competition. Yet the top two brands (La Roche-Posay and Neutrogena, both at 9.4% share of voice) are pulling measurably ahead of the rest. This combination of a long tail and a forming top tier is the hallmark of a category in mid-maturity: the AI hierarchy is visible but not yet locked in, making the current moment high-stakes for brands that can move quickly.
Compare this to a fully mature AI category — enterprise cybersecurity, for example — where a top-3 dominance of 60-70% share of voice leaves little oxygen for challengers. In skincare, the top 2 brands together hold only 18.8% of total mentions. That means 81.2% of all AI mentions are distributed across 82 other brands. The category is still absorbing recommendations from all corners of the internet, and the model has not yet converged on a stable short-list. This is a meaningful structural window for brands that can generate high-quality, authoritative content quickly.
The presence of Colibri Skincare and Nø Cosmetics in the tracked set — both at 15.4% visibility despite being far smaller than the pharmacy giants — demonstrates that AI visibility in this category is not purely a function of heritage or distribution. These brands have likely achieved their foothold through concentrated presence in specific content verticals: natural beauty journalism, sustainability-focused review sites, or direct-to-consumer content ecosystems that AI draws on when answering niche-angle prompts. Their inclusion in the tracked set serves as proof of concept that new entrants can build meaningful AI visibility in skincare without multi-decade brand histories.
The overall picture is of a category where the next 12 to 18 months will likely determine which brands establish lasting AI visibility and which remain permanently in the long tail. As AI search usage grows — particularly for considered purchase categories like skincare, where shoppers increasingly ask AI for personalized recommendations rather than browsing category pages — the brands visible in AI outputs today will benefit from compounding reinforcement: more AI citations generate more content referencing those AI citations, which trains future model versions to surface those brands even more reliably. Getting into the AI recommendation layer now, before the hierarchy hardens, is the critical strategic priority.
Skincare's AI visibility hierarchy is mid-formation: the top is visible but not yet dominant, meaning brands that invest in structured, clinical, and use-case-specific AI content in 2025–2026 have a genuine opportunity to lock in positions before the category matures and mobility shrinks.
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Real questions skincare shoppers ask AI
No wishful thinking: the ranking comes from exactly these prompt types – best-of questions, comparisons, alternatives and use cases.
AI visibility of the tested providers
Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
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Every prompt runs against all major AI models. We count mentions, position, sentiment and the cited sources.
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