Sunscreen Brands: 2025 AI Market Discovery Index

Answer Capsule

In May 2025, the sunscreen category shows a clear two-tier market in AI discovery. La Roche-Posay leads with the highest recommendation coverage and captured value, closely followed by CeraVe and EltaMD. Neutrogena and Cetaphil hold middle positions but trail significantly. Sun Bum, Olay, and Vichy appear in responses but rarely earn ranked recommendations. Kopari Beauty is invisible across all platforms.

Executive Summary

AI systems are reshaping sunscreen brand discovery by acting as de facto shortlist builders. Being mentioned is no longer enough. The brands that earn ranked, positive recommendations across multiple platforms capture the vast majority of AI-driven consumer attention.

La Roche-Posay leads the category with a 21.4% Top 3 recommendation rate and a modeled monthly captured recommendation value of $476,504. CeraVe follows closely with a 21.9% Top 3 rate and $274,093 in captured value, while EltaMD holds a strong third position at $460,076 despite a lower overall recommendation rate of 12.5%. These three brands collectively dominate the consideration phase, where most consumer decisions begin.

The warning signs are concentrated among brands with high awareness but weak AI recommendation architecture. Neutrogena appears in 28.2% of all observations but earns Top 3 recommendations in only 8% of cases. Sun Bum and Olay show similar patterns: present in responses but rarely advanced as top choices. Kopari Beauty registers zero presence across all platforms and all clusters, representing a complete AI discovery gap.

The commercial implication is direct. As more consumers begin product research with an AI query rather than a search engine or retailer, recommendation position becomes the new shelf placement. Brands without a strong evidence layer are not losing ground slowly. They are being excluded from the shortlist entirely.

The AI Discovery Shift in Sunscreen

Consumers increasingly ask AI systems for sunscreen recommendations rather than browsing retailer shelves or searching traditional review sites. This shifts the competitive battleground from shelf presence and paid media to source-layer authority and citation architecture.

AI platforms build shortlists by retrieving and ranking brands based on available public evidence. A brand that appears in dermatologist-reviewed articles, clinical studies, comparison content, and authoritative beauty publications is far more likely to be recommended than a brand that relies on advertising or retail placement alone.

The critical distinction in this category is between being mentioned and being recommended. A brand can appear in dozens of AI responses as a factual reference but never earn a Top 3 recommendation. This gap between visibility and shortlist eligibility defines the competitive dynamics in sunscreen today, and it is widening.

Directional Category Leaders

1. La Roche-Posay

La Roche-Posay appears in 35.1% of all observations and earns valid recommendations in 29% of cases. Its Top 3 recommendation rate of 21.4% is the highest in the category, and it achieves a Rank 1 rate of 6.6%. The brand leads across ChatGPT, Copilot, Gemini, and Perplexity, with particularly strong performance in consideration-stage prompts where consumers ask for the best sunscreen options. No other brand combines this level of recommendation frequency with this level of platform coverage.

The public interpretation: La Roche-Posay has the strongest AI recommendation architecture in the sunscreen category, converting high visibility into consistent shortlist dominance across every major platform.

2. CeraVe

CeraVe appears in 44.6% of observations, the highest raw presence in the category, and earns a 31.6% valid recommendation coverage rate. Its Top 3 rate of 21.9% is nearly identical to La Roche-Posay, and it leads in Rank 1 frequency with 117 first-place recommendations. CeraVe dominates evaluation-stage prompts where consumers compare specific products, suggesting its public evidence layer is particularly strong in comparison contexts.

The public interpretation: CeraVe converts its massive visibility into recommendation power more efficiently than any other brand, particularly in the head-to-head comparison prompts that often precede a purchase decision.

3. EltaMD

EltaMD appears in 23.4% of observations and earns a 15.9% valid recommendation coverage rate. Its Top 3 rate of 12.5% is lower than the top two, but its average recommended rank of 1.57 is the best in the category. EltaMD captures $460,076 in modeled monthly value, driven by strong performance on Gemini and ChatGPT where it frequently earns the top position rather than a secondary mention.

The public interpretation: EltaMD earns fewer total recommendations but tends to appear at the top of the list when it does, making it a disproportionately high-impact player in consideration-stage shortlists.

4. Neutrogena

Neutrogena appears in 28.2% of observations but earns Top 3 recommendations in only 8% of cases. Its valid recommendation coverage of 17.5% is respectable, but its average rank of 2.1 and low Rank 1 rate of 2.3% indicate it is frequently listed below competitors. Neutrogena performs best on ChatGPT and Copilot but struggles on Gemini and Google AI Overviews, where clinical and dermatologist-backed citations appear to carry more weight.

The public interpretation: Neutrogena has strong awareness but weak recommendation positioning, frequently appearing as a secondary option rather than a primary choice, a pattern that will compound over time as AI discovery grows.

5. Cetaphil

Cetaphil appears in 15.5% of observations with a 13.4% valid recommendation coverage rate. Its Top 3 rate of 5.9% and average rank of 2.19 place it firmly in the middle tier. Cetaphil earns high net sentiment at 0.87, the strongest positive sentiment signal among middle-tier brands, but lacks the recommendation frequency to challenge the top three for meaningful captured value.

The public interpretation: Cetaphil is well-regarded when mentioned but does not appear often enough in AI shortlists to capture recommendation value proportionate to its brand equity.

The Buying Moments That Now Decide the Category

Consideration: Best Sunscreen Discovery and Ranking

This cluster accounts for 463 observations and represents the highest-value buying moment in the category. Consumers ask for the best sunscreen for face, daily use, or specific skin types. La Roche-Posay leads with a 37.4% Top 3 rate and $476,161 in captured value. CeraVe follows at 31.8% and $243,493. EltaMD captures $459,879 in this cluster despite a lower Top 3 rate, driven by frequent first-place rankings when it does appear.

Brands that win this cluster control the initial shortlist. Brands that lose here rarely recover in later stages because AI systems tend to surface the same shortlist across follow-up queries from the same consumer session.

Evaluation: Head-to-Head Comparisons

This cluster includes 317 observations where consumers compare specific brands or products directly. CeraVe dominates with an 11% Top 3 rate and $29,852 in captured value. Cetaphil and La Roche-Posay hold secondary positions. Most brands see their recommendation rates drop significantly in this cluster, indicating that comparison prompts favor brands with strong, independently sourced side-by-side evidence rather than broad brand recognition alone.

Decision: Pricing and Plan Evaluation

This cluster of 134 observations shows the narrowest recommendation distribution in the public dataset. CeraVe leads with a 13.4% Top 3 rate, followed by Neutrogena at 3%. Most brands earn minimal or zero recommendation credit in decision-stage prompts, suggesting that pricing content is less influential in AI sunscreen recommendations than clinical or dermatologist-backed authority signals.

Why Recommendation Power Is Concentrating

Recommendation concentration in sunscreen is driven by the evidence layer that AI systems draw on when constructing shortlists. La Roche-Posay, CeraVe, and EltaMD all have citation architectures built on dermatologist endorsements, clinical studies, independent skin-care publications, and structured brand entity data. These sources are more retrievable and more trusted by AI systems than consumer reviews or brand-owned promotional content.

The gap between presence and recommendation is most visible with Neutrogena and Olay. Both brands appear frequently in AI responses, often as factual references or neutral mentions, but they rarely earn the positive, ranked recommendations that drive shortlist inclusion. Their public evidence layer supports awareness but not endorsement, and AI systems are making that distinction clearly.

Brands that invest in structured entity data, authoritative citations, and comparison-ready third-party content are building the infrastructure that AI systems use to recommend. Brands that rely primarily on retail presence, advertising, or brand-owned content are being listed but not chosen. The structural advantage compounds over time as AI systems reinforce patterns they have already established.

The Category's Most Visible Warning Sign

Kopari Beauty registers zero presence across all six AI platforms and all three public clusters. In a study covering 914 observations across consideration, evaluation, and decision prompts, the brand did not appear a single time. This is not a visibility problem. It is a discovery extinction event.

For a brand in the personal care space, having no AI presence means being invisible to the growing share of consumers who begin product research with an AI query. The brand has no entity recognition, no citation footprint, and no recommendation architecture that AI systems can retrieve. Every other brand in this study appears in at least some responses. Kopari Beauty does not appear in any.

The commercial risk is immediate. Consumers who ask an AI for sunscreen recommendations today will not encounter Kopari Beauty as an option. The brand cannot be evaluated, compared, or chosen from a shortlist it never enters.

What This Means for the Category

The sunscreen category is experiencing shortlist compression. Three brands control the majority of AI recommendation value, and the gap between the top tier and the middle tier is widening with each month that AI search adoption grows. Brands outside the top tier are not losing share gradually. They are being structurally excluded from the moments where consumer decisions form.

Competitor displacement is already visible in the data. Neutrogena and Olay, both with significant advertising budgets and mass-market distribution, are being outperformed by brands with stronger clinical and dermatologist citation layers. The brands that win in AI recommendations are not necessarily the brands with the largest marketing spend. They are the brands with the most retrievable, authoritative, and independently verified public evidence.

Trust-source dependency is the new competitive moat. AI systems prioritize sources that are clinically authoritative, professionally endorsed, and independently verified. Brands that build this evidence layer will be recommended. Brands that do not will be mentioned when convenient and excluded when it matters.

AI discovery is becoming a permanent fixture in consumer choice for sunscreen and adjacent personal care categories. Brands that treat AI shortlist eligibility as a strategic priority will capture disproportionate share. Brands that treat it as secondary to traditional media will find their position in the category eroding in ways that are difficult to reverse.

What This Public Benchmark Does Not Include

- Full cluster dataset for all 10 buyer-stage clusters

- Prompt-level response tables showing exact AI outputs by platform

- Citation-source failure maps identifying which sources are missing or underperforming

- Platform-by-platform recovery priorities for brands with gaps on specific AI systems

- Entity and schema diagnostics for brand recognition gaps

- Source-layer gap analysis comparing brand content to competitor citation architecture

- Company-specific content recommendations for improving AI shortlist eligibility

- Exact competitor threat profiles showing displacement risk by cluster

- Full paid opportunity model with investment scenarios

This page shows the market shape. The paid report shows the repair map.

Methodology and Disclaimers

1. Market studied: Sunscreen brands, including facial sunscreen, daily SPF, and broad-spectrum protection products.

2. Brands and entities included: Supergoop, CeraVe, Cetaphil, EltaMD, Kopari Beauty, La Roche-Posay, Neutrogena, Olay, Sun Bum, Vichy. This is not a full market census.

3. Data collection date and window: May 2025, point-in-time snapshot.

4. AI platforms tested: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity.

5. Observations analyzed: 914 observations across all platforms and clusters. Prompt count was not disclosed in the public dataset.

6. Prompt categories: Consideration (best product discovery and ranking), Evaluation (head-to-head comparisons), Decision (pricing and plan evaluation).

7. Definition of a mention: A mention means the brand appeared in an AI-generated response, regardless of sentiment or ranking position.

8. Definition of a valid recommendation: A valid recommendation is a positive, shortlist-quality recommendation that earns recommendation credit. Visibility and recommendation credit are tracked separately and are not interchangeable.

9. Ranking and scoring metrics used: Valid recommendation coverage, Top 3 recommendation rate, Rank 1 rate, average recommended rank, net sentiment score, positive visibility rate, and modeled monthly captured recommendation value.

10. Limitations: This is a point-in-time benchmark. AI outputs change over time as models update and source layers shift. Modeled values are estimates based on observed recommendation patterns and are not revenue figures. This report does not constitute a full audit or a complete market census.

For a company-specific Authority Index report, the deeper analysis would show which prompts each company wins or loses, which AI platforms are under-recognizing the brand, which source layers are shaping recommendations, and what changes may improve AI shortlist eligibility.