La Roche-Posay AI Visibility Market Strategy Report - Sunscreen Brands

Answer Capsule

La Roche-Posay leads sunscreen brand AI recommendations in October 2026 with 92.9% valid recommendation coverage across 622 qualified observations, extending its gap over second-place CeraVe to 16.7 percentage points. The brand holds the strongest recommendation power in the category, appearing in 95.8% of qualified observations and converting nearly all of them into shortlist placements. Its clearest win is top-three placement, which climbed to 72.0% from 56.2% in July 2026. Its clearest weakness is first-position share, where EltaMD leads at 31.4% against La Roche-Posay's 18.8%. The clearest opportunity is converting its dominant shortlist presence into more rank-one recommendations, particularly in high-intent dermatologist-recommendation prompts.

Who This Report Is For

This report is for brand leaders, category managers, and marketing teams at La Roche-Posay and its parent organization who need to understand how AI systems recommend sunscreen brands, where the brand's recommendation power is strongest, and where competitors are capturing first-position recommendations despite lower overall coverage.

Report Card

Executive Summary

La Roche-Posay holds dominant recommendation power in the sunscreen category. The brand recorded 92.9% valid recommendation coverage across 622 qualified observations in October 2026, meaning it was recommended in 578 of those observations. This represents a 3.3 percentage point increase from 89.6% in July 2026 and a 9.2 point increase from September 2026.

The brand's presence rate of 95.8% indicates it appears in nearly every qualified AI response about sunscreen brands. Its top-three recommendation rate climbed to 72.0% in October 2026 from 56.2% in July 2026, a 15.8 point increase. This means La Roche-Posay appears in the top three recommended positions in nearly three out of every four qualified observations.

The gap between La Roche-Posay and second-place CeraVe widened to 16.7 percentage points in October 2026 from 19.2 points in July 2026, even as CeraVe improved its own coverage. This widening gap reflects La Roche-Posay's ability to convert its existing presence into shortlist placements at a higher rate, not a significant increase in how often it appears in AI answers.

The brand's strongest platform signal comes from ChatGPT, where it achieved 97.7% valid recommendation coverage and 100% raw mention presence. Its strongest cluster is the Brand Recommendation cluster, which covers discovery and consideration queries about which sunscreen brand to choose.

The clearest gap is first-position share. EltaMD leads the category with a 31.4% rank-one rate, more than 12 points above La Roche-Posay's 18.8%. While La Roche-Posay dominates shortlist inclusion, it is less frequently the single first recommendation.

Sentiment is uniformly positive across all tracked brands. La Roche-Posay recorded 582 positive mentions, 14 neutral mentions, and zero negative mentions, producing a net sentiment score of 0.9765. No tracked sunscreen brand recorded a negative mention in October 2026.

What La Roche-Posay Is Winning

Questions This Section Answers

• How strong is La Roche-Posay's recommendation coverage compared with other sunscreen brands?
• Which platforms and metrics show La Roche-Posay's strongest recommendation position?
• What does La Roche-Posay's sentiment profile look like across sunscreen queries?

La Roche-Posay holds the strongest overall recommendation position in the sunscreen category. Its 92.9% valid recommendation coverage is the highest among all nine tracked brands and represents the largest valid recommendation count at 578 observations.

The brand's top-three placement rate of 72.0% is the highest in the category, 18.8 percentage points above EltaMD's 53.2% and 23.1 points above CeraVe's 48.9%. This indicates that when AI systems recommend sunscreen brands, La Roche-Posay is almost always included in the shortlist.

ChatGPT represents the brand's strongest platform. La Roche-Posay achieved 97.7% valid recommendation coverage on ChatGPT, with 84 valid recommendations across 86 observations. Its raw mention presence rate on ChatGPT was 100%, meaning it appeared in every qualified ChatGPT observation.

The brand also leads on AI Overviews with 94.8% valid recommendation coverage and on AI Mode with 90.8% coverage. These Google-integrated surfaces represent strong recommendation signals given their prominence in search-adjacent AI responses.

La Roche-Posay recorded zero negative mentions across all platforms and clusters. Its net sentiment score of 0.9765 reflects 582 positive mentions against 14 neutral mentions, with no cautionary or negative framing detected in the qualified observation set.

Where La Roche-Posay Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why does EltaMD capture more first-position sunscreen recommendations despite lower overall coverage?
• On which platforms is La Roche-Posay most displaced from the top recommendation spot?
• What query types does the benchmark not capture that could affect how La Roche-Posay's gaps are interpreted?

The clearest gap is first-position recommendation share. While La Roche-Posay appears in the top three in 72.0% of qualified observations, it ranks first in only 18.8%. EltaMD, which has lower overall coverage at 73.5%, achieves a 31.4% rank-one rate. This means EltaMD is more than 12 percentage points more likely to be the single first recommendation when AI systems respond to sunscreen brand queries.

This pattern suggests that La Roche-Posay is frequently included in recommendation lists but is not consistently positioned as the primary recommendation. The brand's average recommended rank of 2.31 reflects this, placing it behind EltaMD's 2.20 average rank despite La Roche-Posay's substantially higher coverage.

On Copilot, La Roche-Posay achieved 92.4% valid recommendation coverage but only a 15.2% rank-one rate. EltaMD achieved 87.3% coverage on the same platform with a 57.0% rank-one rate. This represents a significant displacement pattern where EltaMD captures first position more than three times as often as La Roche-Posay on Copilot.

The brand's rank-one rate on Perplexity is 10.8%, the lowest among its platform-level rank-one rates. While La Roche-Posay maintains 86.5% coverage on Perplexity, it is less frequently positioned as the primary recommendation on that platform.

The benchmark does not capture pricing, value, or multi-brand comparison queries. All 622 qualified observations fell into the Brand Recommendation cluster. This means the current data cannot show how La Roche-Posay performs when AI systems are asked to compare brands head-to-head or discuss price and value trade-offs.

Biggest Opportunity

Questions This Section Answers

• Where is La Roche-Posay losing first-position recommendations despite strong shortlist presence?
• Which high-intent prompts offer the clearest path to improving La Roche-Posay's rank-one rate?

The clearest opportunity is converting La Roche-Posay's dominant shortlist presence into more rank-one recommendations. The brand already appears in 95.8% of qualified observations and achieves top-three placement in 72.0%. The gap between top-three inclusion and first-position selection represents the largest addressable improvement in the brand's AI recommendation profile.

This opportunity is concentrated in specific platforms and prompt types. On Copilot, where EltaMD achieves a 57.0% rank-one rate against La Roche-Posay's 15.2%, the displacement pattern is most pronounced. On Perplexity, where La Roche-Posay's rank-one rate is 10.8%, the brand has room to strengthen its first-position positioning.

The prompt examples from the dataset include high-intent queries such as "What is the most recommended sunscreen by dermatologists?" and "Which sunscreen is considered the best?" These prompts represent decision-stage questions where first-position recommendations carry the most weight. Strengthening the brand's positioning in these specific prompt types represents the most direct path to improving rank-one rates.

Competitive Landscape

Questions This Section Answers

• How do the leading sunscreen brands compare on top-three rate, rank-one rate, and average recommended rank?
• Which competitor poses the biggest threat to La Roche-Posay's first-position share?

La Roche-Posay holds the strongest recommendation-stage position in the sunscreen category, leading on valid recommendation coverage and top-three placement. CeraVe and EltaMD follow as the strongest challengers, with EltaMD capturing the highest first-position share despite lower overall coverage.

Average recommended rank covers rank-eligible recommendations only.

La Roche-Posay's position at the top of the table reflects its dominant shortlist inclusion, with a top-three rate more than 18 points above the next brand. However, its rank-one rate of 18.81% trails EltaMD's 31.35%, indicating that while La Roche-Posay is almost always included in recommendations, it is less frequently the single first choice. The brand's average recommended rank of 2.31 is slightly behind EltaMD's 2.20, consistent with the rank-one gap.

Prompt Evidence

Questions This Section Answers

• How does La Roche-Posay perform on high-intent sunscreen prompts across ChatGPT, Copilot, Perplexity, and AI Overviews?
• Where did competitors capture first position in La Roche-Posay's strongest prompt examples?

ChatGPT / Brand Recommendation Prompt: "What is the most recommended sunscreen by dermatologists?" Result: La Roche-Posay appeared as a top recommendation with positive framing, contributing to its 97.7% coverage rate on ChatGPT.

Copilot / Brand Recommendation Prompt: "Which sunscreen is best for combination skin?" Result: La Roche-Posay was recommended but EltaMD captured first position, reflecting the platform's 57.0% rank-one rate for EltaMD against 15.2% for La Roche-Posay.

Perplexity / Brand Recommendation Prompt: "What is the best sunscreen for rosacea?" Result: La Roche-Posay appeared in the recommendation set with 86.5% platform coverage but achieved rank-one placement in only 10.8% of Perplexity observations.

AI Overviews / Brand Recommendation Prompt: "Which is the best sunscreen for your face?" Result: La Roche-Posay achieved 94.8% valid recommendation coverage on AI Overviews, its strongest Google-integrated surface performance.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map the specific prompt families where La Roche-Posay achieves top-three placement but not first position, with particular focus on Copilot and Perplexity where the rank-one gap is largest.

Phase 2: Recommendation Readiness Plan Identify the attributes and evidence patterns that AI systems associate with first-position recommendations in the sunscreen category, and assess how La Roche-Posay's current public evidence layer supports or undermines rank-one positioning.

Phase 3: Owned Answer Layer Buildout Strengthen the brand's owned content to emphasize the specific claims, differentiators, and use-case positioning that correlate with first-position recommendations in high-intent dermatologist-recommendation prompts.

Phase 4: Citation / Authority Layer Development Analyze which external sources AI systems cite when recommending La Roche-Posay versus EltaMD in first position, and develop a strategy to strengthen the brand's presence in the editorial and dermatologist-endorsed sources that appear most frequently in rank-one responses.

Phase 5: Monthly AI Visibility and Recommendation Tracking Establish ongoing tracking of rank-one rates by platform and prompt type to measure progress against the specific gap identified in this report.

Why This Matters

Questions This Section Answers

• Why does first-position recommendation share matter more than overall mention presence for sunscreen brands?
• What does the gap between inclusion and first-position selection mean for La Roche-Posay's buyer consideration?

AI presence alone is not enough. La Roche-Posay appears in 95.8% of qualified sunscreen brand observations, yet it ranks first in only 18.8%. The difference between being included in a recommendation list and being the first recommendation matters for buyer behavior. When AI systems respond to questions like "What is the most recommended sunscreen by dermatologists?" the first recommendation often carries disproportionate weight in the buyer's consideration set.

The next move is targeted correction of the prompt, page, and citation layers that influence first-position recommendations. The benchmark shows where La Roche-Posay is winning and where EltaMD is capturing first position despite lower overall coverage. A company-level analysis can identify the specific prompts, sources, and evidence patterns that drive rank-one placement, turning the current standings into a concrete action plan for strengthening the brand's position at the decision moment.

Core Metrics

Sentiment Score

Sentiment Score = (positive mentions × 1 + neutral mentions × 0 + negative mentions × -1) / total mentions

La Roche-Posay's sentiment score is 0.9765, calculated as (582 × 1 + 14 × 0 + 0 × -1) / 596. This score reflects the balance of positive versus negative framing among all mentions of the brand in qualified AI responses.

This matters because unclassified mention counts are misleading. A brand that appears frequently but is described neutrally or cautionarily is not in the same position as a brand that appears frequently with positive recommendation framing. Share of voice is a diagnostic metric, not a business KPI. A positive recommendation, a neutral reference, a cautionary mention, and a competitor-displaced mention are not equal in their impact on buyer behavior.

Counting all mentions as wins is bad measurement. La Roche-Posay's 596 mentions include 582 positive and 14 neutral classifications. The 14 neutral mentions represent factual references or context-setting statements rather than recommendations. Classified sentiment is required before interpreting AI visibility because it distinguishes between being mentioned and being recommended.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of La Roche-Posay's AI recommendation visibility in the sunscreen category. It is not a client implementation case study and does not imply that CiteWorks Studio caused any benchmark outcomes.
2. The reporting month is October 2026. The benchmark series includes monthly measurements from July 2026 through October 2026.
3. Six AI platforms were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. All six platforms carried at least one qualified observation in October 2026.
4. The October 2026 benchmark began with 800 prompt-surface observations and produced 622 qualified observations after qualification. The July 2026 benchmark produced 557 qualified observations.
5. The competitor universe includes nine tracked brands: La Roche-Posay, CeraVe, EltaMD, Neutrogena, Supergoop, Cetaphil, Sun Bum, Vichy, and Kopari Beauty.
6. All 622 qualified observations in October 2026 fell into the Brand Recommendation cluster, which covers discovery and consideration queries about which sunscreen brand to choose. No qualified observations were recorded for pricing and value or multi-brand comparison queries.
7. The benchmark uses a stage 0 extraction process to identify brand mentions, recommendation outcomes, rank positions, sentiment, and citations from prompt-level observations.
8. A mention is defined as any appearance of a tracked brand in a qualified AI response, regardless of whether the brand is recommended or simply referenced.
9. A valid recommendation is defined as a positive recommendation with a rank position of 1 through 10. Neutral references, cautionary mentions, and comparison anchors are not counted as valid recommendations unless the dataset explicitly marks them as such.
10. Brand-level percentages use the 622 qualified observations as the public denominator, not the 800 raw prompt-surface collections.
11. The public benchmark does not measure market share, sales attribution, organic search ranking performance, social media mention volume, or causality from metric movement alone.
12. The benchmark identifies where attention is warranted across the category. A company-level analysis is needed to explain why specific patterns emerged and to identify the prompts, sources, and evidence patterns driving those outcomes.

See How AI Is Recommending Your Brand

The public benchmark shows where La Roche-Posay leads and where competitors are capturing first-position recommendations. A company-level AI visibility audit maps the specific prompts, platforms, competitors, and citation sources that shape how AI systems recommend your brand. That analysis turns the current standings into a prioritized strategy for strengthening recommendation power at the decision moment.