La Roche-Posay AI Market Strategy Report - Sunscreen Brands

Answer Capsule

La Roche-Posay leads the Sunscreen Brands AI Market Discovery Index in September 2026 with valid recommendation coverage of 83.7%, but that figure declined 5.9 percentage points from 89.6% in July 2026, a move beyond normal month-to-month variation. The brand remains the most visible in the category at 95.6% raw mention presence, yet a measurable share of recommendations now flows to other brands even in answers where La Roche-Posay appears. The clearest win is sustained category leadership and a rank-one rate that improved to 15.5%. The clearest weakness is that EltaMD nearly doubles La Roche-Posay on first-position share at 30.6%, and the gap between the two narrowed to 5.4 percentage points. The clearest opportunity is converting existing presence back into shortlist placement across the high-intent discovery prompts where the brand is already mentioned.

Who This Report Is For

This report is for brand, growth, and category leaders at La Roche-Posay and its parent portfolio who need to understand how AI and search surfaces present and recommend the brand at the decision moment, and where recommendation credit is shifting to competitors.

Report Card

Executive Summary

La Roche-Posay holds the strongest overall position in the Sunscreen Brands category, but September 2026 marked a genuine shift beneath the surface. The brand recorded valid recommendation coverage of 83.7% across 522 qualified observations, down from 89.6% in July 2026. That 5.9 percentage point decline moved beyond normal month-to-month variation, making La Roche-Posay one of only two brands in the category to register a statistically meaningful change this month.

The decline is not a visibility problem. Raw mention presence held effectively flat at 95.6%, compared with 95.7% in July 2026. The brand appeared in 499 of 522 qualified observations. What changed is recommendation conversion: the share of answers where La Roche-Posay was mentioned but not included in the valid recommendation shortlist increased. Valid recommendation count fell from 499 in July 2026 to 437 in September 2026.

Placement signals were mixed. The top-three recommendation rate fell from 56.2% to 51.7%, meaning the brand lost ground in the middle of the shortlist. At the same time, the rank-one rate rose from 14.7% to 15.5%, so when La Roche-Posay was recommended first, that first-position strength held. The brand still holds the highest average recommended rank among high-coverage competitors at 2.39.

The strongest platform signal for La Roche-Posay is ChatGPT, where the brand recorded 92.96% valid recommendation coverage and a 35.21% rank-one rate across 71 observations. Copilot also showed strong conversion at 95.59% coverage. The clearest platform gap is Perplexity, where coverage dropped to 72.73% and rank-one rate to 7.58%, well below the brand's ChatGPT performance.

Sentiment framing remains strongly positive. La Roche-Posay recorded 479 positive mentions, 19 neutral mentions, and 1 negative mention, producing a net sentiment score of 0.9579. The single negative mention is an outlier in an otherwise clean framing profile.

The competitive picture is tightening. EltaMD trails La Roche-Posay on overall coverage at 78.3%, but leads on top-three rate at 53.6% and nearly doubles La Roche-Posay on rank-one rate at 30.6%. The gap between the two brands narrowed from 8.8 percentage points in July 2026 to 5.4 percentage points in September 2026. La Roche-Posay remains the category leader, but the margin is thinner and the placement dynamics favor EltaMD at the top of the shortlist.

What La Roche-Posay Is Winning

Questions This Section Answers

• Where does La Roche-Posay lead the sunscreen category in AI recommendation coverage?
• Which platform produces the strongest recommendation signal for the brand?
• How does the brand's sentiment framing compare to other high-volume sunscreen brands?

La Roche-Posay holds the highest valid recommendation coverage in the category at 83.7%, ahead of EltaMD at 78.3% and CeraVe at 66.5%. This is the third consecutive month the brand has led the benchmark.

The brand also holds the highest raw mention presence rate in the category at 95.6%, appearing in 499 of 522 qualified observations. No other brand exceeds 85.1% presence.

La Roche-Posay recorded the strongest rank-one improvement among high-coverage brands, rising from 14.7% in July 2026 to 15.5% in September 2026. When the brand is recommended first, that position is holding even as overall coverage declined.

On ChatGPT, La Roche-Posay achieved 92.96% valid recommendation coverage and a 35.21% rank-one rate, the strongest single-platform recommendation signal in the dataset. The brand also recorded 100% raw mention presence on ChatGPT across 71 observations.

Sentiment framing is the cleanest in the category among high-volume brands. With 479 positive mentions against 1 negative mention, La Roche-Posay's net sentiment score of 0.9579 reflects a public evidence layer that consistently frames the brand positively.

Where La Roche-Posay Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why is La Roche-Posay mentioned in AI answers but not shortlisted as often as before?
• Which competitor is displacing La Roche-Posay at the top of the sunscreen shortlist?
• Where does Perplexity underperform for the brand compared with ChatGPT and Copilot?

The primary gap is recommendation conversion, not presence. La Roche-Posay appeared in 95.6% of qualified observations but received a valid recommendation in only 83.7%. That 11.9 percentage point difference represents answers where the brand was mentioned but not shortlisted. In July 2026, that gap was 6.1 percentage points. The conversion gap has widened.

EltaMD is the clearest displacement threat. While La Roche-Posay leads on overall coverage, EltaMD leads on top-three rate at 53.6% versus 51.7% and dominates on rank-one rate at 30.6% versus 15.5%. When AI systems are asked to recommend a single best sunscreen brand, EltaMD is nearly twice as likely to be named first. This pattern suggests that EltaMD's public evidence layer is more effective at earning first-position recommendations, even though La Roche-Posay appears in more answers overall.

The top-three rate decline from 56.2% to 51.7% indicates that La Roche-Posay lost ground in the middle of the shortlist. The brand is still recommended, but less prominently. Competitors are capturing the second and third recommendation slots that La Roche-Posay held in July 2026.

Perplexity represents the clearest platform-specific gap. La Roche-Posay recorded 72.73% valid recommendation coverage on Perplexity, compared with 92.96% on ChatGPT and 95.59% on Copilot. The rank-one rate on Perplexity was 7.58%, less than a quarter of the ChatGPT rate. Perplexity's answer format and source retrieval patterns appear to favor different brands.

The category-wide decline in recommendation-shaped answer share, from 75.0% in July 2026 to 69.0% in September 2026, means AI systems are producing fewer recommendation-style responses overall. In a less recommendation-heavy answer environment, the brands that maintain shortlist placement will capture disproportionate attention.

Biggest Opportunity

Questions This Section Answers

• Which prompt cluster offers the clearest path to converting La Roche-Posay mentions into recommendations?
• How much recommendation credit is La Roche-Posay losing in discovery prompts where it already appears?
• What would closing the Perplexity coverage gap require?

The clearest opportunity for La Roche-Posay is closing the gap between presence and recommendation conversion in the discovery and evaluation prompt cluster. The brand already appears in 95.6% of qualified observations. The path forward is ensuring that when La Roche-Posay is mentioned, it is also shortlisted and placed in the top three.

This opportunity is specific to the Brand Recommendation cluster, which covers prompts such as "best sunscreen," "sunscreen for face," and "Which sunscreen is best for combination skin?" These are the high-intent discovery prompts where buyers form their shortlist. La Roche-Posay is present in nearly all of them but is losing recommendation credit in approximately 12% of cases where it appears.

The secondary opportunity is Perplexity. The brand's 72.73% coverage on Perplexity trails its performance on every other tracked platform. Improving the source footprint that Perplexity retrieves could close that gap and add recommendation coverage without requiring new presence.

Competitive Landscape

Questions This Section Answers

• How does La Roche-Posay's placement compare to EltaMD on top-three and rank-one rates in sunscreen recommendations?
• Which sunscreen brands form the second tier behind La Roche-Posay and EltaMD?
• What does La Roche-Posay's average recommended rank say about its shortlist position?

La Roche-Posay and EltaMD hold the strongest recommendation-stage positions in the Sunscreen Brands category, with CeraVe, Neutrogena, and Supergoop forming a second tier above 60% valid recommendation coverage. La Roche-Posay leads on overall coverage and presence, while EltaMD leads on placement strength at the top of the shortlist.

Average recommended rank covers rank-eligible recommendations only.

La Roche-Posay ranks second on top-three rate and second on rank-one rate, trailing EltaMD on both placement metrics despite leading on overall recommendation coverage. The brand's average recommended rank of 2.39 is the strongest among high-coverage competitors, indicating that when La Roche-Posay is recommended, it tends to appear near the top of the shortlist. The gap to EltaMD is narrow on top-three rate at 1.92 percentage points but wide on rank-one rate at 15.13 percentage points.

Prompt Evidence

ChatGPT / Brand Recommendation Prompt: "best sunscreen for face" Result: La Roche-Posay received a valid recommendation with strong placement, consistent with the brand's 92.96% coverage and 35.21% rank-one rate on ChatGPT.

Perplexity / Brand Recommendation Prompt: "Which sunscreen is best for combination skin?" Result: La Roche-Posay was mentioned but recommendation coverage on Perplexity dropped to 72.73%, well below the brand's category-leading presence rate.

AI Mode / Brand Recommendation Prompt: "best sunscreen" Result: La Roche-Posay appeared in the answer with a valid recommendation, contributing to the brand's 86.61% coverage on AI Mode across 127 observations.

Copilot / Brand Recommendation Prompt: "What is the best mineral sunscreen for rosacea?" Result: La Roche-Posay received a valid recommendation with a 95.59% coverage rate on Copilot, one of the brand's strongest platform signals.

What CiteWorks Studio Would Do Next

Phase 1: AI Market Discovery Audit Map the specific prompts where La Roche-Posay is mentioned but not shortlisted, and identify which competitors capture the recommendation credit in those answers.

Phase 2: Recommendation Readiness Plan Prioritize the discovery and evaluation prompts where conversion gaps are largest, and define the content and evidence changes needed to restore shortlist placement.

Phase 3: Owned Answer Layer Buildout Strengthen the brand's owned pages so that product, ingredient, and dermatologist-endorsement content directly addresses the high-intent prompts where recommendation credit is being lost.

Phase 4: Citation / Authority Layer Development Improve the external source footprint that AI systems retrieve, particularly for Perplexity, where coverage trails every other tracked platform.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track coverage, top-three rate, rank-one rate, and sentiment by platform and cluster to measure whether conversion gaps are closing month over month.

Why This Matters

AI presence alone is not enough. La Roche-Posay appears in 95.6% of qualified observations, yet receives a valid recommendation in only 83.7%. That gap represents real buyer moments where the brand is visible but not chosen. In a category where EltaMD nearly doubles La Roche-Posay on first-position recommendations, the difference between being mentioned and being recommended first is the difference between being on the shortlist and winning it.

The next move is targeted correction of the prompt, page, and citation layers. The benchmark shows where La Roche-Posay is winning and losing. A company-level analysis shows why. The path from reference to recommendation runs through the specific prompts, sources, and answer formats that AI systems use to form their shortlists.

Core Metrics

Sentiment Score

Questions This Section Answers

• Why is a positive sentiment score alone not enough to measure AI recommendation strength?
• How is La Roche-Posay's net sentiment score calculated for September 2026?
• What does the brand's single negative mention indicate about its public evidence layer?

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

For La Roche-Posay in September 2026: (479 × 1 + 19 × 0 + 1 × -1) / 499 = 478 / 499 = 0.9579.

This score matters because unclassified mention counts are misleading. A brand can appear in hundreds of AI answers without being recommended, and a positive recommendation is not the same as a neutral reference or a cautionary mention. Counting all mentions as wins is bad measurement.

Share of voice is a diagnostic metric, not a business KPI. What matters is whether the brand is recommended, where it appears in the shortlist, and how it is framed. La Roche-Posay's sentiment score of 0.9579 indicates that the public evidence layer consistently frames the brand positively. The single negative mention is an outlier. But sentiment alone does not capture recommendation placement, which is why coverage, top-three rate, and rank-one rate must be read alongside it.

Classified sentiment is required before interpreting AI visibility. A brand with high presence and positive framing but declining recommendation conversion is losing ground at the decision moment, even if its sentiment score remains strong.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of La Roche-Posay's AI recommendation performance in the Sunscreen Brands category for September 2026. It is not a client implementation case study.
2. The reporting window is September 2026, with comparisons to July 2026 and August 2026 where the benchmark provides historical data.
3. Six AI and search surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.
4. The September 2026 benchmark drew on 800 source prompt-surface observations across 667 unique questions. Of those, 540 were relevant to the category and 260 were irrelevant, leaving 522 qualified observations used for brand-level metrics.
5. The competitor universe includes nine tracked brands: La Roche-Posay, EltaMD, CeraVe, Neutrogena, Supergoop, Cetaphil, Sun Bum, Vichy, and Kopari Beauty.
6. Three public high-intent clusters were defined: Best Sunscreen Brands Discovery & Evaluation (consideration stage), Sunscreen Brand Comparisons & Alternatives (evaluation stage), and Sunscreen Pricing, Value & Where to Buy (decision stage). All 522 qualified observations in September 2026 fell into the Brand Recommendation class, which corresponds to the discovery and evaluation cluster. No qualified observations were recorded for pricing or multi-brand comparison queries.
7. Stage 0 extraction retained the query, AI surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. A mention is defined as any appearance of the brand in a qualified observation, regardless of whether the brand was recommended.
9. A valid recommendation is defined as an observation where the brand received a positive recommendation with a rank position of 1 through 10. Neutral, cautionary, comparison-anchor, and listed-only mentions are not counted as valid recommendations.
10. Brand-level percentages use the 522 qualified observations as the public denominator, not the 800 raw prompt-surface collections.
11. The benchmark does not measure market share, sales attribution, organic-search ranking performance, social media mention volume, or causality from metric movement alone.
12. Source presence is evidence about the information environment. It is not automatically proof that the source caused the recommendation.

See How AI Is Recommending Your Brand

The public benchmark shows where La Roche-Posay is winning and losing in AI-generated recommendations. A company-level AI visibility audit maps the specific prompts, competitors, sources, and answer formats behind those percentages, turning the standings in this report into a prioritized strategy for closing recommendation gaps and defending category leadership.