Supergoop AI Visibility Market Strategy Report - Sunscreen Brands

Mark HuntleyBy Mark HuntleyFounder and CEO
11 minutes read

Key Takeaways

  • Supergoop’s valid recommendation coverage dropped 13.3 points from July to October 2026, the largest decline in the sunscreen category.
  • The main issue is reduced mention presence: the brand appears in fewer AI answers, but still places well when it is included.
  • Google AI Overviews and Gemini are the strongest platforms for Supergoop, while Perplexity and ChatGPT show the largest coverage gaps.
  • The best recovery opportunity is in dermatologist-recommendation and skin-type-specific prompts, where competitors have absorbed Supergoop’s visibility.

Answer Capsule

Supergoop holds meaningful AI recommendation presence in sunscreen brand discovery but is losing ground at the recommendation stage. In October 2026, the brand recorded 52.25% valid recommendation coverage across 622 qualified observations, down 13.3 percentage points from 65.5% in July 2026, the sharpest decline of any tracked sunscreen brand. The retreat was driven by falling mention presence rather than placement weakness: Supergoop appears in substantially fewer AI answers than it did at baseline, but where it does appear it still places in the top three at a 26.37% rate. The clearest opportunity is recovering the prompt families that stopped surfacing the brand entirely, particularly in dermatologist-recommendation and skin-type-specific sunscreen queries.

Who This Report Is For

This report is for Supergoop's brand, growth, and ecommerce leadership teams, and for category strategists tracking how AI-generated recommendations are reshaping sunscreen discovery at the consideration stage.

Report Card

Field

Value

Report type

AI Visibility Company Market Strategy Report

Target company

Supergoop

Category / market studied

Sunscreen Brands

Reporting month

October 2026

AI platforms tracked

6 (ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, Google AI Mode)

Public high-intent clusters

1

AI observations analyzed

622

Competitors tracked

8

Executive Summary

Supergoop is visible in AI-generated sunscreen recommendations but is losing recommendation-stage ground faster than any other tracked brand. The brand recorded 52.25% valid recommendation coverage in October 2026, down 13.3 percentage points from 65.5% in July 2026. That decline exceeded normal month-to-month variation and represents the largest baseline-to-current drop in the category.

The retreat was driven by presence, not placement. Raw mention presence fell 14.9 percentage points from 68.9% to 54.0%, while top-three rate slipped only 4.5 points to 26.37% and rank-one rate held essentially flat at 7.40%. Supergoop appears in substantially fewer AI answers than it did in July, but in the answers where it does appear, it still places. This is a coverage-of-answers problem, not a placement problem.

Sentiment framing remains strongly positive. Of 336 mentions, 326 were classified positive and 10 neutral, producing a net sentiment score of 0.9702. No negative mentions were recorded. The brand's framing quality is not the issue.

The strongest platform signal for Supergoop is Google AI Overviews, where the brand recorded 56.21% valid recommendation coverage and a 31.37% top-three rate across 153 observations. Gemini also shows relative strength at 65.38% coverage. The weakest platform signal is Perplexity, where coverage drops to 37.84% with a top-three rate of 12.16%.

The clearest gap is the brand's declining presence in the Brand Recommendation cluster, the only cluster with qualified observations in this benchmark. Supergoop's 14.9-point presence decline means the brand is simply absent from answers where it previously appeared. Competitors including La Roche-Posay, CeraVe, and EltaMD have maintained or improved their positions over the same period.

The category context matters. La Roche-Posay leads at 92.93% coverage, followed by CeraVe at 76.21% and EltaMD at 73.47%. Supergoop now sits fifth at 52.25%, separated from the top cluster by more than 20 percentage points. The brand's rank-one rate of 7.40% also trails EltaMD (31.35%), CeraVe (20.10%), and La Roche-Posay (18.81%) by substantial margins.

What Supergoop Is Winning

Questions This Section Answers

  • Where does Supergoop perform strongest across AI platforms, and what is driving that relative strength?
  • If Supergoop already places well when it appears, why is sentiment framing considered its clearest win?

Supergoop's clearest win is sentiment framing. The brand recorded zero negative mentions across 336 total mentions, with a net sentiment score of 0.9702. This places Supergoop among the most positively framed brands in the category, behind only EltaMD (0.9914) and La Roche-Posay (0.9765) on this measure.

The brand also shows relative strength on Google AI Overviews. Across 153 observations on that platform, Supergoop recorded 56.21% valid recommendation coverage and a 31.37% top-three rate. This is the brand's strongest platform-level performance and suggests that AI Overviews surfaces are retrieving and synthesizing Supergoop content effectively.

Gemini represents a second area of relative strength. Supergoop recorded 65.38% valid recommendation coverage on Gemini across 78 observations, with a 35.90% top-three rate and a 17.95% rank-one rate. The rank-one rate on Gemini is the brand's highest across any platform.

Where Supergoop does appear, it places. The brand's top-three rate of 26.37% and rank-one rate of 7.40% indicate that when AI systems surface Supergoop, they are willing to recommend it in shortlist positions. The problem is not recommendation quality when present; it is recommendation frequency.

Where Supergoop Has the Clearest AI Visibility Gaps

Questions This Section Answers

  • Is Supergoop losing ground because competitors are outranking it within answers, or because it is being left out of answers entirely?
  • Which platforms show the largest coverage gap between Supergoop and its competitors?

Supergoop's most significant gap is the collapse in mention presence. Raw mention presence fell from 68.9% in July 2026 to 54.0% in October 2026, a 14.9-point decline. This means the brand is absent from approximately 15% of qualified observations where it previously appeared. The brand is not being displaced within answers; it is being excluded from answers entirely.

The competitive displacement is measurable. La Roche-Posay, CeraVe, and EltaMD all maintained or improved their coverage over the same period. La Roche-Posay rose 3.3 points to 92.93%, CeraVe rose 5.8 points to 76.21%, and EltaMD declined only 7.3 points to 73.47%. Supergoop's 13.3-point decline is nearly double EltaMD's rate of loss.

The gap is most pronounced on Perplexity. Supergoop recorded only 37.84% valid recommendation coverage on Perplexity across 74 observations, with a top-three rate of 12.16%. By comparison, CeraVe recorded 83.78% coverage on Perplexity, Neutrogena recorded 87.84%, and La Roche-Posay recorded 86.49%. Perplexity represents a significant platform-level gap.

ChatGPT also shows weakness relative to competitors. Supergoop recorded 45.35% coverage on ChatGPT, while CeraVe recorded 93.02%, La Roche-Posay recorded 97.67%, and Neutrogena recorded 82.56%. The brand's top-three rate on ChatGPT was 16.28%, compared to 52.33% for CeraVe and 75.58% for La Roche-Posay.

The rank-one gap is substantial. Supergoop's rank-one rate of 7.40% trails EltaMD (31.35%), CeraVe (20.10%), and La Roche-Posay (18.81%). Even Neutrogena, which has lower overall coverage than EltaMD, records a rank-one rate of 5.14%, placing it within 2.3 points of Supergoop despite Supergoop's higher coverage. The brand is being recommended, but rarely as the first option.

Biggest Opportunity

Questions This Section Answers

  • Which prompt families should Supergoop prioritize to recover lost AI recommendations?
  • Should Supergoop focus on improving placement where it already appears, or on regaining inclusion in answers where it is now absent?
  • Which platforms represent the largest recoverable gap for Supergoop's coverage?

Supergoop's clearest path from reference to recommendation is recovering presence in the high-intent Brand Recommendation cluster, specifically the dermatologist-recommendation and skin-type-specific sunscreen prompts where the brand has lost ground.

The benchmark data shows that all 622 qualified observations fall into the Brand Recommendation cluster, covering discovery and consideration queries. Prompt examples from the dataset include "What is the most recommended sunscreen by dermatologists?", "Which sunscreen is best for acne-prone skin?", "What is the best mineral sunscreen for rosacea?", and "What sunscreen is best for Accutane?". These are exactly the prompt types where Supergoop's presence has declined.

The opportunity is not to improve placement where Supergoop already appears. The brand's top-three rate of 26.37% and rank-one rate of 7.40% show that when present, Supergoop places reasonably well. The opportunity is to restore the brand's inclusion in answers where it is now absent. This requires understanding which prompt families dropped Supergoop entirely and which competitors absorbed those recommendations.

The platform dimension matters. Supergoop's strongest platform is Google AI Overviews at 56.21% coverage, while Perplexity at 37.84% and ChatGPT at 45.35% represent the largest gaps. A targeted effort to improve retrievability on Perplexity and ChatGPT could recover meaningful coverage without requiring changes to how Supergoop is framed when it does appear.

Competitive Landscape

Questions This Section Answers

  • How does Supergoop's top-three and rank-one performance compare to La Roche-Posay, EltaMD, and CeraVe?
  • Why does Supergoop's average recommended rank outpace its overall top-three rate?

La Roche-Posay holds dominant recommendation-stage strength in Sunscreen Brands, with CeraVe and EltaMD as the strongest challengers. Supergoop sits in the middle of the field, separated from the top cluster by more than 20 percentage points on valid recommendation coverage and trailing significantly on rank-one placement.

Brand

Top-3 rate

Rank-1 rate

Avg recommended rank

Sentiment

La Roche-Posay

72.03%

18.81%

2.31

0.9765

EltaMD

53.22%

31.35%

2.20

0.9914

CeraVe

48.87%

20.10%

2.58

0.9636

Neutrogena

31.83%

5.14%

3.46

0.9618

Supergoop

26.37%

7.40%

3.13

0.9702

Cetaphil

14.79%

2.09%

3.40

0.9431

Sun Bum

2.25%

0.80%

4.09

0.8442

Vichy

0.48%

0.32%

4.00

0.9231

Kopari Beauty

0.00%

0.00%

N/A

0.0000

Average recommended rank covers rank-eligible recommendations only.

Supergoop ranks fifth on top-three rate and fifth on rank-one rate among the nine tracked brands. The brand's average recommended rank of 3.13 is fourth-best in the category, ahead of Neutrogena (3.46) and Cetaphil (3.40), indicating that when Supergoop is recommended, it tends to place higher than its overall top-three rate would suggest. The gap between Supergoop's top-three rate (26.37%) and Neutrogena's (31.83%) is 5.5 percentage points, while the gap to CeraVe (48.87%) is 22.5 points.

Prompt Evidence

Google AI Overviews / Brand Recommendation Prompt: "What is the best mineral sunscreen for rosacea?" Result: Supergoop appeared in the recommendation set with positive framing, contributing to the brand's strongest platform-level coverage.

ChatGPT / Brand Recommendation Prompt: "What are the top 10 best skincare brands?" Result: Supergoop was mentioned but placed outside the top three, reflecting the brand's lower rank-one rate on ChatGPT compared to competitors.

Perplexity / Brand Recommendation Prompt: "Which sunscreen is best for combination skin?" Result: Supergoop recorded weaker presence on Perplexity, where the brand's coverage of 37.84% trails competitors by more than 40 percentage points.

Gemini / Brand Recommendation Prompt: "What sunscreen is best for Accutane?" Result: Supergoop appeared with a rank-one placement, contributing to the brand's 17.95% rank-one rate on Gemini, its highest across any platform.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map the specific prompt families where Supergoop's presence declined between July and October 2026, and identify which competitors absorbed those recommendations.

Phase 2: Recommendation Readiness Plan Prioritize recovery of the dermatologist-recommendation and skin-type-specific prompt clusters where Supergoop has lost the most ground, with platform-specific targets for Perplexity and ChatGPT.

Phase 3: Owned Answer Layer Buildout Strengthen Supergoop's owned content to directly address the high-intent questions where the brand is now absent, ensuring product pages and educational content align with the query patterns AI systems retrieve.

Phase 4: Citation / Authority Layer Development Expand Supergoop's presence in the editorial and beauty media sources that AI systems cite most frequently, including allure.com, byrdie.com, and glamour.com, where the brand's own domain does not currently appear among top-cited sources.

Phase 5: Monthly AI Visibility and Recommendation Tracking Monitor Supergoop's coverage, top-three rate, and rank-one rate across all six platforms to measure recovery and detect future presence declines before they compound.

Why This Matters

Questions This Section Answers

  • Why doesn't Supergoop's high sentiment score offset its declining recommendation coverage?
  • What needs to change for Supergoop's AI presence to convert into recommendations?

AI presence alone is not enough. Supergoop's 54.0% mention presence rate means the brand appears in more than half of qualified AI answers, but its 52.25% valid recommendation coverage and 26.37% top-three rate show that presence does not automatically convert to recommendation. The brand is visible but under-recommended relative to its presence, and it is losing presence at the same time.

The next move is targeted correction of the prompt, page, and citation layers. Supergoop's sentiment framing is strong and its placement when present is reasonable. The gap is in the prompts where the brand no longer appears and the sources AI systems retrieve when forming those answers. Recovering those prompts requires understanding which pages and citations are driving competitor recommendations in the same query space, then building Supergoop's own evidence layer to match.

Core Metrics

Metric

Value

Mentions

336

Valid recommendations

325

Top 3 recommendation count

164

Rank #1 recommendation count

46

Average recommended rank

3.13

Positive mentions

326

Neutral mentions

10

Negative mentions

0

Raw mention presence rate

54.02%

Valid recommendation coverage

52.25%

Top 3 recommendation rate

26.37%

Rank #1 recommendation rate

7.40%

Net sentiment score

0.9702

Strongest cluster by recommendation behavior

Brand Recommendation (C01)

Strongest platform by recommendation behavior

Google AI Overviews

Sentiment Score

Questions This Section Answers

  • Why can't raw mention counts be used to gauge Supergoop's AI recommendation strength?
  • What does Supergoop's sentiment score of 0.9702 reveal that its coverage decline does not?

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

Supergoop's sentiment score is 0.9702, calculated as (326 × 1 + 10 × 0 + 0 × -1) / 336.

This score matters because unclassified mention counts are misleading. A brand with 336 mentions could appear to be performing well, but if those mentions include cautionary framing, competitor comparisons, or neutral references, the raw count overstates recommendation strength. Supergoop's score of 0.9702 indicates that nearly all mentions carry positive framing, which is a genuine strength.

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. Counting all mentions as wins is bad measurement. Supergoop's high sentiment score means the brand's framing quality is strong, but it does not offset the 13.3-point decline in recommendation coverage. Classified sentiment is required before interpreting AI visibility, and in Supergoop's case, the sentiment is positive while the coverage is declining.

Sentiment by Platform

Platform

Mentions

Positive

Neutral

Negative

Sentiment Score

Readout

Google AI Overviews

87

86

1

0

0.9885

Strongest public recommendation signal

Gemini

52

52

0

0

1.0000

Positive, but sample smaller than top platforms

Copilot

52

47

5

0

0.9038

Present, but not recommendation-led

ChatGPT

39

39

0

0

1.0000

Positive framing, lower coverage than competitors

Perplexity

30

28

2

0

0.9333

Present as context, not recommendation

Google AI Mode

76

74

2

0

0.9737

Present, but coverage trails category leaders

Methodology

  1. This report is a benchmark-based analysis of Supergoop's AI visibility and recommendation performance in the Sunscreen Brands category for October 2026. It is not a client implementation case study.
  2. The reporting window is October 2026, with comparison data from July 2026, August 2026, and September 2026 where available.
  3. Six AI platforms were tracked: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode. All six carried at least one qualified observation in both July and October 2026.
  4. The benchmark began with 800 prompt-surface observations in October 2026 and produced 622 qualified observations after qualification. The qualified set serves as the public denominator for all brand-level percentages.
  5. The competitor universe includes nine tracked brands: CeraVe, Cetaphil, EltaMD, Kopari Beauty, La Roche-Posay, Neutrogena, Sun Bum, Supergoop, and Vichy.
  6. One public high-intent cluster carried qualified observations in October 2026: Brand Recommendation (C01), covering discovery and consideration queries. Pricing and Value (C02) and Multi-Brand Comparison (C03) recorded no qualified observations.
  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 Supergoop in a qualified AI response, regardless of recommendation status or placement.
  9. A valid recommendation is defined as a positive recommendation with rank 1-10 credit. Neutral, cautionary, comparison-anchor, and listed-only mentions are not counted as valid recommendations.
  10. Unique question count for October 2026 was 653. The public benchmark does not expose prompt-level detail beyond the cluster examples provided.
  11. Brand-level percentages use the 622 qualified observations as the public denominator, not the 800 raw prompt-surface collections.
  12. Month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes. Source presence is evidence about the information environment, not proof that the source caused the recommendation.

See How AI Is Recommending Your Brand

The public benchmark shows where Supergoop stands in AI-generated sunscreen recommendations. A company-level AI visibility audit maps the specific prompts, competitors, and sources shaping those recommendations across all six platforms, turning the category-level standings in this report into a prioritized action plan for recovering recommendation coverage and closing the gap to the category leaders.

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What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.

About The Author

Mark Huntley

Mark Huntley

Founder and CEO

Mark Huntley, J.D. is founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.

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