Sun Bum AI Visibility Market Strategy Report - Sunscreen Brands

Answer Capsule

Sun Bum holds a measurable but thin position in AI-generated sunscreen recommendations for October 2026. The brand appeared in 12.38% of qualified observations and earned a valid recommendation in 10.29%, but reached a top-three placement in only 2.25% of observations. Its clearest weakness is recommendation conversion at the shortlist stage, where La Roche-Posay, CeraVe, EltaMD, and Neutrogena all convert at more than seven times its rate. Its clearest opportunity sits in the consideration cluster, where dermatologist and skin-type questions still surface the brand often enough to build from.

Who This Report Is For

This report is written for Sun Bum's brand, growth, and ecommerce leadership, and for category teams evaluating how sunscreen brands are being shortlisted inside AI-generated answers.

Report Card

Executive Summary

Sun Bum is visible in AI-generated sunscreen answers but is rarely recommended inside them. The October 2026 benchmark recorded the brand in 77 of 622 qualified observations, a raw mention presence rate of 12.38%, yet only 64 of those observations produced a valid recommendation, a valid recommendation coverage rate of 10.29%. That gap between appearing and being chosen is the central finding of this readout.

The brand's placement profile is weaker still. Sun Bum reached a top-three recommendation position in 14 observations, a top-three rate of 2.25%, and appeared as the first recommended option in 5 observations, a rank-one rate of 0.80%. Its average recommended rank of 4.09 places it behind the category's leading brands on placement quality as well as frequency.

Sentiment framing is positive but thinner than the category leaders. Sun Bum recorded 65 positive mentions, 12 neutral mentions, and zero negative mentions, producing a net sentiment score of 0.8442. That is the lowest net sentiment score among the tracked brands with meaningful presence, and it reflects a higher share of neutral, reference-style mentions rather than cautionary framing.

The strongest platform signal for Sun Bum is Google AI Overviews, where the brand recorded a valid recommendation coverage rate of 17.65% and a top-three rate of 2.61%. The weakest platform signal is Gemini, where the brand appeared in 11 of 78 observations but produced only 1 top-three placement and no rank-one placements.

The clearest cluster gap is structural. All 622 qualified observations fell into the Brand Recommendation cluster, which covers discovery and consideration queries. No qualified observations were recorded for pricing and value or multi-brand comparison questions in October 2026, so the benchmark cannot currently show how Sun Bum performs when buyers ask AI systems to compare sunscreen options head to head.

The clearest competitive gap is against La Roche-Posay, which holds a valid recommendation coverage rate of 92.93% and a top-three rate of 72.03%. Sun Bum's coverage sits 82.64 percentage points below the category leader and 41.93 points below Supergoop, the nearest brand above it in the standings.

What Sun Bum Is Winning

Sun Bum's evidence-backed wins are narrow but real. The brand recorded zero negative mentions across all 622 qualified observations, which means no AI-generated answer in the benchmark framed the brand unfavorably. That is a clean framing position, and it is shared by most of the tracked set, so it should be read as an absence of harm rather than a competitive advantage.

The brand's strongest platform by recommendation behavior is Google AI Overviews. Sun Bum earned 27 valid recommendations there, a coverage rate of 17.65%, and 4 top-three placements, a top-three rate of 2.61%. That platform also produced the brand's single rank-one placement outside of Copilot and Perplexity.

Sun Bum also holds a measurable presence in the consideration cluster that the benchmark measures. The brand appeared in 77 qualified observations and earned 64 valid recommendations, which means that when it does surface, it is recommended rather than merely listed in most cases. The conversion problem is one of reach, not of being mentioned without support.

Beyond those points, the win set is thin. Sun Bum does not lead any tracked platform, does not lead the qualified cluster, and does not hold a top-three or rank-one position that outperforms the category's leading brands.

Where Sun Bum Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Where does Sun Bum lose shortlist placement, and what does its top-three rate reveal about AI recommendation conversion?
• Which competitors take the shortlist positions that Sun Bum misses, and how large is that placement gap?

Sun Bum's clearest gap is recommendation conversion at the shortlist stage. The brand is present in 12.38% of qualified observations but reaches a top-three position in only 2.25%. That means roughly four out of every five times Sun Bum appears in an AI answer, it is mentioned without being placed on the shortlist the answer is building.

The gap widens when compared with the brands directly above it. Supergoop holds a valid recommendation coverage rate of 52.25% and a top-three rate of 26.37%, both more than five times Sun Bum's equivalent figures. Cetaphil, which sits below Sun Bum on presence in some months, holds a top-three rate of 14.79%, more than six times Sun Bum's. The brand is not competing for the same shortlist positions as the mid-tier of the category.

The platform picture shows where the displacement is concentrated. On Gemini, Sun Bum appeared in 11 of 78 observations but produced only 1 top-three placement and no rank-one placements, an average recommended rank of 4.86. On ChatGPT, the brand appeared in a single observation and produced no top-three placement. On Copilot, the brand appeared in 10 of 79 observations and produced 1 top-three placement. These are the platforms where the brand is being mentioned as context rather than recommended as an option.

The cluster picture is limited by the benchmark's current scope. All qualified observations fell into the Brand Recommendation cluster, so the report cannot show whether Sun Bum performs differently on pricing, value, or head-to-head comparison questions. That is a measurement gap rather than a confirmed weakness, and it is the single largest unknown in this readout.

Biggest Opportunity

Questions This Section Answers

• Which sunscreen prompt types already surface Sun Bum, and how could they drive top-three AI placements?
• What would closing the dermatologist and skin-type consideration gap do to Sun Bum's shortlist rate?

Sun Bum's biggest opportunity is to convert its existing consideration-stage presence into shortlist placement. The brand already appears in 77 qualified observations and earns a valid recommendation in 64 of them, so the raw material for recommendation-stage visibility exists. What is missing is the placement layer: the brand is being named but not positioned inside the top three, and it is being named on platforms where the leading brands are being positioned first.

The prompts that drive this cluster are specific and repeatable. The qualified prompt set includes questions such as "What is the best mineral sunscreen for rosacea?", "Which sunscreen is best for combination skin?", "What sunscreen is best for Accutane?", and "What is the most recommended sunscreen by dermatologists?". These are skin-type and dermatologist-endorsement questions, and they are the question types where the category leader converts at more than seven times Sun Bum's rate. Closing even a portion of that gap on the consideration cluster would move the brand's top-three rate materially, because the cluster is the only qualified cluster in the benchmark.

Competitive Landscape

Questions This Section Answers

• How does Sun Bum's top-three rate, rank-one rate, and average recommended rank compare with La Roche-Posay, CeraVe, and Supergoop?
• What does Sun Bum's sentiment score and average recommended rank say about its position among sunscreen brands?

La Roche-Posay holds dominant recommendation power in the sunscreen category, with EltaMD, CeraVe, and Neutrogena forming a strong second tier. Sun Bum sits in the lower half of the tracked set, above only Vichy and Kopari Beauty on recommendation-stage strength.

Average recommended rank covers rank-eligible recommendations only.

Sun Bum's top-three rate of 2.25% places it seventh of nine tracked brands, and its rank-one rate of 0.80% places it seventh as well. Its average recommended rank of 4.09 is the lowest among brands with meaningful recommendation volume, meaning that when the brand does receive rank credit, it tends to land further down the list than its competitors. Its net sentiment score of 0.8442 is the lowest in the tracked set, reflecting a higher proportion of neutral mentions relative to positive ones.

Prompt Evidence

Questions This Section Answers

• What does Sun Bum's prompt-level evidence show across Google AI Overviews, Gemini, ChatGPT, and Perplexity?
• Which sunscreen prompts produced a recommendation or rank-one placement for Sun Bum?

Google AI Overviews / Best Dermatologist Recommended Skin Care Brands Prompt: "What is the best mineral sunscreen for rosacea?" Result: Sun Bum appeared in the answer and earned a valid recommendation, contributing to the brand's strongest platform coverage rate of 17.65%.

Gemini / Best Dermatologist Recommended Skin Care Brands Prompt: "Which sunscreen is best for combination skin?" Result: Sun Bum was mentioned but did not reach a top-three placement, consistent with the brand's 1 top-three placement across 78 Gemini observations.

ChatGPT / Best Dermatologist Recommended Skin Care Brands Prompt: "What are the top 10 best skincare brands?" Result: Sun Bum appeared in a single ChatGPT observation across the benchmark and produced no top-three placement on that platform.

Perplexity / Best Dermatologist Recommended Skin Care Brands Prompt: "What sunscreen is best for Accutane?" Result: Sun Bum earned a valid recommendation and one rank-one placement, one of only five rank-one placements the brand recorded across all platforms.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit. Map the specific prompts where Sun Bum appears without placement, and identify which competitors absorb the top-three positions in those same answers.

Phase 2: Recommendation Readiness Plan. Prioritize the skin-type and dermatologist-endorsement question types where the brand already has presence but loses placement, and define the answer attributes that would move it into the shortlist.

Phase 3: Owned Answer Layer Buildout. Strengthen the brand's own pages and product content so that the attributes AI systems associate with top-three sunscreen recommendations are clearly and retrievably stated.

Phase 4: Citation / Authority Layer Development. Build the editorial, dermatologist, and retail source footprint that AI systems draw on when constructing sunscreen shortlists, since no tracked brand's own domain appears among the top 10 cited domains.

Phase 5: Monthly AI Visibility and Recommendation Tracking. Track top-three rate, rank-one rate, and platform-level placement month over month to confirm whether the conversion gap is closing.

Why This Matters

AI presence alone is not enough. Sun Bum appears in more than one in ten qualified sunscreen answers, yet it reaches the shortlist in fewer than one in forty. In buyer-choice terms, the brand is being named in the room but is not being placed on the list the buyer is choosing from.

The next move is targeted correction of the prompt, page, and citation layers. The benchmark shows where Sun Bum is losing placement and which competitors are taking it. It does not show why, and it does not show which sources are shaping those answers. That is the work a company-level analysis is built to do.

Core Metrics

Sentiment Score

Questions This Section Answers

• How is Sun Bum's net sentiment score calculated in this sunscreen benchmark?
• Why can't Sun Bum's positive score be read the same way as a recommendation?

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

For Sun Bum in October 2026, that is (65 × 1 + 12 × 0 + 0 × -1) / 77, which produces a net sentiment score of 0.8442.

This matters because unclassified mention counts are misleading. A brand that appears 77 times with 12 neutral references is not in the same position as a brand that appears 77 times with 77 positive recommendations. 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, and counting all mentions as wins is bad measurement. Classified sentiment is required before interpreting AI visibility, because it separates framing quality from raw presence.

Sun Bum's 0.8442 score is positive but the lowest among tracked brands with meaningful presence. The 12 neutral mentions represent answers where the brand was referenced without a clear recommendation, which is consistent with the placement gap described above.

Sentiment by Platform

Questions This Section Answers

• Which platforms are driving Sun Bum's sunscreen recommendation signal versus neutral context?
• Where does Sun Bum appear positively but remain outside recommendation-led answers?

Methodology

Questions This Section Answers

• How were Sun Bum's qualified sunscreen observations counted for this AI visibility benchmark?
• Which limits should readers apply when interpreting Sun Bum's recommendation and placement metrics?

1. Report orientation. This is a benchmark-based AI Visibility Company Market Strategy Report for Sun Bum in the Sunscreen Brands vertical. It is not a client implementation case study and does not describe work performed by CiteWorks Studio.
2. Reporting window. The report covers October 2026, with comparison points drawn from the July 2026 baseline and the August and September 2026 interim months where the source benchmark provides them.
3. Platforms tracked. Six canonical AI and search surface families carried at least one qualified observation: ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, and Google AI Mode.
4. Observation count. The October 2026 benchmark began with 800 prompt-surface observations and 653 unique questions. Of those, 800 mentioned a tracked brand or competitor, 638 were relevant, 162 were irrelevant, and 622 qualified observations form the public denominator.
5. Competitor universe. Nine brands were tracked: CeraVe, Cetaphil, EltaMD, Kopari Beauty, La Roche-Posay, Neutrogena, Sun Bum, Supergoop, and Vichy.
6. Public clusters used. One qualified cluster was measured in October 2026: Best Dermatologist Recommended Skin Care Brands, a consideration-stage cluster. Two additional clusters, covering brand comparisons and pricing and value, returned no qualified observations.
7. Stage 0 role. Stage 0 extraction produced the prompt-level observations that retain the query, AI surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources.
8. Definition of a mention. A mention is any qualified observation in which Sun Bum appears in the AI-generated answer, regardless of whether the brand is recommended or placed.
9. Definition of a valid recommendation. A valid recommendation is a qualified observation in which Sun Bum receives an explicit recommendation. Mentions that are neutral, cautionary, comparison anchors, or listed only are not counted as valid recommendations unless the dataset marks them as such.
10. Ranking interpretation. Top-three rate is the share of qualified observations where the brand appears in the top three recommended positions. Rank-one rate is the share where the brand is the first recommended option. Average recommended rank covers rank-eligible recommendations only.
11. Dataset normalization. Brand-level percentages use the 622 qualified observations as the public denominator, not the 800 raw prompt-surface collections. Sun Bum's figures were consistent across the industry benchmark, the case study, and the metrics aggregation.
12. Limitations. The benchmark does not measure market share, sales attribution, organic-search ranking performance, social mention volume, private or sponsored AI channels, or causality from a metric movement alone. Source presence is evidence about the information environment, not proof that a source caused a recommendation. The benchmark also cannot currently answer price sensitivity or head-to-head comparison questions for this category, because no qualified observations were recorded in those clusters.

See How AI Is Recommending Your Brand

The public benchmark shows where Sun Bum is being mentioned and where it is being left off the shortlist. A company-level AI visibility audit maps the specific prompts, platforms, competitors, and source patterns behind those placements, and turns the standings in this report into a prioritized plan for closing the recommendation gap.