Tower 28 AI Visibility Market Strategy Report - Clean Makeup Brands

Answer Capsule

Tower 28 holds third position in AI-generated clean makeup recommendations for October 2026, with 40.89% valid recommendation coverage across 626 qualified observations. The brand converts presence into shortlist placement more effectively than most of the category, but it converts that placement into first-position recommendations at a much lower rate than the category leader. Tower 28's clearest win is a top-three recommendation rate of 18.21% built on a falling presence rate, and its clearest gap is rank-one conversion, where it sits at 4.63% against e.l.f. Cosmetics at 20.93%. The biggest opportunity is closing the distance between being shortlisted and being recommended first.

Who This Report Is For

This report is written for Tower 28's brand, growth, and ecommerce leadership, and for category strategists tracking how clean makeup brands are surfaced, shortlisted, and recommended inside AI-generated answers.

Report Card

Executive Summary

Tower 28 is visible in AI-generated clean makeup answers and is regularly shortlisted, but it is not yet the brand AI systems name first. The October 2026 benchmark recorded 302 mentions of Tower 28 across 626 qualified observations, a raw mention presence rate of 48.24%, and 256 valid recommendations, a valid recommendation coverage rate of 40.89%. That places the brand third in the tracked set, behind e.l.f. Cosmetics at 56.7% and Rare Beauty at 51.6%.

The brand's placement profile is stronger than its presence profile. Tower 28's top-three recommendation rate reached 18.21% in October 2026, up from 13.4% in July 2026, while its raw mention presence rate fell 2.4 points to 48.24% over the same window. In other words, Tower 28 was surfaced in fewer AI answers across the series but converted a larger share of those appearances into shortlist positions.

The clearest weakness is first-position conversion. Tower 28's rank-one recommendation rate was 4.63% in October 2026, up from 2.4% in July 2026 but still less than a quarter of e.l.f. Cosmetics' 20.93%. The gap between Tower 28's top-three rate and its rank-one rate is 13.58 percentage points, which means the brand is frequently named as an option without being named as the answer.

The strongest platform signal for Tower 28 is Gemini, where the brand recorded a 47.73% valid recommendation coverage rate and a 22.73% top-three rate, both above its category-wide averages. The clearest platform gap is Perplexity, where Tower 28 recorded a 41.5% valid recommendation coverage rate but only a 12.2% top-three rate, indicating that the brand is referenced on that surface without being placed near the top of the shortlist.

Sentiment is not the problem. Tower 28 recorded 264 positive mentions, 38 neutral mentions, and zero negative mentions in October 2026, producing a net sentiment score of 0.8742. AI systems frame the brand favorably when they mention it. The loss is in how often it is surfaced and how prominently it is ranked.

The category context matters here. The benchmark recorded a recommendation-shaped answer share of 67.4% in October 2026, up 18.4 points from July 2026, and a valid recommendation shortlist share of 88.2%, up 9.9 points. AI systems are producing more recommendation-shaped answers and more shortlists than they were three months ago. Brands that are shortlisted but not ranked first are competing for a smaller share of the top position than the shortlist growth alone would suggest.

What Tower 28 Is Winning

Questions This Section Answers

• Where is Tower 28 converting AI visibility into shortlist placement most effectively?
• Which platform shows Tower 28's strongest shortlist conversion?

Tower 28's strongest evidence-backed win is its top-three conversion. The brand's top-three rate rose 4.8 points from 13.4% in July 2026 to 18.21% in October 2026, a gain that occurred while its raw mention presence rate fell 2.4 points to 48.24%. The brand is being surfaced less often and shortlisted more often, which points to a ranking improvement rather than a visibility increase.

The brand's second win is sentiment quality. Tower 28 recorded zero negative mentions across 302 total mentions in October 2026, and its net sentiment score rose from 0.8 in July 2026 to 0.9 in October 2026. Only e.l.f. Cosmetics, ILIA Beauty, and Thrive Causemetics recorded higher net sentiment scores in the tracked set.

The third win is platform-specific strength on Gemini. Tower 28 recorded a 47.73% valid recommendation coverage rate on Gemini, well above its 40.89% category-wide rate, and a 22.73% top-three rate on that surface. Gemini is the platform where the brand's shortlist conversion is strongest.

These are real gains, but they are narrow. Tower 28 did not move beyond normal month-to-month variation on valid recommendation coverage across the July-to-October series, and its rank-one rate remains in the low single digits. The brand is improving its position within the shortlist without yet winning the shortlist.

Where Tower 28 Has the Clearest AI Visibility Gaps

Questions This Section Answers

• Why is Tower 28's rank-one recommendation rate so much lower than its top-three rate?
• Where is Tower 28 being retrieved without being placed near the top of the shortlist?

The clearest gap is rank-one conversion. Tower 28's rank-one recommendation rate was 4.63% in October 2026, compared with 20.93% for e.l.f. Cosmetics, 9.42% for ILIA Beauty, and 8.95% for Rare Beauty. The brand appears in the top three in 18.21% of qualified observations but appears first in only 4.63%. That 13.58-point spread is the widest between top-three and rank-one rates among the top four brands by coverage.

The second gap is Perplexity. Tower 28 recorded a 41.5% valid recommendation coverage rate on Perplexity, which is above its category-wide average, but its top-three rate on that surface was only 12.2%. On Gemini, by contrast, the same brand recorded a 22.73% top-three rate. The brand is being referenced on Perplexity without being placed near the top of the shortlist, which suggests the surface is retrieving Tower 28 as context rather than as a leading option.

The third gap is the widening distance to the category leader. The benchmark recorded that the gap between e.l.f. Cosmetics and Tarte Cosmetics widened by 16.5 points across the series, and the gap between e.l.f. Cosmetics and ILIA Beauty widened by 16.6 points. Tower 28 held third position but gained only 3.8 points of coverage across the series, while the leader gained 11.0 points and the second-place brand gained 9.1 points. The middle tier, which includes Tower 28, Kosas, and Milk Makeup, held ground but gained no distance on the leaders.

The fourth gap is cluster coverage. All 626 qualified observations in October 2026 fell into the Brand Recommendation cluster. The benchmark recorded zero qualified observations in the Pricing and Value cluster and zero in the Multi-Brand Comparison cluster. Tower 28's position in pricing, value, and head-to-head comparison prompts is not measured in this benchmark, which means the brand's competitive standing in those decision-stage contexts is unknown rather than confirmed.

Biggest Opportunity

Questions This Section Answers

• What evidence does Tower 28 need to close the gap between shortlist placement and first-position recommendations?
• Which citation-layer sources could help Tower 28 become the brand AI systems name first?

Tower 28's biggest opportunity is converting shortlist placement into first-position recommendations. The brand already appears in the top three in 18.21% of qualified observations, which means AI systems have already accepted Tower 28 as a credible clean makeup option. The gap is that the brand is named first in only 4.63% of those observations.

Closing that gap means giving AI systems a reason to lead with Tower 28 rather than list it. That requires strengthening the owned and earned evidence that AI systems retrieve when they form a recommendation: product-specific pages that state clearly what the brand is best for, comparison-friendly content that positions Tower 28 against the brands it is most often shortlisted alongside, and citation-layer sources that AI systems already trust in this category.

The benchmark's citation data supports this direction. Retail and review sites dominate the upper ranks of cited domains, with sephora.com cited 266 times and ulta.com cited 109 times across all platform responses in October 2026. Editorial and social platforms including allure.com, byrdie.com, reddit.com, and instagram.com also appear in the top ten. One tracked brand's own domain, thrivecausemetics.com, appears in the top ten at rank nine with 113 citations. That is direct evidence that a brand's own domain can enter the citation layer in this category.

Competitive Landscape

Questions This Section Answers

• How does Tower 28's top-three and rank-one conversion compare to e.l.f. Cosmetics and Rare Beauty?
• Where does Tower 28 typically land when it receives rank credit in AI recommendations?

e.l.f. Cosmetics holds the strongest recommendation-stage position in clean makeup for October 2026, with Rare Beauty as the clear second. Tower 28 sits third, ahead of ILIA Beauty on coverage but behind both leaders on top-three and rank-one conversion.

Average recommended rank covers rank-eligible recommendations only.

Tower 28's numbers show a brand that is shortlisted more often than it is recommended first. Its top-three rate of 18.21% is close to ILIA Beauty's 19.97%, but its rank-one rate of 4.63% is less than half of ILIA Beauty's 9.42%. Its average recommended rank of 3.53 is the second-highest among the top five brands, meaning that when Tower 28 does receive rank credit, it typically lands in the middle of the shortlist rather than at the top.

Prompt Evidence

Gemini / Brand Recommendation Prompt: "What is the best blush on the market?" Result: Tower 28 recorded a 22.73% top-three rate on Gemini, its strongest shortlist placement across tracked platforms.

Perplexity / Brand Recommendation Prompt: "What are the cleanest makeup brands?" Result: Tower 28 was referenced on Perplexity with a 41.5% valid recommendation coverage rate but a top-three rate of only 12.2%, indicating retrieval without top placement.

ChatGPT / Brand Recommendation Prompt: "What is the best makeup for older skin?" Result: Tower 28 recorded a 27.4% top-three rate on ChatGPT, above its category-wide rate, with a 6.9% rank-one rate.

Copilot / Brand Recommendation Prompt: "What kind of setting powder is best?" Result: Tower 28 recorded a 20.6% top-three rate on Copilot and a 5.5% rank-one rate, consistent with its pattern of shortlist presence without first-position conversion.

What CiteWorks Studio Would Do Next

Phase 1: AI Visibility Market Discovery Audit Map every qualified prompt where Tower 28 is shortlisted but not ranked first, and identify which competitors take the top position in each case.

Phase 2: Recommendation Readiness Plan Prioritize the prompt types and product categories where Tower 28 already appears in the top three, and define what evidence would move the brand to first position.

Phase 3: Owned Answer Layer Buildout Strengthen product and category pages so they state clearly what Tower 28 is best for, who it suits, and how it compares to the brands it is most often shortlisted alongside.

Phase 4: Citation / Authority Layer Development Build presence in the retail, editorial, and review sources that AI systems already cite in this category, including the domains appearing in the benchmark's top ten cited list.

Phase 5: Monthly AI Visibility and Recommendation Tracking Track top-three rate, rank-one rate, and average recommended rank by platform and prompt type to confirm whether shortlist placement is converting into first-position recommendations.

Why This Matters

Questions This Section Answers

• What does the rise in recommendation-shaped AI answers mean for brands that are shortlisted but not ranked first?
• Why is being on an AI shortlist different from being the recommended answer?

AI systems are producing recommendation-shaped answers more often than they were three months ago. The benchmark recorded a recommendation-shaped answer share of 67.4% in October 2026, up 18.4 points from July 2026, and a valid recommendation shortlist share of 88.2%, up 9.9 points. Buyers asking AI systems for clean makeup recommendations are increasingly receiving a shortlist rather than a general reference.

Being on that shortlist is not the same as being the answer. Tower 28 is shortlisted in 18.21% of qualified observations and recommended first in 4.63%. The brand's presence and sentiment are healthy, but the position it occupies inside the shortlist is mid-tier. The next move is targeted correction of the prompt, page, and citation layers that determine which brand AI systems name first.

Core Metrics

Sentiment Score

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

For Tower 28 in October 2026, that calculation is (264 × 1 + 38 × 0 + 0 × -1) / 302, which produces a net sentiment score of 0.8742.

This matters because unclassified mention counts are misleading. A brand with 302 mentions and no sentiment breakdown could be receiving 302 positive recommendations, 302 neutral references, or 302 cautionary mentions, and those are not equivalent outcomes. 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.

Tower 28's sentiment profile is strong. The brand recorded zero negative mentions across the series, and its net sentiment score rose from 0.8 in July 2026 to 0.9 in October 2026. The framing problem for Tower 28 is not tone. It is position. AI systems speak well of the brand when they mention it, but they do not lead with it.

Sentiment by Platform

Methodology

1. This report is a benchmark-based analysis of Tower 28's position in AI-generated clean makeup recommendations for October 2026. It is not a client implementation result.
2. The reporting window is October 2026, with comparison points from July 2026, August 2026, and September 2026 where the benchmark provides them.
3. Six AI and search surface families were tracked: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. All six were represented in the qualified observation set.
4. The October 2026 collection began with 800 prompt-surface observations and 623 unique questions. After qualification, 626 observations formed the public denominator for brand-level rates.
5. The competitor universe contains ten tracked brands: Beautycounter, e.l.f. Cosmetics, Glossier, ILIA Beauty, Kosas, Milk Makeup, Rare Beauty, Tarte Cosmetics, Thrive Causemetics, and Tower 28.
6. One buyer-intent cluster produced qualified observations in this benchmark: Brand Recommendation, covering 626 observations. Pricing and Value and Multi-Brand Comparison clusters recorded zero 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 counted when Tower 28 appears in an AI response in any context, including neutral reference, comparison anchor, or listed-only placement.
9. A valid recommendation is counted when Tower 28 appears in a valid recommendation shortlist, as marked by the dataset. Neutral, cautionary, and listed-only mentions are not counted as valid recommendations.
10. Top-three and rank-one rates are calculated against the 626 qualified observations, not against the raw collection of 800 prompts.
11. Average recommended rank covers rank-eligible recommendations only. Tower 28 recorded 29 rank-one recommendations and 114 top-three recommendations in October 2026.
12. Source presence in the citation layer is evidence about the information environment. It is not automatically proof that a source caused a recommendation.

See How AI Is Recommending Your Brand

The public benchmark shows where Tower 28 is winning and losing in AI-generated clean makeup recommendations. A company-level AI visibility audit shows why, mapping the prompt, surface, competitor, ranking, sentiment, and citation patterns behind the scoreboard into a prioritized visibility strategy.