The implementation task of measuring brand visibility in AI search requires structured evidence collection.
Round Lab has a visibility of 16.1% in AI search, already in the first tier of beauty and skincare brands, with a relatively high citation share. But this industry study, based on 9,345 AI conversations and 12,500 cited sources, shows that high visibility does not equal high coverage.
What truly determines whether a brand can enter the final recommendation list of AI is its evidence coverage capability in high-intent questions, multi-brand comparisons, and scenario-based decisions.
Core Judgment
The most critical judgment of this study is: the AI search competition in the beauty and skincare industry has shifted from "who ranks first" to "who is recommended, cited, and placed in comparison tables". The case of Round Lab precisely illustrates that brand awareness can only solve the problem of "being mentioned," but cannot automatically solve the problems of "being trusted" and "being included in the final choice set."
The Visibility of Round Lab is 16.1%, in the first tier; its Citation Share is 7.7%, relatively high in the sample. This means that when AI needs to provide a basis for recommendations, the probability of Round Lab related content being cited is not low. However, the study also points out that Round Lab still has mention and source gaps in many high-intent content opportunities. In other words, its advantage is concentrated in topics already covered, while in a large number of combined questions of "skin type + problem + ingredient + scenario," it has not yet entered the candidate answers of AI.
Executive Summary
Beauty AI search is a crowded head market. The visibility of COSRX, Round Lab, Axis Y, Skin1004, Purito, and Beauty of Joseon is concentrated in a narrow range of 14%–16%, making it difficult to differentiate based solely on brand awareness.
Visibility and citation share are not the same thing. The study shows that visibility is highly correlated with the number of AI mentions, but citation share is only moderately correlated. Being mentioned does not mean being treated as a credible evidence source by AI.
AI does not only cite official websites. The top five domains by citation volume all come from UGC, social, or retail ecosystems: Reddit, Amazon, Instagram, YouTube, and TikTok. Third-party evidence chains are crucial for beauty AI answers.
The core feature of highly cited content is "helping AI make decisions." Rankings, reviews, comparisons, usage processes, ingredient explanations, community votes, and retail reviews are more likely to be reused by AI than simple brand introductions.
The next stage of growth for Round Lab is not in generic brand exposure, but in extending citation advantages to more high-intent topics and prompt types. It still has insufficient coverage on some high-intent topics, which directly limits its chances of entering the final choice set of AI.
Background and Problem
The growth logic of the beauty industry is undergoing a structural shift. In the past, brand growth relied heavily on search rankings, seeding content, influencer distribution, and e-commerce conversion. Users entered keywords, search engines returned a list of web pages, and the core of brand competition was "who ranks first." But in the AI search environment, users begin to ask complex questions in one sentence, for example: "For sensitive skin, first time buying K-Beauty, should I choose Anua, Skin1004, or Round Lab?" "Which is more suitable for a damaged barrier, PDRN or ceramide?" "Are the products that went viral on TikTok really worth buying?" "
These questions no longer point to a single brand term, but package multiple brands, multiple conditions, and multiple scenarios into one decision task. The AI system needs to complete recommendation, comparison, explanation, and citation when generating answers. The object of brand competition has also shifted from "web page ranking" to "qualification to be recommended, cited, and explained." The study summarizes this shift as a paradigm migration from SEO to GEO.
The beauty industry is particularly affected by GEO for several reasons. First, beauty decisions are inherently complex, with users simultaneously evaluating skin type, ingredients, season, budget, texture, allergy risk, usage order, and competitor alternatives. Second, users rely heavily on third-party experiences, with Reddit, TikTok, YouTube, Amazon reviews, editorial lists, and ingredient databases jointly shaping the judgment of AI. Third, trends change quickly, with topics such as PDRN, exosomes, Heartleaf, centella asiatica, glass skin, and barrier repair requiring continuous updates. Fourth, efficacy claims are highly homogenized, with almost all brands able to claim "soothing," "repairing," "moisturizing," and "brightening," so AI prefers to cite verifiable usage boundaries and real feedback. Fifth, brand competition is no longer single-brand term competition, but choice set competition.
Core Findings
Visibility and Citation Share Are Two Different Competitive Metrics
The study covered 21 beauty brands, 1,000 prompts, 9,345 conversation tests, and 12,500 cited sources. The results show that the visibility gap among top brands is very small, with COSRX, Round Lab, Axis Y, Skin1004, Purito, and Beauty of Joseon all concentrated in a narrow range of 14%–16%. This means that any brand trying to differentiate itself through "a little more brand exposure" has very limited room.
A more important finding is that visibility and citation share are not equivalent. Visibility measures how often a brand is mentioned in AI answers, while citation share measures how often brand-related content is used as an evidence source by AI. The study analogizes Citation Share to the "evidence credit score" of AI: when the model needs to provide a reason for a recommendation, it prioritizes stable, explainable, and easily verifiable sources. High-visibility brands do not necessarily have high citation shares. For example, COSRX ranks first in visibility, reflecting strong brand recognition and long-term content accumulation, but its citation share does not necessarily lead all top brands. Skin1004 has a strong average ranking, tending to appear in a prominent position when mentioned, but its relatively low citation share indicates that AI may recommend it, yet does not always place citations on brand-owned assets.
The Common Feature of Highly Cited Content Is Not "Length" but "Decision-Making"
After reclassifying 12,500 cited URL by title, page type, and domain, the study found that the content most likely to be cited by AI is not generic brand introductions, but content that helps AI make purchase judgments. Common high-citation formats include: ranking lists, first-hand tests, comparisons, usage processes, ingredient and skin type explanations, community consensus, retail reviews, and official facts.
These contents have several common features. First, titles directly match the decision questions of AI users, for example, "Best Aestura Products," "I tried 10 Mixsoon Products," "ANUA vs SKIN1004," "Beginner's Guide to Choosing …". Second, the structure is naturally extractable, with rankings, comparison tables, pros and cons, suitable groups, and summary conclusions more likely to be reused by AI than brand narratives. Third, the content provides third-party judgment, not just official claims. Fourth, highly cited content often spans multiple brands, because AI users often ask "which one should I choose among A/B/C." Fifth, successful content combines scenarios and constraints, with conditions such as sensitive skin, oily skin, damaged barrier, dullness, PDRN, seasonal changes, and pre-makeup use helping AI match specific prompts.
High-Priority Topics Are "Problem + Scenario + Choice Set," Not Brand Terms
Beauty AI users rarely ask only "is this brand good?" They more often place brands into a choice set and ask AI to compare, filter, recommend, and formulate usage plans. The high-priority topics identified by the study include: oil control and pores, acne-prone skin, niacinamide, centella asiatica/Heartleaf soothing, PDRN, barrier repair, cleansing, and sun protection. The common feature of these topics is that they combine skin type, problem, ingredient, and scenario, making them more likely to trigger AI recommendations than single brand terms.
The study also found that content opportunities are very close to purchase decisions. Three prompt types—Final Shortlist, Comparison/Ranking, and Routine/Scenario—should receive the highest priority. Brands need to systematically fill high-intent scenario gaps, building indexable Pillar Pages, FAQ, comparison pages, process pages, and citation-oriented content for topics such as oil control, acne-prone skin, sensitive skin, barrier repair, cleansing, PDRN/ regeneration, and niacinamide.
Cases and Data
The Advantage of Round Lab: Citation Potential Higher Than Pure Brand Exposure
The Visibility of Round Lab is 16.1%, in the first tier. Its Citation Share is 7.7%, relatively high in the sample. The study interprets this as: Round Lab related content or third-party materials are more likely to be used as evidence by AI. In a crowded head market, Citation Share can become a breakthrough variable.
This is noteworthy because it shows that the existing content assets of Round Lab have a certain "citability." When AI needs to provide a basis for recommendations, the probability of Round Lab related materials being selected is not low. The study believes that for brands like Round Lab, the next stage of growth is not to continue pursuing generic brand exposure, but to extend citation advantages to more topics and prompt types.
The Gap of Round Lab: Insufficient Coverage of High-Intent Topics
The study also points out that Round Lab still has mention and source gaps in many high-intent content opportunities. This is a common problem among top brands: AI users ask multi-brand, multi-scenario, multi-condition decision questions, and if a brand lacks matching content, it will not enter the final choice set.
Specifically, when users ask questions like "which products should I choose for oily skin oil control," "how to build a daily routine for acne-prone skin," or "which is more suitable for me, niacinamide or centella asiatica," AI needs to filter and rank from multiple brands. If Round Lab lacks extractable comparison pages, process pages, FAQ, or third-party reviews on these topics, it may be excluded from the answer, even if its overall visibility is high. The study summarizes this phenomenon as: visibility solves "being mentioned," coverage solves "being chosen."
Insights from the Citation Ecosystem: Which Sources Does AI Trust
The top five domains by citation volume all come from UGC, social, or retail ecosystems: Reddit, Amazon, Instagram, YouTube, and TikTok. Official websites do not dominate the citation ecosystem. Based on this, the study proposes that beauty brands cannot rely solely on official articles, but need to proactively build third-party evidence chains that AI can explain and reuse.
Different domains play different roles in AI answers. Community discussions provide real usage feedback, e-commerce and retail platforms provide purchase verification, product databases provide ingredient facts, media lists provide independent reviews, and official sites provide product specifications and usage instructions. A GEO strategy should not treat all sources as generalized "external links," but map them by evidence role: community proof, product facts, independent reviews, retail verification, ingredient education, and scenario explanation.
Action Recommendations
First, extend citation advantages from "brand terms" to "problem + scenario + choice set" topics. The Citation Share of Round Lab is relatively high, indicating that its content is citable, but high-intent topic coverage is insufficient. Brands should prioritize building Pillar Pages, comparison pages, process pages, and FAQ for topics such as oil control and pores, acne-prone skin, niacinamide, centella asiatica/Heartleaf soothing, PDRN, barrier repair, cleansing, and sun protection. These pages cannot only discuss a single brand, but must cover multi-brand choice sets, because AI users often ask the model to rank and recommend among multiple brands.
Second, upgrade content from "articles" to "evidence packages." A single article is difficult to continuously influence AI citations. A more effective unit is a topic evidence package: official fact pages, Pillar Page, comparison pages, process pages, FAQ, third-party reviews, UGC question summaries, and retail review summaries. Each page should include a one-sentence conclusion, suitable/unsuitable group labels, comparison tables, usage order, ingredient explanations, negative feedback, and alternative options. In this way, AI can obtain evidence supporting the same conclusion from multiple sources under different prompt types.
Third, proactively manage the third-party evidence network rather than being defined by third-party content. Brands should proactively answer controversial questions, such as whether a product clogs pores, whether it is suitable for sensitive skin, and where the differences lie with competitors. If official content does not answer, AI will use community and retail reviews. At the same time, brands should organize negative feedback into content assets, clarifying unsuitable scenarios, misuse patterns, and ingredient combination precautions to improve neutrality and AI trust. Product names, core ingredients, usage instructions, and applicable skin types should remain consistent across official websites, Amazon, social media, and media materials, helping AI establish a stable brand entity perception. The structured-evidence priority here is to maintain consistency across all brand touchpoints so that the AI can reliably map product facts to recommendations.
About Dageno AI
Dageno AI is an AI-powered search marketing intelligence platform designed for global market teams. Starting with AI search, it covers 10+ major overseas AI platforms and search experiences, continuously connecting brands, user needs, competitive landscapes, citation sources, organic search, AI Shopping, AI Advertising, and site data. Dageno helps marketing, growth, brand, product, and strategy teams understand their market positioning, purchasing scenarios, and niche category opportunities; trace the source evidence behind AI responses; identify gaps in brand awareness, citations, and channels; and monitor the ongoing impact of key content. All insights can be traced back to specific models, regions, time windows, original answers, and URLs, providing verifiable foundations for GEO optimization and global growth decisions.
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