AI in Beauty Marketing — How Brands Win Product Discovery in the Age of Generative Search
AI in beauty marketing is vetting clean hair care, helping consumers find the best hair products for scalp health, hair growth, clean ingredients, and non-toxic products that drive positive, efficacious results.
Consumers validate across AI summaries + creator content instead of product copy alone.
After four weeks of nightly use I saw a reduction in redness” is more useful to AI than purely emotional praise.
Generative AI is changing how people discover, vet, and buy beauty and wellness products.
Today, consumers don’t only “Google” a product — they ask ChatGPT, Perplexity, Gemini or a social-search box on TikTok and expect a short, confident answer that often includes product suggestions and direct purchase links. For beauty and wellness brands, that means optimizing for a new kind of search: answer engines and AI agents. This post explains how brands can get recommended by ChatGPT & Gemini, the practical differences between AEO / AIO / SEO, where consumers go for product recommendations today, which brands are already winning, how clean-makeup brands benefit from AEO, how creators must change their content, and why this is accelerating frictionless commerce.
How to Optimize Skincare Products for ChatGPT & Gemini Recommendations
Generative engines don’t crawl like a human user in a browser — they ingest and synthesize authoritative content from many places (brand sites, publishers, product pages, reviews, knowledge panels). To get your skincare product into an AI recommendation, focus on three pillars: structured trust, clear answerable content, and third-party signals.
Publish authoritative, answer-focused content
Create product pages with clear, Q&A style sections: “Who is this for?”, “Key ingredients & benefits”, “How to use”, “Clinical / testing claims”, “Contraindications”.
Write long-form guides and FAQ pages that directly answer the typical prompts a user would ask an AI (e.g., “best retinol for sensitive skin”, “vitamin C serum for hyperpigmentation under $50”). AEO-style content is short, scannable, and anticipates follow-ups.
Provide machine-readable signals (structured data / schema)
Add Product schema, FAQ schema, review schema, and Ingredient schema where applicable. These make your content easier to parse and more likely to be cited or excerpted by AI summaries.
Earn and surface third-party validation
AI models favor reputable sources and often synthesize multiple sources. Secure placements in high-quality publications, clinician endorsements, retailer listings (Sephora, Ulta, Amazon), and aggregator databases — these become the “evidence” an AI cites.
Optimize for snippets and microcontent
Include crisp, single-sentence summaries at the top of pages (the kind of line an AI could quote). Use bulleted “best for” lists and ingredient callouts.
Feed conversational assets
Create chat-friendly content such as micro-how-tos, “If you have X, do Y” guides, and short video transcripts. These are convenient for AI to rephrase as a recommendation.
Brands doing this well combine product-level transparency, clinician/third-party references, good schema, and short, direct answers that map to intent. (See guides on optimizing for AEO for more detail.) (Amsive)
AEO vs SEO vs AIO: What Beauty Brands Need to Know
Search and discovery are evolving fast — here’s a practical breakdown and what to prioritize.
SEO (Search Engine Optimization)
Traditional discipline: optimize content so search engines like Google return your pages in organic results. Focuses on keywords, backlinks, page speed, and on-page relevance. SEO still drives organic traffic and is foundational for long-term authority.
AEO (Answer Engine Optimization)
Newer: optimize content so answer engines (ChatGPT, Perplexity, Google AI Overviews, Bing Copilot) cite your content in conversational answers. AEO emphasizes short, factual answers, structured Q&As, trustworthy citations, and content that directly resolves specific questions. The goal is to be the single-line or short-paragraph answer the AI returns. (CXL)
AIO (AI Optimization)
Broader: preparing your entire content, data, assets, and signals so they’re useful to all AI systems across search, chat, and social platforms. That includes structured data, canonical content, accessible multimedia (transcripts, alt text), semantic content architecture, and a content strategy built for AI ingestion and reuse.
How they work together
SEO builds the site authority and traffic foundation.
AEO ensures your product or content becomes the answer when a user asks an AI assistant.
AIO makes your brand’s entire data assets useful and discoverable to multiple AI consumers — from chatbots to retailer recommender systems.
Practical priorities for beauty brands
Keep SEO basics flawless (site health, speed, mobile).
Audit top product pages and convert them into AEO-ready pages (Q&As, schema, single-line summaries).
Build an AIO roadmap: ingredient databases, structured product feeds, video transcripts, and publisher partnerships so AI has clean, trusted inputs to cite. Guides and experiments from AEO experts show this is now a distinct skillset brands must add. (Amsive)
Using Generative AI to Discover New Wellness Brands: What Brands Should Do Now
Consumers increasingly use generative assistants for discovery. Reports show shoppers are using ChatGPT, Perplexity and Gemini as core companions for product research and recommendation. That shift creates both opportunities and responsibilities for brands. (Feedonomics)
What consumers are doing with generative AI today
Asking for tailored product recommendations (e.g., “best serum for melasma with niacinamide and azelaic acid”).
Requesting price-sensitive alternatives (“best vitamin C serum under $50”).
Comparing ingredient tradeoffs and safety profiles.
Requesting routines, not just single products (“morning skincare routine for oily skin in your 30s”).
What brands should do now
Make your product data AI-ready — structured product feeds (with ingredient lists, clinical data, claims substantiation) so AI tools can ingest factual values instead of guessing.
Publish evidence and context — clinical study summaries, dermatologist quotes, and clear use-cases. Generative models favor content with verifiable facts.
Be present on third-party, high-trust sites — retailer listings, respected editorial reviews, and aggregator databases function as signals to AI. Getting featured in those places increases the chances the model will surface your product.
Create conversational content — short how-tos, “if you have X do Y” guidance, and video transcripts that feed the training material for AI answers.
Monitor AI outputs & iterate — run queries in ChatGPT, Perplexity, and Gemini, see what they recommend, and identify gaps or misrepresentations you can correct with content or PR. (There are emerging consultancies and vendor tools to help with this monitoring.)
In short: treat generative AI as both a distribution channel and a reputational frontier. Be the trustworthy source an AI cites.
Where Consumers Are Going for Product Recommendations — and the Average Skincare / Haircare Purchase Path
Top places consumers check today
TikTok / Instagram / YouTube: discovery and initial interest; creators and short-form demos drive curiosity.
Aggregators & retailers (Sephora, Ulta, Amazon): product pages and reviews for validation and purchase.
Generative AI assistants (ChatGPT, Perplexity, Gemini): for quick, curated suggestions and comparison.
Forums & communities (Reddit, beauty communities): for raw peer experience and troubleshooting.
Editorial coverage (Vogue Business, Allure, The Good Trade): for trend validation and clean-beauty vetting. (Vogue Business)
How Consumers are using AEO and AIO to purchase skincare, hair care, makeup and wellness products
It’s rare for a consumer to only check 1 source when purchasing a product. Users will may discover a product on TikTok, see a reminder of the YouTube in a vlog, vet ChatGPT for the clean ingredient list and then evaluate whether to purchase the product directly from the brand’s ecommerce site or on a retailer, such as Sephora. AEO merges all of this into 1 frictionless commerce checkout process, increasing trust in purchase decisions for consumers.
Typical skincare purchase journey
Discover: short video or social post sparks interest (TikTok viral or influencer post).
Research: consumer reads retailer reviews, watches full reviews, or asks an AI assistant for curated options.
Compare / Validate: checks ingredient lists, price, and creator testimonials. May seek peer advice on Reddit or in comments.
Purchase: buys on retailer or brand DTC site (increasingly via shoppable links from social or in-chat links suggested by AI).
Post-purchase: looks for follow-up routines, reviews, and “how to use” content from creators/brand.
An example of a consumer journey to purchase a hair growth serum in the age of AI:
This multichannel flow means brands must be discoverable in social, retail, editorial, and AI simultaneously.
Here’s an example of what a 2025 hair care customer journey might look like for someone buying a hair growth serum. Notice how AI, social search, and frictionless commerce are woven in at each stage:
1. Trigger & Awareness
A 32-year-old consumer notices thinning hair near the hairline.
On TikTok, she sees a creator post a “before/after” transformation video for a hair growth serum. The video has clear captions like “3 months, dermatologist-tested” — easy for both TikTok’s search algorithm and AI crawlers to parse.
Curious, she saves the video and screenshots the product.
2. Exploration & Research
Instead of Googling, she types into Perplexity: “Best hair growth serums safe for women in their 30s with thinning hairline.”
The AI cites Sephora, Allure’s “Best of Beauty,” and Reddit threads — and includes the serum from the TikTok video in its top 3 recommendations.
She clicks a retailer link but also cross-checks with ChatGPT, asking: “Does this product have minoxidil or natural alternatives?”
ChatGPT surfaces the brand’s FAQ (because it uses structured schema and clear ingredient callouts) and confirms the serum is a peptide-based option with clinical trial data.
3. Validation & Social Proof
She goes back to TikTok and searches: “[Brand name] hair serum results.”
Dozens of creators show different hair types and timelines.
She also scrolls through Reddit’s r/FemaleHairLoss where users mention this serum as “less irritating than minoxidil.”
Gemini (via her phone’s AI assistant) summarizes reviews: “70% of verified buyers reported visible improvement after 3 months.”
4. Decision & Purchase
Perplexity presents a shoppable carousel with prices from Sephora, Amazon, and the brand’s DTC site.
She chooses Sephora because she has Beauty Insider points. One click → Apple Pay → purchase complete.
The entire funnel from TikTok awareness → AI vetting → purchase takes under 30 minutes.
5. Post-Purchase & Retention
After purchase, she receives an AI-personalized onboarding email: “Start your 90-day serum routine. Here’s how to track progress.”
ChatGPT reminders integrate into her phone calendar: “Apply serum nightly.”
After 8 weeks, she uploads progress photos to a community forum, fueling more user-generated content that AI engines will later ingest.
She receives a subscription offer: “Keep your hair growth journey going — reorder now with 15% off.”
Key differences vs. 2020 journeys:
Google search is secondary — AI assistants and TikTok drive primary discovery.
Consumers validate across AI summaries + creator content instead of product copy alone.
The path to checkout is nearly frictionless with in-chat shopping integrations.
Brand success depends on structured data, strong creator content, and cross-channel presence.
Would you like me to also map this into a visual funnel diagram (awareness → research → validation → purchase → retention) that you could use in a presentation or blog post?
Which Hair Care, Fragrance, and Skin Care Brands Are Driving Product Discovery Through Generative Engines Right Now
A few trends and examples are visible in the public record:
L’Oréal Group: a leader in AI applications across beauty — virtual try-ons, formulation data, and conversational assistants. Their large footprint across brands means L’Oréal products frequently show up in AI recommendations and retailer databases that AIs ingest. (BeautyMatter)
Olay / Neutrogena / Olay / Estée Lauder (incumbents with deep retailer distribution): These brands provide robust product pages, clinical claims, and retail presence — the exact elements AI systems use to recommend products. (BeautyMatter)
Olaplex, Fable & Mane, LolaVie (haircare winners): editorial recognition (e.g., Allure Best of Beauty) and strong social creator coverage help these haircare brands surface in AI and social queries. Awards and editorial coverage act as high-trust signals. (Allure)
Fragrance bots & personalization experiments: Big players have experimented with fragrance chatbots and quiz flows to recommend scent matches (historical example: L’Oréal’s fragrance advisors / chatbots). These conversational experiences are conceptually similar to the way an AI assistant might recommend a fragrance. (Inference Beauty)
Clean and indie brands (ILIA, Kosas, Milk Makeup, Ami Colé, Fable & Mane): these brands often show up in clean-beauty lists and editorial roundups; because they’re covered in reputable outlets and have clear ingredient claims, they increasingly appear in AI suggestions for “clean” or “non-toxic” recommendations. (The Good Trade)
Note: Many generative models synthesize across retailer pages, editorial, community signals and knowledge graphs — so brands that combine retailer distribution, editorial coverage, and creator buzz are most likely to appear in AI outputs. Tracking exactly which brands appear in an AI’s top-5 recommendations requires live queries and monitoring (an emerging service area).
Clean Makeup Brands and How AEO Helps Consumers Vet Clean Ingredients
Clean-beauty consumers want ingredient transparency, safety vetting, and third-party endorsements. AEO is particularly powerful here:
Why AEO matters for clean claims: When a user asks “Is [brand X] non-toxic?” or “Which foundations are talc-free?”, AI assistants will synthesize evidence from ingredient lists, regulatory statements, editorial vetting (Allure, Credo, Good Trade) and third-party certifications. If your product pages clearly list banned/allowed ingredient status, include certifications and clinical substantiation, and are picked up by reputable editors, an AI is more likely to present your product as a vetted option. (Allure)
How to optimize for clean-ingredient queries:
Use an ingredient-level schema and single-page ingredient glossary.
Publish a clear “clean standard” page that maps your ingredient policy to recognized standards (and link to certifiers).
Get editorial reviews and make sure product pages are listed on clean-beauty marketplaces (Credo, Clean Beauty Awards winners). These act as high-trust citations for AIs.
How and Why Brands Need to Change Creator & Organic Content for Generative Engines
Creators and organic content were built for humans — short, swipeable, and emotional. To win in AI, creators and brands must produce content that is both authentic and structured.
What to change (practical):
Make creator videos more answerable
Include short, explicit statements that an AI can quote: “This serum contains 10% vitamin C, 1% hyaluronic acid; best for dull, dehydrated skin.”
Start videos with a one-sentence summary the AI could use as a recommendation.
Add closed captions and transcripts — AI systems often parse textual transcripts when summarizing or citing multimedia content.
Use structured metadata on video & post pages
Host creator content on your domain (or a partner domain) with schema for VideoObject, Transcript, and Creator. This makes creator content first-class data for AI ingestion.
Publish creator Q&As and micro-FAQ bundles
Transform creator reviews into short FAQs on product pages: “Does this break out oily skin?” “How long until you see results?” These are exactly the prompts AIs answer.
Encourage evidence-backed claims
Creators should reference clinical data, timing, and usage context. “After four weeks of nightly use I saw a reduction in redness” is more useful to AI than purely emotional praise.
Why this works
Generative engines prioritize concise, verifiable answers. By making creator content explicit and machine-readable, you increase the chance that AI will quote creators as supportive evidence in the top results. That means creator marketing stops being only a top-funnel awareness tactic and becomes an input to AI-driven product discovery.
How This Is Driving Frictionless Commerce
Frictionless commerce = fewer steps between discovery and purchase. Generative AI accelerates this in three ways:
Direct recommendations with purchase links
Chat interfaces can give a product suggestion and a link to buy (or even a shoppable carousel). Some AI products now display carousels with pricing and merchant links directly within the chat. This shortens the path from question → consideration → purchase. (LinkedIn)
Personalized, faster decisions
AI curates a small set of options tailored to skin type, budget, and preferences — reducing decision paralysis and increasing conversion rates. Feedonomics and other industry studies show shoppers are adopting AI as a decision shortcut. (Feedonomics)
Seamless cross-channel journeys
An AI can synthesize a TikTok creator’s demo, a Sephora review, and a brand’s clinical claim into one recommendation — then provide a link to purchase on the brand or retailer. Brands that provide clean, structured data and publisher placement make this synthesis easier and thus more likely to be presented in a purchase-ready way.
Result: shorter purchase funnels, higher conversion intent, and a new need for brands to control the quality and structure of their discovery assets.
Quick Tactical Checklist for Brands (Actionable Takeaways)
Audit top product pages for AEO readiness (single-sentence summary, FAQ schema, Product schema, transcript availability).
Convert creator reviews into structured assets: transcripts, timestamps, and video schema.
Build publisher and retail relationships to increase high-trust citations (editorial wins matter).
Maintain a clean ingredient database and a “clean standards” landing page for AEO queries about safety.
Monitor AI recommendations (ChatGPT, Perplexity, Gemini) weekly to see what’s being suggested and fix misinformation fast.
Invest in both creator authenticity and machine-readable structure — you need both to rank in human & AI top-5 results.
Detailed audit + action plan for Tower 28 SOS (Barrier Recovery) Cream
Here’s a detailed audit + action plan for Tower 28 SOS (Barrier Recovery) Cream based on what we could observe in public sources — along with recommendations for schema/content adjustments, plus how one might test generative AI output and prioritize improvements.
Live Audit: What’s Working & What’s Missing (Schema & Content)
We reviewed the product page(s) for Tower 28 SOS Daily Barrier Recovery Cream (aka SOS Recovery Cream) from Tower 28’s site, and also from retailers like Sephora and Credo. (Tower 28 Beauty)
Here’s what they already have, and where gaps appear. Then I’ll suggest what schema/data to add, and content tweaks to make it more AEO/AIO-friendly.
What They Already Do Well
Strong Product Copy: The site includes benefit-led features: “non-greasy,” “reduces redness,” “barrier repair,” “fragrance-free,” etc. The ingredients section is clear: ceramides, 4 types of hyaluronic acid, allantoin. (Tower 28 Beauty)
Clinical / Test Claims: They include statements like “Based on a third party clinical test with 32 people: 100% clinically tested to reduce redness / increase hydration / improve skin texture.” That gives credibility. (Tower 28 Beauty)
Clear Targeting: Sensitive skin, barrier repair, non-comedogenic, fragrance free etc. These help with intent matching. (Tower 28 Beauty)
Retail & Editorial Presence: The cream is listed on Sephora, Credo, Amazon etc, and has reviews. (Credo)
Good User Reviews / Ratings: 4.8 / 5 stars on Tower 28, many reviews. This helps with trust. (Tower 28 Beauty)
Ingredient Transparency & Clean-Beauty Claims: Many clean / sensitive skin-friendly claims, vegan, cruelty-free, etc. Credo labels it under their clean standard. (INCIDecoder)
What Could Be Improved (Gaps or Missing Schema / Content)
Schema Markup: There is evidence that product pages have product name, price, variants, reviews. But it’s not clear whether richer schemas are in place: Ingredient schema, ClinicalTrial schema, FAQ schema, Video schema, etc. I didn’t see explicit transcripts or video FAQs in the main page (though some retailer pages have videos).
FAQ / Q&A Content: There are FAQs on the page, but some seem generic. More question/answer content targeting AI-style queries (“Does this break out skin?”, “How quickly will I see reduced redness?”, “Is it safe to use with retinol?”, etc.) could help.
Transcript / Video Assets: Retailers have some video content, but the brand site may not always have video with matching transcripts, captions, or structured metadata.
Structured Data for Clinical Trials / Test Results: While there are “based on study of 32 people, etc.” claims, the structured format (e.g. ClinicalTrial or Study schema, or clear data tables) is likely missing.
Comparison Content: There is some “compare to other moisturizers” in reviews, but not enough content that explicitly contrasts this product with competitors (“why choose SOS over [competitor]”), ingredient trade-off content, or “natural alternative vs dermatologist formulation.” These help AI answer comparative queries.
Ingredient Safety / Clean Beauty Standard Certifications: While mentioned, more detailed labeling / certification info might help (e.g. listing all the ingredients that are not present, what the clean standards are).
Snippets & Summary Text: For AI summarization, having a one-sentence benefit summary, “best for” bullet list, and perhaps a visible summary like “Reduces redness in 3 days, fragrance free, etc.” is there, but the site could optimize its prominence.
What Schema & Structured Data to Add / Improve
To make the product more likely to be surfaced in generative AI “answer” results, the following schema & structured data elements should be present or enhanced:
Product Schema
FAQ Schema
HowTo / Usage Schema
Ingredient / INCI Schema
Clinical Study / Test Results Schema
Review / AggregateRating Schema
Video Schema + Transcript
Sustainability / Packaging / Ethical Standards Schema
example: Clean beauty / eco claims are strong query topics. Include schema or attribute tags for certifications, “National Eczema Association Seal”, “100% PCR packaging”, etc. Mark them so AI can treat them as cold, trust signals.
Suggested Content Additions / Adjustments
One-Sentence Benefit Summary at or near the top that captures the main selling points (for example: “A lightweight, fragrance-free barrier-repair formula with 4 types of Hyaluronic Acid + Ceramides clinically proven to reduce redness and improve hydration”). That kind of summary is very useful for AI answer snippets.
Comparisons: “How SOS Cream compares vs [other sensitive-skin barrier moisturizers]” sections — pros and cons.
Use Cases / Skin Type Scenarios: E.g., “If you have rosacea”, “If you're using retinoids or actives”, “Best for dry patches / post-procedural skin” — helps match consumer queries.
Timeline / Expectations: Given the claims, adding realistic timelines (“you may begin to see improvement in redness after X days/weeks with consistent AM/PM use”) helps set expectations.
Ingredient Deep Dive: For clean‐beauty consumers, a deeper explanation of what each ingredient does, potential trade-offs, what is not included (fragrance, essential oils, etc.), possibly why certain synthetics are safe and approved.
User-Generated Content (UGC) / Before & After Photos: More visual social proof, especially matched to skin types, lighting, etc. Possibly allowed usage of captions or user stories.
Glossary or Clean Standard Page: Possibly link from product page to a “what clean beauty means to Tower 28” or “ingredient safety / clean standard” page.
How to Test How the Brand / Product Currently Appears in Generative Engines
Steps you might take:
Write sample prompts customers might use, e.g.:
“What is the best moisturizer for sensitive skin with hyaluronic acid and ceramides?”
“Tower 28 SOS daily barrier recovery cream review”
“Is there a barrier cream safe with retinoids that reduces redness?”
Query in ChatGPT / Gemini / Perplexity using those prompts. Observe whether SOS Cream appears in the top-3 / top-5 suggestions; check how it is described, what supporting evidence is used.
Note sources cited by the AI: are they Sephora, Credo, Reddit, brand site, editorial reviews? Check if incorrect or incomplete statements are being made.
Check ranking & content format (does the AI quote ingredient claims, timeline, usage instructions, etc.)
Check also image search / social search: Does content from creators (TikTok etc.) or images of the SOS Cream photos show up in results for “best barrier cream?”, etc.
Based on What we Saw: Likely Weaknesses in Appearance in AI Outputs
From what’s public:
SOS Cream gets good coverage in clean beauty and editorial reviews (Byrdie, Real Simple) which helps position it. (Byrdie)
It’s featured in retailer and clean beauty aggregator sites (Sephora, Credo). That gives good third-party signals. (Sephora)
However, we didn’t see many comparison-prompts where it explicitly shows up vs other barrier creams in AI answer examples. Also, detailed clinical data in a schema form may not be fully accessible to models.
It may not yet dominate answers for safety with actives or how it interacts with other skincare steps (since the FAQs are good but possibly not fully addressing “safe with retinol / acids / for rosacea / etc.” in a way AI will pick up).
The video / creator review content is present, but maybe not in the form that is fully machine-readable (transcripts, clear schema, etc.).
Conclusion — AI in Beauty Marketing Is Not a Toy, It’s a Channel
Generative AI is already reshaping discovery, vetting, and commerce for beauty and wellness. For brands, this is not just a technical SEO experiment — it’s a strategic shift in how you present factual proof, educational content, creator testimony, and product data. Those that invest in AEO + AIO + creator content that’s both authentic and structured will show up in the AI answers consumers increasingly trust — and will convert faster because the commerce itself becomes frictionless.
