What 49 buying keywords, 9 ranking Reddit threads, and one National Eczema Association Seal taught me about how AI search is reshaping DTC clean beauty.
01 ChatGPT’s clean beauty recommendation came from one Reddit thread, not a dermatology database.
Last week I opened ChatGPT and asked it: “What’s the best clean tinted sunscreen for sensitive skin?”
It gave me ILIA. Specifically, the Super Serum Skin Tint SPF 40. Confidently, with reasoning – clean ingredients, broad spectrum, dermatologist-friendly, popular with people who have reactive skin.
Fine recommendation. Probably correct for a lot of people. But I help DTC brands figure out where their customers actually get their information from, so this kind of answer gets my attention. ChatGPT doesn’t have a favourite. It learned its favourite from somewhere.
I went looking for the source. The trail led to one specific Reddit thread, sitting at position six on Google for the same query. The top recommendations in its comments are exactly what ChatGPT gave me: ILIA Super Serum, EltaMD UV Daily Tinted, and a handful of drugstore alternatives.
02 That thread does the work of an editorial team – 2,500 monthly visits, position 6 on Google.
The query “best tinted sunscreen for sensitive skin” gets around 200 searches a month in the US, $0.25 CPC, low competition – the kind of query someone types when they’re three minutes from buying.
The top of the SERP is Healthline, Byrdie, Vogue. Then at position six: r/30PlusSkinCare – “What’s everyone’s favourite tinted sunscreen?“
I went looking.
One thread. Two years old. Pulling more monthly traffic than most brands’ product pages in this category. Every comment is a vote – and ChatGPT is reading the votes.
03 5 of 6 AI Overviews in this category cite Reddit threads as a source.
One query isn’t a pattern. So I expanded.
Across roughly 49 keywords I checked for sensitive-skin and clean-beauty buyers, nine different Reddit threads rank in Google’s top 20. Of the six highest-volume buying keywords I tested in detail, five returned AI Overviews – and all five cited Reddit threads as a source.
Take “best makeup for rosacea.” Roughly 1,000 searches a month, $0.40 CPC. Position four in Google: an r/Rosacea thread. Pulling 300 monthly visits and cited directly in Google’s AI Overview at the top of the page.
The model isn’t synthesising in a vacuum. It’s pulling language from that thread, paraphrasing the comments, and presenting the result as an answer. The same handful of threads ends up shaping the answers being given to everyone searching the query. Like in Google. Or in an LLM.
Same shape on “clean makeup brands.” 6,700 searches a month, $1.20 CPC. Position six: r/moderatelygranolamoms — “Clean Makeup Recs?” — also cited in the AI Overview. ILIA Beauty owns this conversation completely: their domain ranks position five organic with a DR 72 site, the AI Overview cites them, and they’re the most-mentioned name in the thread comments.
04 The brand with the only NEA Seal in complexion makeup has zero Reddit mentions in this conversation.
If you open Tower 28’s product page for SunnyDays SPF 30 (same category as the ILIA Super Serum the model recommended) there’s a line right under the description. The brand calls it the 1st and ONLY complexion makeup product with the National Eczema Association’s Seal.
The NEA Seal is a real third-party certification. The organisation rarely grants it to color cosmetics; SunnyDays was the exception. Tower 28 went further with their SOS line: SOS FaceGuard SPF 30 specifically carries the National Rosacea Society Seal of Acceptance. Across the brand they’re recognised by the NEA, the National Rosacea Society, and the National Psoriasis Foundation (three separate medical bodies).
The most direct version of the mismatch:
SOS FaceGuard SPF 30 carries the National Rosacea Society Seal. The r/Rosacea thread cited by Google’s AI Overview is the literal place people with rosacea ask which sunscreen-friendly product to buy. The brand has the seal for that exact condition. The thread is the source the model reads from. The brand isn’t in the thread.
The credentials live on a Sephora product page. The conversations live somewhere else
05 Even brands that earn the right credentials lose if the language doesn’t match how buyers actually talk.
Here’s the second-order issue. Even if Tower 28 were in those Reddit threads, the credential and the language are slightly out of sync.
The comments in r/30PlusSkinCare and r/Rosacea aren’t saying “I want a complexion makeup product certified by the National Eczema Association”. They’re saying “I want something that won’t make my skin flare”, and “what’s actually working for people with rosacea?”
The credential is the strongest version of the answer to those questions. But it has to be translated into the language people are actually using before it can show up in the recommendation. That translation step is the gap.
If you’ve ever wondered why your strongest claims don’t show up in ChatGPT recommendations, this is usually the reason. The model isn’t ignoring your credentials — it’s never seeing them in the format it reads from. The Reddit comment writes itself in plain English. Your product page writes in marketing English. They’re not the same language.
06 ILIA wins 4 of 6 buying SERPs organically. Tower 28 wins 2 — every appearance is paid.
Here’s the scoreline across the six buying keywords I checked in detail.
The brand with the strongest verifiable claim in the category is paying to be visible. Brands without those credentials are winning the earned conversation that the AI is reading from. Just appearing in the Google Shopping listings is the most expensive way to lose this fight.
07 The same gap shape repeats across every clean beauty brand we checked.
I started checking the same shape for a few other brands I follow.
It repeats. Glow Recipe shows up in plenty of K-beauty conversations but is conspicuously thin in dermatologist-recommended threads. EltaMD wins the medical rosacea conversation easily but is barely present in clean-beauty subreddits. CeraVe and La Roche-Posay carry the drugstore community but rarely get pulled into Reddit threads where someone is asking specifically for clean ingredients.
Different brands, different gaps, same shape. The brand has a credible claim in some buyer conversation. The conversation lives on Reddit. The brand isn’t in it. The model (ChatGPT, AI Overview, Perplexity) is reading from the conversation, not from the brand’s own product page.
A few years ago this didn’t matter much. Search engines respected the website. The product page won most ties. Then Google signed a content licensing deal with Reddit in early 2024, threads started climbing the SERPs aggressively, and the LLMs trained on the same public web. The conversational layer has been quietly promoted from “social channel” to “infrastructure”. It’s underneath everything now.
Your buying conversation is now a three-layer stack. Most brands only measure two.
If you run growth or marketing at a DTC brand, the take-home is uncomfortable but useful. Your buying conversation is split across three layers:
The exercise I ran for clean beauty is replicable for any vertical:
- Pick three buyer-intent queries your category cares about.
- Run them through ChatGPT.
- Do the same searches in Google. Click through to whichever Reddit thread ranks in the top ten.
- Read the comments. See if your brand is in there.
If it is, find out whether the language matches your positioning. If it isn’t, work out why. And decide whether you’re comfortable with that.
The brands that figure out the third layer first are going to compound an advantage that’s pretty hard to claw back later. The ones that don’t will keep wondering why their strongest credentials aren’t showing up in the recommendations.
See what AI is saying about your brand (and which Reddit threads are deciding it)
LeoSEO by blimpp probes ChatGPT, Perplexity and Google AIO across 40+ persona-driven queries in your category and shows you exactly which sources AI is trusting.
