Why this is urgent

• Usage is surging: Bain reports ChatGPT prompt volume grew ~70% in H1 2025 (Sensor Tower sample). That’s not just curiosity that’s real purchase intent flowing into AI search platforms and agents.
  
  
• Zero‑click behavior is normal: ~80% of consumers rely on AI-written results for at least 40% of searches, depressing traditional traffic and pushing decisions into assistants. You need to be selected and cited inside the answer.
  
  
• Platforms are shipping fast:
  
  
  
  • ChatGPT Shopping now shows product carousels with labels and review summaries; results are organic (not ads); OpenAI is exploring merchant feeds and shows UTM tags in outbound links.
    
    
  • Amazon Rufus is available to all U.S. shoppers and answers category and PDP‑level questions using Amazon’s catalog, reviews, Q&A, plus web info.
    
    
  • Perplexity “Shop Like a Pro” adds product cards and one‑click checkout for Pro users via a Merchant Program and payments integrations.
    
    
  • Google AI Mode and Shopping Graph are blending AI visual inspiration, virtual try‑on, and price alerts linked to size/color thresholds.
    
    

Implication: Success = being retrieved, parsed, cited, and clicked—across AI answer surfaces and the retailers they point to.’

Strategy, at a glance (platform comparison)

Below is a condensed view of how the three biggest AI shopping surfaces operate and how to win on each. I also generated a downloadable matrix you can give to your team.

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Quick AI Shopping Comparison (ChatGPT, Rufus, AI Mode)

What actually moves the needle (and why)

1) Access & crawl: let AI find and cite you

• ChatGPT Search: do not block OAI-SearchBot in robots.txt. OpenAI confirms this is search‑only (not training) and adds utm_source=chatgpt.com to outbound referrals so you can attribute traffic.
  
  
• Perplexity: explicitly allow PerplexityBot for discovery—Perplexity’s docs clarify it’s used to surface and link sites in results, not to train foundation models.
  
  
• Keep a simple policy doc defining what you allow for training (e.g., Google‑Extended, GPTBot/Claude training bots) vs search. Training controls don’t affect your appearance in AI shopping, but you should decide and document.
  
  

Why: AI answer engines assemble responses and attribute sources. If they can’t crawl your PDPs and policies, you won’t be cited or surfaced.

2) Structure & identifiers: make products LLM‑parsable

At minimum, emit complete JSON‑LD on PDPs:

• Product (name, description, brand, images, sku, gtin, mpn), Offer/AggregateOffer (price, currency, availability, url, seller), AggregateRating/Review where applicable, hasMerchantReturnPolicy with window/fees/methods. 

Also:

• Normalize identifiers (GTIN/MPN/SKU) across .com, retailers, and feeds so models can reconcile variants and not split review signals. (Google’s docs highlight combining structured data + Merchant feeds to unify product facts.)
  
  
• Publish plain‑English returns and shipping details and embed them via MerchantReturnPolicy. (Google updated guidance in 2025—country specificity now matters for merchant listings.)
  
  

Why: ChatGPT explicitly considers structured metadata and may simplify product titles/descriptions based on provider data; completeness and consistency raise your odds of selection. 

3) Evidence content beats adjectives

LLMs favor evidence they can quote or summarize: real‑world photos, measurable test results, pros/cons, “who it’s for,” comparisons.

• Rufus leans heavily on attributes, reviews, and Q&A—it answers “is this easy to clean?” from PDP content and reviews. Build comparison tables, compatibility notes, and use‑case Q&A into your Amazon listings.
  
  
• Perplexity shows product cards and citations; publishing authoritative explainers (“lip oil vs gloss”, “trail vs road running shoes”) gives it something credible to reference next to your product card.
  
  
• ChatGPT sometimes shows review summaries and labels like “Budget‑friendly” or “Most popular.” If your review corpus makes that claim credible, you’re more likely to earn the label. 

4) Become a source the models already trust

Your earlier Top‑10 Retailers for Beauty/Personal Care dataset is perfect here. In beauty, assistants frequently route shoppers to specialist chains (Sephora/Ulta), mass (Target/Walmart), and marketplaces (Amazon). Use your Top‑10 list to:

1. Map coverage: verify SKU presence and availability across those retailers.
   
   
2. Unify identifiers: same shade names, pack sizes, bundle contents, GTINs everywhere.
   
   
3. Align policy language: publish returns/shipping on your site and mirror them across retailer PDPs where allowed, to avoid mismatched summaries.
   
   
4. Plant credible citations: if category publishers (Allure, Byrdie) and UGC (Reddit) dominate citations in your space, pitch original tests/how‑tos that can be quoted—and earn real reviews on retailer PDPs. (Rufus, in particular, summarizes review/Q&A content.) 

Why: In AI shopping answers, who you are linked from often matters as much as what’s on your site. You’re optimizing across UGC + editorial + retail.

5) Social & brand strategy for AI

Think “promptable”, not just “postable.” Your social content should be findable and quotable by LLMs:

• Format for extraction: concise bullets, labeled pros/cons, “Quick fit notes,” simple ingredients/materials statements.
  
  
• Cross‑post evidence: TikTok/YouTube “how it actually fits/works,” then embed and transcribe on site so models can parse the same claims as text.
  
  
• Community proofs: structured Q&A highlights from Discord/Reddit (with permission) compiled into a /community‑answers hub.
  
  
• Consistency: the same variant names, measurements, and materials used across social captions, PDPs, and retailer listings.
  
  
• Retailer‑specific LPs: “Find us at Sephora/Ulta” pages that map your variant names to their SKU codes and shade chips.

Why: Assistants are multi‑modal and source‑agnostic. They blend social, UGC, editorial, and retail to compose answers. Your job is to make the same facts appear everywhere.

6) Measurement & attribution that actually works

• ChatGPT: OpenAI confirms referral URLs include utm_source=chatgpt.com bucket this into a dedicated AI Search channel so you can track landings, AOV, and conversion.
  
  
• Perplexity: for native Buy with Pro, rely on partner reporting; for cards that link out, use standard UTMs and watch assisted conversions. (Payments integrations with PayPal/Venmo extend native actions.)
  
  
• Rufus: conversions happen inside Amazon - use Brand Analytics, Business Reports, and MMM to quantify halo impact from category queries and Q&A visibility. 

Platform deep‑dives (tactics to implement)

ChatGPT Shopping: how to earn product cards

What we know from OpenAI:

• Display: when shopping intent is detected, ChatGPT shows product carousels and links to where to buy; results are not ads. It may show labels and review summaries based on third‑party data.
  
  
• Selection: intent + structured metadata + model reasoning; order of merchants is currently provider‑inherited; feeds are being explored (merchant interest form live). 
• Tracking: utm_source=chatgpt.com is added to outbound referrals.
  
  

Do this:

1. Allow OAI-SearchBot (search only; not training). Keep your WAF/CDN from false‑flagging it. 
2. Emit complete schema: Product, Offer/ AggregateOffer, AggregateRating/Review, hasMerchantReturnPolicy. Validate in Search Console. 
3. Normalize identifiers and variant names to reduce mismatch and hallucinated titles. 
4. Build evidence‑rich PDPs (BLUF summary; pros/cons; fit notes; care steps) because ChatGPT rewrites titles and summarizes reviews. 
5. Register for feed submissions (future direct ingestion). 

Watchouts: Pricing may lag; review counts/ratings may be aggregated—encourage users to click through for current info. 

Amazon Rufus: how to surface in answers inside Amazon

• Scope: Rufus is live to all U.S. customers (app & desktop). It uses Amazon’s catalog, reviews, community Q&A and info from across the web to answer natural prompts. 

Do this:

1. Complete attributes: materials, dimensions, compatibility, certification claims—Rufus leans on structured attributes to answer “which one fits X?”
   
   
2. A+ Content with tables, usage visuals, and comparison grids (especially “X vs Y” and “best for [use‑case]”).
   
   
3. Seed Q&A and maintain review velocity and quality; Rufus summarizes both.
   
   
4. Resolve conflicting claims across variants and retailers (returns, shipping windows, warranties).
   
   
5. Track share of prompts via Brand Analytics proxies (category term trends) and test messaging that improves PDP Q&A resolution.
   
   

Watchouts: Weak or incomplete listings vanish from recs. This is not just SEO, this is Amazon content operations.

Perplexity “Shop Like a Pro”: how to show up and get 1‑click buys

• What it is: a shopping feature mainly for Pro users showing product cards with images, prices, specs; Buy with Pro enables one‑click checkout; Merchant Program lets retailers share product data; broader payments tie‑ins (e.g., PayPal/Venmo) support agentic purchases. 

Do this:

1. Apply to the Merchant Program; provide complete catalogs (specs, availability, review counts, images).
   
   
2. Enable payments where possible so “Buy with Pro” appears on your SKUs.
   
   
3. Publish authoritative explainers (jobs‑to‑be‑done; comparisons). Perplexity places citations next to product cards—be the cite.
   
   
4. Keep pricing/availability fresh; Perplexity emphasizes up‑to‑date details from partners.
   
   

Watchouts: Visibility and checkout are currently concentrated in U.S. Pro usage; plan for expansion but focus where it’s live now.

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Rethinking “organic growth” for AI shopping

Organic no longer means “blue links.” It means being selected in answer modules, having your product carded, and being cited by sources the model trusts. Four shifts for your playbook:

1. Own your facts: treat product specs, identifiers, pricing, shipping, and returns as your single source of truth. Push the same truth to .com, retailers, feeds, and press. ChatGPT’s selection logic and labels (“Budget‑friendly”, “Most popular”) lean on third‑party facts and reviews—make sure they match your reality.
   
   
2. Design for extraction: assistants extract passages and facts. Use BLUF intros, bullet claims, tables, and clear Q&A blocks that map to common questions.
   
   
3. Build “citation gravity”: prioritize editorial reviews and UGC in the domains assistants cite in your category. Your Top‑10 Retailers (Beauty) work is exactly the retailer leg of that stool—now pair it with publisher hit‑lists.
   
   
4. Instrument the funnel: tag inbound from ChatGPT (utm_source=chatgpt.com), analyze Perplexity direct vs outbound behavior, and quantify Rufus effects via Amazon reporting. 

What to do with your Beauty & Personal Care: Top‑10 retailer dataset

Use it as your AI shelf map:

• SKU/browser parity: guarantee core SKUs (and hero shades/sizes) exist at each retailer with the same GTIN and variant names.
  
  
• Policy clarity: align return windows and shipping policies across retailers; publish MerchantReturnPolicy on your site and ensure retailer pages don’t contradict it.
  
  
• Review distribution: encourage real reviews (photo/video where possible) on retailers that dominate AI recommendations for your subcategory—Rufus summarizes review/Q&A content.
  
  
• Bundle literacy: for kits/sets, list exact contents and sizes uniformly; models struggle when kit naming varies.
  
  
• Retail LPs: “Where to buy” sections that map each retailer’s variant code to your own naming; helps AI resolve duplicates and shoppers find the exact shade.

Google’s AI Mode: plan for inspiration → purchase

Even if you don’t rely on Google for traffic, AI Mode will influence discovery:

• Virtual try‑on (upload your photo) and precise price alerts (size/color/price thresholds) connect inspiration to a purchase moment; later this year, AI‑generated outfit/room inspiration will match to real listings. Build visual assets and structured data that can be matched reliably.
  
  
• Implication: make sure product imagery, angles, backgrounds, alt text, and variant chips are consistent and high‑fidelity. If AI can’t confidently map its generated look to your SKU, someone else gets the click.

Common pitfalls (and how to avoid them)

• Blocking the wrong bots: if you block OAI‑SearchBot or PerplexityBot, you de‑index yourself from their surfaces. If you want to restrict training but allow search, use the vendors’ specific tokens (e.g., Google‑Extended). Document it.
  
  
• Half‑done schema: missing Offer or ReturnPolicy fields limit eligibility for shopping features (especially Google merchant listings); Google tightened return‑policy schema rules in 2025.
  
  
• Identifier drift: different GTINs or shade names across retailers fracture your presence and confuse review aggregation.
  
  
• Review/Q&A silence: if reviews stall or PDP Q&A is empty, Rufus has less to work with—and your listing loses to richer competitors. 

The (near) future of AI Commerce and how to stay ahead

1. Direct feeds into assistants: OpenAI is explicitly exploring merchant feeds. Centralize a clean product feed with canonical IDs, ready to syndicate to assistants (not just Google Merchant Center).
   
   
2. Agentic checkout: native payments (e.g., Perplexity + PayPal/Venmo) reduce friction; expect more one‑click flows and bundled tasks (“build me a routine under $150, deliver by Friday”). Structure logistics and policies for machine consumption.
   
   
3. AI‑labeled shelves: “Best value,” “Most popular,” and “Great for [use case]” steer clicks; invest in reviews and price‑value positioning that legitimately earn those labels.
   
   
4. From blue links to answers: if 60–80% of shoppers accept assistant summaries, the “first page” is now an answer with citations and product cards. Your content, feeds, and retail presence must be aligned to win that composite. 

What Goodie AI adds (quick note)

Goodie’s AEO/GEO toolset focuses on observability (where you surface across ChatGPT, Rufus, Perplexity), optimization (schema, identifiers, content, retailer parity), and attribution (tying AI surfaces back to conversions). If you want, we can plug in your Beauty Top‑10 retailer dataset and show exactly where you’re missing visibility (cards, labels, retailer picks) across assistants.

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