We’re a D2C brand doing $5M/year in revenue. Our products rank well in Google Search, but when I ask ChatGPT or Perplexity about products in our category, we’re completely invisible.
The problem:
When I query AI about “best [our category] for [use case]”:
What we’ve tried:
What we haven’t figured out:
Our stats:
Questions:
This feels like a major blind spot in our strategy.
You’re identifying the right problem. Let me break down the solution:
Step 1: Audit AI Crawler Access
Check your robots.txt for these lines:
User-agent: GPTBot
User-agent: OAI-SearchBot
User-agent: PerplexityBot
User-agent: ClaudeBot
User-agent: anthropic-ai
If any say “Disallow: /”, AI can’t see your site.
Step 2: Check JavaScript Rendering
Disable JavaScript in your browser, visit your product pages. Can you see:
If not, AI crawlers can’t either.
Step 3: Verify Schema Markup
Use Google’s Rich Results Test. Every product page needs:
The reality:
Most D2C brands fail at step 2. Modern storefronts (Shopify themes, etc.) often render critical content via JavaScript that AI can’t parse.
Shopify-specific advice:
Many Shopify themes use JavaScript for:
Quick fix:
Check if your theme outputs product JSON-LD in the page source. Search for "@type": "Product" in view-source.
If it’s there, you’re probably fine. If not, you need an app like JSON-LD for SEO or custom liquid code.
Common Shopify issues:
Product feeds are becoming critical for AI visibility. Here’s why:
The merchant programs:
| Program | Status | What It Does |
|---|---|---|
| Perplexity Merchant | Live | Submit feeds for shopping recommendations |
| OpenAI Product Discovery | Testing | Direct product feed integration |
| Google Merchant Center | Existing | Powers AI Overviews shopping |
Feed optimization for AI:
The same principles that work for Google Shopping work for AI, but with conversational language:
Old product title: “Men’s Running Shoes Size 10 Black Nike”
AI-optimized title: “Nike Men’s Running Shoes - Breathable Mesh, Ideal for Marathon Training, Size 10, Black”
The second version answers questions a user might ask: “running shoes for marathon training”
Required feed fields for AI:
The conversational query angle is crucial. Let me expand:
How AI users shop:
Old: “running shoes size 10” New: “I’m training for my first marathon and tend to overpronate - what running shoes should I get?”
Product page optimization for AI:
Instead of just listing features, connect them to use cases:
Before:
After:
Add FAQ content to product pages:
“Is this shoe good for overpronators?” “What’s the difference between this and [competitor]?” “How do I know my size?”
This gives AI extractable Q&A content for specific queries.
Third-party mentions matter more than you might think.
Our discovery:
We tracked which brands AI recommends for our category. The common factor wasn’t SEO ranking - it was brand mentions across:
The strategy that worked:
Results after 6 months:
AI cites what the internet talks about. Build that conversation.
Technical checklist for e-commerce AI visibility:
Crawlability:
Schema markup:
Content:
Monitoring:
Most e-commerce sites fail on 3+ of these. Fix them systematically.
Interesting perspective: Amazon products get cited because of review volume.
What I’ve observed:
AI frequently cites “according to Amazon reviews” or “users on Amazon report…”
The review content becomes the citation source, not the product listing itself.
Implication for D2C brands:
Our approach:
Post-purchase email asks specific questions:
These prompts generate review content that AI can extract and cite.
Category pages often outperform product pages for AI visibility.
Why:
Product pages answer “tell me about X product” Category pages answer “what’s the best X for Y use case”
Optimization:
Transform category pages from product grids to buying guides:
Before: “Women’s Running Shoes” - product grid
After: “How to Choose Women’s Running Shoes” - buying guide + product grid
Include:
This format is more likely to be cited for research queries than a simple product grid.
Exactly right. The data supports this:
Citation rates by page type:
| Page Type | AI Citation Rate |
|---|---|
| Buying guide pages | 4.2% |
| Category pages (optimized) | 2.8% |
| Product pages | 0.9% |
| Category pages (grid only) | 0.3% |
The pattern:
AI is more likely to cite content that COMPARES and RECOMMENDS rather than content that just DESCRIBES.
Your product pages support conversion. Your buying guides drive AI discovery.
Build both.
I’ve been in the Perplexity Merchant Program for 3 months. Some insights:
What it provides:
Results:
The catch:
Perplexity is still small compared to Google/ChatGPT. But it’s growing fast, and being early gives you an advantage.
My recommendation:
Join the program. It’s free, low effort, and the ROI is positive even at current Perplexity scale.
This thread has given me a complete roadmap. Here’s our action plan:
Immediate (Week 1):
Short-term (Month 1):
Medium-term (Quarter 1):
Metrics to track:
The insight that category buying guides outperform product pages is huge. We’ve been optimizing the wrong pages for AI.
Thanks everyone for the practical guidance.
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