How AI Engines Rank Products: What We Learned from 10,000 Scans

We analyzed responses from 5 AI engines across thousands of product queries to understand how they decide which products to mention, recommend, and cite. Here's what we found.

1. Brand Authority Still Matters

AI engines heavily favor well-known brands. Products from established brands appear in 3x more responses than equivalent products from lesser-known brands. This isn't surprising — AI models are trained on web data where popular brands are mentioned more frequently.

2. Review Sentiment Influences Recommendations

Products with consistently positive reviews across multiple platforms receive more favorable mentions. AI engines seem to aggregate sentiment from review data, product descriptions, and editorial content.

3. Each Engine Has Preferences

Engines don't agree on rankings. Our correlation analysis shows that ChatGPT and Google AI have the highest agreement (r=0.82), while Perplexity and Claude diverge the most. This means optimizing for one engine doesn't guarantee visibility across all of them.

4. Structured Data Helps

Products from sites with proper schema markup, comprehensive product descriptions, and well-structured content tend to score higher. AI engines use structured data as signals of information quality.

5. Citation Patterns Vary

Perplexity cites sources most frequently, while ChatGPT and Claude are more selective. Google AI Overview tends to cite its own search results. Understanding each engine's citation behavior helps you optimize your content strategy.

What This Means for Your Strategy

GEO requires a multi-engine approach. Focus on building brand authority, maintaining positive review sentiment, using structured data, and monitoring each engine individually. NexGeo.ai gives you the tools to do exactly that.