Can We Predict Which Retailers AI Will Recommend?
AI assistants like ChatGPT, Perplexity, and Gemini are becoming the new front page of the internet. When a shopper asks 'What's the best wireless earbuds?' or 'Where should I buy a mattress?' — AI picks the winners. But what makes AI recommend one retailer over another? That's what we set out to answer. We built a scoring engine, tested it against real AI recommendations, and measured the results.
Our Validation Study
60 Retail Pages
Real product pages across 10 retail segments, including mass market, electronics, fashion, outdoor, home, beauty, DTC, pet, food/beverage, and fitness/sports.
300 AI Queries
Real shopping-intent questions submitted to leading AI search engines, the same kind consumers ask every day.
5 Scoring Categories
Every page evaluated on 60 checks across Product Data Quality, AI Accessibility, Buyer Intent, Recommendation Safety, and Consensus Presence.
The goal: Does a higher Cited score actually predict getting recommended by AI? For DTC and mid-market brands, the answer is a strong YES.
What We Found
Higher Scores = More AI Citations
Sites with higher Cited scores were recommended by AI significantly more often. The correlation was strongest for DTC and mid-market brands (r = 0.82), where page optimization is the primary driver of AI visibility rather than brand recognition alone.
We improved dramatically from our initial model, going from anti-predictive to strongly predictive for DTC and mid-market brands, with a correlation of r = 0.82 between Cited scores and actual AI citation rates. For growing brands competing on product quality rather than name recognition, higher scores directly predict AI visibility.
For Growing Brands, Page Quality Beats Name Recognition
One of the most striking findings: a small pet food brand (Open Farm, Cited score: 75) was cited by AI in 60% of relevant queries. Large retailers get cited on brand recognition alone, but growing brands earn citations through the quality and structure of their product information. The difference between being visible to AI and being invisible comes down to page optimization. That's exactly what Cited measures.
Small Brands Can Compete — and Win
The most dramatic gap was between two small brands on the same platform. Open Farm (Cited score: 75) was recommended in 80% of AI queries. Brooklyn Candle Studio (score: 32) was recommended in 0%. Same size. Same Shopify platform. Completely different results. For growing brands, this is the opportunity — the quality of your product pages can be the difference between being recommended by AI and being invisible.
Most Retailers Get Cited — But Not Consistently
The majority of sites in our study were cited by AI at least once. But only a handful were cited consistently. The difference between showing up once and showing up reliably is exactly what Cited measures.
Get the Complete Research Report
Our full validation study includes detailed scoring breakdowns, segment-by-segment analysis, and actionable takeaways for your e-commerce brand.
Free PDF — no credit card required.
Our Framework
Product Data Quality
Is your product information complete, accurate, and structured for AI consumption?
AI Accessibility
Can AI systems find, read, and understand your page content?
Buyer Intent
Does your page answer the real questions shoppers ask AI assistants?
Recommendation Safety
Would an AI assistant feel confident recommending your product?
Consensus Presence
Does the rest of the internet validate that your product is worth recommending?
Categories are weighted by their measured impact on AI citation likelihood. Our scoring methodology is continuously refined based on ongoing research into how AI models select and cite sources.
What This Means for Your Business
AI is the new search.
Consumers increasingly ask AI for product recommendations instead of scrolling Google. The brands AI recommends will capture a growing share of purchase decisions.
Your pages need to be optimized for AI, not just humans.
Traditional SEO focused on keywords and backlinks. AI optimization is about structured data, clear product information, trust signals, and content AI can confidently cite.
Cited gives you the roadmap.
Our analysis across 5 categories tells you exactly what to fix to improve your AI visibility.
You don't need to be a giant to win.
Our data proves that well-optimized DTC and mid-market pages earn more AI citations than poorly-optimized enterprise pages. For growing brands, page quality is the competitive advantage.
AI Glossary
AEO (Answer Engine Optimization)
GEO (Generative Engine Optimization)
The practice of optimizing content so AI assistants like ChatGPT, Claude, Perplexity, and Gemini select your products as direct answers to consumer questions. Endorsed by Forrester as the emerging industry standard for AI search optimization.
The process of optimizing content to be cited and recommended by generative AI engines like Perplexity, ChatGPT, and Gemini.
LLMO (Large Language Model Optimization)
GAIO (Generative AI Optimization)
Optimization focused on how Large Language Models ingest, process, and process information during their massive-scale training and inference phases.
A term used interchangeably with GEO, emphasizing visibility within the broader ecosystem of generative AI productivity and search tools.
AAO (AI Answer Optimization)
AI SEO (Artificial Intelligence SEO)
Techniques specifically designed to increase the likelihood of appearing in curated AI answer summaries and recommendation lists.
The integration of traditional search engine optimization with adjustments for how AI models select, rank, and cite information sources.
Industry analysts project significant growth in AI-driven search
Gartner projects that 25% of all search will be AI-driven by 2027.