How AI Decides Which Products to Recommend

When a shopper asks ChatGPT for product recommendations, how does it pick which brands to mention? The signals that matter and how to influence them.

Mersel AI Team

January 23, 2026

8 min read

A shopper asks ChatGPT: "What's the best standing desk under $500?"

The AI names three brands. Yours isn't one of them.

Why? Your standing desk is well-reviewed, competitively priced, and ranks on Google's first page. But AI product recommendations don't work like Google rankings. The signals are different, the sources are different, and the selection criteria are different.

Understanding how AI picks which products to recommend is the first step to getting your brand into those answers.

AI Doesn't Rank. It Recommends.

Google shows ten results and lets the user decide. AI gives one answer with two or three specific recommendations. That's a fundamental difference in how products get discovered.

When Google shows results, every position gets some traffic. Position seven still gets clicks. With AI, you're either one of the named brands or you don't exist in that conversation.

And this matters financially. ChatGPT referral traffic converts at 15.9% vs. 1.76% for Google organic. The people clicking through from AI recommendations have already decided to buy. They're not comparison shopping. They're purchasing.

The question is: how does AI decide which products make the cut?

The Six Signals AI Uses

Based on analysis of AI citation patterns from Ahrefs and the Prerender.io AI Indexing Benchmark, AI product recommendations are driven by six primary signals.

1. Third-Party Consensus

This is the strongest signal. AI models give the most weight to products mentioned positively across multiple independent sources. A product recommended by Wirecutter, discussed favorably on Reddit, and reviewed on a niche blog carries far more AI citation weight than a product with a great page on its own website.

Think of it as triangulation. AI looks for agreement across sources it considers credible. If three independent reviewers say your standing desk is the best under $500, that's a strong signal. If only your own website says that, AI treats it as marketing.

2. Structured Product Data

AI can only recommend products it can accurately understand. 80% of URLs cited by ChatGPT don't rank in Google's top 100, which means Google ranking isn't what drives citations. What drives them is whether AI can extract precise product attributes: price, specifications, materials, dimensions, warranty terms.

Products with complete schema markup (Product, Offer, Review, FAQ) give AI the structured information it needs to make confident recommendations. Products without schema force AI to guess from raw HTML, and AI that isn't confident about a product's details simply leaves it out.

3. Answer-Ready Content

When a shopper asks "best standing desk for people with back pain", AI looks for content that directly answers that specific question. A product page optimized for "adjustable standing desk" won't match. A buying guide titled "How to Choose a Standing Desk for Back Pain" with specific product recommendations will.

AI prioritizes content structured as answers: Q&A formats, comparison tables, "best for" categories with reasoning. The brands that create this kind of content become the reference material AI synthesizes into recommendations.

4. Specificity Over Superlatives

AI models deprioritize vague marketing language. "The best standing desk on the market" is noise. "Rated to support 300 lbs, 48x30 inch surface, 25-50.5 inch height range, 10-year warranty" is signal.

Products described with specific, measurable attributes get cited more than products described with adjectives. Research from the Prerender.io benchmark confirms that AI surfaces specificity over superlatives. "Rated UPF 50+" beats "great sun protection" every time.

5. Review Volume and Sentiment

AI models use review data as a trust signal, but not the way you'd expect. A product with 2,400 reviews averaging 4.7 stars carries more weight than a product with 50 reviews averaging 5.0 stars. Volume signals market validation.

But the reviews need to be accessible. If your reviews load via a third-party widget (Yotpo, Judge.me, Stamped) after page render, AI crawlers never see them. Your strongest trust signal is invisible.

6. Brand Consistency Across Sources

AI cross-references brand information across your website, Wikipedia, review platforms, social media, and community forums. Inconsistencies create doubt. If your website says one thing, your Amazon listing says another, and your Google Business Profile says a third, AI becomes less confident about recommending you.

Consistent brand information across every platform isn't just good marketing hygiene. It's a direct input to whether AI trusts your product enough to recommend it.

What Your Competitors Are Doing (That You're Probably Not)

The brands that show up in AI product recommendations share a few common traits.

They publish honest comparison content. This sounds counterintuitive, but brands that compare themselves honestly against competitors get cited more. A page titled "Our Standing Desk vs. Uplift vs. Fully: Honest Comparison" that includes real trade-offs signals trustworthiness to AI. One-sided marketing pages don't.

They invest in off-site presence. AI doesn't just read your website. It reads Reddit (r/StandingDesks, r/BuyItForLife), YouTube reviews, Wirecutter roundups, and niche publication reviews. Brands with a rich off-site footprint get recommended more because AI has multiple independent signals to draw from.

They structure product data for machines, not just humans. Complete schema markup, server-side rendered content, and clean HTML aren't nice-to-haves. They're the difference between AI confidently recommending your product and AI leaving you out because it can't parse your page.

They update content regularly. AI models value recency. A buying guide last updated in 2023 gets deprioritized against one updated this month. The brands winning in AI search treat their content as living documents.

How to Get Your Products Into AI Answers

A practical checklist based on what actually drives AI citations.

This Week

Test your AI visibility. Ask ChatGPT, Perplexity, and Gemini to recommend products in your category. Note whether your brand appears, what it says about your products, and whether the information is accurate.
Audit your structured data. Run your top 5 product pages through the Google Rich Results Test. If Product, Offer, and Review schema aren't all present and complete, that's your first fix.
Check your review accessibility. View the page source of a product page. If reviews aren't in the raw HTML, AI can't see them.

This Month

Create 3 to 5 answer-format pages. Buying guides, comparison pages, and "best for [use case]" content structured around the questions shoppers actually ask AI.
Audit brand consistency. Compare your product descriptions, pricing, and claims across your website, Amazon, Google Business Profile, and any review platforms. Fix inconsistencies.
Complete your schema markup. Every product page should have Product, Offer, AggregateRating, and Review schema. Every FAQ section should have FAQPage schema.

Ongoing

Build third-party presence. Pursue editorial reviews, participate genuinely in relevant subreddits, encourage customers to review on independent platforms, not just your site.
Update content quarterly. Keep buying guides, comparison pages, and product descriptions current. AI notices freshness.
Monitor AI answers monthly. Track what AI says about your products and competitors. When the information is wrong, that tells you where your data has gaps.

The Competitive Window

AI product recommendation patterns are still forming. The brands that establish themselves as trustworthy, well-structured sources now will be the default recommendations as AI search scales. AI referral traffic to retail is growing at 4,700% YoY but still represents a small fraction of total traffic. That's the window.

Once AI learns to trust and recommend certain brands in a category, latecomers face the same uphill battle as trying to outrank an established competitor on Google. Except there are only 2 to 3 spots instead of 10.

The question isn't whether your products are good enough to be recommended. It's whether AI can find enough structured, consistent, trustworthy information to confidently recommend them.

Mersel AI helps ecommerce brands become the products AI recommends. We make your structured data, pricing, and reviews readable by AI crawlers so platforms like ChatGPT, Perplexity, and Gemini can confidently recommend you. Book a free AI visibility audit to see how AI currently sees your products.

Frequently Asked Questions

Why doesn't my top-selling product show up in AI recommendations?

AI recommendations depend on structured data, third-party mentions, and review accessibility, not sales volume. If your product data is rendered client-side, your reviews load via JavaScript widgets, or you have limited off-site coverage, AI can't build enough confidence to recommend you.

Do Amazon reviews help with AI recommendations?

Yes. AI models cross-reference information across platforms, and Amazon reviews contribute to the "third-party consensus" signal. But inconsistent product information between your site and your Amazon listing can reduce AI confidence.

How important are Reddit mentions for AI product recommendations?

Very important. Reddit is among the most-cited domains in AI responses. Genuine, positive discussions about your product on relevant subreddits carry significant weight because AI treats community endorsements as independent validation.

Should I create comparison content that mentions competitors?

Yes. Brands that publish honest comparison content get cited more by AI. A page comparing your product against competitors with real trade-offs signals trustworthiness. AI deprioritizes one-sided marketing pages in favor of balanced assessments.

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