"Get recommended by ChatGPT" sounds like one goal, but the path to it looks nothing alike depending on what you sell. A skincare brand and a payroll software vendor are both trying to get named when someone asks an AI assistant for a recommendation. The questions people ask, the sources the AI leans on, and the content that earns a mention are almost entirely different between the two.
Treating B2B and B2C AI visibility as the same problem is why a lot of otherwise solid GEO effort underperforms. Here is where the two actually diverge, and what to do about it.
Consumer questions tend to be short, immediate, and comparison-heavy: "best running shoes for flat feet," "what's a good alternative to Notion," "which meal kit is cheapest." The buyer is often one person making a fast, low-stakes decision.
B2B questions are longer, more qualified, and framed around a role or a use case: "what's the best CRM for a 40-person sales team that uses HubSpot for marketing," "compare Vercel and Netlify for a Next.js app with a small team." The person asking is often researching on behalf of a team, and the AI response gets weighed against budget, integrations, and internal approval, not just personal preference.
If your content only answers the short consumer-style question, it will not surface for the longer, qualified version a B2B buyer actually types.
For consumer categories, AI assistants lean heavily on the sources consumers already trust: review sites, Reddit threads, YouTube reviews, and retailer pages with star ratings. Volume and recency of reviews matter a lot.
For B2B categories, the weight shifts toward comparison sites built for software buyers (G2, Capterra, TrustRadius), analyst commentary, integration and partner pages, and technical documentation that shows the product actually does what it claims. A glowing consumer-style testimonial carries less weight than a specific, verifiable case study with numbers.
A B2C AI recommendation is won on sentiment at scale. A B2B AI recommendation is won on specificity and proof.
| Factor | B2C | B2B |
|---|---|---|
| Typical question | Short, comparative, personal | Longer, qualified by role and use case |
| Key sources | Reviews, Reddit, retailer pages, social | G2/Capterra, docs, case studies, analyst mentions |
| What earns the mention | Volume of positive sentiment | Specific proof: integrations, numbers, named use cases |
| Decision unit | One person, fast decision | A team, slower and more qualified |
| Content that helps most | Comparisons, "best of" style pages | Use-case pages, integration pages, detailed case studies |
If you sell to consumers, the highest-leverage work is usually outside your own site: generating and maintaining reviews, being present in the comparison and community threads people actually read, and keeping your product facts consistent across retailers. Your own site matters, but the AI is drawing heavily from what other people say about you.
If you sell to businesses, the highest-leverage work is often on your own site: pages built around specific use cases ("CRM for agencies," not just "CRM"), a clear integrations page, and case studies with real numbers an AI can quote. Those pages give the model something precise and attributable to lift into an answer, which matters more than general sentiment.
The mistake to avoid is testing your AI visibility with the wrong kind of question. If you sell B2B software, testing "best project management tool" tells you less than testing "best project management tool for a 15-person marketing agency already using Slack." Match the test to how your real buyer actually asks.
The GEO fundamentals, being named accurately and consistently, are the same for everyone. But the sources that build that reputation, and the content that earns the mention, split hard along the B2B and B2C line. Build for the buyer you actually have, not a generic one.
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