AI Shopping Visibility Funnel: How Brands Get Cited in ChatGPT Answers

Generative engines like ChatGPT draw on multiple data sources when shaping shopping answers. Each platform influences a different stage of consumer intent. To win visibility, brands must optimise across all four.

And the numbers back this up. An analysis of millions of ChatGPT responses by Azoma shows:

  • Wikipedia is by far the dominant source across all queries (43%),

  • Reddit provides 12%,

  • YouTube accounts for 5%,

  • While Amazon emerges as a critical source for commerce-related queries (19%).

In shopping-specific prompts, the picture changes:

  • Wikipedia still drives 22% of citations,

  • Amazon surges to 19%,

  • Reddit contributes 15%,

  • YouTube plays a smaller but still noticeable role.

These patterns confirm that Wikipedia anchors authority, Reddit and YouTube shape research, and Amazon drives transactional recommendations.

1. Low Intent (Exploration & Context)

User is browsing, curious, not yet considering specific products.
 Example prompts:

  • “What types of coffee makers are there?”

  • “What is a mirrorless camera?”

Data evidence: Wikipedia dominates here with 43% of citations across all queries, showing its role as the knowledge base for LLMs.

Brand strategy:

  • Build and maintain a credible Wikipedia presence with third-party citations.

  • Ensure flagship products and categories are documented in neutral, well-sourced entries.

2. Medium Intent (Evaluation & Consideration)

User is comparing options, researching features, weighing pros and cons.
 Example prompts:

  • “Is a DSLR better than a mirrorless camera?”

  • “Which laptop is better for students, MacBook Air or Dell XPS?”

Data evidence: Reddit (12–15%) and YouTube (5%) play outsized roles here, as ChatGPT leans on peer experiences and influencer reviews.

Brand strategy:

  • Produce comparison-rich YouTube videos.

  • Partner with credible reviewers and nurture discussions in relevant subreddits.

  • Encourage authentic customer sentiment that gets repeated in answers.

3. High Intent (Purchase-Ready)

User is actively seeking a shortlist of products, often with price or feature filters.
 Example prompts:

  • “Best wireless earbuds under £100.”

  • “Top-rated travel backpacks for digital nomads.”

Data evidence: In commerce queries, Amazon jumps to 19% of citations, second only to Wikipedia. ChatGPT relies on Amazon’s structured data — product titles, reviews, and rankings — to shape buy-now recommendations.

Brand strategy:

  • Optimise product listings with strong titles, enriched descriptions, and review volume.

  • Ensure high-quality assets (images, videos, charts) are attached.

  • Track “share of answer” in AI-generated shopping queries to monitor visibility.

4. Post-Purchase (Trust & Reinforcement)

User seeks validation, reassurance, or long-term product insight.
 Example prompts:

  • “Is Brand X reliable for long-term use?”

  • “How durable are Dyson vacuum cleaners?”

Data evidence: Reddit’s 15% share of commerce queries highlights its role in capturing lived experiences that ChatGPT mirrors back in post-purchase validation.

Brand strategy:

  • Encourage ongoing reviews and discussions that showcase durability and long-term performance.

  • Monitor Reddit and YouTube for recurring feedback loops that might shape future AI answers.

The Strategic Takeaway

  • Wikipedia = Authority Layer (low intent, 43% overall citations).

  • Reddit + YouTube = Research Layer (medium intent, 12–15% + 5%).

  • Amazon = Conversion Layer (high intent, 19% in commerce queries).

  • Reddit + YouTube = Validation Layer (post-purchase, reinforcing sentiment).

In the GEO era, visibility in ChatGPT shopping recommendations depends not on a single platform, but on mastering all four layers of the funnel. The data shows that each plays a distinct role, with Amazon critical at the point of purchase, Reddit shaping peer trust, YouTube driving research, and Wikipedia anchoring credibility.

AI Visibility, Reddit, Wikipedia, YouTube, AmazonFrancesca Tabor