What Black Friday Teaches Us About LLMs and Ecommerce
▼ Summary
– Black Friday acts as a natural stress test for AI-driven discovery, exposing how LLMs reason about products and retail using sources, structures, and behavioral tendencies.
– LLMs overwhelmingly rely on a small cluster of external domains, with YouTube, big-box retailers, and U.S. review media dominating their commercial “knowledge” and recommendations.
– During Black Friday, LLMs shift behavior by leaning harder on social and user-generated content, mirroring consumer reliance on real-time discussion for decision cues.
– Major LLMs like Gemini, OpenAI, and Perplexity exhibit distinct reasoning and output styles, producing different formats, lengths, and structures in their responses.
– The future of retail involves AI-native visibility, where brand success depends on structured, semantically rich content and presence across the off-page ecosystems that shape AI models’ understanding.
The annual Black Friday shopping frenzy provides a unique window into consumer behavior, but this year it also served as a critical real-world laboratory for artificial intelligence. By analyzing thousands of responses from leading language models, we can see how these systems construct their understanding of the retail world. The event acts as a natural stress test for AI-driven discovery, exposing the underlying sources and reasoning patterns that shape how models answer shopping questions. This shift signals a fundamental change in how products are found and compared online.
Our analysis of 10,000 model responses revealed a landscape dominated by a surprisingly narrow set of sources. LLMs overwhelmingly rely on a small cluster of external domains, with YouTube, major big-box retailers, and established U.S. review media forming the core of their commercial knowledge. Generalist retailers like Walmart, Target, and Best Buy captured nearly half of all retail mentions, effectively becoming the default funnel through which models answer a vast array of shopping queries.
The behavior of these AI systems changes dynamically with consumer intent. In the week leading up to Black Friday, responses were anchored in planning, with retail and brand domains dominating. Once the event began, we observed a significant shift. Social and user-generated content (UGC) surged by over 8% as models leaned harder on platforms like Reddit and YouTube for real-time discussion and experiential cues about deals and products. This mirrors how consumers seek validation during periods of high uncertainty and fast-moving inventory.
A crucial insight is the outsized influence of off-page content. Today’s models build their reasoning by absorbing vast amounts of human discussion and comparative data. Third-party domains like Reddit, YouTube, Amazon, and Consumer Reports collectively shape the “external data sources” that LLMs use to compare and recommend products. Content that organizes consumer-driven options and reduces uncertainty with verifiable data holds disproportionate weight in an AI’s decision-making process.
While third-party sources are dominant, a brand’s own digital presence remains vital. A website’s internal structure plays a major role in how a model interprets a brand. The homepage acts as the primary identity layer, while blog and product pages provide the definitional clarity and factual detail models need. Brands that rely on promotional copy or thin content leave significant visibility on the table, as LLMs use owned content to validate and deliver responses only when off-page signals justify the brand’s place in the conversation.
Not all AI platforms reason the same way. Our analysis found that major LLMs exhibit distinct behaviors. Gemini tends to produce expansive, article-length explanations filled with lists. OpenAI generates dense, list-heavy responses that are slightly more concise. Perplexity favors short, direct summaries, compressing information into an executive-brief style. These differences reveal distinct retrieval and reasoning strategies, meaning visibility strategies must respect each platform’s internal logic rather than applying broad strokes.
For retailers and brands, the data points to a clear imperative: AI search is evolving into its own ecosystem. Success requires a dual strategy. On-page, this means building semantically coherent homepages, strengthening product pages with structured factual content, and creating educational content clusters that serve as scaffolding for AI. Off-page, it involves fostering review ecosystems, ensuring presence in comparison-driven media, and investing in rich video content that trains models on product use cases and sentiment.
The stakes are being raised rapidly by developments like OpenAI’s Shopping Research in ChatGPT. This tool captures real-time consumer research behavior, preferences for price, variants, and availability, to build what is essentially a user-trained targeting engine. This marks a shift from passive search optimization to active AI participation. If your content isn’t present, structured, or referenced in these systems, it simply won’t appear in the AI’s answers or the consumer’s journey.
Black Friday offered more than a snapshot of sales; it revealed the emerging architecture of AI-driven commerce. We are moving toward a future where agentic systems, not consumers, increasingly drive product discovery and comparison. Visibility will hinge on structured, semantically rich content aligned with the reasoning patterns of each major model, a discipline we call AI-native visibility. The transformation ahead won’t be won by who ranks highest on a search page, but by who is represented accurately and contextually everywhere AI shows up.
(Source: Search Engine Land)
