The annual Black Friday shopping frenzy has long served as a crucial barometer for consumer behavior, revealing how people search for, compare, and ultimately purchase products under the pressure of limited-time deals. This year, however, the event provided a new, unprecedented layer of insight by acting as a real-world stress test for how large language models (LLMs) interpret the vast and chaotic landscape of e-commerce. A comprehensive analysis of 10,000 AI-generated responses during this peak demand period has uncovered the foundational signals and data sources that shape how these systems reason about products, retailers, and consumer intent. The results offer a compelling preview of the evolving world of AI-driven search and discovery, signaling a fundamental shift in how the entire commerce ecosystem will operate.
1. Rethinking the Funnel How LLMs Interpret E-commerce
Unlike traditional search engines that present a ranked list of blue links, artificial intelligence models operate on an entirely different principle by flipping the conventional discovery funnel on its head. Instead of starting with a query and ending with a results page, an LLM begins with its own internal, compressed map of the world—a complex web of relationships, sources, and signals—to construct a direct and purposeful answer. In the context of shopping, its primary objective is not to facilitate a browsing experience but to deliver a definitive response. An analysis of the top 50 most-cited domains reveals that an LLM’s commercial knowledge is heavily shaped by a small, influential cluster of sources. YouTube leads with 1,509 citations, followed by major retailers like Best Buy (950), Walmart (885), and Target (477), and trusted review sites such as TechRadar (355) and Consumer Reports (325). This concentration demonstrates a clear bias toward large, established retailers and media outlets known for structured comparisons and reviews, creating a powerful feedback loop that solidifies their authority within the AI’s worldview.
The dominance of these specific domains is not accidental; it reflects the types of content that are most useful for an AI model tasked with synthesizing information and making recommendations with confidence. Generalist retailers provide a massive, structured catalog of products, specifications, and pricing data, forming the backbone of the AI’s product knowledge. Meanwhile, platforms like YouTube and review media offer something equally crucial: human-centric context, sentiment analysis, and comparative data. Video reviews and demonstrations train models on product use cases and experiential value, while sites like RTings and Consumer Reports provide the structured, side-by-side comparisons that an AI needs to reason about quality and performance differences. Together, these sources create a rich collection of resources that enable models to generate direct, seemingly authoritative answers across nearly any vertical or consumer need. This reliance on a narrow set of highly structured or sentiment-rich domains effectively makes them the “default funnel” through which LLMs process and understand the entire retail landscape.
2. The Black Friday Effect Shifting Signals and Sources
An examination of LLM behavior in the week leading up to Black Friday compared to the event itself reveals a dynamic and adaptive reasoning process that mirrors consumer patterns. In the preparatory phase, user queries were primarily focused on research and planning, and the AI’s responses reflected this by heavily favoring established domains. Retail and brand websites accounted for a commanding 59.6% of sources, with media outlets making up another 23.4%. During this period, even prompts that explicitly mentioned “Black Friday” often yielded responses aimed at setting expectations, with models sometimes noting that it was still too early for definitive deals. This behavior shows that in a stable, low-uncertainty environment, LLMs anchor their knowledge in official, authoritative sources like brand homepages and established product reviewers. They function as information aggregators, helping users compare baseline prices, research product features, and build their shopping lists in anticipation of the sales event.
Once Black Friday began, however, the informational landscape shifted dramatically, and the models adapted in real time. As pricing and inventory became highly volatile, the AI’s reliance on static retail and media sites decreased, while its dependence on dynamic, conversational content surged. The share of social and user-generated content (UGC) sources jumped from 17% to 25.1%, an increase of over eight percentage points. This sharp pivot indicates that when faced with uncertainty, LLMs lean more heavily on human discussion and experiential content from platforms where real-time information is being shared. They turn to forums, social media, and community-driven sites to find cues about deal quality, stock availability, and genuine consumer sentiment. This behavior demonstrates that models are not just static repositories of crawled data; they are designed to weigh sources differently based on context, prioritizing real-time, conversation-driven signals when authoritative information is in flux, much like a human shopper would.
3. The Power of External Validation and Brand Content
One of the most profound insights emerging from the analysis is the immense weight that third-party domains carry in shaping an AI’s reasoning about products and brands. Today’s most advanced LLMs are engineered to absorb and synthesize vast quantities of human interest signals, and the platforms that supply this information at scale become disproportionately influential. An analysis of external influence in retail identified five domains that consistently dominate as the off-page signals LLMs rely on most: Reddit (34%), YouTube (19.5%), Amazon (15.5%), Business Insider (9.2%), and Walmart (8.9%). Each of these sources contributes a different but essential piece to the model’s decision-making puzzle. Reddit provides a firehose of authentic, unfiltered consumer discussion and sentiment. YouTube offers visual proof and demonstrations of product value. Amazon and Walmart supply structured product data, availability information, and a massive corpus of user reviews. This reliance underscores a critical principle: LLMs build trust and understanding by cross-referencing information across multiple, independent domains that capture genuine human interest.
While external sources form the bedrock of AI reasoning, brand-owned websites still play a vital, albeit different, role in this new ecosystem. A brand’s digital presence serves as a crucial validation layer and a source of foundational, factual information. The internal structure of a brand’s website significantly impacts how a model interprets its identity and offerings. The homepage is paramount, accounting for 40% of brand-site mentions, as it provides the primary identity layer, defining the brand’s positioning and core semantic signals. Following the homepage, blog content (10.6%) and product pages (10.5%) are most important for providing definitional clarity, long-tail context, and the specific details a model needs to answer granular queries. Brands that rely on vague promotional copy, confusing site hierarchies, or thin product content risk becoming invisible. In the current landscape, LLMs use brand content primarily to verify and deliver direct answers, but only after off-page signals have already justified the brand’s inclusion in the conversation.
4. A Divergent Landscape LLM Platforms and Retailer Dominance
Across the entire dataset of AI responses, a clear hierarchy of retailers emerged, with a few key categories dominating the conversation. Generalist retailers like Walmart, Target, and Best Buy were the undisputed winners, capturing an overwhelming 48% share of all retail citations. Their enormous product breadth, high brand familiarity, and extensive content depth position them at the absolute center of an LLM’s commercial reasoning. Following them, electronics specialists claimed a significant 23% share, with Best Buy leading by a wide margin. This effect was likely amplified by the tech-heavy nature of Black Friday queries, but it nonetheless demonstrates that models consistently turn to specialized, authoritative sources for high-consideration categories. In stark contrast, other major retail verticals remained far behind. Fashion, beauty, home improvement, and pet supplies each garnered only small slices of the AI’s attention, even with strong category leaders present. This imbalance reflects the sheer volume of structured content and consumer discussion that generalist and electronics retailers generate compared to more niche verticals.
Further complicating the landscape is the fact that not all AI platforms think alike; each major LLM exhibits distinct behaviors, preferred structures, and unique styles of presenting commercial information. Gemini, for instance, produces the most expansive and detailed outputs, with its responses averaging 606 words and almost always using lists and headings to structure its essay-length explanations. It treats every query as an opportunity to deliver a comprehensive article. OpenAI’s models sit in the middle of the spectrum, averaging 401 words per response and featuring even denser lists, with an average of 32 items. This approach suggests a focus on providing a thorough, yet slightly more consolidated, overview. Perplexity, however, moves in an entirely different direction, favoring brevity and directness. Its typical response is just 288 words and contains far fewer list items, compressing complex information into a format that reads like an executive brief. These fundamental differences in retrieval and reasoning strategies mean that visibility in an AI-driven world will require tailored approaches that respect each platform’s unique internal logic.
5. The Strategic Imperative for Retailers and Brands
The data points to an undeniable conclusion: AI search is rapidly becoming its own distinct ecosystem, governed by a new set of rules where traditional SEO inputs are reinterpreted by language models to deliver a single, clear response. To succeed in this environment, brands and retailers must fundamentally rethink how they communicate value, not just on their own websites but across the entire digital discovery surface. The first step involves critical on-page actions. Homepages must be built to be semantically coherent, clearly reflecting the brand’s identity, product categories, and relevance to core user queries, as LLMs prioritize clarity over clever marketing. Product pages need to be strengthened with structured, factual content, including detailed specifications, variant descriptors, and robust Q&A sections that mirror the language of consumer research. Furthermore, creating educational content clusters tied to core product themes can provide the reusable “content scaffolding” that AI models need to properly contextualize a product and understand its place in the market.
Equally important are the off-page strategies that build a brand’s authority and trustworthiness in the eyes of an AI. Visibility can no longer be achieved in a silo; it requires active participation in the broader digital ecosystem. Fostering robust review ecosystems on platforms like Reddit, Quora, and third-party review sites is essential for generating the trust signals that LLMs associate with product quality. Brands must also ensure a regular presence in comparison- and recommendation-driven media, such as “best of” lists, product roundups, and influencer content, as these are primary sources for AI-driven recommendations. Investing in rich media, especially on YouTube and TikTok, is also critical, as video content effectively trains LLMs on product use cases, user sentiment, and experiential value. Finally, for brands participating in marketplaces like Amazon or Walmart, ensuring product data is accurate, complete, and easily indexable is non-negotiable, as this structured data is increasingly being ingested directly into AI discovery pipelines.
From Discovery to Transaction The Dawn of Agentic Commerce
Black Friday offered more than a simple snapshot of consumer trends; it provided a clear window into how large language models behaved under the pressure of real-world demand, revealing how they reasoned, referenced, and prioritized information across a fragmented digital landscape. The answers these systems generated were not just confident and structured but also increasingly influential in shaping purchasing decisions. Yet, they were also incomplete, shaped more by the sources they encountered most frequently than by the full depth of what brands had to offer. This event signaled the emergence of a new shopping architecture—one where agentic commerce is poised to replace traditional browsing, and AI models, rather than consumers themselves, drive product discovery and comparison.
Initiatives like OpenAI’s Shopping Research feature made this shift unmistakable, demonstrating that these models are evolving from language tools into sophisticated intent engines. They are now being trained not only on static product data but also on the dynamic behaviors of how people actually shop—including their price sensitivity, variant preferences, and need for real-time availability. For brands and retailers, this marked a critical transition from passive search optimization to a new era requiring active AI participation. Visibility will no longer hinge on rankings or ad placements alone. Instead, it was found to depend on having structured, semantically rich content that is surfaced across the right off-page ecosystems and aligned with the unique reasoning patterns of each major model. This new discipline of achieving “AI-native visibility” will be essential for ensuring brands are not just discoverable, but deeply understood by the systems shaping modern commerce. Black Friday was merely the stress test; the real transformation lies ahead.
