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7 B2C Brands Dominating Generative Engine Citations — And What We Can Learn From Them

AI search favors brands with clear, structured, and authoritative content. See how 9 top publishers earn citations in ChatGPT & Google AI — and how your brand can, too.

Large language models aren’t just answering questions — they’re shaping which brands people see first. To understand who’s winning, we examined two recent Ahrefs studies: the Top 100 Most-Cited Domains in Google’s AI Mode and the Top 100 Most-Cited Domains in ChatGPT. These lists reveal which sites are repeatedly referenced in AI-generated answers.

From those rankings, we picked nine brands that piqued our interest across different categories. Here’s why we think they’re being cited so often — and what marketers can take from their playbooks.

📖 Read first: Increasing Brand Visibility in the Age of AI

Byrdie


What’s working: Byrdie publishes highly structured beauty and skincare content: ingredient explainers, dermatologist quotes, and stable “best-of” roundups. For example,“10 Best Vitamin C Serums That Fade Dark Spots and Brighten Skin, Tested” has a clear intro, quick-take product cards, and expert commentary — exactly the type of answer-shaped editorial LLMs can lift when recommending products or explaining ingredients.


💡 Steal this: Build canonical “how to choose” and “best-of” product hubs with consistent facets (eg. ingredients, budget) and expert sourcing so answer engines can use them verbatim.

The Spruce

What’s working: The Spruce is a Dotdash Meredith property focused on home, garden, food, and décor. It blends expert-reviewed how-to guides with structured step-by-step instructions and visuals — a format that maps perfectly to the kind of “how do I…” and “best way to…” prompts people type into AI tools. For example, its “How to Grow and Care for Basil” page opens with a concise overview, lists key specs (soil, sun, water) in a clean table, and then walks through step-by-step care instructions. This gives large language models ready-made paragraphs and bullet lists to reuse when answering gardening or home-care questions.

 

💡 Steal this: If you produce how-to content, lead with a short, declarative summary and a clear “spec sheet” of key facts, then expand into step-by-step instructions. Consistency across your articles and expert citations make your pages easy to trust and easy for AI systems to lift from.

Edmunds

What’s working: Edmunds is a go-to for automotive research. Its detailed car review pages, comparison tools, pricing data (MSRP, incentives, trade-in estimates), and expert commentary create structured and trusted content. LLMs referencing “best cars for fuel economy” or “2025 sedan comparison” are likely to quote rating summaries or pricing blocks from sites like Edmunds.

💡 Steal this: Publish clean comparison tables (e.g. side-by-side specs) and concise summary boxes (“Best for fuel economy,” “Best luxury”), perhaps paired with editorial verdicts — giving models easy, authoritative takeaways to include.

Who What Wear

What’s working: Who What Wear blends trend-driven editorial with structured product curation. A typical example is “Here They Are: The 6 Biggest Boot Trends of Fall 2025” — a trend headline followed by shoppable product sections with specs and prices. That hybrid of style context plus concrete product data makes it a natural source for AI answers on wardrobe ideas and shopping prompts.

💡 Steal this: Combine editorial storytelling with structured product data so LLMs get both context and clean facts in one place.

Study.com

What’s working: Study.com’s Q&A-formatted educational pages map neatly to question-and-answer prompts. For example, its “What Are Macronutrients?” page opens with a crisp definition and then walks through examples — perfect for direct AI answers.

💡 Steal this: Build “explainers with receipts”: short definitions up top, expandable depth below, and schema clarifying entity-topic relationships. (Entity strategy consists of making sure your pages clearly define the people, products, places, or concepts you cover so search engines and AI tools can recognize and cite them correctly.)


Home Depot

What’s working: Home Depot is frequently referenced in consumer product and home improvement queries, thanks to its massive catalog of products, highly structured product pages (with specs, dimensions, install instructions), and robust review sections. AI models often pull from such utility-like content when providing answers related to hardware, home repair, or DIY comparison queries.

💡 Steal this: Build product pages with detailed spec tables, “how to install” or “how to measure” guides embedded — especially in FAQ, bullet, or “spec-list” form — to provide answer-friendly blocks that tools can lift directly into answers.

GoodRx

What’s working: GoodRx’s prescription price data and pharmacy comparisons combine utility with authority, making it a go-to citation in AI answers about drug costs and savings. Its “Drug Prices” section offers structured, stable pages with plain-language explanations.

💡 Steal this: If you manage dynamic data (prices, availability), expose it in stable, human-readable formats and pair it with plain-language explanations.

What These Brands Have in Common

If we look at overall trends, there are a few similarities across the brands featured here.

  • Answer-shaped content. Clear, declarative information up top with structured details below.

  • Entity clarity. Pages map cleanly to people, places, products, or concepts.

  • Consistent templates + schema. Predictable URL patterns, headings, and structured data make citations safer for models and RAG systems.

  • Indirect visibility. Many are also cited on Wikipedia, Reddit, YouTube, or large review hubs — the sources LLMs lean on most.

  • Timely updates. Regular refreshes of specs, prices, and recommendations align with recency signals LLMs use.

How Your Brand Can Improve its Own AI Visibility

Use this as a checklist when coming up with your content auditing, development, and distribution strategies.

  • Audit for “answerability.” Structure key pages to directly answer the questions your audience types into AI tools.

  • Invest in entity hygiene. Consolidate duplicate profiles, ensure consistent naming, and use schema to clarify relationships.

  • Build comparison and utility pages. Tools, calculators, specs, and side-by-side tables are prized by LLMs.

  • Earn citations from intermediaries. Appear in Wikipedia entries, Reddit discussions, or reputable review sites — these act as feeders into LLM outputs.

  • Monitor AI engines directly. Track how often your content is cited or paraphrased in ChatGPT, Perplexity, Google AI Overviews, and others.

For more about winning visibility in AI Search check out the latest research from Stacker!

Conclusion

These nine brands show that LLM visibility isn’t random. It’s a function of clear, structured, authoritative content backed by distribution that models already trust. With deliberate content and entity strategy, any brand can increase its odds of being the paragraph AI engines choose to cite regardless of industry, content type, or subject matter.