When a buyer moves from general research to choosing between named vendors, the content AI assistants cite shifts decisively toward brand-controlled pages. Ten Speed’s Peec AI analysis—covering 170 evaluation-style prompts and 7,387 citation appearances across ChatGPT, Perplexity, Claude and Gemini—found product pages alone accounted for 24.1% of citations while Reddit and YouTube combined were about 4%.

What Ten Speed actually measured

Ten Speed used Peec AI to log which URLs AI assistants referenced in responses to prompts designed to mirror evaluation-stage questions (for example, “Pipedrive vs. HubSpot for sales-led companies”). The prompt set covered Ten Speed clients and look-alike competitors across fintech, physical security, hospitality and IT automation, producing 7,387 citation appearances.

Key citation shares reported:

  • Product pages: 24.1%
  • Articles (blogs, news, PR): 17.4%
  • Comparison pages: ~13%
  • Listicles: ~13%
  • How-to guides: just under 9%
  • Homepages: 7.8%
  • Directory profiles (G2, Capterra): 7.2%
  • Reddit, YouTube and other forums combined: 4.2% (Reddit did most of that; YouTube around 1%)

Combined, brand-controllable content—pages a marketing team creates and maintains—made up 88.3% of the citations in this evaluation-stage slice. The analysis also shows comparison-format prompts (20% of the set) generated roughly 27% of citations, a 1.33x return relative to their prompt share.

Limitations that matter for strategy

These results are useful but not universal. Important constraints:

  • The dataset is drawn from Ten Speed’s B2B SaaS and professional services clients; consumer and e-commerce categories may behave differently.
  • Platform-level citation behavior was pooled: the pull combined ChatGPT, Perplexity, Claude and Gemini without reporting per-model patterns, and those models likely differ in what they cite.
  • The headline 24% vs. 4% split is descriptive rather than statistically tested; it’s a snapshot, not a proven population parameter.
  • Ten Speed corrected presentation errors after follow-up (the prompt total shown as 220 was corrected to 170, and a comparison-category tag inconsistency was reconciled).
  • The firm declined to disclose how many distinct client brands or verticals produced the citations, so external researchers cannot independently assess the sample’s breadth.

Read this as a directional snapshot of how one set of B2B brands show up in AI answers at the vendor-evaluation stage—not as a universal benchmark across industries, platforms or time.

Practical priorities for content teams

If your objective is to appear when buyers are actively comparing named vendors, the dataset points to clear, low-risk shifts in where to focus resources.

  • Audit and simplify product pages and homepages. Together they account for close to a third of evaluation-stage citations. AI assistants favour concise, structured answers: lead with plain-language descriptions of what the product does, who it’s for, key integrations and compliance claims.
  • Build comparison content earlier and broader. Comparison pages outperformed their prompt share. Treat “X vs Y” content as primary and scale beyond a single chief competitor; consistent formats increase the chance models will surface these pages.
  • Manage directory profiles as owned content. G2 and Capterra profiles accounted for 7.2% of citations. Category tags, integration lists and profile copy are structured signals—keep them accurate and current.
  • Keep community content where it belongs in the funnel. Reddit and YouTube matter for early-stage exploration and awareness. But for evaluation-stage visibility in this dataset, brand-owned assets dominate.
  • Vet methodology before reallocating budget. Ask about denominators, whether platforms were pooled, and whether headline splits were statistically tested. Descriptive findings can still guide strategy, but they shouldn’t be treated as absolutes.

What to watch next

The two variables that will determine whether these priorities hold are platform-level citation differences and time-series trends. If ChatGPT, Gemini, Perplexity and Claude begin to cite different surfaces consistently—or if citation patterns shift as models update—teams will need to rebalance priorities. For now, B2B teams focused on conversion should prioritize product, comparison and directory content, and instrument those pages to track clicks, demos and pipeline so citations can be tied to outcomes.