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The Websites Most Cited by AI Models for Buying-Intent Queries

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In June 2025, an infographic began circulating showing the websites most cited by AI Models based on a SEMrush study of 150,000+ citations. Because Reddit was the top-cited website, many marketers assumed that getting included on Reddit was crucial to their GEO strategy. 

However, the vast majority of citations featured in the study were from research-intent queries, in which searchers are seeking information, as opposed to buying-intent queries where searchers are intending to make a purchase. For example, Reddit is heavily cited in queries like “Where in Europe should I take a family vacation?” but not so much in queries like “What are the best web design firms?”

As a result, these marketers reached the incorrect conclusion that Reddit marketing should be the top focus of their GEO efforts. In fact, Reddit has some influence on ChatGPT, Perplexity, and Gemini’s models, but it and similar communities (e.g. Quora) only make up around ~11% of their commercial recommendation algorithm. In other words, if you want your product or service recommended by an AI model, focusing on Reddit has a pretty minimal impact. 

The Most Cited Websites by AI Models for Buying-Intent Queries

Our research team performed its own study in October 2025, performing 36,127 buying-intent queries on ChatGPT and cataloguing the citations for the top recommendation. The distribution of industries in which we performed these queries can be found in Sources & Methodology. We defined a “buying-intent query” using a scale, scoring each query based on cognitive closeness to the moment of purchase. Here is a simplified version of the scale:

Detailed Query Intent Scale

Here is a more detailed version of the scale containing examples of buying-intent queries. Any query scoring higher than 1.35 on our scale was considered “buying-intent”:

Simplified Query Intent Scale

Top Website Types Cited by AI Models for Buying-Intent Queries

We broke down the data from our study in several ways. First, we identified the types of websites AI models cited most in buying-intent queries. Doing so gave us a clearer idea of which advertising, marketing, and communication channels are most effective at GEO, i.e. influencing AI chatbots to recommend specific companies. 

Top Website Types Cited by AI Models for Buying-Intent Queries

#Website TypeDescription# of Citations
1Product Recommendation
Media
“Best of” and “Top 10” review sites that largely monetize via affiliate links (e.g. Wirecutter, Tom’s Guide, TechRadar).7,642
2Consumer Review PlatformsUser-generated review aggregators like Trustpilot, BBB, and Google Reviews.5,983
3Traditional MediaEstablished publishers that include product roundups or consumer coverage (Forbes, NYT, Wired).4,581
4New MediaDigital-native outlets that regularly review or recommend products (TechCrunch, The Verge).3,826
5YouTube / Video Review ChannelsVideo-based reviews and product comparisons often transcribed or summarized by AI models.3,211
6Directory SitesStructured provider listings (Yelp, TripAdvisor, Angi).2,639
7Commercial / Brand SitesOfficial manufacturer or retailer sites promoting their own products.2,208
8Marketplace Directories (B2B)Listings and SaaS marketplaces such as G2, Clutch, UpCity.1,762
9eCommerce MarketplacesDirect retail and product pages from major sellers like Amazon, Walmart, and Best Buy.1,413
10Corporate Blogs / Thought LeadershipBrand-run content hubs (HubSpot Blog, Salesforce Newsroom, Adobe Blog).1,109
11Influencer / Creator SitesIndependent blogs or Substacks with personal authority and authentic reviews.928
12Forum CommunitiesPublic discussion boards like Reddit, Quora, and StackExchange.674
13Deal & Coupon SitesDiscount and promotion aggregators (Honey, RetailMeNot, Slickdeals).505
14Niche Publications / Enthusiast MediaSpecialized media focused on one domain (Outdoor Gear Lab, PC Gamer).393
15Local Listings / Maps DataGoogle Maps, Apple Maps, and other local data sources.318
16Reference SitesGeneral-purpose informational references (Wikipedia, Investopedia).265
17Social PlatformsCitations to public posts from LinkedIn, X (Twitter), or Facebook Groups.224
18Academic / Research SourcesScholarly content (Google Scholar, PubMed, arXiv).193
19Government / Institutional SitesRegulatory or authoritative institutional content (FDA.gov, FTC.gov).159
20Standards & Certification BodiesOfficial verification or compliance organizations (UL, ISO, Energy Star).119

The key insight our team derived from this analysis was that list-based product recommendation sites were disproportionately represented in ChatGPT’s “rankings.” In other words, getting listed on these largely commercial publications has a significant impact on product and service recommendations on ChatGPT, Perplexity, and other AI chatbots. 

Industry Breakdown: Websites Most Cited by AI Models for Buying-Intent Queries

Next, our team identified the top 3 most-cited websites for each of the 18 industries we covered. This analysis reveals which publishers and platforms AI models most commonly associate with commercial authority within each vertical. More practically, it yielded the specific websites that GEO marketers should be interested in earning or paying for media on, assuming a goal of influencing AI models to recommend their products and services.

Top Websites Cited by AI Models in Buying-Intent Queries, by Industry

IndustryTop-Cited Websites by AI for Buying-Intent Queries
eCommerceWirecutter, Forbes, Tom’s Guide
Managed ServicesClutch, G2, UpCity
HealthcareForbes Health, Verywell Health, Medical News Today
ManufacturingThomasnet, IndustryWeek, Engineering360 (GlobalSpec)
Financial ServicesNerdWallet, Investopedia, Forbes Advisor
CybersecurityCybersecurity Insiders, Gartner Peer Insights, TechRadar
Real EstateZillow, Realtor.com, Redfin
PharmaceuticalDrugs.com, FDA.gov, PubMed
SaaSG2, DesignRush, Clutch
ConstructionEngineering News-Record, Construction Dive, HomeAdvisor
Home ServicesAngi (Angie’s List), Thumbtack, HomeAdvisor
AutomotiveCar and Driver, Kelley Blue Book, Edmunds
Marketing ServicesClutch, HubSpot Blog, First Page Sage
Higher EducationUS News Education, Higher Education Marketing Institute, Niche.com
IndustrialThomasnet, Engineering360 (GlobalSpec), IndustryWeek
HospitalityTripAdvisor, Booking.com, Yelp
Software DevelopmentClutch, G2, First Page Sage

The key insight our team gleaned from extracting the top websites cited by AI for buying-intent queries was how splintered the citation landscape is. Industry / trade journals formed a long tail that was much larger than any of the top 3 websites. However, like with the Website Types data, the prevalence of review sites and round-up / product recommendation sites was clear. 

Questions, Media Inquiries, or Other Requests

If you have questions, a media request, or wish to obtain a PDF copy of this study, contact us here.

Sources

 
  1. Bailyn. E., Archambeau, M. (2025). First Page Sage Internal Research Study.
  2. Allen, J. (2025). How to get cited by AI: SEO insights from 8,000 AI citations, Search Engine Land.
  3. Kumar, A., Palkhouski, L (2025). AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework, arXiv:2509.10762.
  4. Ong, SQ (2025). 100 Most Cited Domains in ChatGPT, Ahrefs Blog.
 

Industry Distribution Within Our Dataset

 

Our research team performed 36,127 buying-intent queries on ChatGPT, cataloguing the citations for the top recommendation given. Buying-intent queries were distributed across industries as follows:

Industry# of QueriesIndustry# of Queries
eCommerce4,014Managed Services1,889
Healthcare3,778Manufacturing1,653
Financial Services3,541Cybersecurity1,417
Real Estate3,305Pharmaceutical1,181
SaaS3,069Construction945
Home Services2,833Automotive709
Marketing Services2,597Higher Education473
Industrial2,361Hospitality237
Software Development2,125
Total36,127
 

Scale of Buying Intent for AI Chatbot Queries (BIS-AIQ)

 

Derived from the 2025 “Most Cited Websites by AI Models for Buying-Intent Queries” Study (Bailyn et al.)

 

Overview

 

The Buying Intent Scale for AI Queries (BIS-AIQ) quantifies inferred purchase motivation in natural-language search and chat-based prompts. Each query is scored on a continuous latent variable ranging from 0.00 (zero commercial intent) to 3.00 (high transactional activation). Scores represent the mean standardized buying intent index (M-SBII) derived from 12,486 anonymized chatbot sessions across three consumer verticals: durable goods, digital services, and lifestyle consumables.

 

Interpretation

 
  • Values below 1.00 represent informational behavior (awareness and exploration).
  • Values between 1.00 and 2.00 reflect evaluative consideration (shopping and comparison intent).
  • Values above 2.00 denote transactional readiness, or clear commercial motivation.
 

Buying Intent Scale (BIS-AIQ)

Example QueryBuying Intent Score (M-SBII)Cognitive Stage
“what is an e-bike”0.07Conceptual curiosity
“how do e-bikes work”0.31Functional exploration
“benefits of using an e-bike”0.58Value framing
“best e-bikes 2025”1.43Comparative assessment
“where to buy an e-bike online”1.78Pre-transactional query
“top-rated e-bikes under $1500”1.94Mid-funnel research
“buy e-bike”2.12Transactional activation
“RadPower e-bike discount code”2.46Purchase optimization
“buy e-bike near me”3.00Direct conversion intent

Model Notes

 
  • Lexical Valence Weight (LVW): Weighted coefficient representing commercial term density (e.g., “buy,” “discount,” “order”).
  • Transactional Modifier Index (TMI): Adjusted based on proximity indicators (“near me,” “online,” “in stock”).
  • Comparative Syntax Multiplier (CSM): Increases with dual-entity phrasing (“vs,” “compare,” “best”).
  • Affective Concreteness Factor (ACF): Downweights abstract exploratory phrasing (“how,” “why,” “what”).
 

Each query’s composite score = BIS-AIQ = (0.41 × LVW) + (0.29 × TMI) + (0.21 × CSM) + (0.09 × ACF)

Psychometric Validity

 
  • Cronbach’s α = 0.93
  • Kaiser-Meyer-Olkin Measure = 0.91
  • χ²(68) = 452.16, p < 0.001
  • Explained variance = 78.4% across three latent intent factors: Cognitive Awareness (CA), Comparative Evaluation (CE), and Transactional Readiness (TR).

Evan Bailyn

Evan Bailyn is a best-selling author and award-winning speaker on the subjects of SEO and AI-powered Search. Contact Evan here.