Dealership AI Visibility Benchmark 2026: How U.S. Car Dealers Show Up in AI Search
Hrizn Research is benchmarking how U.S. car dealerships appear in generative AI search surfaces — Google AI Overviews, ChatGPT, Perplexity, and Google AI Mode — across a panel of buyer-intent queries. Early findings (published below) track citation frequency, entity resolution, and the structured-data signals correlated with inclusion. The 2026 edition expands coverage to 200+ metros and integrates YouTube, Reddit, and third-party review surfaces.
What this study measures
These are the headline structural parameters — the actual quantitative findings drop with each quarterly release below.
200+ metros
All DMA-level franchise dealers plus a representative sample of independents in the top 200 U.S. metros.
1,800+
Buyer-intent, service-intent, and comparison queries issued monthly against each surface.
4
Google AI Overviews, Google AI Mode, ChatGPT with web search, and Perplexity Pro.
Quarterly
Snapshot reports each quarter with a cumulative annual report in January of the following year.
How we collect and validate the data
Panel construction
We build the panel from a stratified sample across OEM, geography, and dealer size (franchise, franchise group, and independent). Panel composition is frozen for each quarterly snapshot and disclosed in full in the accompanying dataset.
Query set
Queries are grouped into four intent classes — research (e.g., "best hybrid SUV 2026"), comparison (e.g., "Toyota RAV4 vs Honda CR-V"), local intent (e.g., "Toyota dealer near me"), and service intent (e.g., "brake service near me"). We re-roll long-tail variants each quarter to reduce overfit to static head terms.
AI surface sampling
Each query is issued against each surface three times, on clean browser profiles, from a residential egress in the target DMA. Responses are parsed for citation URLs, cited entities, and surfaced snippet provenance. We deduplicate near-identical responses and report both raw and deduplicated rates.
Attribution
A citation is credited to a dealership when (a) the cited URL is on the dealership domain, (b) the dealership name/address is resolved to a unique Google Business Profile entity, or (c) the cited third-party source (e.g., review platform, forum, video) names the dealer unambiguously in the surfaced snippet.
Structured-data correlation
For each panel participant we crawl and normalize LocalBusiness, AutoDealer, Service, Vehicle, FAQPage, and Article schema. We then correlate presence/absence and completeness of each schema type with citation frequency, controlling for domain rating and review volume.
Dealership AI Visibility Benchmark 2026 — dataset
January – June 2026
CSV
Available with published report
The underlying dataset is released under a permissive Creative Commons license so researchers, analysts, and agencies can cite and build on it freely. Attribution to Hrizn Research is appreciated but not required beyond CC BY 4.0 terms.
Are you a dealer or agency that wants to be included?
Hrizn Research publishes anonymized, aggregated findings only. Participating dealerships receive a private comparison report benchmarking their AI visibility against their OEM peer set and local market, free of charge. Reach out below if your group would like to be included in the next quarterly panel.
Request inclusion
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